Keywords: Firm-level political risk, political uncertainty, trade credit provision, cash management, managerial risk-taking

This dissertation is a compilation of four chapters, one comprehensive theoretical chapter and three empirical essays investigating the effect of political risk on firm financial and managerial choices. The first chapter explores theories and empirical studies on the effects of political risk on businesses and economic outcomes. It sharpens our understanding of the complexity of the twenty-first century political risk and the tools to measure these effects.

The first empirical essay explores the effect of a novel measure of firm-level political risk on trade credit provision. We bring forth two competing hypotheses linking politics to trade credit based on several theories on how trade credit can compete with or substitute other sources of financing during hard political times. Using a panel sample of U.S. firms over the 2001-2016 period, we show that firms' perception of increased political risk levels is associated with higher reliance on trade credit. These results stand up to a battery of robustness checks, including controlling for macroeconomic risks, using alternative measures, and addressing potential endogeneity concerns. In addition, we find that competition, financial constraints, and social trust alter the relationship between political risk and trade credit provision.

In the second essay, we examine the effect of firm-level political risk on corporate cash holdings. We show that higher levels of political risk are associated with more cash holdings among a large panel of publicly traded U.S. firms for the 2002-2016 period.

Firms hold more cash for precautionary and financial flexibility motives in times of political upheavals. We also show that retention of cash flows and the market value of a dollar of cash holdings increase for firms with higher aggregate political risk, and we explain how these cash reserves are channeled. Finally, we document that the positive association between political risk with the level and cash holdings is more pronounced iii for firms operating in a highly competitive market, and less pronounced for firms (i) depending less on R&D, and (ii) whose headquarters are closer to Washington DC.

Overall, our findings imply that political risk is a key determinant of corporate cash holdings.

In the third essay, we investigate the effect of firm-level political risk on managerial risk-taking. Using a large sample of 20,140 firm-year observations covering the 2002-2016 period, we find that the riskiness of managerial decisions in the U.S. is positively associated with political risk. However, this effect becomes reverse over time; managers adjust their behavior on average 3 years after a spike in political risk. We also find that equity-based managerial incentives alter the latter relationship. Our findings are economically significant and are robust to the use of alternative risk-taking measures, various political risk components, and multiple additional tests. Our results have important implications for policymakers and corporate managers by providing direct relevance of political risk to the business decision-making process.

Toutefois, cet effet s'inverse avec le temps ; les managers ajustent leur comportement en moyenne 3 ans après une hausse du risque politique. Nous constatons également que les incitations managériales fondées modifient cette relation. Nos conclusions sont économiquement significatives et résistent à l'utilisation de mesures alternatives de prise de risque, de diverses composantes du risque politique et de multiples tests supplémentaires. Nos résultats ont des implications importantes pour les décideurs politiques et les dirigeants d'entreprise en fournissant une pertinence directe du risque politique sur le processus de prise de décision au sein des entreprises.

Mots-clés :

Risque politique au niveau de l'entreprise ; incertitude politique ; crédit commercial ; gestion de la trésorerie ; prise de risques de gestion. vi guidance and good will to participate in my thesis committee and to provide their constructive comments and thoughtful suggestions.

I am deeply thankful to Professor Bénédicte François for her amazing human qualities and kindness and for her generous support and belief in me. I would also like to thank the former and current heads of the École Doctorale, Organizations, Marchés et Institutions (ED OMI), Professors Sylvie thoron and Rémi Bourguignon for their helpful suggestions and encouragement. I also thank all members and administrative staff of IRG and ED OMI. My special gratitude also goes to Madame Stéphanie Ben Abria for her patience and guidance.

I would like to thank those from whom I learned a lot during different workshops and conferences and who have provided comments on my research work, including Professor Stefano Rossi and the thoughtful discussions on political risk with Professor Branko Milanović. My special recognition goes to Professor Sadok El Ghoul (University of Alberta, Canada) for giving me the opportunity to learn from him during my five-month visit as a research award recipient at the University of Alberta.

My thanks also go to Professors Arnaud Le Ny, Sophie Laruelle, Thierry Paul and every single professor I have had the chance to teach with. I am also thankful to the administrative staff of FSEG Créteil and to all my students for the pleasant teaching experiences and all the heart-warming feedback I got from them. vii I have been fortunate to have great mentors around me, who have always believed in me, and provided me with opportunities to work on the most exciting and challenging projects worldwide, especially Azmat Taufique whose mentorship and leadership is phenomenal, James P. Bond, Sam Chan, David Harris, Hasan Tuluy, and Hildegard Gacek.

I would like to express my genuine gratitude to my friends Yessine, Haikel, Mariana, Dana, Sina, Mahyar, Paria, Alaa, Alberto, Chika for making life more joyful.

I would like to express my heartfelt and profound love for Mantoo for unconditional love and support, and for providing me with the courage, hope, and safety to carry on with this amazing, wholesome life journey together. My love also goes to Abba Abul Hasanat who has been my best teacher and guide and whose kindness and wisdom will always fill my heart.

Finally, my biggest love and sincerest debt of gratitude go to my family: my parents, my sister, my brothers, and my nieces and nephews, to whom my love and attachment are endless. Without their loving support, belief in my decisions and life choices during difficult times, I could have never been able to make it. I thank my parents for always reminding me of my true worth and for never stopping to love and support me.

viii Chapter 1 Table 1-1 This research endeavor is concerned primarily with expanding our understanding of political risk based on empirical data and with its implications mostly for the management of financial resources at the firm-level.

Table of Contents

List of Tables

List of Figures

There is an extensive body of academic work that deals with political risk. However, the fluid nature of uncertainties in today's business environment with respect to the various dimensions of political risk, and how managers interpret and respond to changes as they occur is still underexplored. The consequence of those political changes on business performance is harder to conceptualize, and more challenging to calibrate in today's markets (e.g., [START_REF] Fukuyama | The origins of political order: From prehuman times to the French Revolution[END_REF]Baker, Bloom and Davis, 2016;Rice and Zegart, 2018). The core effort of this dissertation is to consolidate and shed light on the existing literature, to push the boundaries of our understanding and to provide meaningful insights for both practicing managers to use as well as for further academic research. We do so especially by providing more empirical evidence on the relationship between political risk and corporate financial decisions, using more contemporaneous data and novel proxies for political uncertainty.

With regards to the very nature of political risk, we postulate that in spite of the extensive theoretical and practical work done on this matter, our understanding of what constitutes political risk remains nebulous. This is understandable. As compared to more concrete components of business risk such as the market or financial creditworthiness of a firm, an industry or even an economy, political risk is a more amorphous as well as mutable concept (e.g., Zingales, 2017;[START_REF] Giambona | The management of political risk[END_REF]Luo, Chen and Wu, 2017;Rice and Zegart, 2018). Even as we probed deeper into this subject in the course of our research, we found that the scope of what we believe to be political risk should be a more evolving, more expansive, and more dynamic construct. In the past fifty years alone, political systems evolved first, especially through the 1970s and 1980s, with a surge in the number of democracies around the world accompanied by profound changes in underlying social beliefs and economic aspirations. This "golden age" of democratization has been followed more recently in the 21 st century by what [START_REF] Fukuyama | The origins of political order: From prehuman times to the French Revolution[END_REF] refers to as a "democratic recession." Both phenomena have had enormous implications for the dimensions of political risk and therefore for our need to improve and constantly adapt our knowledge.

We therefore start our work in Chapter I with a comprehensive review of the theories and literature of political risk. In this chapter, we sharpen our understanding of the components of political risk and its evolution over time. We find that the earliest academic works appeared to view political risk as mainly stemming from government actions and particularly in the context of cross-border transactions. Thus, as recently as 1970, [START_REF] Truitt | Expropriation of foreign investment: Summary of the post World War II experience of American and British investors in the less developed countries[END_REF] sees political risk generally as "non-business" risks but mainly manifested in events such as nationalization or expropriation by a foreign government. This understanding expanded especially through the eighties and beyond to include domestic events, and politically motivated actions by governments as well as nongovernment actors such as strikes and insurrections which would have the potential to significantly impact the economic outcome for a firm. While expanding the range of forces that create political risk, these broader definitions made the understanding of political risk more complex, multidimensional and more difficult to operationalize and to measure (e.g., Hanseth and Ciborra, 2007). Firms today face increasingly complex and multi-layered facets of political risk and if they ignore it the consequence can be catastrophic.

We argue that in the contemporary context, political risk is even harder to quantify and that no single framework can neatly describe all political upheavals or fit all firm needs. Nevertheless, we try to capture the multiple forms of political risk facing firms in the 21 st century, including geopolitics, internal conflicts, regulatory frameworks, breaches of contract, corruption, extraterritorial reach, natural resource manipulation, social activism, terrorism and cyber threats.

We also explore in the first chapter the theories on the economic effects of political risk and the tools to measure these impacts. We find that unlike for example, financial risk which can be operationalized through metrics such as interest rates, labor supply, and gross domestic product, political risk remains mostly subjective and qualitative. In addition, we note that the measures that do attempt to quantify political risk should be interpreted with care. First, most of these measures generally provide time-bound discontinuous snapshots of political events that can hide substantial global trends.

Second, they usually assess national measures while political risk can emanate from a very local level. Last, there are many political events that have a low probability of occurrence, but when they do occur, the consequence can be catastrophic. We specify that for this dissertation, we use the novel measure of Hassan, Hollander, van Lent, and Tahoun (2019) and the motivations behind our choice of this measure. In the absence of a direct measure of political risk in the literature, this measure is the only one to provide an idiosyncratic assessment of political uncertainty as perceived by the firm. It is a result of textual analysis of corporate conference calls between firm management and analysts, and measures the share of the conversation dedicated to talk about political topics.

In addition to the theory, we review in this chapter a wide body of literature comprising empirical studies on political risk and the effects of political uncertainty at the level of the economy as well as the firm. The influential study of Baker, Bloom and Davis (2016) find for example a high correlation between its innovatively developed index of political uncertainty and stock price volatility, with negative impacts on employment growth rates, investments and aggregate economic output. Other empirical studies demonstrate that increased political risk distorts financing terms and decisions and makes access to financing more difficult (e.g., Julio and Yook, 2012;Gulen and Ion, 2016;[START_REF] Kaviani | Le chapitre examine également la connectivité politique accrue, en particulier des grandes entreprises avec les politiciens[END_REF]. The chapter also looks at the increased political connectedness especially of large corporations (e.g., [START_REF] Khwaja | Do lenders favor politically connected firms? Rent provision in an emerging financial market[END_REF][START_REF] Faccio | Discussion of "Corporate Political Connections and Tax Aggressiveness[END_REF][START_REF] Faccio | Political determinants of competition in the mobile telecommunication industry[END_REF]. Driven by the size and market share of companies, regulatory complexity and increased support to big business across the political spectrum, this phenomenon adds another layer of complexity to the interplay of the relationship between politics and economics. This vast literature remains however inconclusive, indicating that political risk can only be understood by considering the range of interactions between cultural, political, social and economic spheres and the configuration between these and the companies, large and small, local and multinational. To do so, it is essential to have knowledge about the country that is analyzed, to know these connections and to examine the conceivable evolution of these factors.

In Chapter II, entitled 'Firm-level political risk and trade credit,' we move from a review of the existing theoretical and empirical literature on impact of political risk on firms to explore the effect of firm-level political risk on the use of trade credit. Based on previous theoretical and empirical research, we present two competing hypotheses to suggest that trade credit can be either a complement or a substitute to other external sources of funding.

Using a large sample of U.S. firms over the period 2001-2016, we find that high levels of firm-level political risk are associated with more reliance on trade credit. This result is consistent with the second hypothesis that trade credit offers many advantages compared to bank and public lending. Our findings are robust to a battery of robustness checks. Specifically, we show that our evidence persists after using alternative estimation techniques and measures of trade credit, decomposing the aggregate measure of political risk, and addressing endogeneity concerns. We also investigate the potential conditioning effect of other firm and industry-level characteristics and show that the positive effect of political risk on trade credit is stronger for firms operating in competitive environments and periods of higher social trust index, and weaker for firms with tighter financial constraints.

This study contributes to different strands of literature. Specifically, we add to the line of research that examines the effect of uncertainty emanating from politics.

Additionally, our study adds insight to understanding the determinants of corporate financing decisions (i.e., trade credit provision). However, it is worth stressing that this definition of political risk cannot be exhaustive. This measure reflects firm management and stakeholders' perception of political risk and can be quantitatively different from real political risk levels. We cannot uniquely rely on this definition to classify events and risks as being political or not. This proxy includes some judgment; thus care is required both in the construction of the index and in its interpretation.

In Chapter III, entitled 'Firm-level political risk and corporate cash holdings,' we move on from the consequences of political risk on trade credit provision to the management of cash holdings by the firm in response to changes in their assessment of political risk and more specifically, on the management of their cash holdings at the corporate level.

The theoretical basis for this proposition is the precautionary savings theory introduced by [START_REF] Keynes | The general theory of employment, interest and money[END_REF] and developed by [START_REF] Jensen | Agency costs of free cash flow, corporate finance, and takeovers[END_REF] which suggests that the predominant motive for firms to hold more cash is to protect themselves from adverse cash flow shocks. Many other studies empirically and theoretically show that shocks emanating from the political system significantly alter firms' behavior and cause significant fluctuations in investment, cash flows and stock price. This chapter specifically establishes a link between political shocks generating high risk levels and corporate cash holdings. We argue that firms have the incentive to set up financial slack by hoarding cash to avoid the adverse consequences associated with shocks and uncertainty stemming from politics. Furthermore, we test and demonstrate the validity of our hypotheses using a novel firm-level measure of political risk developed by Hassan, Hollander, van Lent, and Tahoun (2019).

Using a large panel of U.S. public firms over the period 2002 to 2016, we show that firms' exposure to political risk indeed increases their cash reserves, cash valuation, and cash flow retention. These additional cash holdings are mainly rooted in reduced investment, increased net working capital and longer maturities in debt issuance. The motive to hold cash in periods of political crises is stronger for firms operating in competitive markets and less pronounced for innovative firms and for firms that are closer to political centers in Washington D.C.

While our finding that political risk is a key determinant of corporate cash holdings is statistically and economically significant, we should note that further research is merited to compare in depth this firm-level variation's effect with the effect of aggregate or sector-level proxies of political risk. Moreover, this measure of political risk embeds qualitative perceptions of managers and participants of the political environment they are in. Thus, it should be interpreted as indicative of risk as it is apparent to firms and stakeholders and indicative of their sentiment because their perceptions may differ from actual risk.

As in nature, the need for adaptability to ensure survival, if not dominance is essential. Chapter IV, entitled 'Firm-level political risk, managerial adjustment for risk-taking and managerial incentives,' concludes that political risk is inherent, pervasive and significantly affect managerial preference for risk-taking over time. Based on prior research, we develop two channels through which political risk can affect managerial risk choices. On one hand and consistent with the agency theory premise of the differentials in risk between principals and agents, managers can become more risk averse in times of high political uncertainty to secure their benefits. On the other hand, the U.S. has strong institutions which strive to maintain economic stability in times of crisis using many bailout tools which can induce moral hazard behavior and induce managers to take more risks.

Using a large panel of U.S. listed firms over the 2002-2016 period, we provide novel evidence that managers engage in riskier activities in periods of high political risk but start to adjust to reduce their risk-taking around the third year. Furthermore, we show the substantial effect of equity-based managerial incentives (mainly Vega) on the relationship between political risk and risk-taking decisions. We run many robustness and additional tests and our results remain quantitatively and quantitatively the same.

We use different measures for firm risk-taking, include additional variables and employ different regression specifications. We also show that the response of firms to political risk is more pronounced for multinational corporations (MNCs) and for financially constrained firms.

This dissertation contributes to literature in finance in various ways. First, it complements previous empirical evidence on determinants of central corporate financial choices, including cash management, trade credit provision and managerial risk-taking.

Second, it explores to what the extent political environment can alter businesses' decision-making and organizational structure. Third, this dissertation presents significant implications for policymakers and corporate managers by emphasizing the growing complexity of the 21 st century political risk environment and by providing direct relevance of political risk to the corporate decision-making process. Finally, improved knowledge about the new environment in which firms operate has substantial implications for organizational researchers; Such times of transition can represent a genuine shift of paradigm, and consequently offer a unique opportunity to test existing theories and develop new ones.

Theories and literature review of political risk

Introduction

Human beings are a bundle of characteristics. One of their main characteristics is that they strive to organize themselves and to master their environments. They are rulefollowing by nature. They conform to norms and entrench rules with transcendent meaning and values (e.g., [START_REF] Hobbes | The Elements of Law[END_REF][START_REF] Locke | Two Treatises of Government[END_REF]. Considered as a result of social forces, political institutions and economic entities such as firms, develop over time as human societies change their organizational structures. [START_REF] Fukuyama | The origins of political order: From prehuman times to the French Revolution[END_REF] documents that underlying the changes in political systems over the last five decades, there was a massive economic and social transformation as well. According to him, these enormous changes are the result of a host of factors, mainly related to the upsurge in the number of democracies during the twenty-year period from 1970 to 1990 followed by the political anxieties and "democratic recession" that have emerged in the first decade of the twentyfirst century. 1 Recently, the occurrence of political changes has been increasing in number 1 In 1973, only 45 of the world's 151 countries were considered as "free" (see, freedomhouse.org). By that era, Spain, Portugal, and Greece, for instance, were ruled by dictators; many Eastern European countries and the Soviet Union seemed to be strong and cohesive societies; China was going through Mao Zedong's cultural revolution; most Latin America had been under military tyrannies; and African countries saw the expansion of the rule by corrupt presidents-for-life groups. By the late 1990s, more than 120 countries were counted as electoral democracies. Liberal democracy had become the accepted form of political system that shaped with it the economic and business landscape. Samuel [START_REF] Huntington | Democracy's third wave[END_REF] refers to it as the third wave of democratization in the history of the modern world. This wave was driven by major external forces, such as (i) the key role played by the European Community (EC) in consolidating democracy in southern Europe and the move toward further free trade economic system and political union; (ii) The withdrawal of Soviet power; (iii) The role of the United States and in magnitude and is fostered by different reasons. [START_REF] Fukuyama | The origins of political order: From prehuman times to the French Revolution[END_REF], among many others, summarizes these reasons to be (i) the greatly expended access to education that made people more aware of their environment and the politics around them; (ii) information technology, which smoothed the spread of ideas; (iii) progress in the modes of transportation and communication and the decreasing costs of transactions; and (iv) higher prosperity, which encouraged people to seek better protection of their welfare and to keep their rights upheld. Rice and Zegart (2018) Markets are therefore not just markets, an entity that is completely isolated from politics. Markets are shaped and constrained by social constructs, regulations, laws, and political decisions. Since the second half of the twentieth century, there has been a strong relationship between economic performance and the survival of political systems (e.g., [START_REF] Diamond | The Democratic Recession. New Ideas on Development after the Financial Crisis[END_REF]. Joseph (2012) also analyzes how social theories portray the world in a particular way and contribute to the global political governance and governmentality.

Thus, understanding how political upheavals can affect businesses is important, especially nowadays in a world that has become more closely intertwined due to the development of information and communication technology (ICT) and the emergence of that acted as a major promoter of democratization and the attractiveness of its economic efficiency as a model to other countries.

2 First, the United Stated and the Soviet Union were the two major superpower rivals in times of the Cold War who set clear divisions between allies and opposers. Trade paths and security politics were also precisely outlined. Overall, the world was delineated, the western capitalist markets on one side and the command of the Soviet Union on the other side.

Second, current supply chains are growing, unlocking firms' potential, thanks to lower shipping and transaction costs, lower offshore wages, and inventory management. Firms expansion overseas is certainly beneficial in terms of growth, but it also rises the likelihood that a political disruption jeopardizes its business. Third, the world nowadays is massively connected through internet, smart gadgets and social media. Henderson Institute has set up an analytical framework applying natural language processing (NLP) to S&P 500 companies' investor communications. It shows that firm management devote more attention to reacting to and preventing the impact of political issues.

Figure 1-1 below displays that U.S. firms devote more resources to discuss economic and political policies in their 10-K files. It also shows that firms with higher exposure to political and economic uncertainty tend to have significantly lower profit margins. The remainder of this chapter provides an overview of political uncertainty definitions, highlights the complexity of the twenty-first-century political risk and outlines the various measures used to assess its effects. Section 3 presents theoretical and empirical literature on the impact of political risk on financial markets and corporate decision-making. Section 4 outlines research on the growing significance of political connectedness between business and political spheres. Section 5 provides some case studies of firms affected by political risk and political connectedness. Finally, Section 6 concludes.

Definitions, complexity, measure and literature of political risk

It is not unusual for all areas of human endeavor to be considered as risk-involving, whether these endeavours are for social, personal, commercial or national purposes (e.g., Adams, 1995;[START_REF] Moore | The business of risk[END_REF]. The literature on risk is abundant, however it is still not the case for political risk literature. Political science and finance literature are haphazard in investigating political risk.

Definitions of political risk

The evolution of the body of knowledge related to the definition and assessment of political risk has not been consistent because it is a nebulous notion. Even though the role of politics in firm decision-making in practice has long been implicitly acknowledged, academic interest has paid attention to this interaction only over the last decade. Political risk sheds light on the gap between discipline and practice. This is reflected in the wide range of political risk definitions.

Nowadays, political risk is generally associated with the risk of financial, market or personnel losses due to political decisions or disruptions.5 Yet, there has been no factual consensus on the precise meaning of what political risk comprises. The definitions range from the general to the specific. In 1962, the primary description of political risk was conveyed by the American Commission on Foreign Investment as every loss of control over ownership or loss of benefits due to government action. [START_REF] Truitt | Expropriation of foreign investment: Summary of the post World War II experience of American and British investors in the less developed countries[END_REF] is among the first academic studies to tackle the spectre of definitions of political risk. 6 Truitt considers 'non-business' risks as political risks and particularly focuses on expropriation and nationalization risks, indicating that political risk is "a broad band of political/economic options available to a host government intent on restricting the activity of the foreign investor […]. Expropriation is only one of a variety of political moves a host government can take against a foreign investor" (1970, p. 22). The magnitude of government actions can be limited such as an occasionally enforced set of rules or severe, like outright confiscation. Subsequently, a strand of literature defining political risk in terms of government or sovereign action had grown. For instance, [START_REF] Ady | Private foreign investment and the developing world[END_REF], [START_REF] Dunning | International investment: selected readings[END_REF], [START_REF] Kronfol | Protection of Foreign Investment: A Study in International Law[END_REF]Aliber (1975) emphasize the negative effects of government interference. More recently, and also with regards to government actions, Jiménez, Durán and De la Fuente (2011) define political risk "as the probability that the State uses its monopoly of legal coercion to renege on the agreements reached with the MC, in order to affect the redistribution of income between the public and private sectors."

Another category of literature portrays political risk with respect to occurrences of political events or constraints of a political nature. Political events are changes in governments or heads of state and any kind of violent disturbance such as riots [START_REF] Robock | Political risk-identification and assessment[END_REF]. Constraints however can be imposed at the industry-or firm-level and involve risks of expropriation, restrictions on profit repatriation, discriminatory taxation, and public sector competition (e.g., Bass, McGregor and Walters, 1977;Zink, 1979;[START_REF] Haner | Rating investment risks abroad[END_REF]. [START_REF] Robock | Political risk-identification and assessment[END_REF] considers such events as discontinuities that directly affect firm activities. According to Robock, political risk exists when "(i) discontinuities occur in the business environment; (ii) they are difficult to anticipate; and (iii) they result from political change. To constitute a 'risk' these changes in the business environment must have the potential for significantly affecting the profit or other goals of a particular enterprise" (1971, p.7). Robock's work leads to a third major cluster of literature that define political risk in terms of environment (e.g., [START_REF] Robock | Political risk-identification and assessment[END_REF][START_REF] Haendel | Overseas investment and political risk[END_REF][START_REF] Rummel | How multinationals analyze political risk[END_REF][START_REF] Hofer | Globescan: A way to better international risk assessment[END_REF]. It differs from the first two strands of definitions providing greater focus on uncertainty within the environmental context. A fourth and last category views political risk also as environmental. It differs, however, from the third category in that it doesn't define political risk per se, but acknowledges sources of risk to firms emanating from the political environment (e.g., [START_REF] Drake | Floundering with foreign investment planning[END_REF]Flint, 2016;Rice and Zegart, 2018). In recent years, as the global political risk scenery has become more turbulent, definitions of political risk have also evolved. Rice and Zegart (2018) state that political risk defined as "the probability that a political action could significantly impact a company's business" is affecting more businesses in more ways than ever before. Today however, political risk is a multidimensional phenomenon [START_REF] Jakobsen | Old problems remain, new ones crop up: Political risk in the 21st century[END_REF].

Table 1-1 below reviews the existing literature with respect to the evolution of political risk definitions. [START_REF] Truitt | Expropriation of foreign investment: Summary of the post World War II experience of American and British investors in the less developed countries[END_REF]; [START_REF] Ady | Private foreign investment and the developing world[END_REF]; [START_REF] Dunning | International investment: selected readings[END_REF]; [START_REF] Kronfol | Protection of Foreign Investment: A Study in International Law[END_REF]; Aliber (1975); [START_REF] Jiménez | El riesgo político como determinante de la estrategia de localización de las empresas multinacionales españolas[END_REF] Political risk derives exclusively from governmental actions or from some of the powers of the State.

Political risk viewed as an action of the normative framework, specifically the executive and legislative power limits operationalization of this risk. According to Alon and Martin (1998), this view may lead to poor selection of data sources and a flawed interpretation of political risk. First, it does not allow the distinction between events or actions that are of concern and which are not. Second, none of the authors of this category has presented political risk in a way that can be linked to corporate decision-making. Third, the scope of political investigation is limited by the focus on discontinuous changes only. Finally, concentration on the negative consequences of governmental actions may strengthen the implicit assumption that government intervention is not effective, and this assumption may not be universally valid. [START_REF] Robock | Political risk-identification and assessment[END_REF] [START_REF] Hofer | Globescan: A way to better international risk assessment[END_REF] Political risk emerges at the junction of the political environment and the firm environment (for example, a nationalization, riots and strikes, etc.), typically associated unfavorable consequences for the firm.

These classifications expand the first categories to contemplate in such studies a large array of actors, both nationally and internationally. Governmental or legislative actors are not the only generators of political risk. There are also non-state actors like political parties, groups of society or unions. However, this view implies that political risk is very complex and multidimensional which makes its operationalization more difficult. Moreover, these definitions also focus on discontinuities for political risk assessment. Since, it considers a broad strand of political risk sources, it does not pursue the nature of these sources in depth.

Drake

and Prager (1977); [START_REF] Simon | Political risk assessment-past trends and future-prospects[END_REF]; [START_REF] Jakobsen | Old problems remain, new ones crop up: Political risk in the 21st century[END_REF]; Flint (2016); Rice and Zegart (2018) Political risk is defined as the probability that a political action emanating from different sources and political action generators that can be state actors or other external sources.

The more thinking and research about political risk are undertaken, the more the operationalization of political uncertainty is refined and redefined. Political uncertainty is viewed as the most influential among all the sources of uncertainty. Using a unique survey on managers' risk aversion, [START_REF] Giambona | The management of political risk[END_REF] document that political risk is more significant than commodity and input risk for firms. They also show that nearly 50% of firms not only reduce but avoid foreign direct investment because of political risk.

Political institutions and their arrangement in the policy decision-making process is a reflection of the interest of different groups. The behavior of these groups is a source of randomness which can results in a variety of political shocks. These shocks are very often associated with high level of uncertainty since interest groups try to protect their own benefits by the means of elections, legal amendments and demonstrations, as well as armed conflicts and wars. Many acrimonious conflicts in politics, around election time for instance, lead often to a significant change in government economic policies.

Therefore, political turmoil increases the risk of economic actions and changes that can alter corporate activities. In the following sections, we try then to respond to the emergent questions: How complex is political risk? What and how significant are the economic and financial costs of political uncertainty?

Complexity of political risk

Today's world is much more crowded, fluid, and uncertain than ever before. This longer and leaner world and global supply chains leave companies in higher vulnerability against disruptions. In addition to market-based issues, firms are also exposed to political decisions. As discussed in the prevoius section, the firm environment is wider and more complex and generators of political risk affecting economic activity are numerous (see, Table 1-2). For instance, technology is massively and increasingly reducing the cost of collective action. With more than 48% of the world being connected, it has become much easier to join a common cause and to escalate issues worldwide. Political risk has long-term effects as the risk increases over time given the large number of potential events and changes over time. [START_REF] Truitt | Expropriation of foreign investment: Summary of the post World War II experience of American and British investors in the less developed countries[END_REF] points out that the definition of political risk is ambiguous and political actions are increasingly complex such that the spectrum of definition is becoming broader. Therefore, twenty-first-century political risk is extremely hard to quantify, and no framework can adequately fit all firm needs or describe all political upheavals. Yet, firms and investors try to investigate and analyze risks stemming from politics to enable active crisis management and continuous learning in a world that keeps changing at a rapid pace.

report entitled "Silicon Valley start-up culture gone awry" delving into the discrimination and sexual harassment within Uber.

Theories and measures of political risk

Theories on the economic effect of politics

The seminal work of Jensen and Meckling (1976) defines the firm as a nexus of contracts. Based on this concept, contemporary economics has developed the prevailing view that contractual obligations solve issues of interest conflicts between contracting parties. This view may still hold for very small private remote firms, but it definitely does not sufficiently hold for large corporations. The seminal work of [START_REF] Nordhaus | The political business cycle[END_REF] and [START_REF] Hibbs | Political parties and macroeconomic policy[END_REF] come to add the political dimension to the determinants of business cycles and the economic activity of firms. Governments can attain political benefits before elections in two ways: (i) by using economic instruments which creates opportunistic political business cycles; and (ii) by satisfying the expectations of electoral bases thereby creating partisan business cycles.

Using politico-economic models, [START_REF] Frey | Politico-economic models and cycles[END_REF] and Frey and Schneider (1978b) argue that governments with strong ideology have more opportunistic and short-sighted economic policies in times of elections. The pre-election popularity of the incumbent strongly accentuates governmental policies. In the same line of thought, [START_REF] Alesina | Partisan cycles in congressional elections and the macroeconomy[END_REF]1995) develop a theory of rational partisan business cycles where progressive and conservative voters anticipate the winning party and the variation in the economic indicators accordingly. They show that there is strong association between macroeconomics and micropolitics. For example, markets expect higher inflation and therefore higher interest rates when a progressive party is more likely to win the elections.

More recently, numerous authors theoretically examine the political effects of institutions. [START_REF] Bachman | The effect of political risk on the forward exchange bias: the case of elections[END_REF], among few others, uses a model based on Krasker's "peso problem" approach to examine the effects of elections in developed countries on the forward bias rate. 8 Fowler (2006) develops a theory of policy risk and extends the rational partisan theory that focuses on uncertainty stemming only from elections. Amongst the substantial studies, Acemoglu and Robinson (2006a;2006b) develop a theoretical model to assess the effect of politics on countries' development and growth. The model of Acemoglu and Robinson (2006a), for instance, explains the reasons behind the industrial development of Germany, Britain, and the U.S. and the blocked growth in Russia and Austria-Hungary in the nineteenth century. The authors argue that politically powerful groups or as they call them political elites can have "political replacement effect" and erode competition and innovation within an economy. Their model also shows that the higher the political stakes, the more significant these elites' incentives would be to block the economy. In a more recent work, [START_REF] Acemoglu | Economics versus politics: Pitfalls of policy advice[END_REF] argue that former frameworks on economic policies have neglected the political dimension. This view implies that economists seek solutions for market failures that do not take into account politics. [START_REF] Acemoglu | Economics versus politics: Pitfalls of policy advice[END_REF] define a new theoretical framework which compares two periods of economic policy decision-making with no economic linkages between the two periods. In the first period, the choice of economic policy is made independently of the political equilibrium. In the second period however, politicians have a "window of policy opportunity." They show that the political aspects of economic policy are important. Economic policy that only focuses on reducing market distortions while leaving out the political dimension is inadequate. [START_REF] Simon | A theoretical perspective on political risk[END_REF] documents the difficulties of the development of a theory of political uncertainty including the very interdisciplinary nature of such analysis and the ongoing corporate and investor skepticism with regard to quantification of non-economic variables. Rice and Zegart (2018) explain that political risk is hard to measure. Even when it is put in quantitative terms (e.g., Brexit polls), it is still not easily understandable. Unlike financial risk which can be operationalized through metrics like interest rates, labor supply, and gross domestic product, political risk remains subjective and qualitative.

Political turmoil demands a fine interpretation and a sense of what is political stability, corruption, cultural norms, geopolitics, etc. However, and despite these obstacles, several authors have tried to put forth a theory of political uncertainty. Some patterns and frequent trends have been identified and analyzed. [START_REF] Brennglass | The Overseas Private Investment Corporation: A Study in Political Risk[END_REF], for instance, develops two theoretical classifications. The first classification is the interaction between speculative risk (i.e., the probability of losses and profits) and pure risk (i.e., the probability of losses or non-losses). The second classification is between fundamental political risk (i.e., socio-economic shocks) and particular risk (i.e., a specific origin of political risk). Several other authors establish a theory of political risk based on the distinction between micro and macro risk (e.g., [START_REF] Robock | Political risk-identification and assessment[END_REF][START_REF] Grosse | The bargaining relationship between foreign MNEs and host governments in Latin America[END_REF]. The micro political risk affects certain industries or certain types of investments or firms. The macro political risk however affects all industries and investments. Other classifications propose a theory of political risk based on a series of sources that political uncertainty stems from (e.g., [START_REF] Haner | Rating investment risks abroad[END_REF][START_REF] Simon | Political risk assessment-past trends and future-prospects[END_REF]Alon and Martin, 1996). Political risk can derive from: (i) internal sources or external sources (e.g., global environment; (ii) government or society; and (iii) economic factors or noneconomic factors.

Measures and proxies for political risk

The current literature builds on the latter theoretical work to find a reliable enough measure of political risk or policy uncertainty. As mentioned in the discussion in the previous section, political uncertainty was at the outset measured by major political events, mainly elections. Different studies assess political risk or policy uncertainty based on national or gubernational elections. A large and growing strand of literature uses timing around national elections or specific political events to account for policy uncertainty and political risk (e.g., [START_REF] Bachman | The effect of political risk on the forward exchange bias: the case of elections[END_REF]Pantzlis, Stangeland, and Turtle, 2000;Fowler, 2006;[START_REF] Białkowski | Stock market volatility around national elections[END_REF][START_REF] Floros | The influence of the political elections on the course of the Athens Stock Exchange 1996-2002[END_REF][START_REF] Repousis | Stocks' prices manipulation around national elections[END_REF]Kelly, Pástor, and Veronesi, 2016;Jens, 2017). Event studies around political events or dummy variables which accounts for periods of political turmoil are employed. For instance, the study of Pantzlis, Stangeland, and Turtle (2000) employs event study around 129 national elections week in 33 countries across the world from 1975 to 1995. In the context of the U.S., Fowler (2006) empirically investigates the predictions of rational partisan theory and policy risk theory. The author conducts an event study around Presidential and Congressional elections over the 1988-2000 period.

Recently, Jens (2017) investigates the effect of political uncertainty on investment.

Political uncertainty is measured by a dummy variable equal to one when a U.S.

gubernatorial election occurs in a specific state and a specific year starting in 1984 and ending in 2008. Table 1-5 shown at the end of this section summarizes the main measures and proxies of political risk used in the finance literature.

Another strand of literature provides other methodologies to measure political risk.

One of these methodologies is index construction based on risk factors. A political risk index can be an aggregation of scores (or weighted scores) assigned to different subcategories of uncertainty. For instance, Howell (2011) developed a measure for political uncertainty known as the International Country Risk Guide (ICRG). The guide comprises 22 factors belonging to three risk subcategories: political, financial and economic. There is an index for each subcategory separately. The political risk index score ranges from 0 to 100, the financial risk index from 0 to 50 and the economic risk from 0 to 50. The aggregate country risk is constructed based on the sum of the three indices divided by two and its score ranges from 0 to 100 (range 80-100 being the category of very low risk and range 0-50 being the category of very high risk).

The political risk index according to [START_REF] Howell | International country risk guide methodology[END_REF] is composed of 12 main risk factors and 17 subcomponents, weighted according to their impact. The twelve variables are: government stability, socio-economic conditions, investment profile, internal conflict, external conflict, corruption, military in politics, religious tension, law and order, ethnic tensions, democratic accountability, bureaucracy quality. Appendix 1-1 provides the detailed composition of this index and the weights related to each factor and subcategory.

Another similar study to note is [START_REF] Muñoz | Quantifying the geopolitical dimension of energy risks: A tool for energy modelling and planning[END_REF].

The authors use a multivariate statistical and a factor analysis technique to quantitatively construct an index of the geopolitical dimension of energy risk for 122 countries spanning the 2000-2010 period. The index is called the geopolitical energy supply risk index (GESRI) and is composed of variables divided into four major vectors based on the correlation and the underlying common features between them. The vectors of risk are economic, energy-specific, socio-political, and EU-relations. The authors had initially started with 143 variables before ending with an index that is composed of 47 selected variables that are represented in Appendix 1-2.

In recent years, and with the development of computer science and computational techniques, several authors have developed measures of policy uncertainty and political risk based on textual analysis and computational linguistics approaches. Several recent studies use these techniques and are summarized in Table 1-5 (e.g., Gentzlow and Shapiro, 2010; Loughran and McDonald, 2011;Wiesen and Wysocki, 2015;Baker, Bloom, and Davis, 2016;Hassan, Hollander, van Lent, Tahoun, 2019;[START_REF] Caldara | Measuring geopolitical risk[END_REF][START_REF] Ahir | The world uncertainty index[END_REF]. For instance, Loughran and McDonald (2011) is one of the first papers to construct a measure of political uncertainty based on the textual analysis of the tone in 50,115 firm-year 10-K report filed between 1994 and 2008. Prior to this study, the most frequently used source for word tone classification had been the Harvard-IV-4 TagNeg (H4N) dictionary, which according to the authors of this study misclassifies almost three-fourths (73.8%) of the words when evaluating tone in financial fields. 9 The authors end up with a new classification of 2,337 words with negative tone in the financial sense (i.e., Fin-Neg dictionary) about half (1,121) of which overlap with the H4N, and are partly related to political risk. Another commonly used measure is the Baker, Bloom, and Davis (2016) All the measures above attempt to provide quantitative measures for political risk.

They are complementary and not in competition. They provide incrementally additional information as political risk is complex in nature. However, they should be interpreted with care. First, most of these measures generally provide time-bound discontinuous snapshots of political events that can hide substantial global trends. Second, they usually assess national measures whereas political risk, as discussed in the previous section, can emanate from a very local level. Last, there are many political events that are less likely to occur, but when they do, they have major consequences on the economic activity. Such events with low-likelihood/huge-effect are very tricky as they are prone to outlier bias.

Another example is when a firm is exposed to cyber threat. It may know the probability for such an event to occur but cannot estimate its costs and consequences.10

Empirical studies on political risk and uncertainty

Effects of political uncertainty

Political uncertainty has long been studied in interaction with general economic outcome. Politics unavoidably affects financial markets. Frey and Schneider (1978) for instance empirically show the interdependence between the political and economic spheres and this relationship is reflected in the popularity function. Using quarterly data from the U.S. over the 1953[START_REF] Nordhaus | The political business cycle[END_REF]period, Frey and Schneider (1978) point out that governments steer economies when the president's popularity is low due to high unemployment and inflation rates. Political uncertainty is shown to be linked to shrinking economic output, a sharp decrease in investment and capital expenditure. Alesina and Tabellini (1989) show that uncertainty stemming from fiscal policies in moments of regime change causes capital flight, lower domestic investment and significantly increases external debt. The authors use data from four Latin American countries to also show that political polarization is also associated with redistributive policy polarization.

More recently and especially with the rising political turmoil over the last decade, a growing body of literature has also empirically delved into the relationship between political risk and economic growth indicators. Julio and Yook (2012) provide evidence that in election times, investment expenditure decreases by an average of 4.8% and that countries with civil law origin suffer more from this temporary decline in investment.

The authors draw their results from a large sample of 248 elections comprising data from 31 countries with legislative elections, 16 countries with presidential elections, and 1 country with prime ministerial elections. Using time-series models over the period spanning 1985-2012, [START_REF] Jones | The time-varying correlation between uncertainty, output, and inflation: Evidence from a DCC-GARCH model[END_REF] show that the relationship between policy uncertainty and economic output is consistently negative while the impact of uncertainty on inflation proportionally depends on crude oil prices.

The famous and most influential study of Baker, Bloom and Davis (2016) develops new proxies of policy uncertainty for the U.S. and eleven economies and highlights its major effects on different features of the economy. Based on the real option theory, the findings show strong positive relationship between the index and firm-level stock price volatility implied by equity options and a substantially significant and negative effect on employment growth rates, investment and overall aggregate economic output. In this same vein, Gulen and Ion (2016) use the same index and empirically show that policyrelated uncertainty is negatively associated with capital investment at the firm-level in the U.S. and at the industry-level across Canada, the U.K., Germany, France and Italy.

This effect can be explained by the increasing cost of borrowing and precautionary delays induced by investment irreversibility. Moreover, and at the macro level, political uncertainty, especially stemming from taxes, government spending, and regulation, has a substantial negative effect on firm acquisitiveness and considerably lengthens the completion time of merger and acquisition deals (Bonaime, Gulen and Ion, 2018) Other studies show that political uncertainty is associated with more stringent access to financing markets (e.g., Cao, Duan, Uysal, 2013;[START_REF] Waisman | The effect of political uncertainty on the cost of corporate debt[END_REF]Gungoraydinoglu, Çolak and Öztekin, 2017;[START_REF] Çolak | Global leverage adjustments, uncertainty, and country institutional strength[END_REF][START_REF] Gad | Firm-level political risk and credit markets[END_REF]. Using the monthly political uncertainty index of Baker, Bloom and Davis (2016) over the 1980-2012period, Waisman, Ye and Zhu (2015) find that presidential and regional elections times affect U.S. corporate bond issuance and pricing. The findings are backed by the hypothesis that bond holders embed additional risks when the levels of political uncertainty are prominent and that reflects on the pricing models. Furthermore, Gungoraydinoglu, Çolak and Öztekin (2017) and [START_REF] Çolak | Global leverage adjustments, uncertainty, and country institutional strength[END_REF] consistently show that political uncertainty positively affects investors' risk premium and the marginal costs of placement and new issuances. The authors also point out that political risk has drastic and negative effects on corporate leverage levels and the speed of adjustment towards their optimal capital structure. Overall, political risk is associated with higher transaction costs, higher equity and debt cost, longer duration between debt issuances, less leverage, longer leverage adjustment process, and reduced frequency as well as volume of capital issued.

Scholars have long been interested in investigating to what extent politics can affect

financial markets and stock price movements. The theory of political cycles is a solid background for research work linking political science to finance and macroeconomics (e.g., [START_REF] Niederhoffer | Presidential elections and the stock market[END_REF][START_REF] Agmon | Domestic political risk and stock valuation[END_REF]Billingsley and Trucker, 1987). This theory distinguishes between (i) the electoral cycle, and (ii) the partisan theory. On one hand, the electoral cycle is characterized by the opportunism of incumbent politicians who try to steer the economy by talking about incentives to win elections. On the other hand, the partisan theory is driven by the policymakers' ideologies that can impact capital markets in different ways.

Along this line of thought, the empirical literature on the relationship between stock market behavior and political risk is extensive, dating back to [START_REF] Niederhoffer | Presidential elections and the stock market[END_REF] study. Allivine and O'Neill (1980), for instance, study market behavior around US elections and point out that stock price movements are not random and follow the electoral cycle. This effect is driven by the change in the macroeconomic policies amid presidential elections in the U.S. [START_REF] Foerster | Stock Market Performance and Elections: Made-in-Canada Effects?[END_REF] documents the same effect in the Canadian stock market. Canadian markets follow the four-year electoral cycle and react to government changes. In addition, [START_REF] Foerster | The transmission of US election cycles to international stock returns[END_REF] Another strand of literature has studied the response of stock markets but from the perspective of voter opinion polls mainly in the U.S. market and finds mixed results on the effect of elections (e.g., [START_REF] Gwilym | The efficiency of stock and options markets: tests based on 1992 UK election opinion polls[END_REF][START_REF] Thompson | The stock market response to voter opinion polls[END_REF][START_REF] Gemmill | Political risk and market efficiency: tests based in British stock and options markets in the 1987 election[END_REF]. For example, in an attempt to update the study of Gemmil (1992), [START_REF] Gwilym | The efficiency of stock and options markets: tests based on 1992 UK election opinion polls[END_REF] find that there is a strong correlation between the opinion polls in the U.K. and the movement of the stock and options markets index.

More available measures for political risk have been contributing to the multiplication of recent studies on the impact of political risk on stock prices and firm valuation. Political uncertainty creates information asymmetry; investors become less informed, financial instruments' prices reflect less information, and managers become reluctant to use such information to make investment decisions. Information asymmetry intensifies as stock prices become noisier because insiders are more informed than outside shareholders (e.g., Bachmann, Elstner and Sims, 2013;[START_REF] Bloom | Fluctuations in uncertainty[END_REF][START_REF] Scotti | Surprise and uncertainty indexes: Real-time aggregation of real-activity macro-surprises[END_REF].

Studies like Bachmann, Elstner and Sims (2013) and [START_REF] Bloom | Fluctuations in uncertainty[END_REF] document that uncertainty is amorphous and that when macroeconomic predictions are noisy, forecasters are less confident about their forecasts that are usually more optimistic than actual outcomes during periods of high uncertainties. Pástor andVeronesi (2012, 2013) likely to deviate from "revelatory price efficiency" and include less private information.

In addition, options price, variance, and tail risks are the most expensive prior to a political upheaval characterized with high uncertainty. In the same vein, Jens (2017) uses gubernational elections as a proxy for political risk and points out that firms are more affected by political uncertainty than economic uncertainty and that political risk is associated with a 5% decline in investment and more stock volatility spikes.

Several empirical studies also shed light on the implications of political risk on firm valuation and other topics related to corporate financial structure (e.g., [START_REF] Henisz | Spinning gold: The financial returns to stakeholder engagement[END_REF]. In times of political upheaval, firms lower values of cash, without stakeholder support, can drive down shareholder value. Nguyen and Phan (2017) provide robust evidence on the negative relationship between political uncertainty and merger and acquisition (M&A) deals both in terms of number and value.

Furthermore, the authors show that the deals take longer to be completed due to the increased prudence of acquirers during periods of high risk as well as their preference for stock payment and lower bid premiums. They also find that deals undertaken during periods of high policy uncertainty are likely to improve acquirer operating performance and create greater shareholder value in the future. In the same line of thought, Bonaime, Gulen, and Ion (2018) show that political and regulatory risks negatively affect both the number and the volume of M&A transactions both in the aggregate and at the firm-level.

Consistent with the real option theory hypothesis, this effect is stronger when the deal is less reversible or when the stock returns are more sensitive to policy uncertainty. The authors further show that there is no mean reversion, indicating that the acquisition deals are lost rather than just delayed. [START_REF] Cao | Political uncertainty and cross-border acquisitions[END_REF] use a sample of 47 countries and find that firms strategically time their cross-border M&A activities according to national election timing to mitigate political risk. The study also shows that acquirers in crossborder M&A deals get higher announcement returns in comparison with local deals. Very recently, [START_REF] Chatjuthamard | The effect of political risk on shareholder value and the mitigating role of corporate social responsibility (CSR)[END_REF] use the novel firm-level political risk measure of Hassan, Hollander, van Lent and Tahoun (2019) and put forth empirical evidence of the moderating effect of corporate social responsibility on the relationship between political risk and shareholder wealth.

Effects of political connectedness

The phenomenon of big corporations that can influence politics is not new. Berle and Means wrote in their 1932 book The Modern Corporation and Private Property: "The rise of the modern corporation has brought a concentration of economic power which can compete on equal terms with the modern state -economic power versus political power, each strong in its own field. The state seeks in some aspects to regulate the corporation, while the corporation, steadily becoming more powerful, makes every effort to avoid such regulation... The future may see the economic organism, now typified by the corporation, not only on an equal plane with the state, but possibly even superseding it as the dominant form of social organization."

Firms can engage in political lobbying or corruption to preserve their benefits over time and to seize a substantial first mover advantage. Zingales (2017) documents that in 2015, ten companies were in the largest 30 entities worldwide, ranking even before most governments. The phenomenon of firms becoming highly politically connected is driven by different factors. Over the last three decades, the power in the U.S. has been concentrated in the business sphere. Corporations have been setting rules and changing the game. Behind this phenomenon, there are three main reasons according to Zingales ( 2017): (i) The size and market shares of companies are becoming bigger and bigger and thus creating monopolies with stronger interests, (ii) Regulation is becoming more complex and that creates room for opportunistic behavior and for vested interest to tilt the playing field to their advantage, and (iii) The diminished anti-business force among Democrats and the increasing support to the interest of big businesses.11 Another factor can be that firms try to hedge against political expropriation or creeping expropriation. Expropriation can be driven by the increasing populist pressures and influenced by monopolies and/or rent seeking of highly politically connected entities.

This risk is becoming more prominent as most of the time power does not arise from meritocracy, a skill gap, or a technological lead, but from a first mover advantage.

It has also been shown that politicians extract rents from companies they manage.

The other way around, generally, firms with high sensitivity to government policy uncertainty are more likely to have political connections and to contribute to political candidates; they donate more to candidates for elected office than less sensitive firms (e.g., Zingales, 2017).

Anecdotal evidence suggests that political risk has major effects on firms' political [START_REF] Acemoglu | Persistence of power, elites, and institutions[END_REF] show the substantial role of individuals or groups of elites in institutional transitions. They argue that these changes affect the political power which encourages elites (or landed elites) to focus on increasing investment in the de facto power to offset the de jure power. In another research work [START_REF] Acemoglu | Dynamics and stability of constitutions, coalitions, and clubs[END_REF] further point out that the dynamics of the collective decision-making of certain elites can reflect in the decision-making of governmental entities. In the same line, Besley and Prat (2006) develop a model of democratic politics in which pressure or bribes from incumbent politicians alter media firms' coverage and make them produce more positive coverage than otherwise.

As large modern corporations have facilitated the massive concentration of economic and political power, a growing body of literature has been examining this relationship. For example, Faccio (2006) conducts a cross-country study to investigate the behavior of firms with controlling shareholders and top management who are also members of parliaments or national governments. The author shows that firms over time have increasing number of connections and political affiliations in Congress and with the executive power. These firms enjoy greater market power (6.7% higher differential market share), better access to financing markets and lower taxation. Political connections are predominantly common in countries with higher levels of corruption, countries imposing restrictions on foreign investments, and countries with less transparent systems.

Corporate political connections are widespread. With respect to the literature on political connections, [START_REF] Kim | Corporate political connections and tax aggressiveness[END_REF] provide new evidence that tax aggressiveness is higher in politically connected firms. This effect is more pronounced with the nomination of politically engaged directors. The underlying hypothesis is that political connections reduce detection risk and transparency pressures and the political costs of aggressive tax planning. 13While the latter study presents important results because evidence of tax-related benefits of political connections has been limited, there has been a more extensive empirical literature documenting other benefits; Politically connected firms have less stringent regulatory oversight [START_REF] Kroszner | Interest-group competition and the organization of congress: theory and evidence from financial services' political action committees[END_REF], more privileged access to credit (e.g., [START_REF] Houston | Political connections and the cost of bank loans[END_REF][START_REF] Khwaja | Do lenders favor politically connected firms? Rent provision in an emerging financial market[END_REF], favorable allocation of government procurement contracts [START_REF] Goldman | Do politically connected boards affect firm value?[END_REF], bailouts [START_REF] Faccio | Political connections and corporate bailouts[END_REF], a lower likelihood of fraud detection [START_REF] Yu | Corporate lobbying and fraud detection[END_REF], and lower enforcement outcomes [START_REF] Fulmer | Negation of sanctions: the personal effect of political contributions[END_REF].

With regards to the determinants of competition/monopolistic pressures, Faccio The FTC antitrust case against Google was dropped in the United States (but not in Europe). The leaked staff report concluded that Google had unlawfully maintained its monopoly over general search and search advertising by "scraping content from rival vertical websites", "by entering into exclusive and highly restrictive agreements with web publishers that prevent publishers from displaying competing search results or search advertisements", and "by maintaining contractual restrictions that inhibit the crossplatform management of advertising campaigns. In spite of this indicting report, the FTC commission unanimously decided to drop the case. One wonders what role did the frequent visits paid by Google employees to the White House play: between Obama's first inauguration and the end of October 2015, employees of Google and associated entities visited the White House 427 times, including 21 small, intimate meetings with President Obama. This effect is more pronounced in countries operating under the civil law.

Jones and Olson (2013) 1985-2012

The U.S. Economic policy uncertainty consistently affects the economic output in a negative manner.

The correlation between uncertainty and inflation becomes positive with the increase of crude oil price and vice-versa.

Political risk and financing decisions and cost

Francis, Hasan and Zhu (2014)

1990-2010

The U.S. When the firm is idiosyncratically exposed to political risk, lenders price that risk and propose loans with higher spreads.

Gilchrist, Sim, and

Zakrajšek (2014)

1976-2012

The U.S. Financial distortions stemming from uncertainty shocks widen loan spreads which leads firms to slash their capital expenditures.

Leverage is procyclical while credit spreads is countercyclical.

Sweden, The U.K., the U.S.

The disruptive effects of political risk generate market frictions that increase transaction costs and slow down the speed of adjustment to the optimal leverage ratio for firms.

Gad, Nikolaev, Tahoun and van Lent (2020)

2002-2016

The U.S. The pricing and liquidity of public debt, the cost of private debt, credit default swaps spreads and recovery rates are critically affected by political risk.

Firms engage in political activism to mitigate these risks.

Political risk, stock markets and firm valuation

Allivine and O'Neill 1980) 1961-1978 The U.S.

(
There is a strong four-year cyclicity between stock prices and shift in the macroeconomic policies prior and after presidential elections.

Foerster and Schmitz (1997)

1957-1996 18 countries

The pervasiveness of the U.S. presidential election cycle affects international stock markets' returns.

Reilly and Luksetich (1980) 1990-1976 The U.S. The market dislikes the uncertainty prior to the elections and prefers

Republicans at least in the short run following the elections.

Gwilym and Buckle

(1994) 1992

The U.K.

A strong correlation is found between the opinion polls and the FTSE100 share index in the stock and options markets of London while the prices of FTSE100 index options do not follow as closely. [START_REF] Goodell | US presidential elections and implied volatility: The role of political uncertainty[END_REF]) 1992[START_REF] Grosse | The bargaining relationship between foreign MNEs and host governments in Latin America[END_REF], 2000, 2004[START_REF] Floros | The influence of the political elections on the course of the Athens Stock Exchange 1996-2002[END_REF] The U.S.

(elections) political risk around U.S. presidential elections affects stock market volatility; the S&P 500 index volatility increases along with positive changes in the probability of the eventual winner.

Kelly, Pástor and

Veronesi ( 2016) 1990-2012 20 countries Options provide valuable protection against the risk stemming from major political events. Option price take into account political uncertainty and is more expensive before a political event as well as variance, and tail risks.

Jens (2017) 1984-2008

The U.S.

(328 elections)

Firms are more affected by political uncertainty than economic uncertainty.

Political uncertainty is associated with a 5% decline in investment and more stock volatility spikes. Firms that are connected to Republican Party have higher stock returns amid such nomination. [START_REF] Yu | Corporate lobbying and fraud detection[END_REF]) 1998-2004 The U.S.

Lobbying firms have significantly lower chances to be detected by regulators for fraud (38% less likely to be) and can avoid detection for longer periods (117 days on average).

Firms engaging in fraud spend 77% more on lobbying than nonfraudulent firms.

Blanes i Vidal, Draca

and Fons-Rosen (2012).

1998-2009

The U.S. Lobbyist firms which are connected to U.S. Senators suffer an average 24% decrease in revenue when their Senator leaves the Senate; an example of rent seeking activity.

Houston, Jiang, Lin and Ma (2014)

2003-2008

The U.S. The cost of bank loans is significantly and negatively related to political connectedness of firm board members. [START_REF] Kim | Corporate political connections and tax aggressiveness[END_REF]) 1999-2009 The U.S.

There is a positive relationship between political connection and tax aggressiveness.

The degree of tax aggressiveness rises with the nomination of politically connected directors.

Cases and examples

The Blackfish: an example of the 21 st century political risk

Gabriela Cowperthwaite, an independent Los Angeles-based filmmaker used to take her twins to watch the orcas show in the water at the San Diego SeaWorld Entertainment theme park. In 2010, Gabriela was touched by an article in the newspaper which reported the death of a popular SeaWorld trainer, Dawn Brancheau, by a 13,000pound orca, Tilikum. She then decided to dedicate the next years to make a short movie/documentary called 'Blackfish' on the various practices related to orcas' captivity conditions. The documentary portrayed SeaWorld's domestic breeding programs, the separation of calves from their mothers and the safety conditions of both the orcas and the trainers. In 2013, 'Blackfish' was aired on CNN and by 2014 it had been seen by more than 60 million people.

The low-budget film 'Blackfish' started a public outcry from students, dozens of celebrities and artists who tweeted negatively about the park and encouraged people to boycott the park and pressured the authorities to enact legislation banning the breeding of orcas. Social media helped raise awareness which also pushed the organization People for the Ethical Treatment of Animals (PETA) to campaign against SeaWorld Parks and Entertainment, Inc. The company has been fighting for its survival with an 84% drop in profits, a 60% plunge in stock price and the resignation of its CEO.

Until today, the stock price still has not recovered.

"Mickey

Conclusion

In this first chapter, we show that political risk has long been studied. However, its context today is changed, and the risks have become more complex, sustained and pervasive. This complexity has invited scholars to develop awareness regarding the necessity of political risk measurement to be taken into account in corporate decisionmaking. Today's geopolitical environment in the era of transformations, technological disruptions, globalization challenges, changes in the "rules of the game" around economic integration and expansion, presents significantly increased risks which businesses need to navigate. This endeavor of political risk calculation should proceed as an approach that integrates different factors and human judgment to better confront the uncertainties of this age. Alesina, A., & Rosenthal, H. (1995). Partisan politics, divided government, and the economy.

Cambridge University Press. Bloom, and Davis (2016), for instance, shows that political crises shrink output and employment. Several other studies point out that policy uncertainty is associated with a sharp decrease in investment and capital expenditure at the firm-level (e.g., Bernanke, 1983;[START_REF] Rodrik | Policy uncertainty and private investment in developing countries[END_REF]Julio and Yook, 2012;Jens, 2017) and industry-level (e.g., [START_REF] Gulen | Policy uncertainty and corporate investment[END_REF]. Nguyen and Phan (2017) and Bonaime, Gulen, and Ion (2018) also

show that uncertainty stemming from politics negatively affects firm acquisitiveness and considerably lengthens the completion time of merger and acquisition (M&A) deals.

Another strand of literature links policy uncertainty with corporate financing decisions. Uncertainty is associated with more stringent access to financing markets. In periods of instability, costs of external financing increases (e.g., Gilchrist, Sim, andZakrajsek, 2013, 2014;[START_REF] Francis | The impact of political uncertainty on institutional ownership[END_REF]Çolak, Flannery, and Öztekin, 2014) and investors therefore require higher risk premia [START_REF] Pástor | Uncertainty about government policy and stock prices[END_REF]Kelly, Pástor, and Veronesi, 2016). Moreover, firms in situations of exposure to high political and economic uncertainty prefer to maintain financial flexibility by staying underleveraged for extended periods (e.g., Cao, Duan, and Uysal, 2013).

This study is based largely upon advances gleaned from research on the effect of uncertainty on firm financing choices. The latter studies tell us little about the effect of uncertainty stemming from the political sphere on trade credit provision. Thus, we fill this gap by examining the relationship between political risk and trade credit, using a novel measure of firm-level political risk. Trade credit is a credit extended by one trader to another for the purchase of goods and services. It simplifies the obtaining of supplies without immediate payment and is commonly considered as a source of short-term financing.

We present two opposing views about the relation between political risk and trade credit. On one hand, political risk makes firms' access to financing harder due to the perceived increasing volatility of future cash flows and therefore their default risk (e.g., Gilchrist, Sim, and Zakrajšek, 2014). This can dampen the willingness of suppliers to allow their customers to delay payment and/or reduce the ability of firms to obtain credit. On the other hand, trade credit can be considered a substitute source of short-term financing when access to other source of private and public debt becomes difficult due to high political risk (Petersen and Rajan, 1997). Trade credit has a competitive advantage compared to bank lending because of the supplier's ability to more easily acquire information about their customers, reclaim goods, and control moral hazard (e.g., Smith, 1987;[START_REF] Frank | Trade Credit. Collateral, and Adverse Selection[END_REF].

To test our hypotheses of the effect of political risk on trade credit, we use a large sample of 40,634 firm-year observations from the U.S. over the 2001 to 2016 period. We also use a firm-level political risk measure constructed by Hassan, Hollander, van Lent, and Tahoun (2019). This novel measure adapts computational linguistic techniques to transcripts of firms' quarterly earnings conference calls between call participants and firm management. It accounts for the share of words in the conversations which are devoted to talk about political risk. This proxy is the first to account for variation of political risk at the firm level and as perceived by the firm rather than at the aggregate or sector level measures. It also allows the distinction between different dimensions of political risk and between political and non-political topics.

Consistent with the substitution argument, we find evidence that firms facing intense political risk tend to have higher trade credit provision. In response to an exogenous shock, when financing market conditions become tighter, suppliers prefer to extend credit and customers prefer to access trade credit over other costly sources of financing. These results robustly hold when we run alternative estimation methods, when we use alternative measures of trade credit and political risk dimensions, and when we control for potential endogeneity concerns. Moreover, we find that competition, financial constraints, and social trust alternate the positive relation between political risk and trade credit.

Our research contributes to the literature in different ways. Our paper provides novel evidence documenting the influence of an idiosyncratic perception of political risk by the firm management on the choices related to trade credit provision. First, it relates to the literature on financing decisions and corporate capital structure under uncertainty.

For instance, Choe, Masulis, and Nanda (1993), [START_REF] Korajczyk | Capital structure choice: macroeconomic conditions and financial constraints[END_REF], Levy and Hennessy (2007), and [START_REF] Erel | Determinants of cross-border mergers and acquisitions[END_REF], among many others, investigate how business cycles affect corporate financing decisions. Furthermore, [START_REF] Friedman | The Role of Monetary Policy[END_REF], [START_REF] Rodrik | Policy uncertainty and private investment in developing countries[END_REF], Higgs (1997) and Hassett and Metcalf (1999), show the detrimental economic repercussions of monetary, fiscal, and regulatory uncertainty. Our study stands out from these studies because the uncertainty embedded in business cycles and the uncertainty embedded in political events-although they might be correlated -affect financing decisions through different channels. Baker, Bloom, Canes-Wrone, Davis, and Rodden (2014) explain that business cycles reflect the cash flow levels that are low in recessions and high in expansions and the levels of the overall economic output.

However, political uncertainty explains the role of different governmental and social changes on the economic outcome, and in our case how it affects trade credit mainly from the supply side.

Second, our study contributes to the stream of research that links politics and policies to corporate financial traits, such as investment, M&A, cost of capital, corporate disclosure and stock market volatility (e.g., Julio and Yook, 2012;[START_REF] Pastor | Political uncertainty and risk premia[END_REF]Baloria and Mamo, 2014;Jens, 2017;DeBoskey, Li, Lobo, and Luo 2017;Nguyen and Phan 2017;Chen, Cihan, Jens, and Page, 2018). There are also a few studies on the effect of policy uncertainty or financial crises on trade credit provision (e.g., Levine, Lin, and Xie, 2018; D'Mello, Jha, and Toscano, 2019) but none of them tackles political risk in its idiosyncratic dimension. We use the novel measure provided by Hassan, Hollander, van Lent, and Tahoun (2019) that accounts for political uncertainty as it is perceived by the firm's management and its stakeholders.

The remainder of this study is organized as follows. Section 2 reviews existing literature on political uncertainty and trade credit and develops our main hypotheses.

Section 3 discusses the data and variables and provides descriptive statistics. Section 4 presents our main empirical analysis and reports results of robustness tests. Section 5 concludes.

Literature review

In this section, we first discuss political risk and policy uncertainty framework.

Then, we build on several theories of trade credit to develop two competing hypotheses linking firms' assessment of political risk to their trade credit provision decisions.

Political uncertainty and access to financing

The degree of political disruptions is very likely to alter firms' financing decisions.

The uncertainty embedded in it can engender financial frictions, increase debt cost and stretch the duration between debt issuances. Gilchrist, Sim, andZakrajsek (2013, 2014) point out that there is a positive relationship between uncertainty and the costs of external financing, since it increases firms' exposure to default risk. Along the same line of thought, Çolak, Flannery, and Öztekin (2014) and Gungoraydinoglu, Çolak, and Öztekin (2017) find that financial intermediation costs for both equity and debt issuance are higher, time of leverage adjustment process is longer, and the frequency and volume of capital issued are reduced in situations of political uncertainty. [START_REF] Francis | The impact of political uncertainty on institutional ownership[END_REF] show that a one-standard-deviation increase in political uncertainty results in a ten-basis-point increase in the U.S. bank loan spread and that institutions significantly reduce their common stock holdings during election years. In consequence, investors require larger risk premia and corporate cost of capital becomes higher, implying that political uncertainty is costly (e.g., Pástor andVeronesi, 2012, 2013;Kelly, Pástor, and Veronesi, 2016). In addition to increasing cost of financing, political risk affects firms'

intertemporal capital structure and its dynamics. Cao, Duan, and Usyal (2013), for instance, point out that there is a negative relation between pollical uncertainty and leverage, indicating that firms tend to remain underleveraged and to maintain their financial flexibility for extended periods. In addition, when facing high political uncertainty, firms prefer sources of financing that protect their financial flexibility, tend to delay debt and equity issuances that are tied to investment (e.g., Jens, 2017). Similarly, the study of Çolak, Durney, and Qian (2017) reports that the number of initial public offerings (IPOs) originating from a state decrease when gubernatorial elections are scheduled. Overall, the foregoing discussion argues how political uncertainty can affect corporate financing decisions and financing markets resulting in tighter conditions and harder access to funds.

Trade credit financing

Trade credit provision has several comparative advantages over other sources of funds, such as bank lending. Suppliers (i.e., lenders) can acquire information more easily, repossess goods and deter moral hazard threats. Prior literature indicates that trade creditors can acquire information about firm creditworthiness at lower cost and can rely on collective punishment in the event a customer defaults. First, with respect to information asymmetry, Smith (1987) argues that interest rates operate as a screening device. Trade creditors can offer their customers discounts for early payments and can also charge more interest on late payments. Late payments by customers signal their higher default risk and trade creditors may accordingly increase monitoring or extend credit with stricter terms. Moreover, Mian and [START_REF] Smith | The investment opportunity set and corporate financing, dividend, and compensation policies[END_REF] explain that suppliers are in a continuous relationship with their customers which make information acquisition about their customers' financial situation easier and more effective; For example, sales representatives can visit very frequently with the customers. Ng, Smith, and Smith (1999) argue that trade creditors are better than financial institutions in terms of gathering information and generating prospects about firms since the formers operate within a network of similar customers and can distinguish therefore between a firm individually defaulting and a general decline in a specific industry. In the same vein, [START_REF] Jain | Monitoring costs and trade credit[END_REF] shows that trade credit provision can be considered as a second layer of financial intermediation.

Banks with asymmetric information about a borrower resulting in high monitoring costs are more likely to provide credit to the better-informed suppliers of that borrower and the supplier can in turn extend credit to the customer /borrower.

Second, with respect to cost advantages, trade credit can be cheaper than funds extended from other competing lenders for several reasons. One of the main reasons is suppliers' ability to repossess the merchandise and resell it under more favorable terms, which can be more advantageous than collateral (e.g., Mian and Smith, 1992). In a more recent study, [START_REF] Frank | Trade Credit. Collateral, and Adverse Selection[END_REF] argue that goods repossession and reselling option makes suppliers less likely to suffer a loss than a bank. Furthermore, Ng, Smith, and Smith (1999) state that reputational effect can explain this cost advantage: (i) customer reputation can reduce concerns about non-repayment; and (ii) supplier reputation can reduce concerns about goods quality. Reputations help mitigate concerns, strengthen relationship between suppliers and customers and curb any potential for opportunism.

Finally, and turning to moral hazard, several previous studies provide an illustration on the enforcement mechanism embedded in trade credit. [START_REF] Burkart | In-kind finance: A theory of trade credit[END_REF], for instance, point out that relative to traditional credit extended by banks, trade creditors are better able to control moral hazard. Banks lend cash while suppliers lend goods (i.e., illiquid assets) and it is typically more difficult for an opportunistic borrower to divert inputs than to divert cash. Moreover, many prior studies emphasize the enforcement and punishment role of trade credit (e.g., [START_REF] Greif | Contract enforceability and economic institutions in early trade: The Maghribi traders' coalition[END_REF][START_REF] Greif | Cultural beliefs and the organization of society: A historical and theoretical reflection on collectivist and individualist societies[END_REF]McMillan and Woodruff, 1999). Trade creditors, as a social network, can engage in collective punishment in case of an opportunistic borrower or a borrower that defaults.

This "sanction effect" discards low-quality customers from the network and disciplines borrowers since they fear collective punishment.

Hypotheses: Trade credit financing under political uncertainty

There are several arguments why political risk can influence a firm's reliance on trade credit provision. In particular, we present arguments for two competing hypotheses that political risk can positively or negatively affect trade credit, implying that trade credit and other sources of financing (mainly bank lending) can be either complements or substitutes (e.g., [START_REF] Burkart | In-kind finance: A theory of trade credit[END_REF]. Uncertainty stemming from politics exacerbates informational asymmetries, imposes higher costs, stiffens market conditions, and exposes firms to higher risk of default (e.g., Julio and Yook, 2012;[START_REF] Pastor | Political uncertainty and risk premia[END_REF]Baloria and Mamo, 2014;Baker, Bloom, and Davis, 2016;Jens, 2017; Nguyen and Phan 2017).

On one hand, we would expect political risk to limit firm access to external sources of financing including interfirm and trade credit for different reasons. First, the decline in capital investments that leads to more cash flow volatility and to higher risk of default can deter firms from obtaining or providing credit. Second, the effects of the increasing cost of financing and financial constraints in both public and private markets can outspread to the interfirm market resulting in harder access to trade credit. Finally, preferring to maintain financial flexibility when they are exposed to high uncertainty, firms may hold more cash as an internal source of financing and be less reliant on trade credit, especially in the short-term.

H1a: There is a negative relation between firm-level political risk and trade credit.

On the other hand, extant literature suggests that trade creditors have a comparative advantage over financial institutions. Relative to other external sources of financing, trade credit can be a tool to reduce information asymmetry, as well as generate better and superior information about industries in which firms operate (e.g., Smith 1987). In addition, to circumvent constraints, trade creditors can extend trade credit at subsidized rates, or repossess and resell products sold earlier to a defaulting customer (e.g., Mian and [START_REF] Smith | The investment opportunity set and corporate financing, dividend, and compensation policies[END_REF][START_REF] Frank | Trade Credit. Collateral, and Adverse Selection[END_REF]. Moreover, Petersen and Rajan (1997) and [START_REF] Burkart | In-kind finance: A theory of trade credit[END_REF], among few others, argue that firms view borrowings from suppliers as an alternative source of financing (i.e., a substitute) when access to other sources of funding declines due to high policy uncertainty. They also show that trade creditors lend more liberally than banks especially in an environment with stringent legal constraints or with limited or nonexistent contract enforceability.

H1b: There is a positive relation between firm-level political risk and trade credit.

Data and sample selection

Sample

To construct our sample, we merge firm financial data from Compustat North America with the firm-level political risk measures of Hassan, Hollander, van Lent, and Tahoun (2019). We exclude observations before 2001 and beyond 2016, periods for which political risk data are not available. We further remove observations related to firms with missing Standard Industrial Classification (SIC) codes, firms with invalid industry classification (SIC between 9900 and 9999) and firms in the financial industry (SIC between 6000 and 6999). Finally, we restrict our sample to firm-year observations with positive assets, cash, leverage, tangible assets, and capital expenditure. These filters yield a sample of 40,634 firm-year observations from the U.S. over the period 2001 to 2016.

Variables

Definitions and sources for all variables used in the main analysis and the robustness tests are summarized in the Appendices. We winsorize all firm-level variables at 1 st and 99 th percentiles to minimize the effect of outliers.

Trade credit provision

Our main measure of trade credit defined as the ratio of accounts receivable due to trade to total sales (i.e., TR). Specifically, trade receivables are the amount (net of applicable reserves) owed by customers for goods and services sold in the ordinary course of business. The ratio of receivables to sales indicates the percentage of sales made through credit. Multiplying this measure by 360 gives the number of days for which suppliers are willing to extend credit assuming that all buyers receive 100% credit. 16 We use this measure as primary measure since Compustat separates accounts receivable due to trade from other receivables; it does not do so for accounts payable. Then, we also use the ratio of accounts payable to cost of goods sold (i.e., TP) as a second measure of trade credit.

To test the sensitivity of our results to the measure of trade credit, in robustness tests, we replace our main measures with the ratio of total of accounts receivables to total sales, trade receivables to total assets and with the ratio of the difference between receivables and payables, again, scaled by total sales. 16 These ratios capture the importance of trade credit in the financing of the economic activity. One advantage of using ratios scaled by flow variables is that these measures control for decline in economic activity (i.e., sales) that are commonly associated with crises. Thus, whenever we find a declining ratio of accounts receivables to sales, we know that accounts receivables have declined more than sales, in percent terms. There are two ways these ratios could be interpreted. If trade credit were extended for the whole year, the ratio of receivables to sales would show what percent of sales is done on credit. However, as trade credit usually has much shorter maturity, the alternative interpretation of such a ratio is the number of days the customers take to repay the credit (assuming all customers receive 100% of credit).

Firm-level political risk

Politics nowadays is a term that encompasses multiple definitions (Flint, 2016).

Partly due to its intrinsically interdisciplinary nature, political risk as such has been neglected as a subject of study in the context of academic research, despite the existence of several studies on the concept of "political instability". When information on political risk is collected, comparisons between the different political risk assessment approaches and relative indices are not easy to carry out. There is no general agreement on which measure is the best proxy for political risk. In this study, however, we use the novel measure constructed by Hassan, Hollander, van Lent, and Tahoun (2019). The measure developed in computational linguistics and natural language processing. 17 The aim is to measure the share of the conversations between firms' management and other stakeholders that are about politics and risk associated with each of the eight political risk components (e.g., economic policy and budget, environment, etc.) and to distinguish between political and non-political language. To do so, they use trainings sets in libraries of political text and non-political text and identify most of bigram combinations that are used in political texts. 18 The baseline measure is the share of transcripts that is linked to political risks. It is a result of the count of the number of occurrences of these bigrams in the conference-call transcripts in conjunction with synonyms of risk and uncertainty, divided by the total length of the conversation.

In this study, we aggregate the quarterly measured levels on annual basis. First, most of the changes in politics and technology in general occur every six to eighteen months. Second, firms review their choices and decisions every year on average. The measure is an accurate indicator of political risk: its intuitive variation over time matches the real variation of political uncertainty and political events and aligns with other measures, like the aggregate economic policy uncertainty of Baker, Bloom, and Davis (2016). We winsorize the political risk variable (PRisk) at 5 th and 99 th percentiles

Other variables

Following prior literature on trade credit (e.g., El Ghoul and Zheng, 2016; Giannetti, Burkart, and Ellingsen, 2011; Love and Zaidi, 2010; Petersen and Rajan, 1997), we control for firm-level characteristics shown to have an important impact on trade credit to isolate the effect of political risk on trade credit. We include firm Size as Ln(assets), the natural logarithm of total assets in $US millions; Capex, the ratio of capital expenditure to the book value of total assets; Lev, the ratio of short-and long-term liabilities to total assets; firm profitability ROA, defined as operating income before depreciation scaled by total assets; Cash, the ratio of cash and short-term investments to total assets; MB, the market value of assets divided by the total book value of assets; tangibility Tang, the ratio of total (net) property, plant, and equipment to total assets. In addition, we include year dummies and industry dummy variables based on the Fama-French 48-industry classification to account for differences that result from variation in the nature of product and market structure. pairwise correlation coefficients for all variables in the main regressions. From this correlation matrix, it is evident that multicollinearity between the independent variables is unlikely, since the correlation coefficients are relatively small. We find that our primary proxy for trade credit provision, is positively correlated with political risk at the 1% level.

Descriptive statistics

These results provide initial support for our hypotheses. Moreover, trade credit provision measures are correlated with most of the control variables at the 1% level, confirming their relevance for trade credit. This table presents summary statistics of the main variables used in our regressions. PRisk, the key independent variable, is a measure of firm-level political risk developed by Hassan et al. (2019) and defined in Appendix 2-2. TR is the dependent variable measuring trade credit and calculated as the ratio of total accounts receivable to total sales. TP is another measure for trade credit, defined as the ratio of accounts payable to the cost of goods sold. Size is measured as the logarithm of the book value of net assets dollars. Capex is the ratio of capital expenditures to total assets. Lev is long-term debt plus debt in current liabilities, scaled by total assets. ROA is the firm profitability, measured as the ratio of operating income before depreciation to the total book value of assets. Cash is the ratio of cash plus short-term investment to total assets. MB is the market-to-book ratio defined as the book value of assets plus the market value of common equity minus the book value of common equity, scaled by the book value of assets. Tang or tangibility is the ratio of total property, plant and equipment to the book value of total assets.

Empirical results

Main regressions

To examine the effect of firm-level political risk on trade credit, we use pooled OLS with standard errors adjusted for heteroskedasticity and clustered at the firm level to reduce concerns about within-firm correlation.

Table 2-3 presents regressions of trade credit using different model specifications.

We begin our analysis by regressing the ratio of accounts receivable from trade to total sales on the firm-level political risk variable and the set of control variables. The results displayed in column (1) provide evidence for a positive relation between political risk and trade credit. Specifically, the coefficient on PRisk is positive and statistically significant at the 5% level, suggesting that firms consider trade credit as a substitute financing mechanism, as demonstrated by their increasing use of interfirm credit in periods of high uncertainty due to political shocks (e.g., Petersen and Rajan, 1997). The

Robustness tests

In this sub-section, we examine the robustness of our main results. First, we test the sensitivity of our results to the measure of trade credit by using alternative proxies for trade credit provision. Second, we address the omitted-variable concern by taking great care to include a host of additional control variables related to macroeconomic risk that may drive our results. Third, we decompose the aggregate measure of political risk into its different dimension to ensure that the effect of political risk is not driven by one source of risk. Finally, we address endogeneity concerns that may alter our results.

Alternative measures

As a robustness check, we repeat our analyses using alternative proxies for the dependent variable to ensure that our main results are not driven by the choice of a specific trade credit measure. 19 For that, we re-examine our hypothesis using four alternative trade credit estimates. The results are reported in Table 2-4. More precisely, in Column (1), we replace the ratio of accounts receivable due to trade to sales with the ratio of accounts payable to cost of goods sold. In Column (2), we use total accounts receivables scaled by total sales. In Column (3), we scale accounts receivable due to trade by total assets instead of sales. Finally, in Column (4), we use the difference between accounts receivable and accounts payable divided by total sales. Table 2-4 points out that our main finding on the effect of political risk on trade credit is virtually unchanged. The coefficient on PRisk remains qualitatively the same, reflecting the positive effect of firm-level political risk on the use of trade credit. 19 Seasonality might also affect the quality of receivables to sales as a proxy for payment terms and trade credit provision.

Additional macroeconomic risk variables

We address the omitted-variable concern by taking great care to include other control variables. Our findings presented thus far show that firms are more likely to rely on trade credit provision when they are exposed to higher political uncertainty pressure.

First, we address the question of whether our results are driven by other dimensions of macroeconomic risk. For this purpose, we include in our baseline regression additional control variables that proxy for different risks. In particular, we include at a first stage a measure of all non-political risks developed by Hassan, Hollander, van Lent, and Tahoun (2019) in specifications 1 and 2 of Table 2-5. We report the results using nonpolitical risk as an additional control variable. Overall, we find that controlling for nonpolitical risk does not alter the positive effect of political risk on firms' use of trade credit.

Since we have built our framework on the political risk effect, a natural step would be to ensure that our results may not simply be capturing the effect of other macroeconomic risk factors that we did not control for, and which are likely to influence the financing decision or firms' reliance on interfirm debt. To this end, we include additional macroeconomic risk variables. More precisely, we control for the cyclically adjusted price-to-earnings ratio of Shiller, commonly known as CAPE, the index of real earnings of the publicly traded firms in the U.S., the inflation rate, the consumer expectations and the consumer sentiment index, the financial stress index, and the partisan conflict index. The partisan conflict index tracks the degree of political disagreement among U.S. politicians at the federal level by measuring the frequency of newspaper articles reporting disagreement in a given month. Higher index values indicate greater conflict among political parties, Congress, and the President.

In columns (3) to (6) of Table 2-5, we report our evidence that political risk has a significant effect on trade credit provision and that its effect remains when we control for other macroeconomic sources of risk and uncertainty.

Endogeneity IV 2SLS regression

The next concern we address is potential endogeneity problems, which plague empirical corporate finance studies. In the context of our study, endogeneity concerns may arise from our inability to randomly assign firms' perception of political risk levels to firms and observe their use of trade credit provision. There are three sources of endogeneity: omitted explanatory variables, simultaneity bias, and measurement errors (Roberts and Whited, 2013). We have already shown that our main findings are not sensitive to using alternative measures of trade credit; below we focus on omitted explanatory variables and simultaneity bias. We address these potential concerns by employing an instrumental variable (IV) approach. We use two instrument variables in our first stage regression of the 2SLS technique. The first instrument is Mood, which is a measure of the actual public mood of a state in a given year. The second instrument is Professionalism and it is Squire's measure of legislative professionalism in every state in the U.S. in a given year. We match these measures using year and the state in which a firm headquarter is located as key identifiers. Both instrumental variables are significantly related to political uncertainty, yet unlikely to influence corporate decisions except through the channel of political risk, thereby meeting the relevance and exclusion conditions of instrument validity. Besides, corporate trade credit decisions are not likely to influence legislative professionalism and the mood in a state, which makes our instruments theoretically relevant and exogenous.

Table 2-8 reports our results. In specifications 1 and 3 that correspond to first stage regressions, F statistics is highly significant, which means that we reject the null hypothesis under which our instruments are weak. Sargan and Basman tests that are built over the hypothesis that error terms are not correlated with the rest of exogenous variables indicate that our instruments are valid and there is no overidentification problem. The coefficients on PRisk in specifications 2 and 4 are significantly positive, suggesting that this approach yields results consistent with our findings that political risk increases the fraction of trade credit.

Additional tests of moderating effects

Our findings are shown to be robust so far. In addition, we perform additional tests to dig deeper into the nature of the political risk and trade credit relationship. Our previous results suggest that firms' exposure to high level political risk is associated with higher reliance on trade credit provision as a source of external finding. In periods of high political uncertainty when financing conditions are stringent and stiff, firms are more willing to extend and to obtain interfirm debt. This finding confirms the substitution effect of trade credit related to the advantages interfirm credit offer compared to other

▪ The role of financial constraints

The severity of financial constraints can alter firms' choices. Prior studies have examined the effect of financial constraints on corporate investment and financing activities. Financial constraints are a major impediment to investment and growth (e.g., [START_REF] Almeida | Financial constraints, asset tangibility, and corporate investment[END_REF], restrict access to credit (e.g., Campello, Graham, and Harvey, 2010), and represent a major source of priced risk (e.g., Whited and Wu, 2006).

Thus, we expect that political risk's effect on trade credit to be more important when financial constraints are high.

To better measure the effect of firm financing risk on the relation between political risk and trade credit, we use interaction terms of the variable PRisk and a proxy for firm financial constraints. More precisely, we use the Kaplan-Zingales Index (KZ) following Kaplan and Zingales (1997). Table 2-9 reports the results of the analysis. Consistent with the arguments above, we find that the effect of the interaction between political risk and the KZ index is statistically and significantly negative. Hence, firms with more binding financial constraints are more likely to have lower access to trade credit with the intensification of political risk.

▪ The role of social trust

Social trust is one of the important indicators of societies and communities' strength and quality across the world. [START_REF] Fukuyama | Trust: The social virtues and the creation of prosperity[END_REF] and Putnam (2001), for instance, state that trust is a major driver for growth and that people in environments with high levels of social trust are likely to behave cooperatively under the norms of honesty, trustworthiness, and reciprocity. Moreover, previous empirical studies, such as Wu, Firth, and Rui (2014), and Levine, Lin, and Xie (2018), find evidence of the positive effect of trust on trade credit. Levine, Lin, and Xie (2018) use firm-level data in 34 countries and show that during crises, firms in high-trust countries extend and obtain trade credit easier and suffer less from the decreasing level of growth and employment relative to firms located in low-trust economies. In the same line of thoughts, we expect that during

Conclusion

In this study, we examine the effect of firm-level political risk on corporate financing decisions, particularly trade credit provision. Based on previous theoretical and empirical research, we present two competing hypotheses in a way that trade credit can be either a complement or a substitute to other external sources of funding. First, we hypothesize that a political crisis can impede access to financing markets and credit including interfirm credit market. Second, we put forth arguments based on the comparative advantages of trade credit relative to other sources of financing. We therefore hypothesize that in periods of high political risk firms would rely more on interfirm sources of financing.

Using a large sample of U.S. firms over the period 2001-2016, we find that high levels of firm-level political risk are associated with more reliance on trade credit. This result is consistent with the second hypothesis that trade credit offers many advantages compared to bank and public lending. Our findings are robust to a battery of robustness checks. Specifically, we show that our evidence persists after using alternative estimation techniques and measures of trade credit, decomposing the aggregate measure of political risk, and addressing endogeneity concerns. We also investigate the potential conditioning effect of other firm and industry-level characteristics and show that the positive effect of political risk on trade credit is stronger for firms operating in competitive environments, higher trust periods, and weaker for firms with tighter financial constraints.

This study contributes to different strands of literature. Specifically, we add to the line of research that examines the effect of uncertainty emanating from politics.

Additionally, our study adds insight to understanding the determinants of corporate financing decisions (i.e., trade credit provision).

However, it is worth stressing that this definition of political risk cannot be exhaustive. This measure reflects firm management and stakeholders' perception of

Lev

The leverage ratio defined as the ratio of debt text that are used in conjunction with synonyms for risk or uncertainty.

Introduction

Markets' concerns about uncertainty fluctuate over time. Research has been exploring questions about the out-turns for firms and financial markets when investors and managers are unsure about the future. Even though markets can anticipate change in risk, uncertainty can still affect business cycles. The perceived complexity of uncertainty in the economic, legal, and political environment can alter how investors and firms make forward-looking decisions. Concerns about political uncertainty and risk have increased in the wake of the 2008 financial crisis and have since then attracted more the attention of scholars (e.g., [START_REF] Pástor | Political uncertainty and risk premia[END_REF]Baloria and Mamo, 2014;[START_REF] Lee | Political uncertainty and cost stickiness: Evidence from national elections around the world[END_REF]Jens, 2017).

Economics and finance literature has long wrestled with financing constraints and uncertainty, which still is a prominent topic in corporate finance. A large strand of literature examines how various forms of uncertainty, including risk, ambiguity and their resulting skepticism about future outcomes affect firm financial performance. The first theoretical work on uncertainty dates at least to Bernanke (1983) whose research shows that firms have an incentive to delay investment and hiring under uncertainty. Other reasons for the undermining effects of uncertainty shocks consist of higher managerial risk-aversion (e.g., [START_REF] Panousi | Investment, idiosyncratic risk, and ownership[END_REF], more volatile stocks (e.g., [START_REF] Pástor | Political uncertainty and risk premia[END_REF], precautionary cutbacks by households, increasing financial distortions (e.g., Gilchrist, Sim, and Zakrajšek, 2014), and raised unemployment (e.g., Leduc and Liu, 2015).

A growing body of academic research has specifically focused on policy uncertainty. [START_REF] Friedman | The Role of Monetary Policy[END_REF], [START_REF] Rodrik | Policy uncertainty and private investment in developing countries[END_REF], Higgs (1997) and Hassett and Metcalf (1999), among others, show that the effects of regulatory, fiscal, and monetary policies and the uncertainty embedded in them are substantial. More recently and based on the latter studies, Pastor andVeronesi (2012, 2013) theoretically examine the relationship between government policy and stock prices fluctuations. Fernández-Villaverde, Guerrón-Quintana, Rubio-Ramírez, and Uribe (2011) and [START_REF] Born | Policy risk and the business cycle[END_REF] use dynamic stochastic general equilibrium (DSGE) models to study the effect of policy uncertainty.

Following seminal works on investment under uncertainty, some studies explicitly focus on how political uncertainty and therefore volatility decreases investment outputs (e.g., [START_REF] Bittlingmayer | Output, stock volatility, and political uncertainty in a natural experiment: Germany, 1880-1940[END_REF]. In the same line of thoughts, Julio and Yook (2012) and Jens (2017), for instance, show that managers undertake less risky investment around the timing of national elections. Managers also take longer time to complete merger and acquisition deals (Nguyen and Phan, 2017), engage in fewer acquisitions (Chen, Cihan, and Jens, 2018), have lower risk-taking incentives (e.g., Tan, 2001;Boubakri, Mansi, and Saffar, 2013) and retain slack resources [START_REF] Lee | Political uncertainty and cost stickiness: Evidence from national elections around the world[END_REF]. More empirical studies document that Political risk affects asset prices, international capital flows, investment, employment growth, and the business cycle [START_REF] Belo | Government spending, political cycles, and the cross section of stock returns[END_REF][START_REF] Gourio | Uncertainty and international capital flows[END_REF][START_REF] Handley | Trade and investment under policy uncertainty: theory and firm evidence[END_REF][START_REF] Kelly | The price of political uncertainty: Theory and evidence from the option market[END_REF][START_REF] Besley | Institutions, volatility, and investment[END_REF].

In this study, we extend this line of research by analyzing the effect of firm-level political risk on corporate cash management. We argue that when political uncertainty is high but to be resolved soon, firms hold more cash as an endogenous response to the ambiguity caused by political upheavals. We put forth this argument based on agency theories and the flexibility hypothesis of [START_REF] Jensen | Agency costs of free cash flow, corporate finance, and takeovers[END_REF]. In response to an exogenous shock, such as a political crisis, market conditions become tighter and firms have, therefore, less flexibility. [START_REF] Harford | Refinancing risk and cash holdings[END_REF], among many others, point out that firms decide to hold more precautionary cash to mitigate refinancing risk and to adjust to stringent market conditions. In the same vein, several empirical studies on corporate cash holdings (e.g., [START_REF] Opler | The determinants and implications of corporate cash holdings[END_REF][START_REF] Harford | Corporate cash reserves and acquisitions[END_REF] find that riskier cash flows and more limited access to financing markets induce firms to have larger cash reserves. Following this reasoning, firms have a precautionary motive for corporate cash holdings and we predict that cash holdings will be positively related to political risk, especially under the need for external funds (i.e., growth opportunities and cash flow variability).

We test the role of political risk in explaining firm cash management using a novel idiosyncratic measure of political risk. The measure is constructed by Hassan, Hollander, van Lent, and Tahoun (2019), adapting computational linguistics to transcripts of firms'

quarterly earnings conference calls between call participants and firm management, which are devoted to talk about political risk. We choose this proxy since it is the first to account for variation at the firm level rather than at the aggregate or sector level. It also allows the distinction between different components and sources of political risk and political from non-political topics.

Our empirical analysis is based on a sample of 33,618 firm-year observations over the period 2002-2016 in the US. Consistent with the argument that cash holdings help firms being flexible at times of high risks emanating from the political system, we find evidence that corporate cash balances increase as a result of substantial firm-level variation in exposure to political risk and uncertainty. We find the same positive relationship between political risk components (i.e., policy, defense, taxes, trade, technology, institutions, economy, health, and environment) and cash holdings, after decomposing the measure of political risk by topic. We also show that firms are more likely to retain cash flows. Following [START_REF] Pinkowitz | Does the contribution of corporate cash holdings and dividends to firm value depend on governance? A cross-country analysis[END_REF] and [START_REF] Dittmar | Corporate governance and the value of cash holdings[END_REF] methodology to determine the market value of an incremental dollar of cash reserves, we find that the value of an incremental dollar of corporate cash reserves is larger for firms facing higher political risk. We further run other tests to show that the precautionary corporate cash holdings in high political risk periods come from lowering capital expenditure, R&D spending and short debt and equity issuance and increasing net working capital capacities and long debt issuance. In a final step, we conduct a range of additional tests. We investigate the impact of exposure to competition, R&D expenditure, and proximity of headquarters to where the political power resides in Washington DC on the relationship between political risk and cash holdings. Our findings show that it is more pronounced for firms that operate in competitive industries, rely less on R&D and with headquarters farther from Washington DC.

Bringing Moreover, our findings seek to advance understanding about the implications of uncertainty, especially for market dynamics and the design of political disruptions, on one of the most critical corporate financial choices (i.e., cash management). This chapter relates to many studies that investigate the effect of risk on cash holdings. Higher refinancing risk, credit risk, aggregate firm risk, and risk related to competition dynamics positively and largely affect corporate precautionary savings (e.g., [START_REF] Haushalter | The influence of product market dynamics on a firm's cash holdings and hedging behavior[END_REF][START_REF] Acharya | Cash holdings and credit risk[END_REF][START_REF] Palazzo | Cash holdings, risk, and expected returns[END_REF][START_REF] Harford | Refinancing risk and cash holdings[END_REF]. Our empirical analysis aligns with the latter results, suggesting that cash holdings are central to understanding the effects of twenty-firstcentury political risk.

The remainder of this chapter proceeds as follows. In Section 2, we discuss theoretical background for our hypotheses. In Section 3, we describe the sample and variables definitions. Section 4 discusses the empirical results and the additional tests.

Finally, Section 5 summarizes and describes our conclusions.

Literature and hypotheses

Complexity of today's political risk

The world of politics is becoming more complex and multifaced. Over the last three decades, many tectonic shifts in geopolitics have had considerable repercussions on the international economic landscape. The growth of China and other emerging countries at unexpected rates, the wave of deindustrialization, the new technological war, and revolutions have reshaped global business opportunities and challenges. In this globalized and connected world, the twenty-first-century political risk is happening everywhere beyond the walls of governments buildings, which has therefore made the intersection of global business and politics worth exploring. In their new book, Rice and Zegart (2018) (re)define political risk as "the probability that a political action could significantly affect a company's business" and claim that they chose the words "political actions" and not "government actions" to "highlight the growing role of risk generators outside of the usual places like capitals and army barracks and party headquarters."

The tasks assigned to elected representatives are more sophisticated. They should decide tax, foreign, health, education, immigration, social policies, and among others.

Each of these policies involves numerous issues that are themselves complex and multidimensional. Thus, it is difficult to find an appropriate way to measure political uncertainty and political risk. Several institutions and academics have started to develop specific methods to assess it, trying to align to the fast-changing dynamics of the internationalisation of trade and investment. When political risk first gained more relevance a few years ago, many studies started to use basic measures indicating the timing of national or gubernatorial elections (e.g., [START_REF] Bachman | The effect of political risk on the forward exchange bias: the case of elections[END_REF]Pantzalis, Stangeland, and Turtle, 2000;Fowler, 2006;[START_REF] Białkowski | Stock market volatility around national elections[END_REF]Jens, 2017).

Recently, a growing number of studies has been identifying variation in aggregate country-and sector-level measures of political risk, such as economic policy uncertainty (EPU) indexes, the differential exposure of specific sectors to shifts in government contracting or levels of political violence. Most of these recent studies have developed measures based on textual analysis of newspaper, reports, online websites and forums, and corporate disclosures as text sources (Gentzkow and Shapiro, 2010;Wiesen and Wysocki, 2015;Baker, Bloom, and Davis, 2016;[START_REF] Koijen | Financial health economics[END_REF][START_REF] Mueller | Reading between the lines: Prediction of political violence using newspaper text[END_REF]. They have implemented methods using computational linguistics and natural language processing techniques. Accordingly, they use pre-defined dictionaries of significant words to process source documents (e.g., Loughran and McDonald, 2011;Baker, Bloom, and Davis, 2016).

Partly due to its intrinsically interdisciplinary nature, the measurement and practice underpinning the existing political risk indices is always questioned and evolving. In the absence of direct proxies, work on political uncertainty conceptualization and measurement seems more and more timely. In this chapter, we follow the work of Hassan, Hollander, van Lent, and Tahoun (2019), which provides a novel firm-level 123 measure of political risk. It is a result of textual analysis of US firms' conference calls transcripts. This method aims at endogenously capturing word combinations that are indicative of political discourse or topics about political risk. The numerous and frequent changes in politics also alter the practices and the speed of how firms try to navigate them. That is why we employ this specific measure since it is the unique to encompass the assessment of political risk from the point of view of firm management and insiders.

Moreover, this measure allows a meaningful distinction between aggregate, sector-level, and firm-level exposure. Besides, it provides a flexible decomposition of the aggregate firm-level index into topic-specific components.

Political risk and cash management

▪ Political risk and cash holdings A firm's holdings of liquid assets, such as cash, play a key role in shaping its economic prospects in moments of distress or recession as access to external financing may be restricted (e.g., [START_REF] Fazzari | Investment, financing decisions, and tax policy[END_REF]. Liquid assets directly affect the probability of corporate default and bankruptcy and its timing, which in turn impacts the firm's decision to retain a cash reserve that could otherwise be invested or paid out to shareholders.

In general, seminal theoretical models identify two general branches of theories for research in cash holding. First, there are capital structure theories: (i) the trade-off theory of [START_REF] Modigliani | Corporate income taxes and the cost of capital: a correction[END_REF], [START_REF] Bradley | On the existence of an optimal capital structure: Theory and evidence[END_REF], and [START_REF] Titman | The determinants of capital structure choice[END_REF]; and (ii) the pecking order theory of [START_REF] Myers | Corporate financing and investment decisions when firms have information that investors do not have[END_REF]. According to these theories, cash reserves level is respectively driven by trade-offs between the benefits and costs of holding cash and the ex-ante information asymmetry that makes external sources of financing pricey. Second, cash holdings decisions also arise from agency conflicts that can define corporate cash levels through five channels: (i) the flexibility hypothesis of [START_REF] Jensen | Agency costs of free cash flow, corporate finance, and takeovers[END_REF] for which internal resources (i.e., cash) give firms financial flexibility to overcome underinvestment problems in case of restricted access to financing; (ii) the spending hypothesis or the agency costs of managerial discretion of Jensen and Meckling (1976) stating that, in the absence of scrutiny, managers overinvest;

(iii) cash can be a tool to deter hostile takeover threats [START_REF] Faleye | Cash and corporate control[END_REF]; (iv) the shareholder power hypothesis of Harford, Klasa, and Maxwell (2008), which joins the flexibility hypothesis and argues that shareholders with effective control over managers' activity let those managers to stockpile large cash holdings to prevent underinvestment problem due capital market frictions; and (v) the contracting theory (e.g., [START_REF] Liu | Corporate cash holdings and CEO compensation incentives[END_REF] that proposes that debt covenants for risky firms are stringent and enforce them therefore to hold more cash.

Political changes inject a lot of uncertainty into business decisions. This uncertainty and the resulting constraints push firms to deal with complex investment, liquidity management, risk management decisions. According to the agency conflicts theory, firms facing higher uncertainty rooted in political crises try to have higher financial flexibility to face capital market frictions. Our study is based largely upon advances gleaned from prior research on the effect of risk and uncertainty on corporate cash holdings. For instance, [START_REF] Opler | The determinants and implications of corporate cash holdings[END_REF] and [START_REF] Harford | Corporate cash reserves and acquisitions[END_REF] find that firms with stronger growth opportunities, riskier cash flows, and more limited access to capital markets hold higher cash levels. [START_REF] Harford | The effect of cash reserves on corporate investment and performance in industry downturns[END_REF] provide evidence that firms with large cash balances carry on investing in their growth opportunities during and closely after industry downturns. Furthermore, [START_REF] Davydenko | When do firms default. A study of the default boundary[END_REF] argues that higher liquid assets reserves make firms safer and have lower credit spreads in time of market frictions, ceteris paribus. Firms also hold more precautionary cash to gain flexibility in dealing with cash flow shocks and difficulties in adjusting their labor resulting from market labor frictions (e.g., [START_REF] Ghaly | Cash holdings and labor heterogeneity: the role of skilled labor[END_REF].

Under high political uncertainty, the decision on how to deploy internal funds gains more prevalence. The ambiguity emanating from the political sphere can foster information asymmetry, create agency problems and tighten capital market conditions. [START_REF] Harford | Refinancing risk and cash holdings[END_REF] show that external financing costs are higher for firms with greater risk due to agency conflicts and information asymmetry about their economic prospects. In this same vein, DeBoskey, Li, Lobo, and Luo (2017) point out that firms increase their corporate political disclosure (CPD) as a response to information asymmetry driven by political uncertainty. Increased political disclosure for firms reduces the cost of debt by mitigating agency problems associated with executives using a firm's resources to pursue personal agendas.

Considering the above discussion, we argue that political risk induces firms to retain more precautionary cash to be financially flexible and able to face tight market conditions and underinvest problems:

H1: Corporate cash holdings increase with higher political risk levels.

▪ Political risk and marginal benefits of cash retention Almeida, Campello, and Weisbenner (2004) show that firms exhibit a superior propensity to save cash from their cash flow during financial crises. Furthermore, [START_REF] Faulkender | Corporate financial policy and the value of cash[END_REF] and [START_REF] Denis | Financial constraints, investment, and the value of cash holdings[END_REF] examine the cross-sectional variation in the marginal value of corporate cash holdings. They respectively find that the marginal value of cash is higher for financially constrained firms that have limited access to external funding sources and that cash "premium" help firms mitigate underinvestment problem in valuable projects that would otherwise go unfunded.

Accordingly, we argue that when firms face higher political risk, the marginal benefit of cash flows retention increases. To the extent that a political crisis is associated with a rise in the costs of external funds and tighter market conditions, this leads to our second hypothesis:

H2: The marginal benefit of cash retention increases, and firms retain more cash from their cash flows when political risk is high.

▪ Political risk and the market value of cash

If obtaining external funds is easy, firms would not place importance on the necessity of holding cash. For instance, [START_REF] Pinkowitz | Does the contribution of corporate cash holdings and dividends to firm value depend on governance? A cross-country analysis[END_REF] provide evidence that cash holdings are more valuable for constrained firms than for unconstrained firms. When risk is high because of political uncertainty, access to capital markets can be costly and limited and firms can therefore pass up positive NPV investments. 20 Studies such as Kaplan andZingales (1997, 2000), Fazzari, Hubbard, and Petersen (1998), [START_REF] Hubbard | Capital-Market Imperfections and Investment[END_REF] and [START_REF] Denis | Financial constraints, investment, and the value of cash holdings[END_REF], among many others, extensively cover the effect of financial constraints on investment. Collectively, these studies support the view that changes in cash flow are an important determinant of marginal capital spending and the value that shareholders place on cash reserves.

Overall, they show that high cash holdings are value increasing in case of market frictions as cash reserves enable firms to take advantage of available investment opportunities.

H3:

The market value of cash held by firms is higher with the intensification of political risk.

▪ Political risk and cash channels

The increase in cash holdings and this substitution from external to internal financing following political changes raise the question of where this precautionary cash comes from. [START_REF] Bliss | Corporate payout, cash retention, and the supply of credit: Evidence from the 2008-2009 credit crisis[END_REF] study the changes in corporate financial choices following the 2008-2009 financial crisis. They document that firms facing higher uncertainty are more likely to significantly reduce corporate payouts for both dividends and share repurchases. For example, they find that corporate cash savings from the decrease in payout during the recent crisis are economically meaningful, representing 31% of the firm's pre-crisis cash balance and 53% of its pre-crisis level of investment. In the same line of thoughts, Duchin, Ozbas, and Sensoy (2010) and Campello, Graham, and Harvey (2010) also report that firms substantially reduce their spending on investment and employment during the onset of the crisis. They show that these results are more pronounced for firms lacking sufficient short-term liquidity, either because of low precautionary cash balances or because of high short-term obligations. Same as a financial crisis, a political crisis represents an unexplored negative shock to both firms and markets. As a consequence, we propose that our main hypothesis of higher cash holdings is also rooted in the following hypotheses:

H4a: Political risk induces firms mainly to reduce their investment (i.e., capital and R&D expenditure).

H4b: Political risk is associated with lower (higher) short (long) debt issuance.

H4c: Political risk is associated with reduced corporate payouts and equity repurchase.

Methodology

Sample selection

To test our hypotheses, we consider a large sample of U.S. listed firms over the 2002-2016 period. The sample period is limited by the availability of the calculations of the novel measure of firm-level political risk. We start with all firms appearing in the Compustat database, where we collect financial data available and match them with our measures of political risk. To avoid any possible effect of regulation, we exclude companies in the financial (SIC codes 6000-6999) industries. Finally, we exclude firm years with nonpositive values for total book assets or cash holdings. These restrictions result in a final sample of 33,618 firm-year observations corresponding to 5,481 unique firms over the period 2002 to 2016.

Variables

Firm-level political risk

The literature has proposed a variety of ways of identifying the level of politicsrelated risk facing firms. However, there is no general agreement on which measure is the best proxy for political risk. In this study, we use the novel measure constructed by Hassan, Hollander, van Lent, and Tahoun (2019). The measure consists of using textual (2008) and Manning et al., (2008), the authors adapt a simple pattern-based sequence-classification method developed in computational linguistics and natural language processing. 21 The aim is to measure the share of the conversations between firms' management and other stakeholders that are about politics and risk associated with each of the eight political risk components (e.g., economic policy and budget, environment, etc.) and to distinguish between political and non-political language. To do so, they use trainings sets in libraries of political text and non-political text and identify most of bigram combinations that are used in political texts. 22 the baseline measure is the share of transcript that is linked to political risks. It is a result of the count of the number of occurrences of these bigrams in the conference-call transcripts in conjunction with synonyms of risk and uncertainty, divided by the total length of the conversation.

In this study, we aggregate the quarterly measured levels on annual basis. First, most of the changes in politics and technology in general occur every six to eighteen months. Second, firms review their choices and decisions every year on average. The measure is an accurate indicator of political risk: its intuitive variation over time matches the real variation of political uncertainty and political events and aligns with other measures, like the aggregate economic policy uncertainty of Baker, Bloom, and Davis (2016). Above, figure 3-1 provides evidence bolstering the interpretation that overall increases in political risk captured by this measure overlaps major political events such as federal elections, the war in Iraq, etc. In addition, Figure 3-1 shows that the firm-level political risk measure is highly correlated with the index of aggregate economic policy uncertainty (EPU).

Other variables

The dependent variable in our study, cash holdings, is measured as natural logarithmic function of the ratio of cash and marketable securities (che) to total net assets computed as total assets minus cash and marketable securities (at -che), following Opler, [START_REF] Opler | The determinants and implications of corporate cash holdings[END_REF] and [START_REF] Harford | Corporate governance and firm cash holdings in the US[END_REF]. We deflate corporate liquid assets by the book value of total assets, net of liquidities (net assets hereafter) with the view that a firm's ability to generate future profits is a function of its assets in place.

Our results are robust to the use of alternative measures used in literature on cash holdings including the ratio of cash to net assets, the ratio of cash to total book assets, the logarithm of the ratio of cash to total assets, and the ratio of cash to sales. Following the latter studies mentioned above and [START_REF] Bates | Why do US firms hold so much more cash than they used to?[END_REF], we control for the following firm-specific characteristics in our regressions:

(i) Size: measured as the logarithm of the firm's book value of net assets in 2003 dollars. [START_REF] Opler | The determinants and implications of corporate cash holdings[END_REF] show that cash holdings decrease with size. The argument is that size is a deterrent for takeovers and substitute cash holdings' role. Thus, we expect size and cash holdings to be negatively related.

(ii) Leverage: measured as long-term debt plus debt in current liabilities, all scaled by the book value of net assets. We expect the coefficient on leverage to be also negative.

This is consistent with the financing hierarchy model and the evidence supporting the static trade-off model.

(iii) Capex: measured as the ratio of capital expenditure to total net assets.

(iv) R&D: measured as the ratio of R&D expenses to net sales and set equal to zero when R&D expenses are missing.

We expect firms with higher capital and R&D expenditure to hold more cash. To the extent good investment opportunities are available and if faced with a cash shortage, the firm has to give up on such profitable projects (e.g., [START_REF] Opler | Corporate cash holdings[END_REF]. Therefore, investors would rather prefer firms to be able to take advantage of them. [START_REF] Myers | Corporate financing and investment decisions when firms have information that investors do not have[END_REF] demonstrate that financial slack is more valuable in firms with good investment opportunities and that the value of cash holdings should be the investment opportunity set of the firm. We expect the relation to be positive in a way that firms with better prospects for investments will have higher levels of liquid assets.

(v) Cash flows: measured as earnings after interest, dividends, and tax but before depreciation scaled by total net assets. [START_REF] Bliss | Corporate payout, cash retention, and the supply of credit: Evidence from the 2008-2009 credit crisis[END_REF] point out that generating more cash flows allows firms to hold more cash.

(vi) Net working capital: measured as working capital minus cash, all scaled by total net assets. Net working capital (NWC) has been long used as a measure of liquid asset substitutes (e.g., [START_REF] Opler | The determinants and implications of corporate cash holdings[END_REF]. Hence, the relationship between NWC and cash holdings is expected to be negative.

(vii) Market-To-Book: defined as the book value of net assets plus the market value of common equity minus the book value of common equity, all scaled by the book value of net assets. A variety of studies find that the market-to-book ratio is an important determinant of corporate financing choices thought to depend on a firm's portfolio of growth options (e.g., [START_REF] Smith | The investment opportunity set and corporate financing, dividend, and compensation policies[END_REF][START_REF] Jung | Timing, investment opportunities, managerial discretion, and the security issue decision[END_REF][START_REF] Barclay | The priority structure of corporate liabilities[END_REF]. Growth opportunities, proxied by market-to-book ratio, should be positively related to cash holdings.

(viii) Acquisition: defined as the ratio of acquisitions to total net assets, with acquisitions reflecting only the associated cash outflows. Consistent with the agency costs of cash retention and free cash flow theories, [START_REF] Harford | Corporate governance and firm cash holdings in the US[END_REF] find that firms spend cash on capital expenditures and acquisitions, especially when they are weakly governed.

(ix) Industry CF volatility: calculated as the average of the standard deviation of firm-level cash flow in each industry defined by two-digit SIC code, the standard deviation in question being the standard deviation of the firm-level ratio of cash flow to assets for the previous five years. The evidence presented in Opler, Pinkowitz, Stulz, andWilliamson (1999, 2001) advocates that industry-level cash flow variability is a key positive determinant of corporate cash holdings. Industry cash flow volatility means higher uncertainty and firms are more likely to hold precautionary cash.

(x) Dividend: defined as a dummy variable, set equal to one in years in which a firm pays common dividends, and zero otherwise. When corporate payouts increase, it means that firms distribute cash to shareholders instead of holding it (e.g., [START_REF] Harford | Corporate governance and firm cash holdings in the US[END_REF][START_REF] Bliss | Corporate payout, cash retention, and the supply of credit: Evidence from the 2008-2009 credit crisis[END_REF]. We expect dividends to be negatively linked to cash holdings.

In our models, we regress the ratio of cash holdings on the firm-level political risk variable (PRisk) and the set of control variables. All continuous control variables are winsorized at the 1% and 99% level to mitigate the effect of outliers. Given that cash holdings are time period and industry specific as they are related to economic conditions, we include dummy variables for firms and both time period and industry using a twodigit SIC classification. 23 We report our results using mainly two specifications fixed effects model on raw measures and ordinary least squares model on standardized values of political risk and all the variables accordingly.

Descriptive statistics

Figure 3-2 and Table 3-1 reports the overall time variation by fiscal year of our variable of interest and main dependent variable.

Figure 3-2. Time variation of the standardized values of political risk and cash holdings

We show that firm-level political risk correlates with firm-level outcomes in terms of cash reserves clearly indicating corporate response to fluctuations in political risk levels accordingly. This correlation is positive as every peak in political risk levels is followed by a peak in corporate cash holdings and vice-versa.

Table 3-2 show the mean and standard deviation of the main measure of political risk used in our study within each industry, where industries are defined by Fama and

French 48 industry classification. 2019), for all firms in a given year. Cash holdings is the annual average of the ratio of cash and marketable securities to net assets for all firms in a given year. Results in Table 3-2 show that certain specific industries are more sensitive to political risk than others and have an overall high risk related to politics. We find that firms, which dedicate a high share of their conference calls to talk about political risk, belong to heavy industries (e.g., pharmaceutical, aircraft, ships and railroads building)

and to sectors that are usually politically connected, such as defense, healthcare, and tobacco. They present PRisk levels that are around 45% above the overall mean of political risk. Such findings corroborate results in [START_REF] Floyd | Payout policy through the financial crisis: The growth of repurchases and the resilience of dividends[END_REF], though not the first focus on their study, they find that credit crisis has stronger impact on repurchase and dividend cuts for heavily industrial firms. Political risk levels are the lowest for retail, food and beverage industries. 24Table 3-3 presents descriptive statistics for the main variables used in our empirical work. The reported statistics show that all firm characteristics are within reasonable levels and are largely in line with previous studies in terms of magnitude (e.g., Hassan, Hollander, van Lent, and Tahoun, 2019;[START_REF] Ghaly | Cash holdings and labor heterogeneity: the role of skilled labor[END_REF]. For example, we find that political risk is prevalent in the U.S. context, since the average value of PRisk for our sample firms is 98.08. We also use standardized values of political risk as in Hassan, Hollander, van Lent, and Tahoun (2019) and report descriptive statistics that ate close to the average value reported the latter authors.

Table 3-4 provides Pearson correlation coefficients between the explanatory variables in our main regression. From this correlation matrix, it is evident that multicollinearity between the independent variables is unlikely, since the correlation coefficients are relatively small. Another noteworthy result in table 3-4 is the initially positive and significant correlation between political risk and corporate cash holdings. This table reports summary statistics for the dependent variable (cash holdings), our key variable of firmlevel political risk (PRisk), and a set of control variables. Our sample consists of 33,618 firm-year observations covering the period 2002-2016. Cash holdings are the natural logarithm of the ratio of cash and short-term investment to total net assets. PRisk is a measure of firm-level political risk developed by Hassan et al. (2019) and defined in Appendix 3-2. Size is measured as the logarithm of the book value of net assets in 1999 dollars. Leverage is long-term debt plus debt in current liabilities, scaled by total net assets. Capex is the ratio of capital expenditures to total net assets. Cash flows are earnings after interest, dividends, and tax but before depreciation, scaled by the book value of total net assets. Net working capital is measured as working capital minus cash, scaled by total net assets. R&D is the ratio of R&D expenses to net sales and is set equal to zero when R&D expenses are missing. The market-to-book ratio is defined as the book value of assets plus the market value of common equity minus the book value of common equity, scaled by the book value of net assets. Acquisitions are defined as the ratio of acquisitions to total net assets.

To measure industry cash flow volatility, we calculate for each firm-year the standard deviation of (firmlevel) cash flow to assets for the previous five years. Industry CF volatility is then calculated as the average of the firm cash flow standard deviations for each industry, classified by two-digit SIC codes. Dividend dummy is a dummy variable set equal to one in years in which a firm pays common dividends, and zero otherwise. This table presents correlation coefficients between the dependent variable (cash holdings), our key variable of firm-level political risk (PRisk), and control variables. Our sample consists of 33,618 firm-year observations covering the period 2002-2016. Cash holdings are the natural logarithm of the ratio of cash and short-term investment to total net assets. PRisk is a measure of firm-level political risk developed by Hassan et al. (2019) and defined in Appendix 3-2. Size is measured as the logarithm of the book value of net assets in 1999 dollars. Leverage is long-term debt plus debt in current liabilities, scaled by total net assets. Capex is the ratio of capital expenditures to total net assets. Cash flows are earnings after interest, dividends, and tax but before depreciation, scaled by the book value of total net assets. Net working capital is measured as working capital minus cash, scaled by total net assets. R&D is the ratio of R&D expenses to net sales and is set equal to zero when R&D expenses are missing. The marketto-book ratio is defined as the book value of assets plus the market value of common equity minus the book value of common equity, scaled by the book value of net assets. Acquisitions are defined as the ratio of acquisitions to total net assets. To measure industry cash flow volatility, we calculate for each firm-year the standard deviation of (firm-level) cash flow to assets for the previous five years. Industry CF volatility is then calculated as the average of the firm cash flow standard deviations for each industry, classified by two-digit SIC codes. Dividend dummy is a dummy variable set equal to one in years in which a firm pays common dividends, and zero otherwise. Numbers in bold refer to significance at the 1% threshold.

4.

Empirical results

Main regressions: the effect of firm-level political risk on corporate cash holdings

The hypotheses presented in this chapter hold that firms adjust their cash holdings proportionally to the level of political risk they idiosyncratically perceive. The argument is implied by [START_REF] Jensen | Agency costs of free cash flow, corporate finance, and takeovers[END_REF] financial flexibility theory. We test these hypotheses by estimating our baseline model where we regress cash holdings on the key variable named

PRisk and the set of control variables.

Table 3-5 presents the regression results on the relation between the degree to which firms are exposed to political risk and their cash holdings. Except for the cross-sectional analysis in Model 5, we report p-values based on heteroskedasticity-robust standard errors, clustered at the firm level. Regressions include year, industry, and/or firm dummies but to conserve space we opt not to report their estimates. We include these fixed effects to address concerns of not controlling for (unobserved) time-invariant firmor industry-level factors (i.e., heterogeneity) may lead to a spurious correlation between the political risk and cash holdings variables.

In model 1, we run a pooled ordinary least squares (OLS) estimation and we control for year and industry fixed effects. In model 2, we run the same estimation but with the standardized values of our variables. Column 3 and column 4 provide results using fixed effects model without and with clustering at the firm level, respectively. In model 5, we perform the [START_REF] Fama | Risk, return, and equilibrium: Empirical tests[END_REF] model. With this approach, a cross-sectional regression is estimated each year. This method addresses the issue of serial correlation in the residuals of a timeseries cross-sectional regression. The Fama-MacBeth model treats each year as an independent cross-section.

Across all model specifications, our findings remain qualitatively the same. Firmlevel political risk has a robust and positive effect on corporate cash holdings. In models 1, 2 and 5 the coefficient on firm-level political risk is significant at the 1% threshold. For example, in model 5, the coefficient is equal to (0.0853). This table provides the results of the regressions of the dependent variable (cash holdings), our key variable of firm-level political risk (PRisk), and a set of control variables using different estimation techniques. Our sample consists of 33,618 firm-year observations covering the period 2002-2016. Cash holdings are the natural logarithm of the ratio of cash and short-term investment to total net assets. PRisk is a measure of firm-level political risk developed by Hassan et al. (2019) and defined in Appendix 3-2. Size is measured as the logarithm of the book value of net assets in 1999 dollars. Leverage is long-term debt plus debt in current liabilities, scaled by total net assets. Capex is the ratio of capital expenditures to total net assets. Cash flows are earnings after interest, dividends, and tax but before depreciation, scaled by the book value of total net assets. Net working capital is measured as working capital minus cash, scaled by total net assets. R&D is the ratio of R&D expenses to net sales and is set equal to zero when R&D expenses are missing. The marketto-book ratio is defined as the book value of assets plus the market value of common equity minus the book value of common equity, scaled by the book value of net assets. Acquisitions are defined as the ratio of acquisitions to total net assets. To measure industry cash flow volatility, we calculate for each firm-year the standard deviation of (firm-level) cash flow to assets for the previous five years. Industry CF volatility is then calculated as the average of the firm cash flow standard deviations for each industry, classified by two-digit SIC codes. Dividend dummy is a dummy variable set equal to one in years in which a firm pays common dividends, and zero otherwise. Robust p-values are in parentheses. *, ** and *** refer to significance at the 10%, 5% and 1% levels, respectively.

Consistent with prior studies' results, we find that cash holdings decrease significantly with size, net working capital, leverage, acquisitions and dividends and increase significantly with R&D, capital expenditures, cash flows, market-to-book, and industry cash flows volatility. The coefficients are not only statistically significant but, in general, they are also economically significant. Overall, our findings are qualitatively similar across a variety of model specifications: political uncertainty has a robust and positive impact on cash holdings. This evidence is consistent with our hypothesis that firms heavily facing risks related to politics hold higher cash balances so as to maintain financial flexibility in moments of upheavals.

We dig deeper into the understanding of the effect of political risk on corporate cash holdings decisions by using the decomposition of the aggregate risk measure into eight different topics, namely: economic policy and budget, environment, trade, institutions and political process, health care, security and defense, tax policy, and technology and infrastructure. Table 3-6 provides the regression results on the relation between the degree to which firms are exposed to each topic or dimension of political risk and levels of cash holdings. We run a fixed effect model in Panel A and a pooled ordinary least squares (OLS) estimation in Panel B. Models 1 to 8 present regression results of cash holdings on each dimension and our set of control variables. Model 9 presents a horserace regression where we include all political risk components. Overall, our results remain the same, indicating that dimensions of firm-level political risk have a positive and significant effect on corporate cash balances. table provides the results of the regressions of the dependent variable (cash holdings), the variables representing sub-components of the firmlevel political risk, and a set of control variables using different estimation techniques. Our sample consists of 33,618 firm-year observations covering the period 2002-2016. Cash holdings are the natural logarithm of the ratio of cash and short-term investment to total net assets. The variables Policy, Defense, Taxes, Technology, Institutions, Trade, Economic, Health, and Environment are measures of the components of firm-level political risk developed by Hassan et al. (2019) and defined in Appendix 3-2. Robust p-values are in parentheses. *, ** and *** refer to significance at the 10%, 5% and 1% levels, respectively.

Political risk and the model cash retention

We follow Almeida, Campello, and Weisbach (2004) to empirically model the cash flow sensitivity of cash for firms facing high political uncertainty. We use a parsimonious model, in which we include firm size and proxies for variables that capture the most information related to the primitives of the model: cash flow innovations and investment opportunities (i.e., cash flows and Tobin's Q), to which we add our variable of firm-level political risk in interaction with cash flows. the results of the empirical model of cash flow retention. We regress the dependent variable (cash holdings) on our key variable of firm-level political risk (PRisk) and non-political risk (Non-PRisk), cash flows, their interaction and a set of control variables Cash holdings are the natural logarithm of the ratio of cash and short-term investment to total net assets. PRisk and Non-PRisk are measures of firm-level political risk and non-political risk, respectively, developed by Hassan et al. (2019) and defined in Appendix 3-2. Size is measured as the logarithm of the book value of net assets in 1999 dollars. Tobin's Q is defined as the market value scaled by the book value of net assets. Robust p-values are in parentheses. *, ** and *** refer to significance at the 10%, 5% and 1% levels, respectively.

The model captures the change in cash holdings in response to a shock to cash flows emanating from politics. It suggests that the cash policy of firms should also be influenced by the attractiveness of future investment opportunities. The results displayed in column 1 of Table 3-7 find support for a significant at the 5% threshold evidence of more cash flow sensitivities of firms facing higher political risk. In column 2, we replace firmlevel political risk by the proxy of non-political risk and the coefficient on the interaction term between non-political risk and cash flows turns insignificant.

4.4.

Political risk and cash valuation [START_REF] Opler | The determinants and implications of corporate cash holdings[END_REF] and [START_REF] Dittmar | Corporate governance and the value of cash holdings[END_REF], among many others, point out that excess cash is positively associated with post-crisis investment, indicating a substantial precautionary cash savings role. In the same vein, other studies emphasize the importance of cash holdings of constrained firms as a value-increasing answer to costly external financing. To the extent that political events are of high magnitude, we expect that the excess cash firms hold increases their value.

Using the definitions of excess cash and the models of cash valuation provided by [START_REF] Pinkowitz | What is a dollar worth? The market value of cash holdings[END_REF] and [START_REF] Dittmar | Corporate governance and the value of cash holdings[END_REF], we report in Table 3-8 and in Table 3-9 the results of the effect of excess cash during political upheavals on cash and firm valuation. We find a strong and positive effect (0.0908) and (0.0044) meaning that the marginal value of cash rises with larger cash holdings during political crises. [START_REF] Dittmar | Corporate governance and the value of cash holdings[END_REF], in which we include firm-level political risk. Tobin's Q is defined as the market value scaled by the book value of net assets. PRisk is a measure of firm-level political risk developed by Hassan et al. (2019) and defined in Appendix 3-2. Mi,t is the market value of equity at time t computed as price times shares. Ci,t is cash at time t. Ei,t is earnings before extraordinary items from year t -1 to t. NAi,t is net assets at time t. RDi,t is R&D expenses, set to zero if missing from year t -1 to t. Ii,t is interest expenses from year t -1 to t. Di,t is common dividends from year t -1 to t. Li,t is leverage at time t and is calculated as Debti,t divided by (Debti,t+Mi,t). Debti,t is long term debt plus short term debt at time t. NFi,t is new finance from year t -1 to t and is equal to net new equity issues plus net new debt issues. Robust p-values are in parentheses. *, ** and *** refer to significance at the 10%, 5% and 1% levels, respectively. [START_REF] Pinkowitz | What is a dollar worth? The market value of cash holdings[END_REF], in which we include firm-level political risk. PRisk is a measure of firm-level political risk developed by Hassan et al. (2019) and defined in Appendix 3-2. Xt is the level of variable X in year t divided by the level of assets in year t. dXt is the change in the level of X from year t-2 to year t divided by total assets in year t ((Xt-2 -Xt)/At). dXt+2 is the change in the level of X from year t+2 to year t divided by assets in year t ((Xt+2 -Xt)/At). MV is market value of equity. E is earnings defined as earnings before extraordinary items plus interest plus income statement deferred tax credits plus investment tax credits. NA is net assets, which is defined as assets minus cash. RD is research and development expense. When R&D is missing, it is set to zero. I is interest expense. D is common dividends. C is cash and marketable securities. Robust p-values are in parentheses. *, ** and *** refer to significance at the 10%, 5% and 1% levels, respectively.

Political risk and cash management and channeling

Understanding corporate cash holdings decisions implies understanding firms' investment and financing behavior correspondingly. How likely are these firm to substitute a cash flow shortfall and what is the source of the additional cash reserves?

Channeling here denotes the channel through which a firm can spend cash flows: the change in investment, the change in net working capital, the change in short-term debt, the change in long-term debt, equity issuance, payout and repurchase. We investigate the effect of firm-level political risk on the variation in these channels to better comprehend how firms react to uncertainty emanating from political disruptions in terms of cash management decisions.

We report the results of our analysis in Table 3-10. We find that firms facing higher levels of political risk generally reduce their investment expenses. The coefficients on PRisk in model 1 and 2 in Panel A and Panel B of Table 3-10 are strongly significant and negative. These findings are consistent with prior literature (e.g., Bernanke, 1983;[START_REF] Bittlingmayer | Output, stock volatility, and political uncertainty in a natural experiment: Germany, 1880-1940[END_REF]Julio and Yook, 2012;Jens, 2017). Furthermore, the additional corporate cash reserves during political crisis are likely to be channeled through higher net working capital (Model 4) and longer debt maturities (Model 5 and 6). Political risk is also associated with lower equity issuance. However, we find no significant effect of firm-level political risk on the change in equity repurchase and the change in payout policies.

Overall, firms that are exposed to risks emanating from the political system overcome financing shortfalls and stringent financing conditions by increasing their cash holdings through reducing capital expenditure, investment spending, short debt and equity issuance, increasing their net working capital and undertaking longer maturity debt. table provides the results of the regressions of the change in various dependent variables (through which cash can be channeled within a company), our key variable of firm-level political risk (PRisk), and a set of control variables using fixed effect model in Panel A and pooled OLS in Panel B. PRisk is a measure of firm-level political risk developed by Hassan et al. (2019) and defined in Appendix 3-2. Δ Capex is the change in the ratio of capital expenditures to total net assets from year t -1 to t. Δ R&D is the change in the ratio of R&D expenses to net sales, set equal to zero when R&D expenses are missing from year t -1 to t. Δ NWC is measured as the change in working capital minus cash, scaled by total net assets from year t -1 to t. Δ Acquisitions is defined as the change in the ratio of acquisitions to total net assets from year t -1 to t. Δ Short debt is the change in short-term nonoperating liabilities from year t -1 to t. Δ Long debt is the change in long-term nonoperating liabilities from year t -1 to t. Δ Equity is the change in share issuance from year t -1 to t. Δ Dividend is the change in common dividends from year t -1 to t. Robust p-values are in parentheses. *, ** and *** refer to significance at the 10%, 5% and 1% levels, respectively.

Additional tests

This study shows that firms' exposure to political uncertainty induces firms to hold more cash. This finding is consistent with the financial flexibility argument of the agency costs theory. In our analyses, we implicitly assume that the relation between firm-level political risk and cash holdings is uniform across all firms. In this section, we aim to more deeply understand the cross-sectional heterogeneity in this relation by examining the role of exposure to competition, innovation, and the distance separating a firm's headquarter

to Washington DC where the political power is concentrated.

▪ Role of competition

Competition plays an important role in increasing firms' risk and their exposure to uncertainty. The pressure stemming from competition is likely to lead to an uncertain and rapidly changing environment since firms in competitive markets are inclined to continuously change their products and innovate more, consistent with the "escapecompetition effect" of [START_REF] Aghion | Competition and innovation: An inverted-U relationship[END_REF]. On the one hand, competing over profit opportunities and market shares with rivals may erode gains related to innovation or investment activity, thus leading to risky profits and greater uncertainty about future performance (e.g., [START_REF] Gaspar | Idiosyncratic volatility and product market competition[END_REF][START_REF] Akdogu | Product markets and corporate investment: Theory and evidence[END_REF]. Other studies suggest that in stiff competition environments, such as the U.S. economy, firms experience an increase in idiosyncratic cash flow volatility, suggesting competition is a driver of firm-level risk and that market power is a hedge against firm-specific fluctuations [START_REF] Irvine | Idiosyncratic return volatility, cash flows, and product market competition[END_REF]. On the other hand, Hoberg, Phillips, and Prabhala (2014) find that competition increases the cash held by firms, especially for firms with less access to financial markets. Therefore, we expect the relationship between firm-level political risk and cash holdings to be stronger in competitive environment.

To better gauge the role of exposure to competition, we use interaction terms from variables that reflect exposure to competition (Herfindahl Hirschman Index, hereafter 153 HHI). Table 3-11 reports our results. In column 1, we use the HHI calculated as the sum of the squared market shares of all firms operating in the same industry developed by [START_REF] Hoberg | Text-based network industries and endogenous product differentiation[END_REF]. 25 We show that, as predicted, the effect of political risk on cash reserves is more pronounced in competitive industries. The coefficient on the interaction term is negative and significant at the 5% level.26 regression results on the impact of a firm's exposure to competition, level of innovation, and distance to Washington DC on the relation between the dependent variable (cash holdings) and our key variable of firm-level political risk (PRisk). Cash holdings are the natural logarithm of the ratio of cash and short-term investment to total net assets. PRisk is a measure of firm-level political risk developed by Hassan et al. (2019) and defined in Appendix 3-2. R&D is the ratio of R&D expenses to net sales and is set equal to zero when R&D expenses are missing. Distance to Washington DC is the distance in miles separating the county in which the headquarter is located and Washington DC calculated using longitude and latitude data. Robust p-values are in parentheses. *, ** and *** refer to significance at the 10%, 5% and 1% levels, respectively.

▪ Role of innovation

Studies show that a political crisis strongly and negatively affects firms' investment strategies (e.g., Julio and Yook, 2012;[START_REF] Lee | Political uncertainty and cost stickiness: Evidence from national elections around the world[END_REF]Jens, 2017), reducing mainly their capital and R&D expenditure to provide more cash reserves as we show in the previous section. Brown and Peterson (2011) and [START_REF] Lyandres | Cash holdings, competition, and innovation[END_REF] state that R&D investment has an important role in shaping corporate liquidity policies because R&D has high adjustment costs, especially in response to transitory finance shocks. These studies show that firms rely extensively on cash holdings to smooth R&D to face financing frictions and that successfully innovate compete better with using cash as a commitment device for implementation of successful innovations.

In column 2 of Table 3-11, we rerun our baseline regression model by adding an interaction term between each of our political risk variable and innovation proxies. The coefficient on the interaction term is negative and statistically significant, suggesting that the positive relation between political risk and cash holdings is weaker for firms with higher R&D.

▪ Role of distance to Washington DC

In 2018, Amazon decided to build two headquarter offices, one in New York City and another in Northern Virginia near Washington, D.C. The firm has already had a considerable presence in those locations and this decision aims to strengthen its footprint in America's financial and political centers. Amazon has been facing criticism since then over its political connections. The incentive for firms to become politically connected has been studied for many years. Faccio (2006), for example, finds that corporate value significantly increases around the time of the announcements of directors or large shareholders getting involved in politics. The author defines a firm as politically connected if at least one of its large shareholders has connections with a politician. This value can have various sources including lighter taxation, preferential treatment, relaxed regulatory oversight for politically connected firms compared to their rivals, and rent extraction (e.g., Shleifer and Vishny, 1994).

In this study, we provide a proxy for firm likelihood to lobby or to build political connection. We calculate the distance in latitude and longitude (in miles) separating the county in which a firm's headquarter is located and Washington DC (i.e., where political actors and power are established). Column 3 in Table 3-11 reports our results. We find that the positive relation between firm exposure to political risk and its decision to hold more cash is less pronounced if it is located near Washington DC. This finding implies that politically connected firms perceive less risk because of the privileged treatment and the relaxed regulatory oversight which they enjoy, and therefore, they are less likely to have high levels of cash reserves.

Conclusion

The main key to the comprehension of corporate demand for cash is the precautionary saving theory introduced first by [START_REF] Keynes | The general theory of employment, interest and money[END_REF] and developed by [START_REF] Jensen | Agency costs of free cash flow, corporate finance, and takeovers[END_REF]. From this theory perspective, the predominant motive for firms to hold more cash is to protect themselves against adverse cash flow shocks. Many studies empirically and theoretically show that shocks emanating from the political system significantly alter firms' behavior and cause a lot of fluctuations in investment, cash flows, stock price (e.g., [START_REF] Belo | Government spending, political cycles, and the cross section of stock returns[END_REF][START_REF] Gourio | Uncertainty and international capital flows[END_REF][START_REF] Handley | Trade and investment under policy uncertainty: theory and firm evidence[END_REF][START_REF] Kelly | The price of political uncertainty: Theory and evidence from the option market[END_REF][START_REF] Besley | Institutions, volatility, and investment[END_REF].

A political crisis raises uncertainty about the future supply of funds and the conditions to access financing markets. Hence, it may lead to increase in the demand for internal funds. Specifically, this chapter establishes a link between political shocks generating high risk levels and corporate cash holdings. We argue that firms have the incentive to set up financial slack by hoarding cash to avoid the adverse consequences associated with shocks and uncertainty stemming from politics.

We use a large panel of U.S. public firms over the period 2002 to 2016. Consistent with the idea of apparent benefits of maintaining larger cash reserves in moments of distress, we show that firms' exposure to political risk increases their cash reserves, cash valuation, and cash flow retention. These additional cash holdings are mainly rooted in reducing investment, increasing net working capital and longer maturities in debt issuance. The motive to hold cash in periods of political crises is stronger for firms operating in competitive markets and less pronounced for innovative firms and firms that are closer to political centers in Washington DC.

This study adds to different literature topics that investigate the change in firms'

behavior and decision-making under uncertainty and belongs to a growing theoretical and empirical body of literature examining the interaction between politics and corporate financial decisions. However, the main contribution is that we use a novel firm-level measure of political risk developed by Hassan, Hollander, van Lent, and Tahoun (2019).

In the absence of a direct measure of political risk in the literature, this measure is the only one to provide an idiosyncratic assessment of political uncertainty as seen by the firm. It is a result of textual analysis of corporate conference calls between firm management and analysts and measures the share of the conversation dedicated to talk about political topics.

Measuring political risk is often too complex. Even if such measures reveal compelling and offer relevant patterns of the dynamics of political risk, we can still pinpoint a few limitations that are inherited in it. First, although the association between firm-level variation in political risk and firm cash holdings are statistically and economically significant, we do not compare in depth this firm-level variation's effect with the effect of aggregate or sector-level proxies of political risk. Second, this measure of political risk embeds qualitative perceptions of managers and participants of the political environment there are in. Thus, it should be interpreted as indicative of risk as it is apparent to firms and stakeholders and also indicative of their sentiment because their perceptions may differ from actual risk. Finally, management can use pretexts to

As above

Leverage Firm leverage measured as the ratio of total liabilities to total net assets.

As above

Capex

Capital expenditures defined as the ratio of capital expenditures to total net assets.

As above R&D R&D expenditures measured as the ratio of R&D expenses that are set on zero if missing to total net assets.

As above

Tobin's Q Tobin's Q defined as the sum of market value of equity plus book value of debt divided by total net assets. text that are used in conjunction with synonyms for risk or uncertainty.

Introduction

Understanding whether political risk has effects on the risk-seeking behaviors of firms is important for shaping policy-making processes and corporate governance frameworks (e.g., [START_REF] Mihet | Effects of culture on firm risk-taking: a cross-country and cross-industry analysis[END_REF]. Risk is a fundamental determinant of managerial decisions and has been substantially studied in strategic and financial management fields [START_REF] Ruefli | Risk measures in strategic management research: auld lang syne?[END_REF]. In the past, numerous studies have focused on the risk-taking determinants from the perspective of decisions makers' characteristics, such as optimism, over-confidence, gender, executive team heterogeneity, etc. (e.g., [START_REF] Hambrick | The influence of top management team heterogeneity on firms' competitive moves[END_REF][START_REF] Yung | Managerial ability and firm risk-taking behavior[END_REF]. Other studies have explored the determinants of risk behavior focusing on firm policies and organizational structures (e.g., [START_REF] Bromiley | Testing a causal model of corporate risk taking and performance[END_REF][START_REF] Mcnamara | Risk and return in organizational decision making[END_REF][START_REF] Das | Trust, control, and risk in strategic alliances: An integrated framework[END_REF].

Managers' profile and managerial incentives have significant effects on the outcomes achieved by firms. From the upper echelons' theory perspective, [START_REF] Hambrick | Upper echelons: The organization as a reflection of its top managers[END_REF] and [START_REF] Hambrick | Upper echelons theory: An update[END_REF], among many others, argue that managers' interpretations of the complexity of actual decision-making situations they face, and therefore their choices, depends on their idiosyncratic experiences and values. In line with this theory, [START_REF] Bertrand | Managing with style: The effect of managers on firm policies[END_REF] that a big extent of heterogeneity in firm practices is explained by the presence of manager fixed effects. A large strand of literature delves into the central premise of managerial incentives in shaping the riskiness profile of their decisions (e.g., [START_REF] Raith | Competition, risk, and managerial incentives[END_REF]Coles, Daniel and Naveen, 2006;[START_REF] Low | Managerial risk-taking behavior and equity-based compensation[END_REF][START_REF] Agarwal | Role of managerial incentives and discretion in hedge fund performance[END_REF][START_REF] Kini | Tournament incentives, firm risk, and corporate policies[END_REF][START_REF] Milidonis | Managerial incentives, risk aversion, and debt[END_REF][START_REF] Baixauli-Soler | Executive stock options, gender diversity in the top management team, and firm risk taking[END_REF].

More recently, a growing set of literature has examined the effects of exogenous and external factors that are beyond the control of the manager on firm risk-taking. The legal and political environments in which firms operate can constrain or foster managerial incentives for risk-taking and therefore for the economic outcomes of these firms. The political climate defines businesses and managerial practices (e.g., Tan, 2001;Boubakri, Mansi, and Saffar, 2013;[START_REF] Ashraf | Political institutions and bank risk-taking behavior[END_REF]Akey and Lewellen, 2017). Our study is motivated by the body of work on the effect of politics on managerial risk choice. More specifically, we examine the relationship between firm-level political risk and managerial risk-taking

and to what extent equity-based managerial incentives can affect this relationship.

To date, the link between political risk and firm risk choices remains relatively under-understood, especially with the mounting complexity of political uncertainty.

Following the rise of political turmoil in the last decade, we explore the relationship between a novel measure of idiosyncratic perception of political risk on managerial risktaking. Former research work adds to the understanding of why managers make decisions ad they do amid changing business conditions due to changing environments.

For instance, Tan (2001) shows that the political change during the Chinese economic transition has widened the difference between managers and entrepreneurs in terms of willingness to make risky decisions. A few other studies investigate the relationship between the soundness of political institutions and firm riskiness. For example, Boubakri, Mansi and Saffar (2013) show that this relation is consistent with the hypothesis that better political institutions encourage risk-taking by boosting the credit market competition and by the bailouts in times of uncertainty which create moral hazard issues. [START_REF] Ashraf | Political institutions and bank risk-taking behavior[END_REF] however find a competing result consistent with the hypothesis that in countries with sound political institutions, political risk is less pronounced because of the reduced expropriation risk and the increased information transparency, and therefore firms engage in less risky activities. Whether political risk increases or lowers managers' risk-taking in decision-making is still an open question and political uncertainty offers two potential channels of arguments with conflicting predictions.

We test both hypotheses to gauge the interdependence between the uncertainty in the political sphere and the riskiness in the managerial decision-making processes. We use a new proxy for political risk, developed by Hassan, Hollander, van Lent, and Tahoun (2019). It is a text-based measure that reflects firm perception of political risk through the share of quarterly earnings conference calls between top managers and stakeholders that is dedicated to talk about political risk related topics. As for our main dependent variable, we calculate managerial risk-taking based on equity risk measure for firm risk for several reasons, the most important being that executive stock options are written on firm equity.

Our sample comprises 3,095 U.S. listed firms for the period spanning 2002 through 2016, for which the firm-level political risk index is available. Our empirical analysis finds that there is a strong positive association between political risk and the riskiness of managerial choices during the first two years after being exposed to political turmoil and before adjusting their risk-taking behavior towards less risky choices. Besides, managers act on the idiosyncratic risk-taking while have little effect on systematic risk. This time lag can be explained by different reasons including the "outrunning the bear" mind-set, agency problems, managerial inertia facing risk and/or the moral hazard stemming from governmental bailouts.

Our analysis next investigates how the relation between firm-level political risk and risk-taking varies in the cross-section. First, we investigate whether equity-based compensation (i.e., Delta and Vega) affects managers' risk-taking behavior in response to an increase in political uncertainty. Then, we argue that the risk-increasing effect of political turmoil on the riskiness of managerial decision-making can be distorted by the moderating effect of financial constraints and some firm policies. Furthermore, we argue that this relationship can be of particular interest to multinational corporations (MNCs)

and to foreign investors. Overall, we conclude that the effect of political risk on firm risktaking is more pronounced for multinational firms, financially constrained firms and for firms with higher CEO portfolio Vega.

Our evidence is robust to several sensitivity tests. We use different measures of firm decision-making riskiness and we check the response of managers in terms of risk-taking to political risk with regards to the granularity of its components (i.e., economic policy, defense, trade, health, environment, taxes, technology, and institutions). Our results also stand up to a battery of robustness checks, including additional control variables and addressing endogeneity concerns that can stem from the measurement of political risk in our case.

Our goal is to study the interaction between firms' sensitivity to political uncertainty and firm risk-taking. The underlying hypothesis can be related to two major strands of existing literature. First, it adds to the growing literature examining the effects of aggregate political uncertainty on firm outcomes and asset prices [START_REF] Durnev | The real effects of political uncertainty: Elections and investment sensitivity to stock prices[END_REF][START_REF] Boutchkova | Precarious politics and return volatility[END_REF]Julio and Yook, 2012;Pástor andVeronesi, 2012, 2013;Brogaard and Detzel, 2015;[START_REF] Gulen | Policy uncertainty and corporate investment[END_REF]Baker, Bloom and Davis, 2016;Kelly, Pástor, and Veronesi, 2016;Jens, 2017). These papers commonly point out that aggregate risk-taking is reduced during periods of high uncertainty. Moreover, it adds an additional support for the relevance of legal and political institutional framework in shaping corporate decisions (e.g., Tan, 2001;[START_REF] Boutchkova | Precarious politics and return volatility[END_REF][START_REF] Durnev | Politics, instability, and composition of international investment flows[END_REF][START_REF] Boubakri | Post-privatization state ownership and bank risk-taking: Cross-country evidence[END_REF].

Second, we contribute to the firm risk-taking literature and suggest that managers' risk-taking impacts organizations' behavior over and above time and firm-specific characteristics, especially in hard economic times. Our study adds to the literature on determinants of firm risk-taking, such as a change in legislation (e.g., [START_REF] Low | Managerial risk-taking behavior and equity-based compensation[END_REF], managerial and board members' heterogeneity (e.g., Yung and Chen, 2017;Sila, Gonzalez, Hagendorff, 2015), religiosity (e.g., Adhikari and Agrawal, 2016) and uncertainty (e.g., [START_REF] Palmer | Decoupling risk taking from income stream uncertainty: A holistic model of risk[END_REF][START_REF] Popescu | Uncertainty, risk-taking, and the business cycle in Germany[END_REF]Akey and Lewellen, 2017). To date, however, these strands of the literature have not looked at how twenty-first-century political risk interacts with managerial risk-taking.

The rest of the chapter proceeds as follows. Section 2 hypothesizes relationship between political risk and managerial risk-taking. Section 3 introduces data and variables. Section 4 presents empirical results, including robustness tests and the additional effects. Final section concludes.

Motivation and hypothesis development

Political risk and managerial risk-taking

In a continuously intricate and globalized markets, fostering our understanding of managerial decision-making and risk choices has gained growing attention. Advocates of agency theory argue that managers are opportunist and often engage in activities to maximize their own wealth (Jensen and Meckling, 1976). One other stream of research in line with agency theory premise argues that principals are risk neutral while mangers (agents) tend to be risk averse. Prior studies on risk, such as [START_REF] Hoskisson | Managerial incentives and investment in R&D in large multiproduct firms[END_REF] and [START_REF] Beatty | Managerial incentives, monitoring, and risk bearing: A study of executive compensation, ownership, and board structure in initial public offerings[END_REF], suggest that risk orientations of shareholders and managers are divergent. Therefore, if there would be an activity involving high levels of risk, managers can be reluctant to undertake risky options because their benefits from risk-taking are limited, while principals expect from them to engage in such risky activities and maximize shareholders' investment returns. Furthermore, shareholders are risk-neutral since they can easily diversify their portfolios of investment to reduce risks (e.g., [START_REF] Baysinger | Effects of board and ownership structure on corporate R&D strategy[END_REF].

Moreover, managers are usually concerned about a decline in firm performance or a rising risk of bankruptcy, especially when the ties between the firm and its human capital are strong (e.g., [START_REF] Baysinger | The composition of boards of directors and strategic control: Effects on corporate strategy[END_REF]. Consistent with the agency theory, the underlying assumption is that managers prioritize survival, stable income flow over profit maximization to protect their benefits and reduce risks related to human capital [START_REF] Baysinger | Diversification strategy and R&D intensity in multiproduct firms[END_REF]. Hence, to align these differences, agency costs are usually incurred (e.g., [START_REF] Baysinger | Effects of board and ownership structure on corporate R&D strategy[END_REF][START_REF] Beatty | Managerial incentives, monitoring, and risk bearing: A study of executive compensation, ownership, and board structure in initial public offerings[END_REF]. The research focusing on firm R&D investment choices show that when projects involve uncertainty, managers are less likely to spend on R&D (e.g., [START_REF] Hansen | Are institutional investors myopic? A time-series study of four technology-driven industries[END_REF][START_REF] Laverty | how valid are r&d measures in empirical tests of" shorttermism[END_REF][START_REF] Ettlie | R&D and global manufacturing performance[END_REF]. When projects are associated with high risks and failure rates, and take longer durations in realizing profits, proponents of agency theory support the argument that managers are more likely to under invest and to avoid risk-taking.

Political risk increases uncertainty and complexity of the environment in which firms operate. Businesses have arguably never been exposed to such a large scope of challenges as they do today. Investment and trade decisions are significantly susceptible to political risk, posing serious threats to firm outcome and interest (e.g., Rice and Zegart, 2018). 27 The growing literature discussing the effect of political uncertainty on firm financial decisions reports that firms' risk-taking and performance are highly sensitive to elections outcome. (Akey and Lewellen, 2016), for instance, find that highly sensitive firms to government policy take less risk, invest less and contribute more to political candidates' campaigns prior to elections. A few other studies show that higher political constraints and government predatory policies (i.e., expropriation and corruption) make managers avoid risk-taking (e.g., [START_REF] John | Corporate governance and risk-taking[END_REF][START_REF] Popescu | Uncertainty, risk-taking, and the business cycle in Germany[END_REF]Caprio, Faccio and Parsley, 2011). Political risk emanating from government actions affects managerial risk-taking behavior and encourages firms to channel their cash into less liquid assets to make expropriation by politicians and bureaucrats harder and more costly. Following this line of thought, we argue that political risk is associated with lower levels of managerial risk-taking because managers'

objectives and interests are in fortifying their personal wealth and employment security:

H1a: There is a negative relationship between firm-level political risk and managerial risktaking choices.

Whether political risk and political institutions increase or decrease managers' risk-

taking remains an open question. There are two potential hypotheses on how political uncertainty can affect managerial risk choices. The competing hypothesis is that political risk can positively affect managers' appetite for risk-taking through the moral hazard channel resulting from the soundness of institutional schemes and bailouts in moments of upheavals. At least over this study period (2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016), the institutions in the United the sensitivity of managers' wealth to equity risk. These incentives (i.e., Delta or the payperformance sensitivity, and Vega or the risk-taking incentives) are awarded to managers in order to control risk-related incentive problems and to better manage the convexity and slope of the relation between performance and managers' wealth. They can reduce managers' opportunistic risk aversion that usually leads to decreased firm risk at the expense of shareholder's profit maximization.

Some studies have delved into the relationship between executive equity-based compensation and risk-taking. Coles, Daniel and Naveen (2006) find that CEO wealth sensitivity to stock volatility (i.e., Vega) is significantly and positively related to riskier managerial choices, including investment policy and debt policy. 28 In this same vein, prior studies examining the effect of on firm decision-making find a positive association between Vega and debt ratios (e.g., [START_REF] Cohen | Do executive stock options encourage risk-taking[END_REF][START_REF] Florackis | Managerial incentives and corporate leverage: evidence from the United Kingdom[END_REF], uncertain investment activities like oil exploration [START_REF] Rajgopal | Empirical evidence on the relation between stock option compensation and risk taking[END_REF], and stock return volatility (e.g., [START_REF] Guay | The sensitivity of CEO wealth to equity risk: an analysis of the magnitude and determinants[END_REF] and corporate fraud (e.g., [START_REF] Johnson | Managerial incentives and corporate fraud: The sources of incentives matter[END_REF]. The paper of [START_REF] Hoskisson | Managerial incentives and investment in R&D in large multiproduct firms[END_REF] however argues that incentives that are based on short-term financial performance are negatively associated to risk-taking in terms of firm D&D intensity and diversification strategies.

Results related to equity-based risk-taking incentives are generally inferred through investment and/or debt policies rather than firm risk-taking per se. Nevertheless, a few papers show that option-based managerial compensation is positively associated with risk-taking such as [START_REF] Chen | Does stock option-based executive compensation induce risk-taking? An analysis of the banking industry[END_REF] and [START_REF] Lewellen | Financing decisions when managers are risk averse[END_REF]. Therefore, we add to this line of thoughts by testing the following hypothesis:

H2: The relationship between firm-level political risk and managerial risk-taking is more pronounced in firms with high equity-based compensation for their managers.

between 6000 and 6999 and utilities (SIC between 4900 and 4999), as they are often subject to heavy federal regulations. We merge these data with the firm-level political risk measures of Hassan, Hollander, van Lent, and Tahoun (2019). We exclude observations before 2002 and beyond 2016, periods for which political risk data are not available.

Finally, we restrict our sample to firm-year observations with positive assets and leverage and we winsorize all continuous variables at 1% level in both tails. These filters yield a final sample, consisting of an unbalanced panel of 3,095 firms with a total of 20,140 firmyear observations from the U.S. over the period 2002 to 2016. We also match the main sample to the Execcomp database for data on managerial compensation structure.

Firm-level political risk variables

We rely on the first direct measure of firm-level political risk developed by Hassan, Hollander, van Lent, and Tahoun (2019). The authors use a simple pattern-based sequence-classification method developed in computational linguistics to extricate language related to political topics from non-political matters in the quarterly earnings conference-call transcripts between management and analysts and other interested stakeholders. For their baseline model, they use a training set of political words (i.e., an undergraduate textbook on American politics and articles from the political section of US newspapers) and a training set non-political words (i.e., an accounting textbook, articles from non-political sections of US newspapers, and transcripts of speeches on nonpolitical issues). The measure is calculated like indicated in the equation below as the sum of bigrams in conjunction with synonyms for "risk" or "uncertainty" between ten bigrams on both sides weighted by their instance frequency and divided by the total number of bigrams in the call transcript.

𝑃𝑅𝑖𝑠𝑘 𝑖𝑡 = ∑ (1[𝑏 ∈ 𝑃\𝑁] × 1[|𝑏 -𝑟| < 10] × 𝑓 𝑏,𝑃 𝐵 𝑝 ) 𝐵 𝑖𝑡 𝑏 𝐵 𝑖𝑡
There are several justifications for the use of equity risk measure instead of accounting measures of firm risk-taking. First and most importantly, using equity risk is consistent with the additional tests using managerial equity compensation incentives as executive stock options are written on the firm's equity. Second, it has been shown in the literature that accounting measures for risk-taking can be plagued with issues (e.g., Shin andStulz, 2000a, 2000b;[START_REF] Low | Managerial risk-taking behavior and equity-based compensation[END_REF]. For instance, calculating decision riskiness based on firm debt while data on private and public debt are not readily available or based on the annual cash flow volatility can be subject to several issues. Third, equity risk is the widely used measure in the finance literature. Finally, we control for firm policies in all our regressions, such as the impact of leverage on managerial risk choices. 30This being said, we also use the accounting-based risk-taking measures and other equity measures stemming from models other than the capital asset pricing model (CAPM) in our robustness checks in the empirical results section. Overall, our results remain unchanged to the use of alternative measures for firm risk-taking.

3.4.Control variables

Following the literature, we include a set of control variables found to be relevant in explaining the variation of managerial risk choices. Appendix 4-1 provides variable definitions and data sources in details. These variables are:

Lagged firm size (Lsize): firm size is the natural logarithm of the total book assets at the beginning of the year. Prior studies argue that smaller firms consistently have much higher risks as large firms usually have higher returns and are much more well established [START_REF] Audia | Less likely to fail: Low performance, firm size, and factory expansion in the shipbuilding industry[END_REF][START_REF] Mendeloff | Are small businesses riskier than larger ones?[END_REF].

Lagged return on assets (Lroa):

it is a proxy for firm profitability. According to the prospect theory of [START_REF] Kahneman | Prospect theory: an analysis of decision under risk[END_REF], firms outperforming the industry average is more likely to be risk-averse and would undertake less risky projects. [START_REF] Wiseman | Toward a model of risk in declining organizations: An empirical examination of risk, performance and decline[END_REF] empirically provide evidence that lower financial performance is associated with higher risk propensity and that high performers are more reluctant to take risks.

Lagged market-to-book (Lmb): it is a proxy for investment opportunities. [START_REF] Guay | The sensitivity of CEO wealth to equity risk: an analysis of the magnitude and determinants[END_REF], for instance, show that larger investment opportunities and growth options induces firm to take more risk.

Managers can also alter firm risk-taking through different policies that can be driven by reasons other than risk aversion like leverage, capital expenditures, research and development (R&D) expenditures, and firm focus (Coles, Daniel, and Naveen, 2006).

Therefore, we further control for the contemporaneous values of the policy variables:

Leverage (Lev):

The use of leverage in the capital structure is a source of additional risk to firm riskiness profile (e.g., [START_REF] Faccio | Large shareholder diversification and corporate risk-taking[END_REF].

Net capital expenditure (Netcapex):

Especially in periods of high uncertainties, managerial risk-taking behavior can change to channel their cash into less liquid assets to make expropriation for instance harder (e.g., Boubakri, Mansi and Saffar, 2013).

R&D (RD):

Consistent with [START_REF] Stein | Takeover threats and managerial myopia[END_REF] who argues that in times of regime shift or high risk managers can be myopic, we argue that managers are expected to increase their capital expenditure (Netcapex) and R&D expenses in a way to forgo long-term projects for short-term profits.

For further analysis, we also use segment sales Herfindahl (Saleherf) as a measure of firm diversification or focus, number of geographical segments (Segments) and the dummy variable for multinational firms (MNCs) to account for the effect of firm internationalization policies. Besides, we measure the effects of CEO portfolio payperformance sensitivity (Delta) and risk-taking incentives (Vega) values to evaluate its effect on change in firm risk under political uncertainty. Appendix 4-2 provides a description on how we calculate Delta and Vega. with previous literature both on political risk and managerial risk-taking in terms of magnitude (e.g., [START_REF] Low | Managerial risk-taking behavior and equity-based compensation[END_REF]Hassan, Hollander, van Lent, and Tahoun, 2019). We take the standardized values of political risk over firms and find that political risk is predominant in the U.S. context with mean value of 1.543 and maximal value of 4.071 in line with what is reported in the study of Hassan, Hollander, van Lent, and Tahoun (2019). variables in our main regressions. This matrix shows the absence multicollinearity issues between the independent variables as the correlation coefficients are generally small. Besides, it is worth noting that there is an initial positive correlation between the measure of political risk and the equity-based measures of managerial risk-taking.

Summary statistics

Empirical results

Univariate analysis

This section presents a simple univariate analysis to initiate our understanding of the relationship between firm-level political risk and risk-taking.

Main regression: effects of political risk on managerial risk-taking

We run a multivariate analysis to better gauge the effect of firm-level political risk on managerial risk preference. We regress our variable of total firm equity-based risk and then each of its components (i.e., idiosyncratic risk and systematic risk) on our proxy for political risk and other control variables describing firm characteristics and policies. We also include year, industry, and firm dummies to control for unobservable determinants of firm risk-taking that are fixed across time, industrial activities and firms. For all our regression specifications, we cluster the errors by firm and use strongly robust to heteroskedasticity and cross-sectional standard errors. Table 4-3 presents the main regression results on the relation between the degree to which firms are exposed to political risk and the riskiness of their managerial choices.

First, we run a pooled ordinary least squares (OLS) estimation and we control for year and industry fixed effects. Then, we run a fixed effect model estimation and we control for year and firm fixed effects to address concerns that may lead to a spurious correlation between our variables. Across both model specifications, our findings remain qualitatively the same. Firm-level political risk has a significant, immediate and positive effect on firm risk-taking.

In both models, coefficients on political risk are significant at the 1% threshold when it comes to total risk-taking and idiosyncratic risk but turn insignificant when we regress systematic risk on political risk. This is completely normal and understandable.

Consistent with prior literature (e.g., [START_REF] Low | Managerial risk-taking behavior and equity-based compensation[END_REF]Yung and Chen, 2017), managers are mainly concerned about and act on risks they can diversify away. Furthermore, we find that managerial risk-taking increases with high leverage ratios and capital expenditures and significantly decreases with size, profitability, investment in R&D. These results are consistent with our prediction as well as prior studies' empirical results.

These findings initially hold under the hypothesis stating that amid worst economic times, companies in countries with highly sound and democratic governments, such as the U.S. in our case, can expect help from the local and national authorities and therefore show more moral hazard concerns. Political risk may promote higher risk-taking by generating the moral hazard due to the anticipation that a government would deliver the bailouts.

We further sharpen our understanding of this association between political uncertainty and firm riskiness that seems counter-intuitive to the agency theory premises.

We run further regressions to examine whether managers would dial down or eliminate resource allocation in certain types of activities when faced with the risk of a loss because of a change in the political environment and whether they adjust/reduce the riskiness of their choices over time. To test this hypothesis, we check if the effects of lagged variables of political risk on firm riskiness in the first set of regressions of Table 4-4 presented in columns (1), (2), and (3). Columns (4), ( 5), and (6) of the same table however provide regression results on the effect of firm-level political risk at year t on managerial risktaking at year t+3 and other firm characteristics.

Results suggest indeed that managers change their preference for risk-taking over time. A significant and negative effect of political risk on managerial risk-taking is observed on average in the third year after a surge in political uncertainty levels. We raise the question:

How do we explain managerial risk-taking behavior in face of emergence of Political Risk?

We try to provide plausible explanations of the dynamics in the risk-taking behavior of managers over time in the face of political risk. Until further empirical evidence can disentangle the real reason or reason(s), here are our explanation:

(i) Asymmetrical incentives: Misalignment of incentives between owners of capital and managers of capital; managerial compensation is typically skewed towards shortterm results. Even when incentives are somewhat more balanced towards longer term outcomes, the compensation structures and realization of returns are almost never symmetrical.

(ii) Outrunning the bear" mind-set: Managers may believe they can stay one step ahead or may have an inbuilt optimism bias and/or over-confidence bias in their ability to "manage" the risk.

(iii) Momentum/Inertia: History of past/ongoing profits or "sunk cost" in certain activities or business models may cloud managers' thinking. It is harder to change course, to cut losses or face lower profits even if it is the "rational" thing to do.

(iv) Behavioral explanation of time lags:

There could be a distinction or time lag between the "emergence" of political risk, in other terms the likelihood or probability of a problem affecting outcome, and the "recognition" of the heightened political risk and its possible consequences. Hindsight, which is what our data on the emergence of political risk is based on is 20/20. The cognitive process of emergence, perception, acknowledgement, acceptance and action regarding a certain type of political risk and its consequence can take time. Moreover, managers may believe they can afford to delay action in the hope that the risk will diminish or go away until the risk translates into an actual political risk "event." And then, they scramble or hide. the results of the two sets of regressions using fixed effect models. The first set presents regressions of managerial risk-taking at year t on the lagged variables of firm-level political risk and other firm characteristics. The second set presents regressions of managerial risktaking at year t+3 on firm-level political risk at year t and other firm characteristics. The list of variables definitions and sources are provided in the section 3 of this chapter and the Appendix 4-1. P-values are reported in parentheses. t-values are based on robust standard errors adjusted for clustering by firm. *, ** and *** refer to significance at the 10%, 5% and 1% levels, respectively.

Effects of equity-based compensation

We investigate whether managers who are awarded equity-based compensation mitigate or increase their risk-taking when they face political uncertainty. We address this hypothesis by examining whether managers with high Delta and Vega react in different manner than others in times of political turmoil. provides the results of the regressions of the managerial risk-taking on firm-level political risk, the interaction between risk-taking and equity-based compensation variables and other firm characteristics using Fixed Effect Model. The list of variables definitions and sources are provided in the section 3 of this chapter, the Appendix 4-1 and the Appendix 4-2. P-values are reported in parentheses. tvalues are based on robust standard errors adjusted for clustering by firm. *, ** and *** refer to significance at the 10%, 5% and 1% levels, respectively. Table 4-7 above presents regression results where we include our measures of Delta and Vega as well as the interaction terms with the firm-level political risk measure.

Consistent with our hypothesis, we find that executive stock options encourage more managerial risk-taking when they face higher level of political risk. The explanation is that increases in stock return volatility due to higher risks increase the value of the options [START_REF] Haugen | Resolving the agency problems of external capital through options[END_REF][START_REF] Smith | The determinants of firms' hedging policies[END_REF][START_REF] Low | Managerial risk-taking behavior and equity-based compensation[END_REF]. Stock options substantially increase the sensitivity of managers' wealth to equity risk and therefore reduces their risk-aversion even when political uncertainty is high. Furthermore, results

show that Vega has a stronger impact on the relationship between political risk and managerial risk-taking, as proven in prior studies.

Additional tests

Notwithstanding the robustness of our results thus far, we perform additional tests to delve more deeply into the nature of the political-firm risk relationship. Our earlier findings suggest that managers risk-taking increases with the rise in political risk and that this relationship is more pronounced for firms which award managers with equitybased compensation incentives. In this section, we examine how the association between political risk and managerial risk-taking depends on other factors like multinationality and the severity of financial constraints.

Multinational corporations and risk-taking

The political environment in which firms operate is important to determine the riskiness of firm decision-making and the risk-taking behavior of managers. This issue is of particular interest to multinational corporations (MNCs) seeking partnership abroad (Boubakeri, Mansi and Saffar, 2013;[START_REF] Stulz | The limits of financial globalization[END_REF]. In this study, we explore how multinationality affects the increased firms' risk levels during high political turmoil periods. This examination builds on the argument from real options theory that international operations can offer switching options to multinational corporations and can also be different sources of coordination costs that can mitigate the benefits of operational flexibility [START_REF] Tong | Real options in multinational corporations: Organizational challenges and risk implications[END_REF].

Table 4-8 reports our regression results for the impact of political uncertainty on firm riskiness conditional to whether the firm is local or multinational. We employ a measure of the number of geographical segments as well as a dummy variable that equals one if the firm is multinational and zero otherwise. The coefficients on interaction terms are significant and negative suggesting that the degree of multinationality mitigates the additional managerial risk-taking when they are exposed to political risk. Overall, our findings corroborate the real option theory premise. provides the results of the regressions of managerial risk-taking on firm-level political risk, the interaction between risk-taking and the number of geographical segments (Segments) or between risktaking and the dummy (MNC) indicating if the firm has an international presence and other firm characteristics using Fixed Effect Model. The list of variables definitions and sources are provided in the section 3 of this chapter and the Appendix 4-1. P-values are reported in parentheses. t-values are based on robust standard errors adjusted for clustering by firm. *, ** and *** refer to significance at the 10%, 5% and 1% levels, respectively.

Financial constraints

A better understanding of the impact of political constraints on firms' strategic choices is likely to help managers in their own decisions. It has long been discussed that firms with higher financing risk are more subject to face and navigate greater levels of risk (e.g., Fresard and Valta, 2016). Financial constraints can also represent a proxy for firm complexity and a determinant of risk. On one hand, they may lead managers to undertake riskier projects as it incentivizes them to transfer wealth and benefits from bondholders to shareholders [START_REF] Leland | Agency costs, risk management, and capital structure[END_REF]. On the other hand, a higher probability of facing financial distress may restraint the firm's tendency to engage in risky activities [START_REF] Friend | An empirical test of the impact of managerial self-interest on corporate capital structure[END_REF]. table provides the results of the regressions of managerial risk-taking on firm-level political risk, the interaction between risk-taking and the Kaplan-Zingales (1997) index for financial constraints and other firm characteristics using Fixed Effect Model. The list of variables definitions and sources are provided in the section 3 of this chapter and the Appendix 4-1. P-values are reported in parentheses. t-values are based on robust standard errors adjusted for clustering by firm. *, ** and *** refer to significance at the 10%, 5% and 1% levels, respectively.

To better gauge the impact of firm financing risk on the relation between political uncertainty and risk-taking, we use interaction terms of firm-level political risk and a proxy for firm financial constraints. We use the Kaplan-Zingales Index (KZ) according to Kaplan and Zingales (1997) which increases with the severity of firms' financial constraints. Table 4-9 reports the results that are consistent that managers of financially constrained firms tend to exhibit higher risk-taking preference when they operate in a politically turbulent environment. 200

Robustness checks

Alternative risk-taking measures

To measure firm risk-taking behavior, we use additional widely used measures in prior studies. Following Faccio, Marchica and Mura (2011), for instance, we use as a proxy for firm riskiness the standard deviation of return on assets (ROA) computed over a 3-year rolling window. 31 Standard deviation of ROA is generally employed to reflect firm's income stream variance or riskiness [START_REF] Bettis | Risk/return performance of diversified firms[END_REF][START_REF] Cool | Business strategy, market structure and riskreturn relationships: A structural approach[END_REF]. We also use additional equity-based risk-taking measure calculated using [START_REF] Fama | Common risk factors in the returns on stocks and bonds[END_REF] Across all model specifications, we provide strong evidence of the significant and positive association between firm-level political risk and different measure of managerial 31 We also compute σ(ROA) over a 5-year rolling window and our results remain the same. 32 See also, [START_REF] Berger | Diversification's effect on firm value[END_REF]. 201 risk-taking behavior. We also regress the different firm riskiness measures on the lagged variables of firm-level political risk, and we find that consistent with our previous results, managers start to reduce their risk-taking from the third year after a spike in political uncertainty.

Additional controls

We address the omitted-variable concern by taking great care to include a host of additional control variables that may drive our results. Following prior literature, we include Hassan, Hollander, van Lent and Tahoun (2019) measure of all non-political risk in Column (1) of Table 4-11. In Column (2), we include firm Beta computed using the CAPM model. In column (3), we add to the main regression specification an indication of industry concentration following [START_REF] Low | Managerial risk-taking behavior and equity-based compensation[END_REF] and [START_REF] Berger | Diversification's effect on firm value[END_REF]. Finally, we control for firm governance using E-index. 33Our results in Table 4-11 show that the coefficient on firm-level political risk still loads significantly and positively after controlling for various variables that capture additional risks and corporate governance quality. The same results hold for the components of total firm risk-taking: idiosyncratic risk and systematic risk.

Additional evidence on managerial risk-taking adjustment

We include lagged firm-level political risk separately in the main regression to check if political risk at year t-3 still drives the change in managerial risk-taking behavior. Table 4-12 provides evidence that facing high political levels, managers increase their risktaking for year t and year t+1 and significantly invert their behavior in year t+3 after a 203 spike in political uncertainty. Using idiosyncratic risk as dependent variable, results remain the same, but they are not reported in this table. the results of the regressions of managerial risk-taking on the lagged firm-level political risk taken separately and other firm characteristics using different estimation techniques: Ordinary Least Squares and Fixed Effect Model. The list of variables definitions and sources are provided in the section 3 of this chapter and the Appendix 4-1. P-values are reported in parentheses. t-values are based on robust standard errors adjusted for clustering by firm. *, ** and *** refer to significance at the 10%, 5% and 1% levels, respectively.

Conclusion

The interaction between politics and business has long been debated. It is important to understand this interaction and how it shapes managerial decisions and firm riskiness.

A large body of literature highlights the role political institutions can play in driving the risk-taking behavior of managers by affecting the operating environment [START_REF] Stulz | The limits of financial globalization[END_REF].

On the one hand, increased political risk under an authoritarian government characterized by few checks and balances or political constraints, managers are more likely to lower their risk-taking to reduce their exposure to government overregulation, extraction of bribes, and expropriation (Caprio, Faccio and McConnell, 2011). This view also complements the agency theory premise on the risk differentials between shareholders and their agents. On the other hand, the expectation of a bailout that generates moral hazard can encourage more risk-taking by managers (e.g., [START_REF] Boutchkova | Precarious politics and return volatility[END_REF][START_REF] Cukierman | Monetary policy and institutions before, during, and after the global financial crisis[END_REF].

To examine the relationship between political risk and managerial risk-taking, we use a large sample of 20,140 firm-year observations covering the 2002-2016 period. We

show that the novel measure of firm-level political risk we employ in this dissertation is positively associated with the riskiness of managerial decisions in the U.S. However, this effect becomes reverse over time; managers adjust their behavior on average 3 years after a surge in political risk. Furthermore, we show that managerial sensitivity to equitybased incentives increases managerial preference for risk-taking immediately when they face political uncertainty. Our findings are economically significant, and are robust to alternative risk-taking measures, various political risk components, and multiple additional tests.

Our results have important implications for policymakers and corporate managers by providing direct relevance of political risk to the business decision-making process.

Further tests on the managers' behavior regarding the time lag in adjusting to political risk can be conducted to see what alters the relation between political risk and firm riskiness.

General Conclusion

We set out on this research dissertation to examine the nature of political risk and its possible effects on managerial and corporate decision-making, especially as those decisions relate to corporate financial management. It could be argued that politics and businesses have been interlinked since the dawn of civilization. But it seemed to us that the linkages and the complexities that all these forces generate for managerial decisionmaking have never before been so pervasive, so intense and so instantaneously manifested. The rapid pace of innovation and adoption of information and communication technologies, globalization and social media, have undoubtedly contributed to the intensification of these forces. The fundamental perception however is that political developments were playing an increasingly important role in the outcome of business results, and that the pace of these developments was intensifying across the world.

In the face of these challenge, the question in our mind was how business managers anticipate or identify political developments, how do they measure their impact on their businesses, how do they manage the risks that are generated, and what insights or lessons could we draw and that might be helpful for businesses, policymakers and possibly, for additional academic research.

As we try to formulate the scope and course of our research, we were confronted with two threshold issues. The first related to the very definition of "political risk." We find that finance and economics literature was awash with various descriptions but that there has been no clear consensus on a comprehensive understanding of what constitutes political risk. We realize that despite its widespread usage today, political risk still is an amorphous and constantly evolving concept. Therefore, for the purpose of our research Vishny, 1997), political uncertainty is not reduced. This is explained by the partisan variations in taxes, social benefits, health care systems, and regulations that may affect business cycles (see, e.g., Alesina, 1987;Alesina and Roubini, 1992).

Our research on the interplay between managers' perceptions of political risk (as measured through the Hassan, Hollander, van Lent and Tahoun (2019)) and trade credit concludes that interfirm credit can substitute other sources of external financing in times of turmoil as these periods are characterized by more stringent access to credit and capital markets. These results are robust to a variety of checks, and we further show that trade credit provision increases in periods of high social trust.

On cash management, the examination of the effect of firm-level political risk leads us to conclude that firms maintain higher cash reserves and liquid resources when they face high political uncertainty. Firms hold more cash for precautionary and financial flexibility motive in times of political upheavals, consistent with Jensen (1989) theory. We also show that retention of cash flows and the market value of a dollar of cash holdings increase for firms with higher aggregate political risk and explain how these cash reserves are channeled. Finally, we document that the positive associations between political risk with the level and cash holdings are more pronounced for firms operating in a highly competitive market, depending less on R&D and whose headquarters are closer to the centers of political power in Washington D.C.

On managerial risk-taking behavior, we conclude that firm-level political risk proportionally induces managerial risk-taking in the very short run. However, managers adjust their risk-taking preference by reducing it over time. Firms that award their managers with stock-options and equity-based incentives are more likely to experience this positive association between the level of risk in the political sphere and the level of risk-taking within the firm. However, managers exhibit more risk aversion when they run multinational corporations and operate in a politically turbulent environment.

219 Throughout this dissertation, our studies contribute to different strands of literature. Specifically, we add to the line of research that examines the effect of uncertainty emanating from politics. Additionally, our study adds insight to understanding the determinants of corporate financing, strategic and organizational decisions. However, it is worth stressing that this definition of political risk we employ cannot be exhaustive. This measure reflects firm management and stakeholders' perception of political risk and can be quantitatively different from real political risk levels. We cannot uniquely rely on this definition to classify events and risks as being political or not. This proxy includes some judgment thus care is required both in the construction and in the interpretation of the index.

However, our understanding of the scope of today's political risk, its complexity, and the various forces -that shape its various expressions -is deeper, more nuanced and evolving. This dissertation used the various literature and our own empirical analysis to

iv

  Essais sur le risque politique et la finance d'entrepriseRésuméCette thèse est une compilation de quatre chapitres, un chapitre sur la littérature du risque politique et trois essais empiriques qui étudient l'effet du risque politique sur les choix financiers et managériaux des entreprises. Le premier chapitre explore les théories et les études empiriques sur les effets du risque politique sur les entreprises et les indicateurs économiques. Il affine notre compréhension de la complexité du risque politique au XXIe siècle et les outils permettant de mesurer ces effets. Le premier essai empirique explore l'effet d'une nouvelle mesure du risque politique au niveau de l'entreprise sur l'octroi de crédits commerciaux. Nous avançons deux hypothèses concurrentes liant la politique au crédit commercial, basées sur plusieurs théories débattant la manière dont le crédit commercial peut concurrencer ou remplacer autres sources de financement en période de difficultés politiques. En utilisant un échantillon d'entreprises américaines sur la période 2001-2016, nous montrons que la perception des entreprises d'un niveau de risque politique accru est associée à une plus grande dépendance au crédit commercial. Ces résultats restent inchangés face aux différents tests de robustesse, notamment en contrôlant d'autres risques macroéconomiques, en utilisant des mesures alternatives et en prenant en compte des problèmes potentiels d'endogénéité. En outre, nous constatons que la concurrence, les contraintes financières et la confiance sociale agissent sur la relation entre le risque politique et l'octroi de crédits commerciaux. Dans le deuxième essai, nous examinons l'effet du risque politique au niveau de l'entreprise sur les niveaux de liquidités au sein des entreprises. Nous montrons que des niveaux de risque politique plus élevés sont associés à des liquidités plus importantes parmi un large échantillon d'entreprises américaines cotées en bourse pour la période 2002-2016. Les entreprises détiennent plus de liquidités pour des raisons de précaution et v de flexibilité financière en période de bouleversements politiques. Nous montrons également que la rétention des flux de trésorerie et la valeur marchande d'un dollar de liquidités augmentent pour les entreprises qui font face à un risque politique plus élevé et nous expliquons comment ces réserves de liquidités sont canalisées. Enfin, nous documentons que les associations positives entre le risque politique et le niveau et la valeur des liquidités sont plus prononcées pour les entreprises qui opèrent sur un marché hautement concurrentiel, qui dépendent moins de la R&D et dont le siège est plus proche de Washington D.C. Dans l'ensemble, nos conclusions impliquent que le risque politique est un déterminant clé des liquidités des entreprises. Dans le troisième essai, nous étudions l'effet du risque politique au niveau de l'entreprise sur la prise du risque managérial. À partir d'un large échantillon de 20 140 observations couvrant la période 2002-2016, nous constatons que le risque politique est positivement associé au caractère risqué des décisions de gestion aux États-Unis.

  Résumé de la thèse en Français .............................................................................................. xi

  . Definitions of political risk ....................................................................................................................Table 1-2. The firm environment is becoming wider and more complex ......................................................... Table 1-3. Five levels of action generating political risk ...................................................................................... Table 1-4. Types of the twenty-first-century political risk .................................................................................. Table 1-5. Measures of political risk ....................................................................................................................... Table 1-6. Empirical studies on the effects of political risk and political connectedness ................................ Chapter 2Table 2-1. Descriptive statistics of main variables ................................................................................................ Table 2-2. Correlation analysis ................................................................................................................................ Table 2-3. Main regressions ..................................................................................................................................... Table 2-4. Alternative measures of trade credit .................................................................................................... Table 2-5. Additional macro-economic risk variables ......................................................................................... Table 2-6. Political risk dimensions ........................................................................................................................ Table 2-7. Regression analysis relating trade credit to prior period's political risk ........................................ Table 2-8. Endogeneity: IV 2SLS regression .......................................................................................................... Table 2-9. Additional analysis ............................................................................................................................... Chapter 3 Table 3-1. Firm-level political risk and cash holdings by fiscal year ............................................................... Table 3-2. Firm-level political risk by industry ................................................................................................... Table 3-3. Summary statistics ................................................................................................................................ Table 3-4. Correlations ........................................................................................................................................... Table 3-5. Main regression: The effect of firm-level political risk on corporate cash holdings .................... Table 3-6. The effect of the different components of firm-level political risk on corporate cash holdings Table 3-7. Empirical model of cash flow retention ............................................................................................. Table 3-8. Valuation of cash (the model of Dittmar and Mahrt-Smith, 2007) ................................................. Table 3-9. Valuation of cash (the model of Pinkowitz and Williamson, 2002) ............................................... Table 3-10. Political risk and cash channeling .................................................................................................... Table 3-11. Additional tests ................................................................................................................................... xii Chapter 4 Table 4-1. Descriptive statistics for main regression variables ......................................................................... Table 4-2. Correlation matrix ................................................................................................................................ Table 4-3. Main regressions: effect of political risk on firm-risk taking .......................................................... Table 4-4. Empirical evidence on the managerial risk-taking adjustment to political risk over time ......... Table 4-5. Effects of different components of political risk on total managerial risk-taking ........................ Table 4-6. Evidence on components of political risk and managerial risk-taking adjustment .................... Table 4-7. Analysis of equity-based compensation effect.................................................................................. Table 4-8. Effects of internationalization ............................................................................................................. Table 4-9. Effect of financial constraints .............................................................................................................. Table 4-10. Alternative measures for firm risk-taking ....................................................................................... Table 4-11. Additional controls ............................................................................................................................. Table 4-12. Additional evidence on managerial risk-taking adjustment ........................................................ xiii

Figure 1

 1 Figure 1-1. BCG Henderson Institute Politics and Economics Index ................................................................

Figure 1 - 2 .

 12 Figure 1-2. Google/White house meetings during Obama's presidency .........................................................

Figure 2

 2 Figure 2-1. Time variation of political risk and trade credit ...............................................................................

Figure 3

 3 Figure 3-1. Time variation of political risk and the economic policy uncertainty (EPU) ..............................

Figure 3

 3 Figure 3-2. Time variation of the standardized values of political risk and cash holdings ..........................

Figure 4

 4 Figure 4-1. Univariate analysis .............................................................................................................................

  also analyze the new forces behind the twenty-first-century political risk. They document that there are three megatrends shaping the politics of the global world, which are (i) the substantial changes in politics after the end of the Cold War; (ii) supply chain innovations; and (iii) the technological revolution. 2

Figure 1 -

 1 Figure 1-1. BCG Henderson Institute Politics and Economics IndexPolitical risk factors generate systematic and non-systematic risk in capital markets and for firms. In the wake of the 2008 global financial crisis, the Arab Spring, the multiplication of terrorist attacks, and the comeback of nativism, populism, protectionism and isolationism, political uncertainty is omnipresent, and its economic effects cannot be overlooked by firms, investors and regulators. Political connections between businesses and politicians have become widespread in an effort towards risk management and uncertainty mitigation(Faccio, 2006). Hence, studying political uncertainty generators and developing accurate measures of political risk have gained increasing attention for researchers. Due to the complexity of the subject, several strands

  examine the pervasiveness of the U.S. presidential election cycle and show that U.S. presidential elections over 1957-1996, and across 18 countries, negatively affect stock market capital gains in the second year of the electoral cycle. Moreover, Reilly and Luksetich (1980) try to test the hypothesis "the Democrats are the party of labor, the Republicans the party of business" and provide evidence that Wall Street prefers the Republican Party at least in the months after the presidential elections.

  argue that policy uncertainty increases uncertainty with respect to expected future cash flows for firms, and therefore decreases the quality of the information contained in stock prices. Using a general equilibrium model of government policy choice, the authors show that political uncertainty increases the volatility of stocks, decreases their price and alters the correlation between individual stocks. Similar patterns are shown in the work of Goodell and Vähämaa (2013), Brogaard and Detzel (2015) and Kim, Pantzalis and Park (2012). Pasquariello and Zafeiridou (2014) also find reduced trade volume before U.S. national elections since investors become skeptical about the quality of information, they receive during the electoral period consistent with the ambiguity hypothesis. Furthermore, Kelly, Pástor and Veronesi (2016) investigate whether option market contain the political uncertainty stemming from major political events such as summits and elections. Their results are consistent with the baseline model as political uncertainty expressed by the uncertainty of a change in the government increases the price of financial protection tools against uncertainty, namely options. Stock prices are more

  connectedness. For instance, a 2013 survey by PwC shows that U.S. CEOs were more worried about policy uncertainty than about any other type of uncertainty, and around the same period, aggregate corporate political contributions touched the highest levels ever recorded during the 2013-2014 U.S. congressional election cycle. 12 Another example is the healthcare industry in the U.S. According to OpenSecrets.org, Political Action Committee ("PAC") contributions associated with the health care industry had grown by 150% in 10 years over the 2000-2010 election cycles as the industry faced unprecedented policy uncertainty. Politics are indissolubly related to Economics all over modern history. It dates back to the study of Kalechi (1943) which models the effects of political factors on labor. The study finds that the influence exercised by capital holders on democratically elected governments to generate a business cycle is proportionate to the evolution of the unemployment cycle. Shleifer and Vishny (1994) develop a theoretical model of bargaining between politicians and top management to understand the dynamics of state firms' behavior. Furthermore, in their model of simultaneous change and persistence in institutions,

and

  Zingales (2017), for instance, point out that in the telecommunication industry, political connections play a crucial role in designing the rules of the game. Politically connected firms can distort the rules initially designed by local governments in terms of prices, concentration and competition. Overall, political connections can be a key determinant of exposure to political risk and thus affect access to capital conditions (e.g.,[START_REF] Johnson | Cronyism and capital controls: evidence from Malaysia[END_REF][START_REF] Leuz | Political relationships, global financing, and corporate transparency: Evidence from Indonesia[END_REF] Akey and Lewellen, 2017), tax payment (e.g., Arayavechkit, Saffie and Shin, 2017), stock price and firm value (e.g.,[START_REF] Hu | Political connections and stock price crash risk[END_REF]. For instance,[START_REF] Brown | All the president's friends: Political access and firm value[END_REF] provide novel evidence that the share price of firms whose executives visited the White House increased an extra 1% in the following two months. During Obama's presidency alone the Chairman and CEO of Honeywell International visited the White House 30 times and the one of General Electric 22 times (See example in the Figure 1-2 below).

Figure 1 - 2 .

 12 Figure 1-2. Google/White house meetings during Obama's presidency 14

Francis

  affects the cost of bank loans driving an increase of 5% in spreads on average at the ex-post and ex-ante exposures to political uncertainty.

Kim

  Proximity to political power affects stock returns; Forms headquartered in states with high political alignment index (PAI) outperform those located in low PAI states. the probable new government policies increases risk premia as well as volatilities and correlations of stock returns.

Fulmer

  firms are more likely to increase political campaign funding and donations. These firms have their investment and risk strategies more affected by the loss or win of a political connection. Large Firms executives' meetings with White House officials are associated with significantly positive cumulative abnormal returns (CARs).

  consists of using textual analysis tools on 175,797 quarterly earnings conference-call transcripts to construct firm-level indexes of the extent and type of political risk confronted by 9,478 individual firms listed in the U.S. and how it fluctuates over time. The transcripts are extracted from the Thomson Reuters' StreetEvents database from the year 2001 up to 2016. Following the work of Song and Wu (2008) and Manning et al., (2008), the authors adapt a simple pattern-based sequence-classification method

Figure 2 -

 2 Figure 2-1 below also shows the primary positive correlation between firm-level political risk and trade credit's mean value variation over fiscal years.

Figure

  Figure 2-1. Time variation of political risk and trade credit

  results of the regressions of the firm-level political risk on trade credit and other firm characteristics taking into account the value of time. In the first set of regressions, lagged independent variables are used while in the second set, we account for the change in political risk . P-values are reported in parentheses. T-statistics are based on robust standard errors adjusted for clustering by firm. The list of variables, definitions, and sources are provided in the Appendix 2-1 and Appendix 2-2. *, ** and *** refer to significance at the 10%, 5% and 1% levels, respectively.

  , they also weight each bigram with a score that reflects how strongly the bigram is associated with the discussion of political topics (the third term in the numerator), where 𝑓 𝑏,𝑃 is the frequency of bigram b in the political training library and 𝐵 𝑝 is the total number of bigrams in the political training library. The measure is overall the share of the conversation devoted to risk associated with political topics and is thus the weighted sum of bigrams associated with political (rather than non-political)

  analysis tools on 175,797 quarterly earnings conference-call transcripts to construct firmlevel indexes of the extent and type of political risk confronted by 9,478 individual firms listed in the U.S. and how it fluctuates over time. The transcripts are extracted from the Thomson Reuters' StreetEvents database from the year 2002 up to 2016. Following the work of Song and Wu

Figure 3 - 1 .

 31 Figure 3-1. Time variation of political risk and the economic policy uncertainty (EPU)

  as the natural logarithm of total net assets.

  defined as working capital minus cash scaled by total net assets. defined as the residuals from regressing cash on a set of variables.As above the standard specification, they also weight each bigram with a score that reflects how strongly the bigram is associated with the discussion of political topics (the third term in the numerator), where 𝑓 𝑏,𝑃 is the frequency of bigram b in the political training library and 𝐵 𝑝 is the total number of bigrams in the political training library. The measure is overall the share of the conversation devoted to risk associated with political topics and is thus the weighted sum of bigrams associated with political (rather than non-political)

  Figure 4-1 below shows the univariate regression estimation between the mean values of political risk and total risk of the firm, indicating an ascending regression line. The slope is positive which gives us an initial idea of the positive association between our main dependent variable and key independent variable and is more consistent with our hypothesis H1b.

Figure

  Figure 4-1. Univariate analysis

  three factor model and[START_REF] Fama | A five-factor asset pricing model[END_REF] five factor model.32

  results of the regressions of total risk-taking on firm-level political risk and other firm characteristics using Fixed Effect Model. The list of variables definitions and sources are provided in the section 3 of this chapter and the Appendix 4-1. P-values are reported in parentheses. t-values are based on robust standard errors adjusted for clustering by firm. *, ** and *** refer to significance at the 10%, 5% and 1% levels, respectively.

  convey a different or additional insight into the different dimensions of the twenty-first century political risk and its effect on businesses and economies. The improved knowledge about the new environment and firm decisions within such an environment has significant implications. Such transitional times can offer researchers, practitioners and policymakers an opportunity for a genuine shift of paradigm to test the validity of existing organizational theories and practices and to develop new ones. The validity of current policies is at stake.For future research endeavor, we acknowledge that over the last five years the world has been facing unprecedent political upheaval (e.g., the election of Trump in the U.S., Brexit in the U.K.). In the last year alone, riots, protests, and political turmoil have become significant in numbers and in impact regarding: Yellow Vests in France and protests in Algeria against exclusion; protests in Lebanon and Colombia against corruption and rent-seeking; protests in Chile and Iran against inflation; protests in Bolivia and Venezuela against oppression; protests in Hong Kong and Catalonia for Aujourd'hui, le risque politique dans ses dimensions d'aujourd'hui est certainement moins bien examiné et moins bien géré.Ce projet de recherche vise principalement à approfondir notre compréhension du risque politique sur la base de données empiriques et de ses implications pour la gestion des ressources financières au niveau des entreprises. Il y a de nombreux travaux universitaires qui traitent du risque politique. La nature fluide des incertitudes dans l'environnement commercial/économique actuel en ce qui concerne les dimensions du risque politique, la façon dont les dirigeants interprètent les changements et y réagissent lorsqu'ils se produisent n'a pas été encore largement étudié. Cependant, la conséquence de ces changements politiques sur les performances des entreprises est plus difficile à conceptualiser et plus difficile à calibrer sur les marchés actuels[START_REF] Fukuyama | The origins of political order: From prehuman times to the French Revolution[END_REF] Baker, Bloom et Davis, 2016 ; Rice et Zegart, 2018). L'objet principal de cette thèse est de consolider et d'éclairer la littérature existante, de repousser les limites de notre compréhension et de fournir des informations utiles aux gestionnaires en exercice ainsi qu'à la recherche universitaire. Nous le faisons notamment en fournissant davantage de nombreuses preuves empiriques sur la relation entre le risque politique actuel et les décisions financières des entreprises, en utilisant des données plus contemporaines et de nouveaux indicateurs de l'incertitude politique.En ce qui concerne la nature même du risque politique, nous postulons qu'en dépit des nombreux travaux théoriques et empiriques réalisés sur cette question, notre compréhension de ce qui constitue un risque politique reste nébuleuse. Cela est compréhensible. Par rapport à des composantes plus concrètes du risque commercial, telles que la solvabilité d'une entreprise, d'un secteur ou même d'une économie, le risque politique est un concept plus amorphe et plus granuleux(Zingales, 2017 ;[START_REF] Giambona | The management of political risk[END_REF] Luo, Chen et Wu, 2017 ; Rice et Zegart, 2018). Même si nous avons approfondi ce sujet au cours de nos recherches, nous constatons que la portée de ce que nous considérons comme un risque politique devrait être, presque par définition, une construction plus évolutive, plus étendue et plus dynamique. Rien qu'au cours des cinquante dernières années, les systèmes politiques ont d'abord évolué, en particulier dans les années 70 et 80, avec une augmentation du nombre de démocraties dans le monde accompagnée de profonds changements dans les croyances sociales et les aspirations économiques sous-jacentes. Cet "âge d'or" de la démocratisation a été suivi plus récemment, au XXIe siècle, par ce que[START_REF] Fukuyama | The origins of political order: From prehuman times to the French Revolution[END_REF] appelle une "récession de la démocratie". Ces deux phénomènes ont eu d'énormes répercussions sur les dimensions du risque politique et donc de notre besoin d'améliorer et d'adapter constamment nos connaissances. Nous commençons donc notre travail au chapitre 1 par un examen complet des théories et de la littérature sur le risque politique. Dans ce chapitre, nous affinons notre compréhension des composantes du risque politique et de son évolution dans le temps. Nous constatons que les premiers travaux universitaires semblaient considérer que le risque politique découlait principalement des actions des gouvernements et en particulier dans le contexte des transactions transfrontalières. Ainsi, pas plus tard qu'en 1970, Truitt (1970) considère le risque politique comme un risque généralement "non commercial", mais qui se manifeste principalement dans des événements tels que la nationalisation ou l'expropriation par un gouvernement étranger. Cette conception s'est étendue, en particulier dans les années 80 et au-delà, pour inclure les événements nationaux et les actions à motivation politique des gouvernements et des acteurs non gouvernementaux, telles que les grèves et les insurrections, qui pourraient avoir un impact significatif sur le résultat économique d'une entreprise. Tout en élargissant l'éventail des forces qui créent le risque politique, ces définitions plus larges ont rendu la compréhension du risque politique plus complexe, multidimensionnelle et plus difficile à opérationnaliser et à mesurer (Hanseth et Ciborra, 2007). Les entreprises sont aujourd'hui confrontées à des facettes de plus en plus complexes et multidimensionnelles du risque politique et si elles l'ignorent, les conséquences peuvent être catastrophiques. Nous soutenons que dans le contexte contemporain, le risque politique est encore plus difficile à quantifier et qu'aucun cadre ne peut à lui seul répondre à tous les besoins des entreprises ou décrire tous les bouleversements politiques. Néanmoins, nous essayons de saisir les multiples formes de risque politique auxquelles les entreprises sont confrontées au XXIe siècle, notamment la géopolitique, les conflits internes, les cadres réglementaires, les ruptures de contrat, la corruption, la portée extraterritoriale, la manipulation des ressources naturelles, l'activisme social, le terrorisme et les cyber menaces. Dans le premier chapitre, nous explorons également les théories sur les effets économiques du risque politique et les outils permettant de mesurer ces impacts. Nous constatons que contrairement au risque financier, par exemple, qui peut être opérationnalisé par des mesures telles que les taux d'intérêt, l'offre de main-d'oeuvre et le produit intérieur brut, le risque politique reste essentiellement subjectif et qualitatif. En outre, nous constatons que les mesures qui tentent de quantifier le risque politique doivent être interprétées avec prudence. Premièrement, la plupart de ces mesures fournissent généralement des aperçus ponctuels et discontinus d'événements politiques qui peuvent cacher des tendances mondiales importantes. Deuxièmement, elles évaluent généralement des mesures nationales alors que le risque politique peut émaner d'un niveau très local. Enfin, de nombreux événements politiques ont une faible probabilité de se produire, mais lorsqu'ils se produisent, les conséquences peuvent être néfastes. Nous précisons que pour cette thèse, nous utilisons la nouvelle mesure de Hassan, Hollander, van Lent et Tahoun (2019) et nous expliquons les motivations qui ont motivé notre choix de cette mesure. En l'absence d'une mesure directe du risque politique dans la littérature, cette mesure est la seule à fournir une évaluation idiosyncrasique de l'incertitude politique telle qu'elle est perçue par l'entreprise. Elle est le résultat d'une analyse textuelle des conférences téléphoniques d'entreprise entre la direction de l'entreprise et les analystes et mesure la part de la conversation consacrée à parler de sujets politiques. Outre la théorie, nous passons en revue dans ce chapitre un vaste ensemble de littérature comprenant des études empiriques sur le risque politique et les effets de associés à une plus grande dépendance au crédit commercial. Ce résultat est conforme à la deuxième hypothèse selon laquelle le crédit commercial offre de nombreux avantages par rapport aux prêts bancaires et publics. Nos résultats sont robustes après une série de tests de robustesse. Plus précisément, nous montrons que nos preuves persistent après avoir utilisé des techniques d'estimation et des mesures alternatives du crédit commercial, décomposé la mesure agrégée du risque politique et répondu aux préoccupations d'endogénéité. Nous étudions également l'effet conditionnel potentiel d'autres caractéristiques des entreprises et du secteur et montrons que l'effet positif du risque politique sur le crédit commercial est plus fort pour les entreprises opérant dans des environnements concurrentiels, des périodes de confiance plus longues et plus faible pour les entreprises soumises à des contraintes financières plus strictes.Cette étude contribue à différents domaines de la littérature. Plus précisément, nous ajoutons à la ligne de recherche qui examine l'effet de l'incertitude émanant de la politique. En outre, notre étude apporte un éclairage sur les déterminants des décisions de financement des entreprises (c'est-à-dire l'octroi de crédits commerciaux). Toutefois, il convient de souligner que cette définition du risque politique ne peut être exhaustive.Cette mesure reflète la perception du risque politique par la direction de l'entreprise et les parties prenantes et peut être quantitativement différente des niveaux des vrais risques politiques. Nous ne pouvons pas nous fier uniquement à cette définition pour classer les événements et les risques comme étant politiques ou non. Cette approximation inclut un certain jugement, ce qui exige une certaine prudence tant dans la construction de l'indice que dans l'interprétation.Dans le chapitre 3, intitulé "Risque politique au niveau de l'entreprise et la gestion de liquidités des entreprises", nous passons des conséquences du risque politique sur l'octroi de crédits commerciaux à la gestion des liquidités des entreprises en réponse aux changements de leur évaluation du risque politique et, plus précisément, au niveau des liquidités qui les détiennent et leur valeur. La base théorique de cette proposition générale est la théorie de l'épargne de précaution introduite par[START_REF] Keynes | The general theory of employment, interest and money[END_REF] et développée par[START_REF] Jensen | Agency costs of free cash flow, corporate finance, and takeovers[END_REF], qui suggère que la principale motivation des entreprises pour détenir davantage de liquidités est de se protéger contre les chocs négatifs de flux de trésorerie. De nombreuses autres études montrent empiriquement et théoriquement que les chocs émanant du système politique modifient considérablement le comportement des entreprises et provoquent des fluctuations importantes des investissements, du cours des actions et surtout des flux de trésorerie. Ce chapitre établit spécifiquement un lien entre les chocs politiques générant des niveaux de risque élevés et les liquidités des entreprises. Nous soutenons que les entreprises sont incitées à mettre en place une marge de manoeuvre financière en thésaurisant des liquidités afin d'éviter les conséquences négatives associées aux chocs et à l'incertitude découlant de la politique. En outre, nous testons et démontrons la validité de nos hypothèses en utilisant une nouvelle mesure du risque politique au niveau de l'entreprise, développée par Hassan, Hollander, van Lent et Tahoun (2019). En utilisant un large panel d'entreprises publiques américaines sur la période 2002-2016, nous montrons que l'exposition des entreprises au risque politique augmente en effet leurs réserves de liquidités, leur valorisation et leur rétention de liquidité. Ces liquidités supplémentaires sont principalement dues à la réduction des investissements, à l'augmentation du fonds de roulement net et à l'allongement des échéances des émissions de dette. La motivation à conserver des liquidités en période de crise politique est plus forte pour les entreprises opérant sur des marchés concurrentiels et moins prononcée pour les entreprises innovantes et celles qui sont plus proches des centres politiques de Washington D.C. Bien que notre conclusion selon laquelle le risque politique est un déterminant clé des liquidités des entreprises soit statistiquement et économiquement significative, nous devons noter que des recherches supplémentaires sont nécessaires pour comparer en profondeur l'effet de cette variation au niveau de l'entreprise avec l'effet des approximations du risque politique au niveau agrégé ou sectoriel. En outre, cette mesure du risque politique intègre les perceptions qualitatives des gestionnaires et des 229 participants de l'environnement politique dans lequel ils se trouvent. Elle doit donc être interprétée comme une indication du risque tel qu'il est perçu par les entreprises et les parties prenantes et comme une indication de leur sentiment, car leurs perceptions peuvent différer du risque réel. Le chapitre 4, intitulé "Risque politique au niveau de l'entreprise, ajustement de la prise du risque managérial et incitations aux managers", conclut que le risque politique est inhérent, omniprésent et affecte de manière significative les préférences au niveau du management pour la prise de risque dans le temps. Sur la base de recherches antérieures, nous développons deux canaux par lesquels le risque politique peut affecter les choix de gestion du risque. D'une part, et conformément au principe de la théorie de l'agence sur les différences de risque entre les actionnaires et les agents, les managers peuvent devenir plus réticents à la prise de risque en période de forte incertitude politique pour garantir leurs avantages. D'autre part, les États-Unis disposent d'institutions fortes qui s'efforcent de maintenir la stabilité économique en temps de crise en utilisant de nombreux outils de sauvetage qui peuvent inciter les comportements d'aléa moral et inciter les managers à prendre plus de risques. En utilisant un large panel d'entreprises américaines cotées en bourse sur la période 2002-2016, nous fournissons des preuves inédites que les dirigeants s'engagent dans des activités plus risquées en période de risque politique élevé, mais commencent à s'adapter pour réduire leur prise de risque a partir de la troisième année qui suit un changement du niveau de risque politique. En outre, nous montrons l'effet substantiel des incitations managériales basées sur les actions (principalement Vega) sur la relation entre le risque politique et les décisions de prise de risque. Nous effectuons de nombreux tests de robustesse et des tests supplémentaires et nos résultats restent quantitativement et qualitativement les mêmes. Nous utilisons différentes mesures de la prise de risque des entreprises, nous incluons des variables de contrôle supplémentaires et nous employons différentes spécifications de régression. Nous montrons également que la réaction des

  globalization. The last global financial crisis of 2008-2009 and its epicenter in the United States (U.S.) raised many questions on what causes financial crises and what moves financial markets. While some answers to such questions can be rooted in economic and technical features, scholars and practitioners have long debated that such economic phenomena also have political foundations.[START_REF] Eichengreen | Capital flows and crises[END_REF] andBryant (2003), for instance, argue that beyond government actors, non-state actors, private actors in the financial industry, and other actors in civil society were also involved in the financial crisis. The way today's world is politically structured affects the efficiency of resource allocation and the economic outcome.

	physical, business, and reputational risks. Recently, Boston Consulting Group (BCG)
	manner. Companies operating in different industries from manufactured products to
	high technology are shifting their organizational structure to include high strategic
	monitoring to assess global political trends and to adapt their decision-making to global
	14

The financial crisis and other economic anxieties of recent years have revealed that domestic and international political factors are closely linked. Business environments are more volatile and far less predictable than they have ever been. Yet firms are still mostly pursuing traditional approaches to strategy that only work under more stable times. As a result, companies are failing to face disruptions and are dying younger. A study conducted on more than 30,000 public firms in the U.S. over a 50-year span documents that public companies have now a delisting rate that is six times higher than the rate of 40 years ago. The five-year survival rate has fallen from 95% in 1970 to around 68% today and this is regardless of firm size, age, or industry. 3 In addition, according to the 20 th Global Trade Alert Report, trade barriers increased by 257% between 2009 and 2018 and the number of discriminatory trade interventions implemented globally peaked in 2018, to 1,308. 4

Thus, it is the challenge of researchers, experts, and firms to seek a wider insight and to integrate political uncertainty in their analyses in a more systematic and rigorous

Table 1 -1. Definitions of political risk Authors Definitions and postulates Discussion

 1

Table 1 -2. The firm environment is becoming wider and more complex

 1 What makes political uncertainty a complex concept is the multitude of types of risks it can reveal. The external challenges of firms or as called the "out there" risks can be of many types. 7 Table 1-4 enumerates and describes ten types of political risk to which firms could be exposed. In addition to market-based and economic risks that companies are already aware of, firms are affected by many other political actions that are summarized in the table below.

	Level	Nature	Firms
	First	The Population	The Company
		Individual organisms of the same	Individual employees
		species
	Second The Natural Ecosystem	The Business Ecosystem
		Populations of species that	Companies that depend on and compete
		depend on and compete with one	with one another
		another
	Third	The Broader Natural Environment	The Business Environment
		Neighboring ecosystems and	Overlapping ecosystems and other
		nonbiological elements, such as	stakeholders, such as NGOs, government
		the climate	entities, and civil society

Source: Harvard Business Review

(Jan-Feb 2016)

. "The biology of corporate survival."

In Table

1-3

, examples and classification of different political risk generators are presented. In their definition of the twenty-first-century political risk,

Rice and Zegart (2018)

chose the phrase "political action," not "government action" to emphasize the growing impact of risk generators other than governments and heads of States. They state in their book that "These days, political activities that affect business are happening almost everywhere-inside homes, on the streets, and in the cloud; in chat rooms, dorm rooms, and boardrooms; in neighborhood bars and summit sidebars." Companies that want to stay competitive have to face the increasing potential of political risk stemming from a widening array of risk generators.

Table 1 -3. Five levels of action generating political risk

 1

	Level

Source:

Rice and Zegart (2018)

, with modification.

Table 1 -4. Types of the twenty-first-century political risk

 1

	Type	Example

Internal conflicts Social unrest, ethnic violence, migration, nationalism, separatism, federalism, civil wars, coups, revolutions In the case of separatist movements many businesses can decide to change the location of their headquarters or plants to or from the country in question. A survey by the Scottish Business Poll shows that more than one third of firms will relocate if the Scotland secedes from the United Kingdom. however, corruption can put the firm under heavy penalties. In the U.S., firms can face severe criminal and civil prosecution according to the American Foreign Corrupt Practices Act (FCPA). In the U.S., under the section 3 II of the Patriot Act, the Treasury Department's Financial Crimes Enforcement Network can sanction financial institutions all over the world if they engage in any money laundering activity. Passengers video-recorded Dao as he was violently dragged from his seat and posted the footage on Twitter and Facebook. Two days later, United's stock had lost $255 million in shareholder value, and analysts began worrying about the ramifications for the airline in the Chinese market, where commenters on social media shared the view that Dao was Source: Rice and Zegart (2018), with modification.

Table 1 -5. Measures of political risk Author Study Period Context Measure used Main Contribution Timing of elections or political events

 1

	Bachman (1992)	1973-1985	Canada, France,	A dummy variable that takes zero for	Elections affect the forward
			the U.K., and the	periods before national elections and	bias.
			U.S.		one for a window period after	Political events affect forward
					elections.	exchange risk premium.
					13 major elections are considered.
	Pantzlis,	1974-1995	33	countries	Event study around national	There are positive abnormal
	Stangeland,	and	across the world	elections week.	returns during the two-week
	Turtle (2000)				129 elections are considered divided	period prior to the election
					as follows: 79 in Europe, 17 in Asia	week.
					and Australia, 10 in North America,
					and 10 across the rest of the world.
	Fowler (2006)	1988-2000	The U.S.	Event study around U.S.

Presidential and Congressional elections.

						winning the elections decreases
						interest rates and inflation.
	Białkowski,	1983-2004	27	OECD	Event study around national	During the week around
	Gottschalk,	and	countries	(all	elections.	election, stock market volatility
	Wisniewski (2008)	OECD		134 elections are considered.	experiences	strong	and
			countries,			significant increase.
			except	for
			Iceland,
			Luxembourg,
			and Slovakia)
	Floros (2008)	1996-2002	Greece		A dummy variable that equals one if	Overall, political elections have
					it is national elections month and	negative effects on the course of
					zero otherwise.	Athens Stock Exchange (ASE).
	Repousis (2016)	1996-2009	Greece		Event study around the Greek
						An increase in the probability
						that Democrats win the
						Presidency, or the Congress
						induces an increase in interest
						rates and inflation.
						A positive change in the
						probability of the incumbent

national Parliamentary elections.

		in 2002 and
		2006).
	Jens (2017)	1984-2008	The U.S.	A dummy variable equal to one when	Firms exposed to political
				a gubernatorial election occurs in a	uncertainty reduce investment
				specific state and a specific year.	by 15%.
				328 elections are considered which	Overall, investment declines by
				define 1,250 state-year observations.	5% before all elections.
							Political information has no
							significant effect on banks' stock
							prices.
	Kelly, Pástor, and	1990-2012	20 countries	Uncertainty	around	national	Equity option which are
	Veronesi (2016)	(Only U.S. data		elections and global summits.	affected by political uncertainty
		begin in 1990,					have higher prices.
		data of other
		countries begin

Index construction based on risk factors

	Howell (2011)	1984-Now	140 countries	An index that is composed of
		(updated
		monthly)

12 main risk factors and 19 subcomponents,

						A risk-factor-based index that
						covers a broad spectrum of
					weighted according to their impact.	political and social attributes
						worldwide.
	Muñoz,	García-	2000-2010	122 countries	An index that aggregates all the
	Verdugo, and San-			components of the Geopolitical
	Martín (2015)			Energy Supply Risk.

It has 47 variables related to 4 vectors:

		Developed economies have
		lower levels of geopolitical
		energy risk, whereas less-
		developed	countries	have
	economic, energy-specific, socio-	higher levels of risk unrelatedly
	political and EU-relations.	to their energy production.

Textual analysis and computational linguistics approaches

	Ahir, Bloom, and	1996-Now	143 countries	433 newspapers are considered. 'deficit', 'congress', 'legislation', or The index relies on the quarterly	fifth of the variation in Global uncertainty is
	Furceri (2019)		1955-Now (for			'white house'. Economist Intelligence Unit (EIU)	consumer increasing.	preferences	is
			34	advanced			12,000 newspaper articles are country reports to count the	explained by the variation of the Uncertainty levels are higher in
			economies)			considered. frequency of the word "uncertainty."	index of media slant. developing countries.
	Loughran Hassan, Hollander, and	1994-2008 2001-2016	The U.S. The U.S.		Tone (positive and negative) and The measure is the sum of the	The authors improve the It is the first firm-level and The relationship between
	McDonald (2011) van Lent, and					sentiment analysis on 10-K reports weighted occurrence of bigrams	Harvard-IV-4 idiosyncratic political uncertainty political TagNeg risk and
	Tahoun (2019)						based on a more accurate word synonyms of political risk and	dictionary for word polarity measure. democracy is an inverted U-
							classification dictionary developed political uncertainty between the set	classification. Firm-level shaped.	political	risk
							by the authors. of 10 words surrounding another	correlates positively with stock Uncertainty positively
	Wiesen	and			The U.S.		An annual firm-level measure of synonym and divide by the total	The authors provide a new price and volatility. correlates with economic policy
	Wysocki (2015)						firms' overall regulatory constraints number of bigrams in the text.	firm-level Firms that devote more time to time-varying uncertainty and stock market
							using textual analysis of corporate This study considers 178,173	measure of regulation risk for discuss risks relating to politics volatility, and negatively with
							10-K files. conference call transcripts held in	future empirical research use. are more likely to lobby. GDP growth.
							It uses the scaled frequency of conjunction with an earnings
							reference to "regulation words." publication of 7,357 listed firms.
	Baker, Bloom, and Caldara, and	1985-2014 1985-2018	The U.S. The U.S., the	An index reflecting scaled monthly A monthly index of geopolitical risk	Policy uncertainty increases High levels of geopolitical risk
	Davis (2016) Iacoviello (2018)				U.K.,	and	counts of articles containing words that counts the frequency of articles	stock price volatility and are associated with lower
					Canada.		such as 'uncertain' or 'uncertainty', related to geopolitical risks in leading	negatively affects investment, economic activity, lesser stock
							'economic' or 'economy', and one or international newspapers.	growth and employment. returns, and a shift in
	Gentzkow	and	2005		The U.S.		An index of media slant that is a more policy relevant terms: The authors also construct a long-	Consumer movements of capital flows demand reacts
	Shapiro (2010)						result of the textual analysis of 'regulation', 'federal reserve', span historical index (GPRH) dating	significantly to the fit between a from emerging markets to
							newspapers political content. back to 1900.	newspaper's slant. Around one advanced markets.

Table 1 -6. Empirical studies on the effects of political risk and political connectedness Author(s) Study Period Context Main Contribution(s) Political risk and economic outcome

 1

	Frey and Schneider	1953-1975	The U.S.		When the popularity of a government decreases due to unemployment
	(1978)				and inflation pressures, it engages in boosting the economy.
	Alesina and Tabellini	1967-1980	Argentina,	Political uncertainty is associated with high external debt levels, lower
	(1989)		Chile,	Peru,	capital outflows and investment.
			Uruguay	Political polarization is associated with polarized policies of
					redistribution.
	Julio and Yook (2012)	1980-2005	48 countries
			248	national

elections

Political risk in times of elections significantly and negatively affects investment expenditures.

Mouse is not a Republican or a Democrat"

  However, is it random that the copyright law is extended every time Walt Disney Co's copyrights on Mickey Mouse is close to expiration?15 Disney has strongly been honing lobbying to promote the copyright term extension bill (HR2589) on Capitol Hill. Without copyright term extension, the firm would collect no fees for the showing of classic movies and other entities and the public would freely use Disney's characters. For example, the copyright for Mickey Mouse was expiring in 2003 (i.e., 75 years after the appearance in 1928 in the Disney cartoon) when the company lobbied to change legislation and to extended copyright to the dead authors.

	"Mickey Mouse is not a Republican or a Democrat," said Joe Shapiro, who oversaw
	Disney's Washington lobbying office in the early 1990s. "If you take a strong position either
	way, you are looking at offending roughly half of the people. Michael Eisner is very informed about

public policy issues. But as a matter of personal style, he's not out front. I believe he doesn't think 57 it's appropriate."

  Alesina, A., & Tabellini, G. (1989). External debt, capital flight and political risk. Journal

	1.	Introduction
		Geopolitical risk has recently become a major mover of markets and companies. It
	carries important business cycle fluctuations that can prove destabilizing. For example,
	of international Economics, 27(3-4), 199-220. both the European Central Bank's economic bulletin of April 2017 and the International
	Aliber, R. Z. (1975). Exchange risk, political risk, and investor demand for external Monetary Fund's World Economic Outlook of October 2017 assert that uncertainties
	currency deposits. Journal of Money, Credit and Banking, 7(2), 161-179. stemming from geopolitical upheavals are a salient risk to economic outcome. Political
	Allvine, F. C., & O'Neill, D. E. (1980). Stock market returns and the presidential election and regulatory upheavals that result in economic volatility do not generally help growth
	cycle: Implications for market efficiency. Financial Analysts Journal, 36(5), 49-56. and value creation, especially with the increasing interconnectedness of businesses and
	politics that causes disturbances to spread faster.
		Evidence	from	business	survey	data. American	Economic	Journal:
		Macroeconomics, 5(2), 217-49.
		Journal, 20(4), 535-551.
	Besley, T., & Prat, A. (2006). Handcuffs for the grabbing hand? Media capture and
		government accountability. American economic review, 96(3), 720-736.

Alon, I., & Martin, M. A. (1998). Political Risk Assessment. Multinational Business Review. Arayavechkit, T., Saffie, F., & Shin, M. (2017). Too Big to Pay: Tax Benefits and Corporate Lobbying. Mimeo University of Maryland. Bachman, D. (1992). The effect of political risk on the forward exchange bias: the case of elections. Journal of International Money and Finance, 11(2), 208-219. Bachmann, R., Elstner, S., & Sims, E. R. (2013). Uncertainty and economic activity: Baker, S. R., Bloom, N., & Davis, S. J. (2016). Measuring economic policy uncertainty. The quarterly journal of economics, 131(4), 1593-1636. Bass, B. M., McGregor, D. W., & Walters, J. L. (1977). Selecting foreign plant sites: economic, social and political considerations. Academy of Management Białkowski, J., Gottschalk, K., & Wisniewski, T. P. (2008). Stock market volatility around national elections. Journal of

Banking & Finance, 32(9), 1941-1953.

Over the last few years, the economic consequences of political and policy uncertainty have been a research topic of increased interest. A study conducted by

Baker,

Table 2 -

 2 1 reports summary statistics for our main measures of trade credit, the measure of firm-level political risk and control variables. The average fraction of sales made on credit is 30% with a standard deviation of 1.16. Table 2-2 provides Pearson

Table 2 -1. Descriptive statistics of main variables

 2

	Variable	N	Mean	Std.Dev.	Min	25%	Median	75%	Max
	PRisk	40,634	1.57	1.18	0.13	0.57	1.28	2.42	4.07
	TR	40,634	0.30	1.16	0.00	0.10	0.15	0.21	15.92
	TP	40,634	0.47	3.93	0.00	0.07	0.12	0.19	126.63
	Size	40,634	6.91	2.08	2.24	5.48	6.81	8.25	12.80
	Capex	40,634	0.05	0.06	0.00	0.01	0.03	0.06	0.31
	Lev	40,634	0.23	0.21	0.00	0.02	0.20	0.36	1.00
	ROA	40,634	0.07	0.18	-0.95	0.05	0.10	0.16	0.40
	Cash	40,634	0.20	0.21	0.00	0.04	0.11	0.29	0.90
	MB	40,634	1.90	1.34	0.57	1.08	1.46	2.19	8.53
	Tang	40,634	0.26	0.25	0.00	0.06	0.17	0.41	0.90

Table 2 -2. Correlation analysis

 2 This table presents correlation coefficients between political risk, trade credit and other control variables. The list of variables, definitions, and sources are provided in the Appendices 2-1 and 2-2. * refers to significance at the 1% level.

		PRisk	TR	TP	Size	Capex	Lev	ROA	Cash	MB	Tang
	PRisk	1
	TR	0.0372*	1
	TP	0.0309*	0.5257*	1
	Size	0.0218*	0.1517*	0.1083*	1
	Capex	-0.0097*	-0.1017*	-0.0420*	0.0839*	1
	Lev	-0.0035	0.0586*	-0.0100*	0.3051*	0.1120*	1
	ROA	-0.0506*	-0.0790*	-0.0681*	0.3668*	0.1457*	0.0584*	1
	Cash	0.0450*	-0.0247*	0.0057	-0.4263*	-0.2115*	-0.4050*	-0.3856*	1
	MB	0.0125*	-0.0591*	-0.0185*	-0.2482*	-0.0131*	-0.2103*	-0.0698*	0.4275*	1
	Tang	-0.0147*	-0.1336*	-0.0649*	0.2552*	0.6577*	0.3176*	0.1752*	-0.4165*	-0.2111*	1

Table 2 -3. Main regressions

 2 This table provides the results of the regressions of the firm-level political risk on trade credit and other firm characteristics using different estimation techniques. P-values are reported in parentheses. T-statistics are based on robust standard errors adjusted for clustering by firm. The list of variables, definitions, and sources are provided in the Appendix 2-1 and Appendix 2-2. *, ** and *** refer to significance at the 10%, 5% and 1% levels, respectively.

		(1)	(2)	(3)	(4)
	VARIABLES	OLS	Fama-MacBeth	Newey-West	Fixed Effects
		TR	TR	TR	TR
	PRisk	0.0131**	0.0203***	0.0131***	0.0011*
		(0.01)	(0.00)	(0.00)	(0.08)
	Size	0.0818***	0.1313***	0.0818***	0.0267***
		(0.00)	(0.00)	(0.00)	(0.00)
	Capex	0.3098**	0.6867***	0.3098***	-0.0067
		(0.04)	(0.00)	(0.00)	(0.93)
	Lev	-0.0730	0.2773***	-0.0730	-0.0521**
		(0.48)	(0.00)	(0.10)	(0.02)
	ROA	-0.8188***	-0.8892***	-0.8188***	-0.2654***
		(0.00)	(0.00)	(0.00)	(0.00)
	Cash	-0.2987***	-0.1898***	-0.2987***	-0.3115***
		(0.00)	(0.00)	(0.00)	(0.00)
	MB	-0.0158***	-0.0259***	-0.0158***	0.0104***
		(0.01)	(0.00)	(0.00)	(0.00)
	Tang	-0.4745***	-1.0406***	-0.4745***	-0.2276***
		(0.00)	(0.00)	(0.00)	(0.00)
	Constant	-0.3248***	-0.3417***	-0.3248***	0.1953***
		(0.00)	(0.00)	(0.00)	(0.00)
	Observations	40,333	40,634	40,333	40,634
	Year FE	YES	NO	YES	YES
	Industry FE	YES	NO	YES	NO
	Cluster by Firm	YES	NO	NO	NO
	Firm FE	NO	NO	NO	YES
	Adjusted R-squared	0.373	0.099		0.018
	Number of groups		16
	Number of gvkey				5,786

Table 2 -4. Alternative measures of trade credit

 2 This table provides the results of the regressions of the firm-level political risk on trade credit and other firm characteristics using various measures of trade credit. P-values are reported in parentheses. Tstatistics are based on robust standard errors adjusted for clustering by firm. The list of variables, definitions, and sources are provided in the Appendix 2-1 and Appendix 2-2. *, ** and *** refer to significance at the 10%, 5% and 1% levels, respectively.

		(1)	(2)	(3)	(4)
	VARIABLES	TP	TR_Receiv	TR_At	RC
	PRisk	0.0452**	0.0075**	0.0014**	0.0044*
		(0.04)	(0.02)	(0.04)	(0.06)
	Size	0.2752***	0.0526***	-0.0066***	-0.0129***
		(0.00)	(0.00)	(0.00)	(0.00)
	Capex	1.1039*	0.2592***	0.0414**	-0.1603**
		(0.08)	(0.00)	(0.04)	(0.04)
	Lev	-1.5727***	-0.0004	-0.0153	0.2541***
		(0.00)	(0.99)	(0.11)	(0.00)
	ROA	-2.5710***	-0.4308***	0.0387***	0.5398***
		(0.00)	(0.00)	(0.00)	(0.00)
	Cash	-0.4506*	-0.2048***	-0.1884***	-0.2545***
		(0.05)	(0.00)	(0.00)	(0.00)
	MB	0.0078	-0.0149***	0.0010	-0.0111***
		(0.67)	(0.00)	(0.24)	(0.00)
	Tang	-0.4572***	-0.3822***	-0.2015***	-0.2838***
		(0.00)	(0.00)	(0.00)	(0.00)
	Constant	-1.5960***	-0.1331**	0.2269***	0.1412***
		(0.00)	(0.04)	(0.00)	(0.00)
	Observations	40,333	40,333	34,568	34,568
	Year FE	YES	YES	YES	YES
	Industry FE	YES	YES	YES	YES
	Cluster by Firm	YES	YES	YES	YES
	Firm FE	NO	NO	NO	NO
	Adjusted R-squared	0.165	0.428	0.234	0.086

Table 2 -5. Additional macro-economic risk variables

 2 This table provides the results of the regressions of the firm-level political risk on trade credit and other firm characteristics using various additional control variables related to macro-economic risks. P-values are reported in parentheses. T-statistics are based on robust standard errors adjusted for clustering by firm. The list of variables, definitions, and sources are provided in the Appendix 2-1 and Appendix 2-2. *, ** and *** refer to significance at the 10%, 5% and 1% levels, respectively.

		(1)	(2)	(3)	(4)	(5)	(6)
	VARIABLES	TR	TP	TR	TP	TR	TP
	PRisk	0.0082*	0.0305*	0.0180*** 0.0634***	0.0131**	0.0452**
		(0.09)	(0.06)	(0.00)	(0.01)	(0.01)	(0.04)
	Size	0.0814***	0.2740***			0.0818***	0.2752***
		(0.00)	(0.00)			(0.00)	(0.00)
	Capex	0.3093**	1.1025**			0.3098**	1.1039*
		(0.05)	(0.02)			(0.04)	(0.08)
	Lev	-0.0728	-1.5721***			-0.0730	-1.5727***
		(0.48)	(0.00)			(0.48)	(0.00)
	ROA	-0.8165*** -2.5641***			-0.8188***	-2.5710***
		(0.00)	(0.00)			(0.00)	(0.00)
	Cash	-0.2992*** -0.4520***			-0.2987***	-0.4506*
		(0.00)	(0.00)			(0.00)	(0.05)
	MB	-0.0160***	0.0072			-0.0158***	0.0078
		(0.01)	(0.65)			(0.01)	(0.67)
	Tang	-0.4727*** -0.4518***			-0.4745***	-0.4572***
		(0.00)	(0.00)			(0.00)	(0.00)
	Non-PRisk	0.0170**	0.0502***
		(0.03)	(0.00)
	Cape			0.0029	-0.0239	0.0042	-0.0268
				(0.56)	(0.27)	(0.39)	(0.22)
	Earn-R			-0.0008	-0.0074*	0.0012	-0.0000
				(0.49)	(0.08)	(0.29)	(0.99)
	Inflation			-0.0105	-0.2600	-0.0140	-0.2152
				(0.76)	(0.19)	(0.67)	(0.29)
	CE			-0.0078	-0.0991**	0.0094	-0.0454
				(0.47)	(0.05)	(0.39)	(0.39)
	CST			0.0068	0.0857*	-0.0112	0.0364
				(0.50)	(0.06)	(0.27)	(0.44)
	Financial Stress Index			-0.0352	-0.2627**	-0.0011	-0.1175**
				(0.23)	(0.04)	(0.97)	(0.04)
	Partisan Conflict Index			-0.0002	-0.0035	0.0003	-0.0008
				(0.54)	(0.17)	(0.49)	(0.75)
	Constant	-0.3430*** -1.6500***	0.1727	2.7369*	-0.2549	0.4227
		(0.00)	(0.00)	(0.57)	(0.09)	(0.42)	(0.81)
	Observations	40,333		40,333	40,333	40,333	40,333
	R-squared	0.374	40,333	0.349	0.146	0.374	0.166
	Year FE	YES	0.166	YES	YES	YES	YES
	Industry FE	YES	YES	YES	YES	YES	YES
	Cluster by Firm	YES	YES	YES	YES	YES	YES
	Adjusted R-squared	0.373	0.164	0.348	0.145	0.373	0.165

Table 2 -6. Political risk dimensions

 2 This table provides the results of the regressions of different dimensions (components) of the aggregate firm-level political risk on trade credit and other firm characteristics. P-values are reported in parentheses. T-statistics are based on robust standard errors adjusted for clustering by firm. The list of variables, definitions, and sources are provided in the Appendix 2-1 and Appendix 2-2. *, ** and *** refer to significance at the 10%, 5% and 1% levels, respectively.

		(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)
	VARIABLES	TR	TR	TR	TR	TR	TR	TR	TR	TR
	Tax policy	0.0174***								0.0201**
		(0.01)								(0.01)
	Institutions		0.0153***							0.0142*
			(0.01)							(0.06)
	Security defense			0.0080						-0.0106
				(0.18)						(0.23)
	Environment				0.0171***					0.0131
					(0.01)					(0.13)
	Trade					0.0093				0.0028
						(0.16)				(0.71)
	Economic policy						0.0126*			0.0014
							(0.05)			(0.88)
	Health							0.0161***		0.0127*
								(0.01)		(0.07)
	Technology								0.0085	0.0012
									(0.16)	(0.86)
	Observations	40,079	40,195	40,304	40,273	39,983	40,274	40,237	40,150	39,727
	Controls	YES	YES	YES	YES	YES	YES	YES	YES	NO
	Year FE	YES	YES	YES	YES	YES	YES	YES	YES	YES
	Industry FE	YES	YES	YES	YES	YES	YES	YES	YES	YES
	Cluster by Firm	YES	YES	YES	YES	YES	YES	YES	YES	YES
	Adjusted R-squared	0.375	0.374	0.373	0.373	0.374	0.374	0.373	0.374	0.351

Table 2 -7. Regression analysis relating trade credit to prior period's political risk

 2

		Lagged independent variables	Change in PRisk
		(1)	(2)	(3)	(4)
	VARIABLES	TR	TP	TR	TP
	PRisk	0.0106*	0.0655**	0.0120*	0.0575*
		(0.07)	(0.01)	(0.06)	(0.05)
	Δ PRisk (%)			0.0003	0.0091*
				(0.86)	(0.09)
	Size	0.0822***	0.2948***	0.0833***	0.2930***
		(0.00)	(0.00)	(0.00)	(0.00)
	Capex	0.1964	0.8105	0.2679	1.1317
		(0.17)	(0.10)	(0.11)	(0.15)
	Lev	-0.1026	-1.6418***	-0.1463	-1.7386***
		(0.34)	(0.00)	(0.16)	(0.00)
	ROA	-0.7736***	-2.7716***	-0.8515***	-2.8054***
		(0.00)	(0.00)	(0.00)	(0.00)
	Cash	-0.2413***	-0.2526	-0.3040***	-0.4434*
		(0.00)	(0.30)	(0.00)	(0.09)
	MB	-0.4247***	-0.4016**	-0.0165**	0.0053
		(0.00)	(0.02)	(0.01)	(0.79)
	Tang	0.0822***	0.2948***	-0.4118***	-0.4026**
		(0.00)	(0.00)	(0.00)	(0.02)
	Constant	-0.3497***	-1.7167***	-0.3313***	-1.6743***
		(0.00)	(0.00)	(0.00)	(0.00)
	Observations	33,562	33,562	33,562	33,562
	Year FE	YES	YES	YES	YES
	Industry FE	YES	YES	YES	YES
	Cluster by Firm	YES	YES	YES	YES
	Adjusted R-squared	0.405	0.191	0.408	0.191
	This table provides the

Table 2 -8. Endogeneity: IV 2SLS regression

 2 This table provides the results of the regressions of the firm-level political risk on trade credit and other firm characteristics using instrumental variable regressions to address endogeneity issues. P-values are reported in parentheses. T-statistics are based on robust standard errors adjusted for clustering by firm. The list of variables, definitions, and sources are provided in the Appendix 2-1 and Appendix 2-2. *, ** and *** refer to significance at the 10%, 5% and 1% levels, respectively.

		(1)	(2)	(3)	(4)
		First stage	Second stage	First stage	Second stage
	VARIABLES	TR		TP
	PRisk		0.5138*		2.4375*
			(0.09)		(0.06)
	Size	0.0277***	0.0320***	0.0277***	0.0158
		(0.00)	(0.00)	(0.00)	(0.68)
	Capex	0.4632***	0.2448	0.4632***	0.3358
		(0.01)	(0.25)	(0.01)	(0.69)
	Lev	-0.0177	0.2532***	-0.0177	-0.0617
		(0.63)	(0.00)	(0.63)	(0.61)
	ROA	-0.2464***	-0.4141***	-0.2464***	-0.8558**
		(0.00)	(0.00)	(0.00)	(0.01)
	Cash	0.1769***	-0.2757***	0.1769***	-0.4325
		(0.00)	(0.00)	(0.00)	(0.10)
	MB	0.0054	-0.0173***	0.0054	0.0001
		(0.34)	(0.00)	(0.34)	(1.00)
	Tang	-0.1127**	-0.4390***	-0.1127**	-0.2305
		(0.03)	(0.00)	(0.03)	(0.31)
	Mood (IV)	0.1187**		0.1187**
		(0.05)		(0.05)
	Professionalism (IV)	-0.1174**		-0.1174**
		(0.02)		(0.02)
	Constant	0.8351***	-0.5221*	0.8351***	-2.2979*
		(0.00)	(0.09)	(0.00)	(0.05)
	Observations	31,481	31,481	31,481	31,481
	Year FE	NO	YES	NO	YES
	Industry FE	NO	YES	NO	YES
	Cluster by Firm	NO	YES	NO	YES
	Adjusted R-squared	0.0256		0.0256
	Chi2 (p-value)		0.00		0.00
	F-statistics (p-value)	0.01		0.01
	Sargan test (p-value)	0.5365		0.3673
	Basman test (p-value)	0.5370		0.3678

Table 2 -9. Additional analysis

 2 This table provides regression results on the moderating effects of competition, financial constraints and social trust on the relationship between the firm-level political risk and trade credit. P-values are reported in parentheses. T-statistics are based on robust standard errors adjusted for clustering by firm. The list of variables, definitions, and sources are provided in the Appendix 2-1 and Appendix 2-2. *, ** and *** refer to significance at the 10%, 5% and 1% levels, respectively.

		Competition	Financial constraints	Social Trust
		(1)	(2)	(1)	(2)	(1)	(2)
	VARIABLES	TR	TP	TR	TP	TR	TP
	PRisk	0.0201***	0.0886**	0.0169***	0.0581***	0.0437**	0.0989**
		(0.00)	(0.02)	(0.00)	(0.00)	(0.01)	(0.02)
	HHI	-0.0908***	-0.8239***
		(0.00)	(0.00)
	PRisk_HHI	-0.0216*	-0.1334*
		(0.09)	(0.06)
	KZ			0.0000***	0.0000**
				(0.00)	(0.01)
	PRisk_KZ			-0.0000**	-0.0000*
				(0.04)	(0.06)
	ST					0.0301**	0.0299**
						(0.03)	(0.03)
	PRisk_ST					0.0098**	0.074**
						(0.02)	(0.03)
	Observations	40,333	40,333	37,589	37,589	40,333	40,333
	Controls	YES	YES	YES	YES	YES	YES
	Year FE	YES	YES	YES	YES	YES	YES
	Industry FE	YES	YES	YES	YES	YES	YES
	Cluster by Firm	YES	YES	YES	YES	YES	YES
	Adjusted R-squared	0.374	0.168	0.409	0.174	0.421	0.188

  together the literature on two important strands of research, namely,

	political risk and corporate cash holdings, our study makes several contributions. Recent
	work has shown that political uncertainty shocks have important effects on economic
	activity (e.g., Bloom, 2009; Baker, Bloom, and Davis, 2016), causing an undershoot in
	investment, employment and productivity. Moreover, it complements other firm-level
	studies on the effect of shocks emanating from the political sphere on investment, M&A,
	for example, use an index for economic policy uncertainty (EPU) based on the analysis of
	dozens of thousands newspaper articles that cover movements in policy-related
	economic uncertainty. Jens (2017), among others, exploits U.S. gubernatorial elections in
	the state of corporate headquarters as a plausibly exogenous source of political
	uncertainty. Instead, we use a new idiosyncratic measure of political risk developed by
	Hassan, Hollander, van Lent, and Tahoun (2019), which evaluate how firms

cost of capital, corporate disclosure and stock market volatility (e.g.,

Julio and Yook, 2012;[START_REF] Pastor | Political uncertainty and risk premia[END_REF] Baloria and Mamo, 2014; Jens, 2017; DeBoskey, Li, Lobo, and Luo 2017;[START_REF] Lee | Political uncertainty and cost stickiness: Evidence from national elections around the world[END_REF] Nguyen and Phan 2017; Chen, Cihan, and Jens, 2017)

. Our study relates to the broad literature on politics and finance that investigates how the degree of policy and political uncertainty affects the economy, corporate decisions and firm financial policies by studying the endogenous corporate cash holdings response to political uncertainty.

Our study differs from those cited above in that it uses a new measure that accounts for firm-level variation of political risk. In the absence of direct measure of political risk because of its complexity, other papers use aggregate proxies at the country-and sectorlevel or event studies in periods leading up to elections.

Baker, Bloom, and Davis (2016)

, endogenously perceive politics-related issues that may significantly affect their business and decision-making.

Table 3 -1. Firm-level political risk and cash holdings by fiscal year

 3

		PRisk	Cash holdings	PRisk (standardized)	Cash holdings (standardized)
	Fiscal year	Mean	Std. Dev.	Mean	Std. Dev.	Mean	Std. Dev.	Mean	Std. Dev.	Freq. %
	All	98.0798	89.0391 -2.1394	1.8833	-0.0803	0.5298	0.0000	1.0000	100
	2002	96.5600	90.2679 -2.1563	1.9235	-0.0893	0.5371	-0.0089	1.0213	4.0425
	2003 104.1902	92.4396 -2.0657	1.8764	-0.0439	0.5500	0.0392	0.9963	5.4316
	2004	87.2589	80.0237 -2.0183	1.8101	-0.1447	0.4762	0.0643	0.9611	5.7618
	2005	82.1876	78.2463 -2.0481	1.8270	-0.1748	0.4656	0.0485	0.9701	6.3656
	2006	86.1364	82.8803 -2.1223	1.9029	-0.1514	0.4932	0.0091	1.0104	6.7137
	2007	87.1436	83.6608 -2.1710	1.9230	-0.1454	0.4978	-0.0167	1.0211	7.0795
	2008 105.9161	87.6579 -2.2387	1.8782	-0.0337	0.5216	-0.0527	0.9973	7.4157
	2009 113.7226	90.0761 -2.0377	1.7910	0.0128	0.5360	0.0540	0.9510	7.1718
	2010 105.4929	94.4837 -2.0445	1.8239	-0.0362	0.5622	0.0504	0.9685	6.9665
	2011 104.3817	92.2643 -2.1573	1.8729	-0.0428	0.5490	-0.0095	0.9945	7.9303
	2012 107.9328	96.3727 -2.1866	1.8443	-0.0217	0.5734	-0.0250	0.9793	7.8619
	2013 101.3356	92.0667 -2.1766	1.8502	-0.0609	0.5478	-0.0197	0.9824	7.0349
	2014	93.3377	88.8762 -2.1547	1.9972	-0.1085	0.5288	-0.0081	1.0605	7.7845
	2015	92.3193	86.1449 -2.1404	2.0095	-0.1146	0.5126	-0.0005	1.0670	7.8291
	2016	98.9051	89.1452 -2.4121	1.8468	-0.0754	0.5304	-0.1448	0.9806	4.6106
	This table shows the mean, standard deviation and frequency in terms of number of observations of firm-

level political risk and the level of corporate cash holdings by fiscal year. The sample comprises 33,618 observations covering 5,481 unique firms for the period spanning 2002 through 2016. PRisk is the annual average of political risk, calculated as in

Hassan et al. (

Table 3 -2. Firm-level political risk by industry

 3 This table shows the mean, standard deviation and frequency in terms of number of observations of firmlevel political risk within each industry, classified according to Fama and French 48 industry categories. The sample comprises 33,618 observations covering 5,481 unique firms for the period spanning 2002 through 2016. PRisk is the industry time series averages of political risk, calculated as inHassan et al. (2019), for all firms within a given industry.

	PRisk	PRisk (standardized)
		Std.		Std.	Freq.
	Mean	Dev.	Mean	Dev.	%

Table 3 -3. Summary statistics

 3

	Variable	Obs	Mean	Std. Dev.	Min	Max
	Panel A
	Political risk
	PRisk	33,618	98.0798	89.0391	5.4884	340.0621
	Policy	33,618	7202.7660	1786.3380	4890.9520	11495.1800
	Defense	33,618	53.2569	43.1435	0.0000	160.8028
	Taxes	33,618	11.0544	10.5462	0.0000	37.5469
	Technology	33,618	11.9390	11.3854	0.0000	40.7162
	Institutions	33,618	15.4742	13.9334	0.0000	50.4216
	Trade	33,618	9.5023	10.5364	0.0000	37.7613
	Economic	33,618	38.3588	30.6710	0.0000	112.6610
	Health	33,618	25.5513	28.1983	0.0000	110.9970
	Environment	33,618	35.0210	34.4440	0.0000	129.8135
	Independent variables
	Cash holdings	33,618	-2.1394	1.8833	-13.2391	6.2695
	Size	33,618	6.4387	2.2120	1.0417	11.3891
	Leverage	33,618	0.3020	0.3466	0.0000	2.2654
	Capex	33,618	0.0692	0.0727	0.0009	0.3951
	Cash flows	33,618	-0.1103	0.8836	-6.4745	0.5061
	Net working capital	33,618	-0.0076	0.3387	-1.9877	0.5226
	R&D	33,618	0.1677	0.5692	0.0000	4.2428
	Market-To-Book	33,618	2.7134	4.3087	-13.7079	24.9807
	Acquisitions	33,618	0.0311	0.0739	-0.0062	0.4122
	Industry CF volatility	33,618	0.2550	0.3831	0.0032	1.8326
	Dividend	33,618	0.3850	0.4866	0.0000	1.0000
	Panel B: standardized variables
	Political Risk
	PRisk	33,618	-0.0803	0.5298	-0.6312	1.3596
	Policy	33,618	-0.0375	0.7982	-1.0674	1.8699
	Defense	33,618	-0.1657	0.5284	-0.6919	1.4321
	Taxes	33,618	-0.1304	0.8237	-0.9210	3.8416
	Technology	33,618	-0.0772	0.7148	-0.6104	3.6515

Table 3 -4. Correlations

 3

		(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)	(10)	(11)	(12)
	Cash holdings (1)	1.0000
	PRisk (2)	0.0794	1.0000
	Size (3)	-0.4804 -0.0252	1.0000
	Leverage (4)	-0.0380	0.0324	0.0436	1.0000
	Capex (5)	0.0277 -0.0242 -0.0306	0.0500	1.0000
	Cash flows (6)	-0.3815 -0.1131	0.4290 -0.2839 -0.1087	1.0000
	Net working capital (7)	-0.3329 -0.0885	0.2380 -0.3559 -0.1751	0.6315	1.0000
	R&D (8)	0.4865	0.1086 -0.4492	0.2116	0.1111 -0.8676 -0.6287	1.0000
	Market-To-Book (9)	0.1584	0.0044 -0.0601 -0.0448	0.0374 -0.0313 -0.0670	0.0896	1.0000
	Acquisitions (10)	-0.0741 -0.0284	0.0576 -0.0049 -0.1395	0.0818	0.0478 -0.0740	0.0137	1.0000
	Industry CF volatility (11)	0.3395	0.1264 -0.2500	0.1536 -0.0131 -0.4002 -0.2677	0.4647	0.1026 -0.0192	1.0000
	Dividend (12)	-0.2910 -0.0030	0.5072 -0.0254 -0.0508	0.1685	0.1212 -0.2025 -0.0017 -0.0088 -0.1308 1.0000

Table 3 -5. Main regression: The effect of firm-level political risk on corporate cash holdings

 3

		(1)	(2)	(3)	(4)	(5)
		OLS	OLS	FE	FE	Fama-
					clustering	MacBeth
		Cash	Cash holdings	Cash	Cash	Cash
	VARIABLES	holdings	(standardized)	holdings	holdings	holdings
	PRisk	0.0007***	0.0853***	0.0001**	0.0001*	0.0005***
		(0.00)	(0.00)	(0.05)	(0.07)	(0.00)
	Size	-0.1662***	-0.2848***	-0.6173***	-0.6173***	-0.2434***
		(0.00)	(0.00)	(0.00)	(0.00)	(0.00)
	Leverage	-0.4100***	-0.0243**	0.0368	0.0368	-0.6690***
		(0.00)	(0.04)	(0.14)	(0.31)	(0.00)
	Capex	1.6416***	0.0952***	0.7803***	0.7803***	-0.7594***
		(0.00)	(0.00)	(0.00)	(0.00)	(0.00)
	Cash flows	0.2801***	0.2917***	0.0840***	0.0840***	0.4397***
		(0.00)	(0.00)	(0.00)	(0.00)	(0.00)
	NWC	-0.8209***	-0.1162***	-0.1160***	-0.1160***	-0.8277***
		(0.00)	(0.00)	(0.00)	(0.01)	(0.00)
	R&D	1.0428***	0.3225***	0.2679***	0.2679***	1.2996***
		(0.00)	(0.00)	(0.00)	(0.00)	(0.00)
	Market-To-Book	0.0292***	-0.0038	0.0056***	0.0056***	0.0352***
		(0.00)	(0.13)	(0.00)	(0.00)	(0.00)
	Acquisitions	-1.3119***	-0.0459***	-0.6154***	-0.6154***	-0.9702***
		(0.00)	(0.00)	(0.00)	(0.00)	(0.00)
	Industry CF volatility	0.0632	0.0376**	0.1046**	0.1046*	0.7501***
		(0.36)	(0.02)	(0.01)	(0.05)	(0.00)
	Dividend	-0.1291***	-0.0224**	0.0693***	0.0693**	-0.3037***
		(0.00)	(0.04)	(0.00)	(0.02)	(0.00)
	Constant	-2.3725***		1.3172***	1.3172***	-0.6731***
		(0.00)		(0.00)	(0.00)	(0.00)
	Observations	33,618	33,618	33,618	33,618	33,618
	Year FE	YES	YES	YES	YES	NO
	Industry FE	YES	YES	NO	NO	NO
	Firm FE	NO	NO	YES	YES	NO
	Adjusted/Average R-	0.480	0.423	0.152	0.151	0.390
	squared

Table 3 -6. The effect of the different components of firm-level political risk on corporate cash holdings

 3

	VARIABLES	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)
	Panel A
	Defense	0.0003**								0.0000
		(0.01)								(0.88)
	Taxes		0.0011**							0.0003
			(0.03)							(0.64)
	Technology			0.0015***						0.0009*
				(0.00)						(0.09)
	Institutions				0.0012***					0.0008
					(0.00)					(0.13)
	Trade					0.0017***				0.0012**
						(0.00)				(0.04)
	Economic						0.0005**			-0.0000
							(0.01)			(0.96)
	Health							0.0004*		-0.0000
								(0.07)		(0.98)
	Environment								0.0002	-0.0002
									(0.20)	(0.26)
	Observations	33,618	33,618	33,618	33,618	33,618	33,618	33,618	33,618	33,618
	Control variables	YES	YES	YES	YES	YES	YES	YES	YES	YES
	Year FE	YES	YES	YES	YES	YES	YES	YES	YES	YES
	Industry FE	NO	NO	NO	NO	NO	NO	NO	NO	NO
	Firm FE	YES	YES	YES	YES	YES	YES	YES	YES	YES
	Adjusted R-squared	0.152	0.152	0.152	0.152	0.152	0.152	0.152	0.152	0.152
	Panel B
	Defense	0.0737***								0.1072***
		(0.00)								(0.00)
	Taxes		0.0417***							-0.0136
			(0.00)							(0.57)
	Technology			0.0490***						0.037**
				(0.00)						(0.04)
	Institutions				0.0391***					-0.0046
					(0.00)					(0.80)

Table 3 -7. Empirical model of cash flow retention

 3

	(1)	(2)
	FE	FE

Table 3 -8. Valuation of cash (the model of Dittmar and Mahrt-Smith, 2007)

 3

	VARIABLES

Table 3 -9. Valuation of cash (the model of Pinkowitz and Williamson, 2002)

 3 This table reports the results of cash valuation, similar to the model of

	VARIABLES

Table 3 -10. Political risk and cash channeling

 3

		(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(10)
	VARIABLES	Δ Capex	Δ R&D	Δ Acquisitions Δ NWC Δ Short debt Δ Long debt Δ Equity Δ Dividend Δ Purchase
	Panel A
	PRisk	-0.0000***	-0.0001*	0.0000	0.0001**	-0.1294*	0.1190	-0.1862*	-0.0004	0.1118
		(0.00)	(0.06)	(0.35)	(0.03)	(0.09)	(0.36)	(0.08)	(0.10)	(0.33)
	Observations	26,907	26,907	26,907	26,907	13,001	13,331	11,746	13,254	12,056
	Control variables	YES	YES	YES	YES	YES	YES	YES	YES	YES
	Year FE	YES	YES	YES	YES	YES	YES	YES	YES	YES
	Industry FE	NO	NO	NO	NO	NO	NO	NO	NO	NO
	Firm FE	YES	YES	YES	YES	YES	YES	YES	YES	YES
	Adjusted R-squared	0.0211	0.0403	0.00524	0.0591	0.00346	0.0123	0.010	0.00163	0.00788
	Panel B
	PRisk	-0.0418***	-0.0501***	0.0245*	0.0326**	-0.0091	0.0435***	-0.0102	-0.0008	0.0036
		(0.00)	(0.00)	(0.09)	(0.02)	(0.30)	(0.00)	(0.37)	(0.94)	(0.74)
	Observations	26,907	26,907	26,907	26,907	26,202	26,907	23,822	26,907	24,391
	Control variables	YES	YES	YES	YES	YES	YES	YES	YES	YES
	Year FE	YES	YES	YES	YES	YES	YES	YES	YES	YES
	Industry FE	YES	YES	YES	YES	YES	YES	YES	YES	YES
	Firm FE	NO	NO	NO	NO	NO	NO	NO	NO	NO
	Adjusted R-squared	0.0126	0.143	0.00285	0.132	0.00152	0.0473	0.00371	0.002	0.00437
	This

Table 3 -11. Additional tests

 3

	(1)	(2)	(3)

Table 4 -

 4 1 shows means, standard deviations and other descriptive statistics for main regression variables covering the period spanning 2002 throughout 2016. The reported statistics show that all firm characteristics are within reasonable levels and are in line

Table 4 -1. Descriptive statistics for main regression variables

 4 This table presents summary statistics for the variables used in our main regressions. The sample comprises 20,140 observations of 3,095 unique U.S. firms and for the period spanning 2002 through 2016. The list of variables definitions and sources are provided in section 3 of this chapter and in the Appendix 4-1.

	Variable	Obs	Mean	Std. Dev.	Min	Max
	PRisk	20,140	1.5428	1.1420	0.1257	4.0710
	ln_TOTR	20,140	-3.5812	0.4687	-5.0896	-0.9884
	ln_IDIOSR	20,140	-3.7236	0.5181	-5.2148	-1.0732
	ln_SYSTR	20,140	-4.5506	0.5975	-7.9481	-1.9253
	Lsize	20,140	6.5980	2.0128	2.8209	12.7745
	Lroa	20,140	-0.0275	0.2077	-0.8731	0.2616
	Lmb	20,140	2.2461	1.4967	0.6755	8.1851
	Lev	20,140	0.1870	0.2042	0	0.9640
	Netcapex	20,140	0.0309	0.0397	0	0.2633
	RD	20,140	0.0862	0.1254	0	0.7315

Table 4 -

 4 2 presents Pearson correlation coefficients between the explanatory

Table 4 -2. Correlation matrix

 4 This table provides correlation coefficients between firm-level political risk, managerial risk-taking and other control variables. The sample comprises 20,140 observations of 3,095 unique U.S. firms and for the period spanning 2002 through 2016. The list of variables definitions and sources are provided in the Appendix 4-1. Numbers in Bold indicates statistical significance at the 1% level.

		(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)	(10)
	PRisk (1)	1.0000
	ln_TOTR (2)	0.0789	1.0000
	ln_IDIOSR (3)	0.0682	0.9745	1.0000
	ln_SYSTR (4)	0.0538	0.3880	0.2276	1.0000
	Lsize (5)	-0.0076	-0.5901	-0.6632	0.1352	1.0000
	Lroa (6)	-0.0532	-0.5080	-0.5308	-0.0095	0.4274	1.0000
	Lmb (7)	0.0531	0.0337	0.0560	-0.0050	-0.2024	-0.1243	1.0000
	Lev (8)	-0.0237	-0.0854	-0.0993	0.0324	0.3026	-0.0589	-0.0796	1.0000
	Netcapex (9)	-0.0157	-0.0050	0.0102	-0.0039	-0.0175	0.0959	0.1110	-0.0031	1.0000
	RD (10)	0.0874	0.3725	0.3975	-0.0020	-0.4276	-0.6028	0.3182	-0.1046	-0.0795	1.0000

Table 4 -3. Main regressions: effect of political risk on firm-risk taking

 4 This table provides the results of the regressions of managerial risk-taking on firm-level political risk and other firm characteristics using different estimation techniques: Ordinary Least Squares and Fixed Effect Model. The list of variables definitions and sources are provided in the section 3 of this chapter and the Appendix 4-1. P-values are reported in parentheses. t-values are based on robust standard errors adjusted for clustering by firm. *, ** and *** refer to significance at the 10%, 5% and 1% levels, respectively.

			Ordinary Least Squared Model		Fixed Effect Model
	VARIABLES	Total risk	Idiosyncratic risk	Systematic risk	Total risk	Idiosyncratic risk	Systematic risk
	PRisk	0.0116***	0.0126***	-0.0002	0.0077***	0.0071***	0.0013
		(0.00)	(0.00)	(0.97)	(0.00)	(0.00)	(0.63)
	Lsize	-0.1169***	-0.1510***	0.0690***	-0.0804***	-0.1123***	0.1212***
		(0.00)	(0.00)	(0.00)	(0.00)	(0.00)	(0.00)
	Lroa	-0.5748***	-0.6143***	-0.1582***	-0.3063***	-0.3313***	-0.0432
		(0.00)	(0.00)	(0.00)	(0.00)	(0.00)	(0.17)
	Lmb	-0.0064**	-0.0166***	0.0660***	0.0052*	-0.0049*	0.0642***
		(0.01)	(0.00)	(0.00)	(0.05)	(0.08)	(0.00)
	Lev	0.2120***	0.2411***	0.0604*	0.2086***	0.2391***	0.0444
		(0.00)	(0.00)	(0.08)	(0.00)	(0.00)	(0.22)
	Netcapex	0.2143**	0.3259***	0.1451	-0.4345***	-0.5067***	-0.0920
		(0.01)	(0.00)	(0.29)	(0.00)	(0.00)	(0.47)
	RD	-0.0659*	-0.0979**	0.0284	-0.2123***	-0.2139***	-0.2102***
		(0.07)	(0.01)	(0.68)	(0.00)	(0.00)	(0.00)
	Constant	-2.4684***	-2.3085***	-5.0978***	-2.6565***	-2.5400***	-5.0780***
		(0.00)	(0.00)	(0.00)	(0.00)	(0.00)	(0.00)
	Observations	20,140	20,140	20,140	20,140	20,140	20,140
	R-squared	0.671	0.683	0.427	0.582	0.505	0.543
	# of gvkey				3,095	3,095	3,095
	Year FE	YES	YES	YES	YES	YES	YES
	Industry FE	YES	YES	YES	NO	NO	NO
	Cluster by Firm	YES	YES	YES	YES	YES	YES
	Firm FE	NO	NO	NO	YES	YES	YES
	Adjusted R-squared	0.667	0.679	0.421

Table 4 -4. Empirical evidence on the managerial risk-taking adjustment to political risk over time

 4

		(1)	(2)	(3)	(4)	(5)	(6)
		Total risk	Idiosyncratic risk	Systematic risk	Total risk	Idiosyncratic risk	Systematic risk
	VARIABLES				(t+3)	(t+3)	(t+3)
	PRisk	0.0075***	0.0081***	-0.0004	-0.0043**	-0.0032*	-0.0040
		(0.00)	(0.00)	(0.90)	(0.03)	(0.09)	(0.15)
	Lag1(PRisk)	0.0035*	0.0044*	-0.0025
		(0.09)	(0.05)	(0.40)
	Lag2(PRisk)	-0.0015	0.0001	-0.0007
		(0.48)	(0.96)	(0.81)
	Lag3(PRisk)	-0.0036**	-0.0024*	-0.0018
		(0.04)	(0.08)	(0.54)
	Lag4(PRisk)	-0.0005	0.0024	-0.0033
		(0.82)	(0.29)	(0.27)
	Lsize	-0.0648***	-0.0933***	0.1118***	-0.0085	-0.0115*	0.0055
		(0.00)	(0.00)	(0.00)	(0.15)	(0.07)	(0.51)
	Lroa	-0.2677***	-0.3122***	-0.0016	-0.0412**	-0.0526***	-0.0175
		(0.00)	(0.00)	(0.95)	(0.01)	(0.00)	(0.47)
	Lmb	0.0064**	-0.0046	0.0579***	-0.0108***	-0.0132***	0.0028
		(0.03)	(0.14)	(0.00)	(0.00)	(0.00)	(0.41)
	Lev	0.1774***	0.2093***	0.0569*	0.0668***	0.0779***	0.0998***
		(0.00)	(0.00)	(0.07)	(0.00)	(0.00)	(0.00)
	Netcapex	-0.5046***	-0.5660***	-0.0674	0.0225	0.0412	-0.0308
		(0.00)	(0.00)	(0.61)	(0.77)	(0.63)	(0.78)
	RD	-0.1885***	-0.1927***	-0.2798***	0.2051***	0.2670***	-0.1086*
		(0.00)	(0.00)	(0.00)	(0.00)	(0.00)	(0.08)
	Constant	-3.3832***	-3.2218***	-5.7799***	-3.6618***	-3.7158***	-4.8207***
		(0.00)	(0.00)	(0.00)	(0.00)	(0.00)	(0.00)
	Observations	9,738	9,738	9,738	11,892	11,892	11,892
	R-squared	0.669	0.556	0.675	0.622	0.497	0.636
	# of gvkey	1,720	1,720	1,720	2,059	2,059	2,059
	Year FE	YES	YES	YES	YES	YES	YES
	Industry FE	NO	NO	NO	NO	NO	NO
	Firm FE	YES	YES	YES	YES	YES	YES

Table 4 -5. Effects of different components of political risk on total managerial risk-taking

 4 the results of the regressions of total managerial risk-taking on the different components of firm-level political risk and other firm characteristics using Fixed Effect Model. The list of variables definitions and sources are provided in the section 3 of this chapter and the Appendix 4-1. P-values are reported in parentheses. t-values are based on robust standard errors adjusted for clustering by firm. *, ** and *** refer to significance at the 10%, 5% and 1% levels, respectively.

		(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)
	VARIABLES	Total risk	Total risk	Total risk	Total risk	Total risk	Total risk	Total risk	Total risk	Total risk
	Tax policy	0.0049***								-0.0010
		(0.00)								(0.63)
	Institutional risk		0.0094***							0.0058***
			(0.00)							(0.00)
	Security/Defense			0.0065***						-0.0015
				(0.00)						(0.50)
	Environment				0.0087***					0.0049**
					(0.00)					(0.03)
	Trade					0.0029*				-0.0031
						(0.09)				(0.11)
	Economic policy						0.0086***			0.0036**
							(0.00)			(0.04)
	Health care							0.0069***		0.0014
								(0.00)		(0.50)
	Technology								0.0073***	0.0027
									(0.00)	(0.18)
	Constant	-2.6498***	-2.6532***	-2.6542***	-2.6530***	-2.6479***	-2.6537***	-2.6520***	-2.6523***	-2.6575***
		(0.00)	(0.00)	(0.00)	(0.00)	(0.00)	(0.00)	(0.00)	(0.00)	(0.00)
	Observations	20,081	20,109	20,133	20,126	20,066	20,124	20,130	20,093	19,999
	R-squared	0.582	0.583	0.582	0.582	0.582	0.582	0.582	0.582	0.583
	# of gvkey	3,060	3,075	3,089	3,085	3,048	3,085	3,086	3,066	3,011
	Controls	YES	YES	YES	YES	YES	YES	YES	YES	YES
	Year FE	YES	YES	YES	YES	YES	YES	YES	YES	YES
	Cluster by Firm	YES	YES	YES	YES	YES	YES	YES	YES	YES
	Firm FE	YES	YES	YES	YES	YES	YES	YES	YES	YES
	This table provides

Table 4 -6. Evidence on components of political risk and managerial risk-taking adjustment

 4 the results of the regressions of total managerial risk-taking on the lagged values of different components of firm-level political risk and other firm characteristics using Fixed Effect Model. The list of variables definitions and sources are provided in the section 3 of this chapter and the Appendix 4-1. P-values are reported in parentheses. t-values are based on robust standard errors adjusted for clustering by firm. *, ** and *** refer to significance at the 10%, 5% and 1% levels, respectively.

		(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)
	VARIABLES	Total risk	Total risk	Total risk	Total risk	Total risk	Total risk	Total risk	Total risk	Total risk
	Lag3(Tax policy)	-0.0004								0.0003
		(0.84)								(0.91)
	Lag3(Institutional risk)		-0.0007							0.0003
			(0.73)							(0.92)
	Lag3(Security/Defense)			-0.0011						0.0002
				(0.59)						(0.95)
	Lag3(Environment)				-0.0040**					-0.0055**
					(0.05)					(0.03)
	Lag3(Trade)					-0.0016**				-0.0011**
						(0.04)				(0.04)
	Lag3(Economic policy)						-0.0001			0.0024
							(0.96)			(0.43)
	Lag3(Health care)							-0.0001		0.0015
								(0.97)		(0.52)
	Lag3(Technology)								-0.0014*	-0.0005
									(0.09)	(0.82)
	Constant	-3.2515***	-3.2512***	-3.2501***	-3.2463***	-3.2509***	-3.2517***	-3.2518***	-3.2506***	-3.2497***
		(0.00)	(0.00)	(0.00)	(0.00)	(0.00)	(0.00)	(0.00)	(0.00)	(0.00)
	Observations	11,889	11,891	11,892	11,892	11,886	11,891	11,892	11,887	11,878
	R-squared	0.644	0.644	0.644	0.644	0.644	0.644	0.644	0.644	0.644
	# of gvkey	2,056	2,058	2,059	2,059	2,056	2,058	2,059	2,057	2,051
	Controls and Lags	YES	YES	YES	YES	YES	YES	YES	YES	YES
	Year FE	YES	YES	YES	YES	YES	YES	YES	YES	YES
	Cluster by Firm	YES	YES	YES	YES	YES	YES	YES	YES	YES
	Firm FE	YES	YES	YES	YES	YES	YES	YES	YES	YES
	This table provides

Table 4

 4

	-7 presents analyses of

Table 4 -7. Analysis of equity-based compensation effect

 4

		Total risk	Idiosyncratic risk	Systematic risk
	VARIABLES	(1)	(2)	(1)	(2)	(1)	(2)
	PRisk	0.0065***	0.0074***	0.0054***	0.0072***	0.0012	0.0003
		(0.00)	(0.00)	(0.00)	(0.00)	(0.56)	(0.63)
	Delta	-0.2166***		-0.1930***		-0.0011*
		(0.00)		(0.00)		(0.07)
	Vega		0.4155***		0.6221***		0.0088*
			(0.00)		(0.00)		(0.09)
	PRisk x Delta	0.0023***		0.0017***		-0.0048
		(0.00)		(0.00)		(0.14)
	PRisk x Vega		0.0043***		0.0057***		0.0343
			(0.00)		(0.00)		(0.21)
	Constant	-2.8730***	-2.3088***	-2.6776***	-2.4481***	-4.9869***	-5.0022***
		(0.00)	(0.00)	(0.00)	(0.00)	(0.00)	(0.00)
	Observations	20,140	20,140	20,140	20,140	20,140	20,140
	R-squared	0.776	0.781	0.715	0.717	0.431	0.411
	Year FE	YES	YES	YES	YES	YES	YES
	Cluster by Firm	YES	YES	YES	YES	YES	YES
	Firm FE	YES	YES	YES	YES	YES	YES
	This table

Table 4 -8. Effects of internationalization

 4

		Total risk	Idiosyncratic risk	Systematic risk
	VARIABLES	(1)	(2)	(3)	(4)	(5)	(6)
	PRisk	0.0174***	0.0312***	0.0181**	0.0318***	0.0169**	0.0176
		(0.00)	(0.00)	(0.01)	(0.00)	(0.04)	(0.26)
	Segments	-0.0042***		-0.0056***		0.0042***
		(0.00)		(0.00)		(0.00)
	PRisk x Segments	-0.0014***		-0.0018***		-0.0012**
		(0.00)		(0.00)		(0.02)
	MNC		-0.1418***		-0.1740***		0.0124
			(0.00)		(0.00)		(0.72)
	PRisk x MNC		-0.0346***		-0.0386***		-0.0157
			(0.00)		(0.00)		(0.34)
	Constant	-3.6179***	-3.5443***	-3.7791***	-3.6980***	-4.9709***	-4.9347***
		(0.00)	(0.00)	(0.00)	(0.00)	(0.00)	(0.00)
	Observations	12,672	12,672	12,672	12,672	12,672	12,672
	R-squared	0.404	0.413	0.350	0.358	0.475	0.474
	Controls	YES	YES	YES	YES	YES	YES
	Year FE	YES	YES	YES	YES	YES	YES
	Cluster by Firm	YES	YES	YES	YES	YES	YES
	Firm FE	YES	YES	YES	YES	YES	YES
	This table

Table 4 -9. Effect of financial constraints

 4

		(1)	(2)	(3)
	VARIABLES	Total risk	Idiosyncratic risk	Systematic risk
	PRisk	0.0087***	0.0084**	0.0044
		(0.01)	(0.03)	(0.30)
	KZ	-0.0000***	-0.0000***	-0.0000*
		(0.00)	(0.00)	(0.09)
	PRisk x KZ	0.0000***	0.0000***	0.0000**
		(0.00)	(0.00)	(0.04)
	Constant	-3.5429***	-3.7311***	-4.2389***
		(0.00)	(0.00)	(0.00)
	Observations	18,372	18,372	18,372
	R-squared	0.394	0.347	0.391
	Controls	YES	YES	YES
	Year FE	YES	YES	YES
	Cluster by Firm	YES	YES	YES
	Firm FE	YES	YES	YES
	This

Table 4 -10. Alternative measures for firm risk-taking

 4 the results of the regressions of alternative measures of managerial risk-taking on firm-level political risk and other firm characteristics using Fixed Effect Model. The list of variables definitions and sources are provided in the section 3 of this chapter and the Appendix 4-1. P-values are reported in parentheses. t-values are based on robust standard errors adjusted for clustering by firm. *, ** and *** refer to significance at the 10%, 5% and 1% levels, respectively.

		Fama and French 3 Factor Model	Fama and French 5 Factor Model	St. Dev.
	VARIABLES	Idiosyncratic risk Systematic risk Idiosyncratic risk Systematic risk	ROA
	PRisk	0.0002***	0.0001*	0.0002***	0.0001**	0.0032**
		(0.00)	(0.08)	(0.00)	(0.05)	(0.01)
	Constant	0.0379***	0.0210***	0.0368***	0.0226***	0.1536***
		(0.00)	(0.00)	(0.00)	(0.00)	(0.00)
	Observations	20,140	20,140	20,140	20,140	14,147
	R-squared	0.310	0.567	0.306	0.561	0.078
	# of gvkey	3,095	3,095	3,095	3,095	2,331
	Controls	YES	YES	YES	YES	YES
	Year FE	YES	YES	YES	YES	YES
	Cluster by Firm	YES	YES	YES	YES	YES
	Firm FE	YES	YES	YES	YES	YES
	This table provides

Table 4 -11. Additional controls

 4

		(1)	(2)	(3)	(4)
	VARIABLES	Total risk	Total risk	Total risk	Total risk
	PRisk	0.0058***	0.0074***	0.0077***	0.0054***
		(0.00)	(0.00)	(0.00)	(0.00)
	Lsize	-0.0803***	-0.1125***	-0.0804***	-0.0806***
		(0.00)	(0.00)	(0.00)	(0.00)
	Lroa	-0.3060***	-0.2707***	-0.3063***	-0.3032***
		(0.00)	(0.00)	(0.00)	(0.00)
	Lmb	0.0053**	-0.0147***	0.0052*	0.0053**
		(0.05)	(0.00)	(0.05)	(0.05)
	Lev	0.2089***	0.1770***	0.2084***	0.2082***
		(0.00)	(0.00)	(0.00)	(0.00)
	Netcapex	-0.4307***	-0.3613***	-0.4346***	-0.4377***
		(0.00)	(0.00)	(0.00)	(0.00)
	RD	-0.2118***	-0.1473***	-0.2124***	-0.2141***
		(0.00)	(0.00)	(0.00)	(0.00)
	Non-PRisk	0.0063***
		(0.00)
	Beta		0.2905***
			(0.00)
	Saleherf			-0.0078
				(0.78)
	E-index				0.0104***
					(0.00)
	Constant	-2.6658***	-2.8210***	-2.6509***	-2.7520***
		(0.00)	(0.00)	(0.00)	(0.00)
	Observations	20,140	20,140	20,140	20,140
	R-squared	0.583	0.678	0.582	0.580
	Number of gvkey	3,095	3,095	3,095	3,095
	Year FE	YES	YES	YES	YES
	Cluster by Firm	YES	YES	YES	YES
	Firm FE	YES	YES	YES	YES
	This table provides the

Table 4 -12. Additional evidence on managerial risk-taking adjustment

 4

		(1)	(2)	(3)	(4)
	VARIABLES	Total risk	Total risk	Total risk	Total risk
	Lag1(PRisk)	0.0039**
		(0.02)
	Lag2(PRisk)		-0.0013
			(0.45)
	Lag3(PRisk)			-0.0044**
				(0.02)
	Lag4(PRisk)				-0.0008
					(0.69)
	Constant	-2.6846***	-3.0880***	-3.2450***	-3.3794***
		(0.00)	(0.00)	(0.00)	(0.00)
	Observations	16,888	14,169	11,892	9,900
	R-squared	0.595	0.617	0.644	0.667
	Number of gvkey	2,672	2,332	2,059	1,769
	Controls	YES	YES	YES	YES
	Year FE	YES	YES	YES	YES
	Firm FE	YES	YES	YES	YES
	Cluster by Firm	YES	YES	YES	YES
	This table provides

https://hbr.org/2016/01/the-biology-of-corporate-survival

https://www.globaltradealert.org/

In the literature, the terms "political risk," "political uncertainty," and "geopolitical risk" are synonymously used.

See also, Nehrt (1967).

It is worth mentioning that sometimes the biggest political risks are "within the firm risks" emanating from the culture and practices of the company itself. Uber, for instance, hurt itself with their toxic culture towards its female employees. Uber founder and CEO was forced out after a former employee wrote a

The peso problem in finance is an issue raised when infrequent discrete event happening can affect the behavior of asset prices in a way that contradicts rational and conventional predictions.

Words such as tax, cost, capital, board, liability, foreign, and vice are on the Harvard H4N negative list while they are not typically negative in a financial context.

To measure risk, one should dispose of two components: likelihood and impact. Usually, risk assessment is based on historical data. This can sometimes lead to misjudgment as some outliers are not taken into account. After the 2008 Financial Crisis, Warren Buffet wrote: "Investors should be skeptical of historybased models… Beware of geeks bearing formulas."

An example in the legislation can be the 2005 Bankruptcy Abuse Prevention and Consumer Protection Act described inHart and Zingales (2012, chapter 4). In the words of one legal scholar: "Never before in our history has such a well-organized, well-orchestrated, and well-financed campaign been run to change the balance of power between creditors and debtors."

http://www.pwc.com/gx/en/ceo-survey/2013/pdf/us-ceo-survey-2013.pdf

This study is explorative and while it proposes sound and reliable reasons why political connections should matter from a tax planning perspective, it got some critics by[START_REF] Faccio | Discussion of "Corporate Political Connections and Tax Aggressiveness[END_REF].

Copyright is a form of intellectual property right protection provided by the laws of the United States (title 17, U.S. Code) to the authors of "original works of authorship," including literary, dramatic, musical, artistic, and certain other intellectual works. This protection is available to both published and unpublished works. Section 106 of the 1976 Copyright Act generally gives the owner of copyright (among other things) the exclusive right to reproduce the work in copies or phonorecords, to prepare derivative works based upon the work, to distribute copies or phonorecords of the work to the public by sale or other transfer of ownership, to rent, lease, or lend the work, and to perform the work publicly. The copyright for an individual creator like an artist lasts for the life of the creator plus 50 years. For a work owned by a corporation, the duration is 75 years from the date of publication or 100 years from the date creation. Whatever comes first.

This chapter presents the various definitions of political risk. Moreover, it reviews the theoretical and empirical literature related to the consequences of political uncertainty. This vast literature remains however inconclusive, indicating that political risk can only be understood by considering the range of interactions between cultural, political, social and economic factors. The understanding must also take into account the configuration between these factors and other influences, including knowledge of the region or the country, to better guage these connections and their evolution over time.

See, appendix 2-2 for more details.

The political text of the libraries used inHassan, Hollander, van Lent, and Tahoun (2019) are an undergraduate political science textbook and text from the political section of newspapers. The text of training library of non-political language is from an accounting textbook, text from non-political sections of newspapers, and transcripts of speeches on non-political topics.

Difficulties and costs of accessing the financial markets can be either explicit or implicit. Explicit costs encompass flotation costs and tax consequences. Implicit costs are related to asymmetric information, underpricing, and time.

See, appendix 3-2 for more details.

The political text of the libraries used inHassan, Hollander, van Lent, and Tahoun (2019) are an undergraduate political science textbook and text from the political section of newspapers. The text of training library of non-political language is from an accounting textbook, text from non-political sections of newspapers, and transcripts of speeches on non-political topics.

We also use theFama and French (1997) 48 industry categories instead and it does not impact our inferences.

In this study, we remove financial firms because of their statutory capital requirements and other regulatory restrictions. However, we find that the industry time series averages of political risk are the highest for financial sectors, when we include financial firms again.

We also use the time varying Text-based Network Industry Classification (TNIC) and our results remain qualitatively and quantitively the same.

An increase in the HH index generally indicates a decrease in competition and an increase of market power, whereas a decrease indicates the opposite.

See also, the World Economic Forum's Global Risks Report 2019.

Usually, higher Vega are associated with increased investment in R&D and reduced investment in property, plant and equipment when it comes to investment policies.

Firm risk summarizes the net effect of all managerial risk-taking activities, including some that cannot be easily measured by econometricians, and thus provides a more accurate portrayal of managerial risktaking behavior.

For example, we control for firm profitability (ROA) since it has been shown that high ROA volatility may potentially emanate from poor managerial ability rather than risk-taking choices.

To account for E-index as an indicator of the strength of corporate governance, we sum the 6-provision subset of the G-Index. The index ranges from a feasible low of 0 to a high of 6; a high score is associated with weak shareholder rights and a low score is associated with high shareholder rights.

Acknowledgments

I place on record my deepest and sincerest gratitude to my thesis supervisor, Professor Emmanuelle Dubocage, without whom this thesis would not have been possible. I am truly appreciative of her great support and guidance throughout the process of this PhD for her quality as a professor as well as for her thoughtfulness and kindness.

I would like to thank the jury members Professors Fabrice Hervé, Jérôme Glachant, Bouchra M'zali and Vincent Bouvatier for generously offering their time, support,

La Porta, R., Lopez-de-Silanes, F., Shleifer, A., & Vishny, R. W. (1997). Legal determinants of external finance. The journal of finance, 52(3), 1131-1150.

Levine, R., Lin, C., & Xie, W. (2018). Corporate resilience to banking crises: the roles of trust and trade credit. Journal of Financial and Quantitative Analysis, 53(4), 1441-1477. Levine, R., Lin, C., & Xie, W. (2018). Corporate resilience to banking crises: the roles of trust and trade credit. Journal of Financial and Quantitative Analysis, 53(4), 1441-1477. Levy, A., & Hennessy, C. (2007). Why does capital structure choice vary with macroeconomic conditions? Journal of monetary Economics, 54(6), 1545-1564. Love, I., & Zaidi, R. (2010). Trade credit, bank credit and financial crisis. International Review of Finance, 10(1), 125-147. Manning, C. D., & Schutze, P. et-al. 2008. Introduction to Information Retrieval. Cambridge University Press, New York, NY, USA, 3, 38-48. McMillan, J., & Woodruff, C. (1999). Interfirm relationships and informal credit in Vietnam. The Quarterly Journal of Economics, 114(4), 1285-1320. Meltzer, A. H. (1960). Mercantile credit, monetary policy, and size of firms. The Review of Economics and Statistics, 429-437. Mian, S. L., & Smith Jr, C. W. (1992). Accounts receivable management policy: theory and evidence. The Journal of Finance, 47(1), 169-200. Ng, C. K., Smith, J. K., & Smith, R. L. (1999). Evidence on the determinants of credit terms used in interfirm trade. The journal of finance, 54(3), 1109-1129. Nguyen, N. H., & Phan, H. V. (2017). Policy uncertainty and mergers and acquisitions. Journal of Financial and Quantitative Analysis, 52(2), 613-644. Pastor, L., & Veronesi, P. (2012). Uncertainty about government policy and stock prices. The journal of Finance, 67(4), 1219-1264. 110 Pástor, L., & Veronesi, P. (2012). Uncertainty about government policy and stock prices. The journal of Finance, 67(4), 1219-1264. Pástor, Ľ., & Veronesi, P. (2013). Political uncertainty and risk premia. Journal of Financial Economics, 110(3), 520-545. Petersen, M. A., & Rajan, R. G. (1997). Trade credit: theories and evidence. The Review of Financial Studies, 10(3), 661-691. Putnam, R. (2001). Social capital: Measurement and consequences. Canadian journal of policy research, 2(1), 41-51. Roberts, M. R., & Whited, T. M. (2013). Endogeneity in empirical corporate finance1. In Handbook of the Economics of Finance (Vol. 2, pp. 493-572). Elsevier. Rodrik, D. (1991). Policy uncertainty and private investment in developing countries. Journal of Development Economics, 36(2), 229-242. Smith, J. K. (1987). Trade credit and informational asymmetry. The journal of finance, 42(4), 863-872. Song, M., & Wu, Y.B. (2008). Handbook of research on text and web mining technologies. IGI global. Verrecchia, R. E. (1983). Discretionary disclosure. Journal of accounting and economics, 5, 179-194. Verrecchia, R. E., & Weber, J. (2006). Redacted disclosure. Journal of Accounting Research, 44(4), 791-814. Whited, T. M., & Wu, G. (2006). Financial constraints risk. The Review of Financial Studies, 19(2), 531-559. Wu, W., Firth, M., & Rui, O. M. (2014). Trust and the provision of trade credit. Journal of Banking & Finance, 39,[146][147][148][149][150][151][152][153][154][155][156][157][158][159]

List of Appendices

Appendix 1-1. Components, subcomponents and political risk scores according to the Howell ( 2011

General Introduction

Risk is inherent in nature. The assessment, management and mitigation of risk or the adaptation to uncertainty in our environment is at the heart of the evolutionary imperative and it is the core stimulus to survival. On an arguably more positive note, an astute understanding of the nature of uncertainty also presents opportunities for better outcomes.

Business and politics are pervasively interconnected and sometimes no different.

The understanding and management of risk in the economy or in business at the level of the firm, whether intuitive or more cognitive is fundamental to economic survival, or prosperity. Expressed from a different perspective, the judicious assessment, indeed exploitation of risk can lead on to fortune. Miscalculations, on the other hand, can be disastrous.

The above propositions are broadly well understood, researched, and documented, not the least in finance and economic literature. However, even within the business context, risk has many dimensions and is often complex. Within the range of risks associated with the deployment of economic resources and on their returns, the more commonly understood forms of risks such as those relating to market performance, credit allocations, financial structures, competition, inflation and even exchange rates generally have a wide body of theoretical and empirical literature that informs the understanding and management of these uncertainties by both academics and market practitioners. Less well understood and certainly less well managed is the subject of political risk. (1960) who shows that it is undoubtedly easier for large firms to obtain non-bank funds and with Almeida, Campello, and Weisbach (2004) and [START_REF] Barrot | Trade credit and industry dynamics: Evidence from trucking firms[END_REF] who state that small firms are more vulnerable to capital market imperfections. Capex is also positively related to trade credit indicating that firms with higher capital expenditures tend to offer more sales on credit. The coefficients on profitability (ROA), Cash, and growth (MB) are negative and significant at the 1% level, suggesting that firms that generate and hold less internal cash or that face a decline in sales tend to extend more credit, in line with the idea that firms in financial trouble may extend more trade credit to preserve sales and the idea that customers are reluctant to repay financially troubled suppliers (Petersen and Rajan, 1997). Tangibility loads significantly negative, consistent with [START_REF] Giannetti | What you sell is what you lend? Explaining trade credit contracts[END_REF]. Our base line model has a value of an adjusted R2 of 0.373, like in prior literature for the U.S., which indicates that our baseline model is valid.

In specification (2), we compute the standard errors using the [START_REF] Fama | Risk, return, and equilibrium: Empirical tests[END_REF] procedure to mitigate concerns about cross-sectional dependence. The coefficient on the firm-level political risk is positive and significant at the 1% threshold.

In addition, the positive impact of PRisk on trade credit provision continues to hold when we account for serial correlation of standard errors under the Newey-West specification in Column (3). Our results remain qualitatively the same after using the Fixed Effects Model in specification (4) to control for the average differences across firms in any observable and unobservable characteristics and to soak up all the cross-group actions.

Overall, our results lend support to the trade credit substitution effect associated with the comparative advantages it offers over other sources of external lending.

Effects of different dimensions of political risk

Hassan, Hollander, van Lent, and Tahoun (2019) define eight major sources that construct the firm-level political risk measure. These different sources are: taxes, institutions, security and defense, environment, trade, economic policy, health and technological disruptions. We check whether our findings on the relation between political risk and trade credit are driven by one specific source of risk. To do so, in Table 2-6 we decompose the main aggregate measure of political risk into the eight different measures, each accounting for a perceived risk related to one of the latter dimensions.

This allows us to mitigate concerns stemming from unrealistic assumptions in each single model that may produce spurious inferences and to make sure that the effect of political risk on trade credit is not driven by the dominance of one dimension.

We show that the positive effect of political risk on trade credit is rooted in different sources, mainly taxes, institutions, environment, economic policy and health. Overall, we find that our main results continue to go through even when we run the horserace regression where we include all the dimensions together.

Effects of change in political risk and prior period effect

The evidence provided so far shows that political risk in a given period affects concurrent trade credit policy to alleviate simultaneity concerns. The question that can be raised here is whether the impact of political risk on trade credit provision extends beyond the current period. Thus, as an additional test, we re-estimate our baseline regression but where we replace all time-variant independent variables by their lagged measures of the previous year-end. In Table 2-7, specifications 1 and 2 put forth evidence that the impact of high political risk on trade credit extends beyond the current period since the coefficient on the lagged PRisk is significantly positive.

Given that previous year's political uncertainty affects this year's interfirm credit, we further investigate how yearly variations in political risk affects firms' use of trade credit. To do so, we include the percentage change in the levels of firm-level political risk relative to the previous year in the baseline model. We present the results of our analysis in specifications 3 and 4 of Table 234567. We find positive and significant coefficients on the PRisk variable at the previous-year end and on the percentage change of PRisk. These results indicate that firms adjust their trade credit provision quickly in response to changes in political uncertainty. In addition, trade credit reacts to both the level and changes in risk embedded in politics.

external sources of financing. In this sub-section, we investigate the moderating effect of competition, financial constraints, and social trust on the relationship between firm-level political risk and trade credit provision.

▪ The role of competition A potential explanation for the moderating role of competition is that political risk effect may not be the same across firms with different sensitivities to competition. In periods of high political risk, information asymmetry becomes higher and access to external sources of financing becomes costly. Verrecchia (1983) and [START_REF] Dedman | Perceived competition, profitability and the withholding of information about sales and the cost of sales[END_REF], among others, point out that competition exacerbates informational asymmetries, since it is considered a deterring force against transparency. Firms operating in environments with stiffer competition may experience even more stringent and costly access to external funds. Moreover, firms are more reluctant to disclose information to the public or to other financing institutions (e.g., Verrecchia and Weber, 2006;[START_REF] Bagnoli | Oligopoly, disclosure, and earnings management[END_REF]. Following this reasoning, we expect that the effect of political risk on trade credit to be more pronounced in more competitive industries and that the comparative advantages that trade credit offers over other sources of financing are more relevant in the context of high competition.

To test the competition moderating effect, we focus on the Herfindahl-Hirschman Index (HHI). The HHI is defined as the sum of the squared market shares of all firms operating in each industry. We report our findings in Column 1 and Column 2 of Table 2-9. In these regressions, we include our proxy for competition exposure as well as interaction terms with the firm-level political risk variable. Consistent with the reasoning developed above, we find that the positive relation between political risk and the proportion of trade credit provision is stronger for firms having a weaker market position. The idea is that, unlike firms with strong market power, firms having disadvantaged positions regarding their rivals' competitive threats are more likely to face harder access to financing markets which makes them more sensitive to the effect of political risk.

political crises, social trust would play a positive moderating role on the effect of political risk on the use of trade credit.

We follow prior studies (e.g., [START_REF] Knack | Does social capital have an economic payoff? A crosscountry investigation[END_REF]La Porta, Lopez-de-Silanes, Shleifer, and Vishny, 1997;Levine, Lin, and Xie, 2018) in constructing our measure of social trust (ST). The World Values Survey (WVS) offers six waves of surveys that aim to measure the "beliefs, values, and motivations of people" across countries. We restrict the sample to the last three survey waves and focus on the answers to the following question "Generally speaking, would you say that most people can be trusted, or that you can't be too careful in dealing with people?" The WVS offers three possible responses: (1) most people can be trusted; (2) you can't be too careful in dealing with other people; and (3) I don't know. TS measure is calculated as the percentage of respondents who reply that most people can be trusted.

The interaction between firm-level political risk and our social trust measure is statistically significant and positive, suggesting that the positive effect on political risk is more significant for the years of high social trust in the U.S. Overall, we conclude that social trust boosts trade credit provision under high risk emanating from political spheres.

political risk and can be quantitatively different from real political risk levels. We cannot exclusively rely on this definition to classify events and risks as being political or not.

This proxy includes some judgment thus care both in the construction of the index and in the interpretation is required. The firm-level measure of political risk is defined by a measure of the number of weighted words in the conversation transcripts between call participants and firm management that centers on risks associated with political topics divided by the total number of words.

This measure can be interpreted as a proxy for the political risk and uncertainty that individual firms face. Rather than a priori deciding on specific words associated with different topics, Hassan et al. (2019) distinguish political from non-political topics using a pattern-based sequence-classification method developed in computational linguistics.

Using this approach, they correlate language patterns used by conference-call participants to that of a text that is either political in nature (e.g., an undergraduate political science textbook) or indicative of a specific political topic (e.g., speeches by politicians about health care). Similarly, they identify the association with risk simply by the use of synonyms of the words "risk" and "uncertainty" in conjunction with this language. Specifically, they construct the measure of overall political risk by first defining the set of 10 words surrounding a synonym for "risk" or "uncertainty" on either side, and divide by the total number of bigrams in the transcript:

where 𝑃\𝑁 is the set of bigrams contained in P but not N, and r is the position of the nearest synonym of risk or uncertainty. The first two terms in the numerator thus simply count the number of bigrams associated with discussion of political but not non-political justify their bad performance. For example, managers can have incentives to discuss risks associated with political topics and blame politicians using 'cheap talking' when they underperform.

Appendix 3-1. Main variables description

All continuous variables are winsorized at the 1st and 99th percentiles of their distributions to mitigate the effect of outliers.

Variable Definition Source

Cash holdings Cash holdings defined as the natural logarithm of the ratio of cash and marketable securities to net assets computed as total assets minus cash and marketable securities.

Appendix 3-2. Political risk measure construction

The firm-level measure of political risk is defined by a measure of the proportions of the words in conversations between call participants and firm management that centers on risks associated with political topics. This measure can be interpreted as a proxy for the political risk and uncertainty individual firms face. Rather than a priori deciding on specific words associated with different topics, Hassan et al. (2019) distinguish political from non-political topics using a pattern-based sequence-classification method developed in computational linguistics. Using this approach, they correlate language patterns used by conference-call participants to that of a text that is either political in nature (e.g., an undergraduate political science textbook) or indicative of a specific political topic (e.g., speeches by politicians about health care). Similarly, they identify the association with risk simply by the use of synonyms of the words "risk" and "uncertainty" in conjunction with this language. Specifically, they construct the measure of overall political risk by first defining a training library of "political" text, archetypical of the discussion of politics, P, and another training library of "non-political" text, archetypical of the discussion of non-political topics, N. Each training library is the set of all adjacent two-word combinations ("bigrams") contained in the respective political and non-political texts (after removing all punctuation). They then similarly decompose each conference-call transcript of firm i in quarter t into a list of bigrams contained in the transcript b = 1,...,Bit. They count the number of occurrences of bigrams indicating discussion of a given political topic within the set of 10 words surrounding a synonym for "risk" or "uncertainty" on either side, and divide by the total number of bigrams in the transcript:

where 𝑃\𝑁 is the set of bigrams contained in P but not N, and r is the position of the nearest synonym of risk or uncertainty. The first two terms in the numerator thus simply count the number of bigrams associated with discussion of political but not non-political

States are known to be strong and to promote effective and accountable measures according to the United Nations' Sustainable Development Goals reports.

Studies provide evidence that prevailing political parameters increase the likelihood of bailouts when needed (e.g., [START_REF] Dam | Bank bailouts and moral hazard: Evidence from Germany[END_REF][START_REF] Cukierman | Monetary policy and institutions before, during, and after the global financial crisis[END_REF][START_REF] Antzoulatos | Institutions, moral hazard and expected government support of banks[END_REF][START_REF] Ashraf | Political institutions and bank risk-taking behavior[END_REF]. In moments of high political uncertainty when economic shocks hit, governments strive to use various schemes to guarantee systemic financial stability and avoid the collapse of the business environment under what is deemed "too many to fail" or "too big to fail." These schemes can take the form of capital injections, credit risk guarantee tools, deposit insurance, etc. or simply decrease information asymmetry [START_REF] Bushman | What determines corporate transparency[END_REF] which can generate moral hazard issues and encourage managers to maintain or increase their risk-taking.

In this same vein, [START_REF] Antzoulatos | Institutions, moral hazard and expected government support of banks[END_REF], among others, claim that a substantial part of the expected bailouts can be attributed to the political environment and provide evidence that a good institutional environment is linked to higher expected bailouts. Therefore, amid worst economic times, companies in countries with highly sound and democratic governments, such as the U.S. in our case, can expect help from the local and national authorities and therefore generate more moral hazard concerns.

Therefore, we extend this debate by testing the hypothesis that political risk may promote higher risk-taking by generating the moral hazard due to the anticipation that a government would deliver the bailouts:

H1b: There is a positive relationship between firm-level political risk and managerial risktaking choices.

The key role of managerial incentives

The finance literature argues that managerial incentives of equity-based compensation is used to overcome managerial risk aversion and to reduce risk differentials between managers and investors in strategic management. For example, [START_REF] Smith | The determinants of firms' hedging policies[END_REF] and [START_REF] Guay | The sensitivity of CEO wealth to equity risk: an analysis of the magnitude and determinants[END_REF] argue that stock options substantially increase

It is crucial to distinguish between the between the slope of the relation between managers' compensation and stock price (i.e., Delta) and the Vega, which is the convexity, meaning the sensitivity of managers' compensation to stock return volatility (e.g., [START_REF] Guay | The sensitivity of CEO wealth to equity risk: an analysis of the magnitude and determinants[END_REF][START_REF] Low | Managerial risk-taking behavior and equity-based compensation[END_REF]. On one hand, Delta increases managers exposure to risk and its effects on managerial risk-taking is inconclusive, because of the mixed incentives it encompasses. Moreover, high levels of Delta are likely to lead to lower the manager's portfolio value when the firm stock price declines.

On the other hand, Vega is shown to significantly offset managerial risk aversion regarding projects which themselves are results of an increased Delta. [START_REF] Guay | The sensitivity of CEO wealth to equity risk: an analysis of the magnitude and determinants[END_REF] shows that there is a positive relationship between Vega and risk-taking, in other words, between Vega and stock return volatility. In this same vein, Coles, Daniel, and Naveen (2006) point out that riskier firms offer more equity-based managerial incentives, which in its turn leads managers to undertake riskier projects and engage in high-risk policies. [START_REF] Low | Managerial risk-taking behavior and equity-based compensation[END_REF] provides further evidence on how increased CEO pay-performance sensitivity (delta) do not affect firm risk-taking as Vega does. On the contrary and consistent with the principal agent framework, [START_REF] Aggarwal | Executive compensation, strategic competition, and relative performance evaluation: Theory and evidence[END_REF] demonstrate that higher risk leads to lower Delta and that higher risks make providing equity-based incentives costlier. We argue then that equity-based compensation, in particular Vega, can help align managers and shareholders' risk preferences:

H3: Vega has a stronger effect on the relationship between political risk and managerial risktaking than Delta.

Data and sample selection

Sample selection

To be included in the sample, a firm had to have accounting, financial and stock records available on both Compustat and Center for Research in Security Prices (CRSP)

databases. We remove financial firms with standard industrial classification (SIC)

Where 𝑃\𝑁 is set of bigrams contained in 𝑃 but not in 𝑁, and 𝑟 is the position of the nearest bigram of risk or uncertainty. 𝑓 𝑏,𝑃 is the frequency of bigram 𝑏 in the training set related to political words and 𝐵 𝑝 is the total number of bigrams in the same set.

Managerial risk-taking variables

Recent methodological studies in the finance literatures have, however, raised serious questions about the two most widely used measures of firm and business-level risk: beta (or its derivatives) from the Capital Asset Pricing Model and simple variance (or its variants).

The main dependent variable is firm total risk-taking. 29 Total risk is calculated following [START_REF] Low | Managerial risk-taking behavior and equity-based compensation[END_REF] by relying on the variance of daily stock returns over the fiscal year.

The variance is then annualized and taken a natural logarithmic transformation (ln_TOTR). Besides, managers can have substantial effect on both total risk and the composition of risk. Thus, we break down total risk into its two components, systematic risk and idiosyncratic risk, by employing the market model and CRSP value-weighted market portfolio as our measure for market portfolio. Non-synchronous trading is adjusted by adding five leads and five lags of market returns following [START_REF] Dimson | Risk measurement when shares are subject to infrequent trading[END_REF].

All these measures are calculated when at least 60 days of returns data are at our disposal.

Systematic risk is defined and calculated as the variance of the firm beta multiplied by market daily returns. We include predictions of part of the leads and lags of market portfolio returns using the stock return regression. Systematic risk (ln_SYSR) is a result of annualized variance and natural logarithmic transformation. Finally, Idiosyncratic risk consists of the annualized variance of the residuals from the market model before taking a natural logarithmic transformation.

Effects of different components of political risk on managerial risk-taking

We take advantage of the granularity our proxy of the aggregate firm-level political risk and decompose it into its eight dimensions, namely: economic policy and budget, environment, trade, institutions and political process, health care, security and defense, tax policy, and technology & infrastructure.

Table 4-5 provides the regression results on the relation between the degree to which firms are exposed to each topic or dimension of political risk and riskiness levels of managerial choices. Columns (1) to ( 8) present regression results of total risk-taking on each dimension and our set of control variables. Column (9) of Table 4-5 is a horse-race regression where we include all political risk components. Overall, our results remain the same, indicating that dimensions of firm-level political risk have a positive and significant effect on managerial risk-taking. In As above

E-index

The E-Index is constructed from IRRC data as described in Bebchuk, Cohen, Ferrell (2004) Coles, Daniel, Naveen (2006) and Daniel, Li and Naveen (2013). 34 However, I make the following changes:

▪ I make the code executable on a standalone basis. Instead of using three heavy external databases, I use a self-contained code that can be run through an SSH connection;

▪ Update the measure until the end year 2016 as the initial code generates the variables only up to 2010.

▪ I change the sum function used to calculate "newdelta" is Naveen's code as it mistakenly generates a missing output variable if any variable (instead of all variables at the same time) on the right side is missing.

34 https://sites.temple.edu/lnaveen/data/ we would need to deal with this matter as a starting point to create a clearer framework for our discussions.

The second threshold issue was whether it was even possible to precisely measure elements of political risk as a basis to calibrate the managerial responses that we examine.

On this point, our search has led us to a novel tool developed by Hassan, Hollander, van Lent and Tahoun (2019). This innovative tool allowed us to reliably measure firm-level perception of various types of political risks by adapting computational linguistics techniques to transcripts of quarterly earnings conference calls of publicly listed firms in the United States.

We use this tool to examine three key issues in corporate finance in the face of the managers' perceptions of measurable increase or decrease in political risk: trade credit, cash management, and managerial risk-taking behavior. Thus, while the first chapter of this dissertation is devoted to our findings on the nature and composition of political risk, the remaining three chapters document our approach and findings on these three questions of relevance to both practitioners of corporate finance and to academics.

With respect to the nature of political risk, we trace the evolution of this concept and various attempts at defining its parameters over the past six decades. We concluded that because the understanding of what constitutes political risk remains amorphous and mutable, our contribution must include a more comprehensive framework that would reflect the contemporary realities and would be a useful updated addition to the understanding and management of political risk. Therefore, in addition to a wideranging review of the literature to date, this new framework constitutes an important component of what we presented in Chapter 1 of this dissertation.

Firms are increasingly dealing with uncertainty related to the timing and content of government policy and regime shifts, as well as the latent effect that these policies have on firm economic outcomes. Even though over our study period, the U.S. enjoys a stable and democratic political environment (La Porta, Lopez-de-Silanes, Shleifer and independence; not to mention the violence in Iraq, Syria and Yemen to which people have become habituated. In 2020, the entire world has been hit by the strongest pandemic in the century jeopardizing the survival of many industries and workers.

Aware of these changes and the great laboratory these last few years offer to measure new dimensions and effects of political risk, we are in the process of developing and patenting a novel measure of political risk based on natural language processing techniques by analyzing the content of politicians' social media accounts.

Résumé de la thèse en Français

Le risque est inhérent à la nature. L'évaluation, la gestion et l'atténuation du risque ou l'adaptation à l'incertitude dans notre environnement sont au coeur de l'impératif