Ralph De Haas
email: dehaasr@ebrd.com

Sergei Guriev
email: sergei.guriev@sciencespo.fr.

Alexander Stepanov
email: stepanoa@ebrd.com

State Ownership and Corporate Leverage Around the World *

Keywords: D22, F36, G32, G38, H11, H81, L33 State ownership, privatization, corporate debt, state banks

Does state ownership hinder or help rms access credit? We use data on almost 4 million rms in 89 countries to study the relationship between state ownership and corporate leverage. Controlling for country-sector-year xed eects and conventional rm-level determinants of leverage, we show that state ownership is robustly and negatively related to corporate leverage. This relationship holds across most of the rm-size distributionwith the important exception of the largest companiesand is stronger in countries with weak political and legal institutions. A panel data analysis of privatized rms and a comparison of privatized with matched control rms yield similar qualitative and quantitative eects of state ownership on leverage.

Introduction

Corporate indebtedness has risen sharply in the wake of the global nancial crisis as low interest rates enticed many rms to issue bonds and borrow from banks. This debt surge occurred in Western countries and emerging markets alike [START_REF] Abraham | Growth of Global Corporate Debt: Main Facts and Policy Challenges[END_REF]. As a result, the total debt of non-nancial companies increased from 84 percent of global GDP in 2009 to 92 percent in 2019 (IIF, 2020). The recent Covid-19 pandemic has pushed up debt levels even further, creating fears of a corporate debt bubble that could threaten the health of the global economy (IMF, 2019;[START_REF] Boone | Debt: The Eye of the Storm[END_REF].

The past decade has also been characterized by a steady rise in state ownership of corporate equity. Many governments not only nationalized banks during the global nancial crisis, but also took stakes in non-nancial corporations. The increasing popularity of Chinese-style state capitalism further contributed to this resurgence in state ownership [START_REF] Megginson | Privatization, State Capitalism, and State Ownership of Business in the 21st Century[END_REF] as did the Covid-19 pandemic, which led many governments to take equity stakes in nancially distressed companies (EBRD, 2020).

The parallel increase in state ownership and corporate leverage around the world raises the question: to what extent can the latter increase be explained by the former? The existing literature identies two countervailing forces [START_REF] Borisova | Government Ownership and the Cost of Debt: Evidence from Government Investments in Publicly Traded Firms[END_REF]. On the one hand, state ownership can benet rms because it implicitly guarantees that debt will be repaid [START_REF] Faccio | Political Connections and Corporate Bailouts[END_REF][START_REF] Iannotta | The Impact of Government Onwership on Bank Risk[END_REF]. This lowers borrowing costs and may therefore increase leverage. On the other hand, state ownership may increase the cost of debt if the state's non-nancial objectives clash with those of for-prot lenders.

1 This would give rise to a negative relationship between state ownership and corporate leverage.

In order to study which of these eects dominates, we use an exhaustive rm-level data set. We start by splicing multiple historical editions of Bureau van Dijk's Orbis database. This allows us to carefully track the ownership structure of individual companies over time, and to identify the shares of all shareholders classied as public authorities or the state. We then create continuous measures of state ownership as well as dummy variables that indicate 1 For example, state ownership can entail outright political interference [START_REF] Shleifer | Politicians and Firms[END_REF][START_REF] Ben-Nasr | The Political Determinants of the Cost of Equity: Evidence from Newly Privatized Firms[END_REF]; increased risk taking (Gropp, Gruendl, and Guettler, 2004); or weaker nancial discipline (Qiu and Yu, 2019). Note that a negative relationship between state ownership and leverage emerges only if political distortions harm creditors relatively more than shareholders.

1 Electronic copy available at: https://ssrn.com/abstract=4105153 whether the state owns more than 20, 50 or 99 percent of a rm's equity, either directly or indirectly. Our nal data set spans 89 countries over 20 years (20002019) and contains 20 million annual observations on almost 4 million rms. The comprehensive nature of our data allows us to explore heterogeneity in the relationship between state ownership and corporate leverage across several important rm-and country-specic dimensions.

We rst conduct a cross-sectional analysis to uncover key patterns and stylized facts about state ownership and rm leverage. Throughout this analysis we control for standard (time-varying) determinants of rm leverage as suggested by corporate nance theory: rm size; protability; asset tangibility; and the size of the non-debt tax shield. Importantly, we also control for country-sector-year xed eects in all regressions, thus comparing rms with dierent levels of state ownership in the same country, sector and year.

The rst key result is that state ownership, both at the extensive and the intensive margin, is robustly and negatively related to rm leverage, dened as total debt normalized by total assets. This implies that, on average, the negative impact of state ownership more than osets any benets rms may derive (in terms of borrowing capacity) from the state as a shareholder. The magnitude of the eect is substantial: within the same country-sectoryear, rms with any state ownership on average have a 5 percentage point lower debt/assets ratio. This is about one-quarter of the median leverage of 18.6 percent in our global sample.

Our second main result is that the negative relationship between state ownership and corporate leverage holds across most of the rm-size distributionwith the important exception of the very largest rms. We nd that only in the top percentiles of the rm-size distributionthat is, rms owning more than approximately USD 3 billion of assetsstate ownership is associated with higher corporate leverage. In other words, only the largest rms in a country benet from (partial) state ownership through implicit bailout guarantees and cheaper credit. This nding is corroborated by analogous results for corporate borrowing costs: for smaller rms, state ownership is associated with more expensive debt while, for larger ones, the relationship has the opposite sign.

Third, we nd that the negative relationship between state ownership and corporate leverage is considerably weaker in richer countries with a stronger rule of law; better control of corruption; stronger insolvency rights; and better investor protection. This indicates that, in better institutional environments, private creditors worry less about distortions due to state interference so that the negative impact of state ownership on rm leverage is smaller.

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We also show that the relationship between state ownership and corporate leverage depends critically on the structure of the banking system, in particular the presence of foreign and state banks (relative to domestic private banks). We nd that (smaller) state-owned rms are (even) less levered, relative to privately owned ones, in countries where foreign banks play a bigger role. This indicates that foreign bank ownership imposes nancial discipline and reduces the likelihood of the state channeling credit to state-owned enterprises.

In line with this interpretation, we nd that state-owned rms, especially smaller ones, pay higher interest rates relative to equivalent privately owned rms in countries where foreign banks play a bigger role. Our results on state banks are more nuanced. While, on average, a larger presence of state banks is associated with a stronger negative relationship between state ownership and rm leverage, this eect is reversed for larger companies. This suggests that countries use state banks to allocate credit to favored national champions (though we do not nd that they do so at subsidized interest rates).

Finally, we complement our cross-rm results with a within-rm analysis based on panel data on privatized rms, as well as with results based on a matching estimator that systematically compares privatized rms with observationally similar (non-privatized) state-owned enterprises. Both of these exercises yield empirical results that are very similar to those from the cross-rm analysis, both qualitatively and quantitatively. We nd that rms typically increase their leverage by about 5 percentage points (27 percent of the sample mean) in the ve years after privatization and relative to comparable (matched) non-privatized rms.

The comprehensive nature of our data setin terms of its coverage of rms of very dierent sizes and of countries with very dierent institutional environmentsallows us to bring four new insights to the literature. First and foremost, we shed light on how the relationship between state ownership and rm leverage varies across the rm-size distribution. Previous papers relied on relatively small samples of large (or very large) listed companies. For example, [START_REF] Dewenter | State-owned and Privately Owned Firms: An Empirical Analysis of Protability, Leverage, and Labor Intensity[END_REF] analyze state-owned enterprises among the 500 largest non-US rms in 1975, 1985 and 1995. They show thatafter controlling for business cycles, rm size, location and industrystate-owned enterprises are leveraged more and perform less well than comparable private rms. They also show that leverage falls after privatization events. [START_REF] Borisova | Government Ownership and the Cost of Debt: Evidence from Government Investments in Publicly Traded Firms[END_REF] use bond credit spreads from 226 large, publicly traded companies from 43 countries. For these large rms they nd that, while in normal times government ownership is associated with a higher cost of debt, the credit spreads of state rms were lower during the global nancial crisis (when implicit bailout guarantees were particularly important). [START_REF] Borisova | Does Government Ownership Aect the Cost of Debt? Evidence from Privatization[END_REF] use a sample of publicly traded bonds by 60 large European companies to show how credit spreads initially increase during privatization while fully privatized rms experience a drop in their cost of debt. In a similar vein, [START_REF] Boubakri | The Financial and Operating Performance of Newly Privatized Firms: Evidence from Developing Countries[END_REF]79 large companies); D 'Souza and Megginson (1999;85 large companies) and [START_REF] Megginson | The Financial and Operating Performance of Newly Privatized Firms: An International Empirical Analysis[END_REF]61 large companies) also nd that, after privatization, companies reduce their debt ratios. Interestingly, Boubakri and Saar (2019;453 large companies) nd that newly privatized rms may continue to benet from government support after (partial) privatization as residual state ownership correlates positively with bank borrowing.

Our contribution is to show how the impact of state ownership on the cost of borrowing, and therefore on corporate indebtedness, depends crucially on rm size. For the bulk of rms, state ownership and the associated ineciencies entail higher average borrowing costs.

2 This eect of state ownership changes sign for the largest enterprises. For such "na- tional champions"large rms of national strategic importancewe nd that the downsides of state ownership are typically more than compensated for by the implicit (and sometimes explicit) bailout guarantees of the state. By analyzing the full spectrum of state rmswe use data on 46,039 rms with at least a 20 percent government stakewe thus uncover important heterogeneity that helps to put earlier empirical ndings into a broader perspective. Second, the granular and time-varying nature of our data allows us to explore the intensive and extensive margins of the eect of state ownership. This is important because, over the past two decades, many governments have expanded their minority stakes in a broad variety of enterprises [START_REF] Faccio | The Ultimate Ownership of Western European Corporations[END_REF]. As yet, little is known about the impact of such stakes on the nancial policies and borrowing behaviors of such enterprises. Our data show clearly that the negative eect of state ownership on rm leverage already occurs when the state takes minority stakes of as little as 1 percent, suggesting that even small equity stakes can carry considerable power (such as when the state holds a golden share). An analysis estimating the marginal eect of state ownership at various rm sizes reveals clearly how the impact of state ownership on rm leverage increases monotonically as the state owns 2 This is in line with recent work showing that losses are especially common among small state rms [START_REF] Musacchio | Fixing State-Owned Enterprises. New Policy Solutions to Old Problems[END_REF] and the observation that in China many small (but only very few large) state-owned enterprises have gone bankrupt during recent times.

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Electronic copy available at: https://ssrn.com/abstract=4105153 progressively larger equity stakes.

Third, we shed new light on the importance of the structure of the banking system, by distinguishing between countries with dierent levels of foreign and state ownership in their banking sectors. Earlier work shows that lending by state banks is often driven by political motivations, electoral cycles in particular.

3 Such lending can distort the allocation of capital throughout the economy, for example, when state banks prefer to lend to stateowned enterprises, crowding out loans to the private sector [START_REF] Gordon | Government as a Discriminating Monopolist in the Financial Market: The Case of China[END_REF][START_REF] Allen | Law, Finance, and Economic Growth in China[END_REF][START_REF] Li | Ownership, Institutions, and Capital Structure: Evidence from China[END_REF][START_REF] Molnar | State-owned Firms Behind China's Corporate Debt[END_REF].

4 [START_REF] Panizza | State-Owned Commercial Banks[END_REF] uses a global data set of state banks to show that both state-owned rms and (large) rms that are part of a conglomerate are more likely to borrow from state banks.

Our contribution is to demonstrate how state banks limit access to credit for smaller state rms, but ease the borrowing constraints of larger state-owned companies. This suggests that state banks help to redistribute nancial resources from smaller rms to larger, strategically more important ones. Moreover, we show that foreign banks impose nancial discipline by reducing the leverage of (smaller) state-owned rms compared to similar privately owned ones. This is in line with foreign banks relying on arm's-length techniques when lending to distant borrowers, thus reducing credit access to relatively opaque clients in destination countries [START_REF] Mian | Distance Constraints: The Limits of Foreign Lending in Poor Economies[END_REF][START_REF] Detragiache | Foreign Banks in Poor Countries: Theory and Evidence[END_REF]. Our results imply that such distance constraints bind in particular for rms in which the state holds an ownership stake and can exert inuence.

Fourth, we contribute to the rich literature on state ownership of productive assets and the rm-level impacts of privatization. Various papers show that full or partial privatization can boost rms' eciency and performance provided the right institutional and regulatory preconditions are in place (D 'Souza, Megginson, and Nash, 2005).

5 [START_REF] Megginson | The Financial and Operating Performance of Newly Privatized Firms: An International Empirical Analysis[END_REF] compare the pre-and post-privatization performance of 61 large companies and nd that a majority of these large rms decrease their leverage ratios after privatization. They argue that this reects the combined eect of the state's withdrawal 3 See, for example, [START_REF] Shleifer | Politicians and Firms[END_REF]; [START_REF] Shleifer | State versus Private Ownership[END_REF]; La [START_REF] Porta | Government Ownership of Banks[END_REF]; [START_REF] Sapienza | The Eects of Government Ownership on Bank Lending[END_REF]; [START_REF] Dinç | Politicians and Banks: Political Inuences on Government-Owned Banks in Emerging Markets[END_REF]; [START_REF] Khwaja | Do Lenders Favor Politically Connected Firms? Rent Provision in an Emerging Financial Market[END_REF]; [START_REF] Carvalho | The Real Eects of Government-Owned Banks: Evidence from an Emerging Market[END_REF] and [START_REF] Bircan | Lending Cycles and Real Outcomes: Costs of Political Misalignment[END_REF]. 4 On the other hand, state banks may overcome market failures by nancing socially desirable investments that the private sector is unwilling or unable to fund [START_REF] Jiménez | Burning Money? Government Lending in a Credit Crunch[END_REF].

5 For example, La Porta and López de Silanes (1999); [START_REF] Megginson | From State to Market: A Survey of Empirical Studies of Privatization[END_REF]; Megginson (2005); [START_REF] Bortolotti | Privatisation Around the World: Evidence from Panel Data[END_REF][START_REF] Estrin | The Eects of Privatization and Ownership in Transition Economies[END_REF]. Even partial privatization, where the government remains in control of management, can benet productivity and protability [START_REF] Gupta | Partial Privatization and Firm Performance[END_REF].

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Electronic copy available at: https://ssrn.com/abstract=4105153 of debt guarantees and the rm's improved access to public equity. Our contribution is to demonstrate how the eect of privatization diers markedly for smaller rms. For these rms, the main nancial impact of privatization is improved access to debt as lenders worry less about political interference (these small rms never beneted from implicit bailout guarantees in the rst place). Only for larger rms is this eect reversed, as these rms can access global equity markets and may experience an uptick in the cost of debt once implicit government guarantees disappear.

The remainder of this paper is structured as follows. In Section 2, we briey review the corporate nance literature on optimal capital structures and outline the main mechanisms through which government ownership can aect rm leverage. Sections 3 and 4 then describe our data and empirical approach, respectively. We present our results in Section 5, and conclude in Section 6.

2 The Drivers of Firm Leverage

Theories of optimal capital structure

The corporate nance literature oers three models to explain rms' capital structures. The rst, trade-o theory, describes the trade-o between the advantageous and adverse eects of debt nancing on rm value. [START_REF] Modigliani | The Cost of Capital, Corporation Finance and the Theory of Investment[END_REF] have shown that, in the absence of transaction costs, a rm's value is independent of its nancial structure. This changes in the presence of bankruptcy costs or taxes that are not neutral to leverage. On the one hand, higher leverage then means rms benet more from debt tax shields. On the other, higher leverage also leads to higher (expected) costs of nancial distress (such as the administrative costs of liquidation). Firms then choose an optimal capital structure that resolves this trade-o.

The second model is the pecking-order hypothesis. It argues that rms prefer internal over external nancing due to asymmetric information between managers and investors.

Moreover, if the rm relies on external funds, as in [START_REF] Myers | Corporate Finance and Investment Decisions when Firms have Information Investors do not Have[END_REF], then it prefers debt to equity due to the lesser impact of information asymmetries (Donaldson, 1961;[START_REF] Myers | Corporate Finance and Investment Decisions when Firms have Information Investors do not Have[END_REF]. Trade-o behavior and pecking-order considerations need not be mutually exclusive. For example, rms may have a target leverage ratio but follow the pecking order to reach it.

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Moreover, while trade-o considerations can be important in the longer term, pecking-order behavior may matter (more) in the short term [START_REF] Mayer | A New Test of Capital Structure[END_REF].

The third theoretical framework comprises agency theory [START_REF] Jensen | Theory of the Firm: Managerial Behavior, Agency Costs, and Capital Structure[END_REF].

It focuses on conicts that can occur between shareholders and managers, as well as between shareholders and debt holders. These conicts determine an optimal capital structure that trades o agency costs with other nancing costs. For example, debt may commit a rm to larger cash payments so less cash remains available for self-interested managers to waste [START_REF] Jensen | Agency Cost and Free Cash Flow, Corporate Finance and Takeovers[END_REF].

Determinants of rms' capital structure

The aforementioned capital structure theories generate the following predictions about the relationship between leverage and rm characteristics such as size, tangibility of assets and cash ows. First, as larger rms tend to be more diversied, have stable cash ows [START_REF] Rajan | What Do We Know about Capital Structure? Some Evidence from International Data[END_REF][START_REF] Fama | Testing Trade-O and Pecking Order Predictions about Dividends and Debt[END_REF] and are therefore less likely to go bankrupt, trade-o theory predicts a positive association between rm size and leverage. In contrast, pecking-order theory highlights that larger rms are more transparent and less subject to information asymmetries, which leads them to prefer equity over debt.

Second, trade-o theory predicts a positive relationship between asset tangibility and leverage because outsiders can easily value tangible assets. This will lower expected distress costs [START_REF] Frank | Capital Structure Decisions. Which Factors Are Reliably Important?[END_REF]. That is, when assets are tangible and can serve as collateral, a rm's leverage will be higher, all else equal [START_REF] Rajan | What Do We Know about Capital Structure? Some Evidence from International Data[END_REF]. In contrast, pecking-order theory predicts an inverse relationship between tangibility and leverage because tangibility lowers information asymmetries. This increases the attractiveness of equity relative to debt.

Third, according to trade-o theory, protable rms have more income to shield from taxation and will therefore have higher leverage. Agency theory predicts that protable rms are more likely to encounter free cash-ow problems and may therefore use leverage to control their managers [START_REF] Jensen | Agency Cost and Free Cash Flow, Corporate Finance and Takeovers[END_REF]. In contrast, pecking-order theory implies protable rms will be less leveraged: these rms will prefer to use their internal funds (retained earnings) over external debt and equity nancing [START_REF] Myers | Corporate Finance and Investment Decisions when Firms have Information Investors do not Have[END_REF].

Fourth, higher non-debt tax shields, such as depreciation, reduce the tax advantage of debt nancing. Therefore, trade-o theory implies a negative association between non-debt tax shields and leverage [START_REF] Deangelo | Optimal Capital Structure under Corporate and Personal Taxation[END_REF]). Yet, [START_REF] Bradley | On the Existence of an Optimal Capital Structure: Theory and Evidence[END_REF] nd a positive correlation between rms' non-debt tax shields and leverage, and suggest this may reect that high non-debt tax shields also proxy for asset tangibility.

A large empirical literature assesses the empirical relevance of these theoretical predictions regarding the determinants of corporate leverage.

6 Across the board, this body of work concludes that leverage is positively related to the size of the rm; the tangibility of its xed assets; its non-debt tax shields; and its growth opportunities. In contrast, leverage tends to be negatively correlated with rm-level income volatility and protability. 7

State ownership and rm leverage

How can a rm's ownershipand, in particular, ownership by the stateimpact its capital structure? The main eect of state ownership is that it dramatically changes the trade-o between the benets and risks of taking on debt. On the one hand, the implicit or explicit bailout guarantees that accompany state ownership can reduce the cost of debt because banks and other lenders worry less that a rm will default on its obligations [START_REF] Faccio | Political Connections and Corporate Bailouts[END_REF][START_REF] Iannotta | The Impact of Government Onwership on Bank Risk[END_REF]. This may particularly apply to large rms that are politically and socially important, and to which the government cannot credibly deny funding [START_REF] Kornai | Resource-constrained versus Demand-constrained Systems[END_REF]. Such soft budget constraints may be particularly common in countries where a large part of the domestic banking system is in state hands [START_REF] Cull | Institutions, Ownership, and Finance: The Determinants of Prot Reinvestment among Chinese Firms[END_REF][START_REF] Megginson | State Ownership, Soft Budget Constraints, and Cash Holdings: Evidence from China's Privatized Firms[END_REF].

On the other hand, state ownership can increase the cost of debt if the state's nonnancial objectives (such as ensuring high levels of employment) clash with those of forprot lenders. Governments can also extract rents from state rms by paying below-market prices for outputs [START_REF] Ahroni | The Evolution and Management of State-Owned Enterprises[END_REF] or by implementing price controls to cater to voters. Not only large strategic state rms, but also smaller state-owned enterprises that receive less attention engage in such distorting activities [START_REF] Musacchio | Fixing State-Owned Enterprises. New Policy Solutions to Old Problems[END_REF]. Finally, state ownership can also make borrowing more expensive because the managers of state 6 See [START_REF] Harris | The Theory of Capital Structure[END_REF] for an early review. 7 For example, [START_REF] Rajan | What Do We Know about Capital Structure? Some Evidence from International Data[END_REF] provide an empirical assessment of the key correlates of rm leverage in G7 countries, while Booth, Aivazian, Demirgüç-Kunt and Maksimovic (2001) asses the drivers of corporate leverage in ten developing countries.

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Electronic copy available at: https://ssrn.com/abstract=4105153 rms are not adequately incentivized or monitored [START_REF] Shleifer | State versus Private Ownership[END_REF]Megginson, 2005 and[START_REF] Firth | Corporate Performance and CEO Compensation in China[END_REF][START_REF] Firth | Corporate Performance and CEO Compensation in China[END_REF].

Importantly, the conict between governments' political objectives and private investors' prot motives applies both to private lenders and to private equity holders. If state ownership is expected to undermine private shareholders' payos, it should also raise the marginal cost of raising external equity, not just external debt.

Data

Firm-level data

We create a new rm-level data set that splices multiple historical editions of Bureau van Dijk's Orbis database. We focus on rms that report information on total assets and debt structure in any year between 2000 and 2019. As our goal is to identify the relationship between state control and corporate leverage, we exclude companies without reliable ownership information. Our nal data set spans 89 countries, the period 20002019 and contains about 20 million annual observations on almost 4 million rms. A total of 46,039 (equivalent to about 1 percent) of these rms have at least a 20 percent government stake.

Corporate leverage and covariates

For each rm we have (time-varying) information on their ownership, prot-and-loss statements, and balance sheetincluding their leverage, which we dene as total debt normalized by total assets. Panel A of Table 1 shows there is wide variation across rms in their relative indebtedness: leverage ranges between zero and 200 percent and is, on average, 18.6 percent.

Figure 1 shows a small decline in the leverage of non-state owned rms over the past 1.5 decades, whereas (partially or fully) state-owned rms experienced a slow but steady increase in leverage.

We create four rm covariates for which corporate nance theory suggests co-determine leverage (see Section 2.2) and use them as controls throughout our analysis. These are rm size (log total assets); protability (earnings before interest, taxes, depreciation and amortization (EBITDA)/total assets); asset tangibility; and the non-debt tax shield. We follow [START_REF] Berkowitz | Do Property Rights Matter? Evidence from a Property Rights Law Enactment[END_REF] and [START_REF] Liu | The Eect of Property Rights Protection on Capital Structure: Evidence from a Chinese Natural Experiment[END_REF] and use xed assets 9

Electronic copy available at: https://ssrn.com/abstract=4105153 over total assets as a measure of asset tangibility. A rm's non-debt tax shield is proxied by total depreciation and amortization over total assets. For about half of all observations, we can also create a proxy for the rm's cost of debt by calculating the ratio between total interest expenses and total formal debt (trimmed at the 5th and 95th percentiles). The average implied interest rate is 8.7 percent (Table 1).

Firm-size categories

We allocate each rm to one of ve predened and mutually exclusive size buckets: micro, small, medium-sized, large and super-large rms. In line with ocial EU denitions 8 , micro rms own less then EUR 2 million in total assets; small rms less than EUR 10 million; and medium-sized rms less than EUR 43 million. Large rms are all other rms with the exception of super-large ones, which own more than EUR 1 billion in assets.

Figure 2 presents the share of all enterprises that are state owned, for various rm size categories. The dark gray bars indicate state-owned enterprises' share of total assets owned by all rms in a particular size category. The light gray bars indicate the share of state rms in the total number of rms in each category. The gure shows that state ownership is concentrated among larger rms. For example, among super-large rms, state-owned enterprises account for about 12 percent of total assets and 9 percent of the total number of rms. These numbers are much lower, at 2 percent and 0.4 percent among micro rms.

Figure 3 shows a bin scatter plot in which we group all rms with any state ownership into 20 equal bins: 15 percent state ownership; 610 percent state ownership; etc. Firms with 100 percent state ownership are assigned to a separate bin. The tted curve shows a clear negative relationship between the intensity of state ownership and rm size. Governments tend to hold minority stakes in (very) large rms and often hold large majority stakes in smaller rms.

State ownership

The Orbis database contains subsidiary-shareholder pair information on direct and total ownership in each year. 9 By splicing various historical editions of Orbis, we carefully track the 8 https://ec.europa.eu/growth/smes/sme-denition_en. 9 Orbis provides information on voting shares for those rms whose shares are split into voting versus non-voting shares. This makes the database particularly well-suited to identify control (Kalemli-Özcan et 10 Electronic copy available at: https://ssrn.com/abstract=4105153 ownership structure of companies over time and then summarize the shares of all shareholders classied as public authorities, the state or the government. This includes minority stakes by state-owned investment funds and sovereign wealth funds, an increasingly important form of state ownership [START_REF] Megginson | Privatization, State Capitalism, and State Ownership of Business in the 21st Century[END_REF].

It is important to account not only for direct but also indirect state ownership. Once we have identied all rms where state agencies directly own more than 20 percent of all shares, we next identify the rms in which these directly state-owned rms cumulatively own more than 20 percent. These indirectly state-owned rms are then added to the list of state rms. We proceed with this iteration until we have identied all rms that are linked to state agencies in a way that, at each point in the ownership chain, the state controls at least 20 percent.

Last, we also create a continuous measure of state ownership that ranges between 0 and 100. The average government stake in rms' equity is very small: 0.8 percent (Table 1, Panel A). The median rm has no state ownership. We then dene dummy variables that indicate whether the state owns more than 1, 20, 50 or 99 percent of a rm's equity. Only about 1 percent of all rms is at least 20 percent state-owned. Moreover, we create dummies for whether a company falls within one of the following state-ownership intervals: [1%; 20%), [20%; 50%), [50%; 99%) and [99%; 100%].

Industry-level data

We explore the role of cross-industry variation in rms' dependence on external nance, their liquidity needs and their access to tangible (and therefore pledgable) assets. Our second industry-level characteristic is liquidity needs. Firms in industries that require more working capital will typically need more liquid funds to operate. We use the primary measure of sector-level liquidity needs by [START_REF] Raddatz | Liquidity Needs and Vulnerability to Financial Underdevelopment[END_REF]: the median ratio of inventories over sales for US public companies during 19801989.

Third, we measure asset tangibility as the median value of tangible xed assets over total assets in an industry. In the vein of [START_REF] Rajan | Financial Dependence and Growth[END_REF], we take all US rms for which we have data on tangible xed assets in any year between 2000 and 2019. For each of these rms, we then calculate their median asset tangibility over this period.

Last, we compute the median asset tangibility for each four-digit sector. In line with the literature, we expect higher asset tangibility to support rm borrowing and thus to increase leverage, all else equal [START_REF] Harris | The Theory of Capital Structure[END_REF][START_REF] Almeida | Financial Constraints, Asset Tangibility, and Corporate Investment[END_REF]. This implies that any negative eect of state ownership on rm leverage should be attenuated in sectors where rms own relatively more tangible assets.

Country-level data

We explore country-level heterogeneity in the relationship between state ownership and rm leverage across several key dimensions. We rst assess the role of the ownership composition of national banking systems. To do so, we collect information on the share of domestic banking assets held by the government and by foreign investors. We source data on state banks' assets from the World Bank's Bank Regulation and Supervision Survey and data on assets held by foreign banks from the Global Financial Development Database, also from the World Bank. State (foreign) banks are dened as institutions where the government (foreign investors) holds at least 50 percent of all equity. Across our country sample, on average, 15 percent (34 percent) of all banking assets are in the hands of state (foreign) banks (Table 1, Panel B).

We also explore variation in domestic income levels and institutional quality. National income levels are measured as log GDP per capita, converted to constant 2017 dollars using 11 Both measures are based on US data from the 1980s to the early 1990s. However, Duygan-Bump et al (2015) extend the analysis to services whereas [START_REF] Rajan | Financial Dependence and Growth[END_REF] We start our analysis by running cross-sectional regressions in which we explain the leverage (L) of rm i in sector s in country c and year t by one or several time-varying measures of state ownership (S ). In all specications, we control for a matrix Z of standard timevarying determinants of rm leverage as suggested by corporate nance theory: rm size; protability; asset tangibility; and the size of the non-debt tax shield. Importantly, we saturate all specications with highly granular sector-country-year xed eects, ϕ sct , thus comparing rms with dierent levels of state ownership in the same sector, country and year. Because we systematically compare state-owned enterprises and private rms within the same sector, we exclude by construction any sectors in which state rms have a natural monopoly. We cluster standard errors at the rm level. Our baseline OLS specication is:

L isct = β 0 + β 1 S it + γ ′ Z it + ϕ sct + ϵ i (1)
In some specications, we replace the dependent variable L isct by I isct : the rm-specic cost of debt. Moreover, to explore cross-country heterogeneity, we also estimate interaction regressions following Equation 2:

L isct = β 0 + β 1 S it + β 2 S it × M ct + γ ′ Z it + ϕ sct + ϵ i (2)
where M ct indicates time-varying country-level measures of the ownership structure in the banking sector or proxies for the quality of the institutional environment. In some specications, we replace M ct with M st : time-varying sector characteristics such as industries' external nance dependence, liquidity needs or asset tangibility. In either case, the eect of M ct or M st itself is absorbed by our xed eects ϕ sct .

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4.2 Analysis of privatization events 4.2.1 Panel data analysis of privatized rms

For a deeper insight into the relationship between state ownership and rm leverage, we study changes in corporate indebtedness for rms that underwent a privatization process.

To do so, we create a comprehensive global data set of privatization events by extracting all privatizations agged by Bureau van Dijk's Zephyr database and that are of the Acquisition deal type. Privatizations are deals where a government, council or other state-owned entity disposes of a company or stake in a company that it owns, and acquisitions are those deals where the acquirer ends up with 50 percent or more of the equity of the target.

Our dataset includes detailed information on 2,714 rms privatized during 20002019.

Most privatizations took place in Russia (1,098 cases), Serbia (267), Poland (192), Ukraine (140) and Bulgaria (118). Our analysis considers rms' debt levels within ve-year periods directly before and after privatization. To ensure our results are not biased by rms disclosing nancial information only in one period, we focus on rms for which we have at least three years of data before and after privatization. This reduces the sample to 946 companies. We run regressions following Equation 3 and cluster standard errors at the rm level:

L isct = β 0 + β 1 P P it + γ ′ Z it + ψ i + θ ct + µ st + ϵ i (3)
The variable of interest is a rm-specic pre-privatization dummy (P P it ) that takes the value `1' in the years before privatization and `0' in the years afterwards (the privatization year itself is excluded from the analysis). If privatization leads to an increase in rm leverage, we would therefore expect β 1 to be negative. Importantly, this setup allows us to include rm xed eects (ψ i ) that absorb all observable and unobservable time-invariant rm character- istics that might otherwise confound estimates of the impact of state ownership on leverage.

We thus obtain a clean within-rm estimate of the privatization impact. We also control for country-year (θ ct ) and sector-year (µ st ) xed eects. These absorb time trends that aect specic countries (such as their business cycle) and specic sectors (such as industry-level technological change), respectively.

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Matching estimator of average treatment eects on privatized rms

To further identify the causal eect of privatization on the debt structure of (previously) state-owned rms, we use a matching estimator [START_REF] Abadie | Large Sample Properties of Matching Estimators for Average Treatment Eects[END_REF] to systematically compare privatized rms with similar peers that remained for at least 20 percent statecontrolled throughout the observed period. We estimate the average treatment eect on the treated (ATT) with treatment being the privatization event.

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We condition on several key pre-treatment variables by matching exactly on country, twodigit NACE rev. 2 sector and year. In addition, we match on rm size, tangibility (ratio of xed assets to total assets), productivity (ratio of operating revenue to total assets), leverage (the outcome variable) and the ratio of total informal debt to total assets. As privatization expectations and preparations may aect these covariates, we use the mean of the years T-3, T-4, and T-5 as the reference period for matching (where T is the year of privatization). In order to nd the optimal covariate balance, we use a genetic search algorithm as proposed by [START_REF] Diamond | Genetic Matching for Estimating Causal Eects: A General Multivariate Matching Method for Achieving Balance in Observational Studies[END_REF] and Sekhon and Grieve (2011).

13 A privatized rm s is matched to a rm z that stayed state-owned throughout the observed period in such a way that the extended Mahalanobis distance m sz is minimized:

m sz (x is , x iz ) = (x is -x iz ) T Σ -1 2 WΣ -1 2 (x is -x iz )
where x ic is a vector of K observable covariates for rm i of type c = s, z; Σ -1 2 is the inverted Cholesky decomposition of the empirical variance-covariance matrix of the covariates, Σ, while W is a matrix of weights obtained via a genetic algorithm that optimizes the covariate balance. Matching is performed with replacement, so a control rm can be linked to multiple treated rms. We match privatized rms s to control rms z one-to-one, conservatively accepting a higher variance for our estimates in exchange for a lower bias.

Our outcome variable is rm leverage at T-5 to T+5 : from ve years before privatization to ve years after. Because Orbis does not always contain data for all years, we run separate 12 See [START_REF] Campello | Real Assets and Capital Structure[END_REF]; [START_REF] Kahle | Access to Capital, Investment, and the Financial Crisis[END_REF]; and Gropp, Mosk, Wix and Ongena (2019) for recent applications of this matching estimator in the corporate nance literature. Estimating consecutive cross-sectional ATT's is equivalent to applying a dierence-in-dierences approach to matched treated and control rms, as long as matching takes into account pre-treatment outcome levels (as we do). See also [START_REF] Athey | Identication and Inference in Nonlinear Dierence in Differences Models[END_REF]. 13 We implement this algorithm in R using the Matching package of [START_REF] Sekhon | Multivariate and Propensity Score Matching Software with Automated Balance Optimization: The Matching Package for R[END_REF].

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analyses for each year. The analysis of the privatization eect in years T+1 and T+2 can therefore be based on slightly dierent treated and control sub-samples because some rms only report outcomes in year T+1 and others only in year T+2. For each year, we estimate the ATT with and without a correction for multiple covariates bias and perform statistical inference by calculating standard errors based on conventional formulas [START_REF] Abadie | Large Sample Properties of Matching Estimators for Average Treatment Eects[END_REF]. Out of all treated rms selected for this exercise, 656 were matched exactly on country, year and two-digit sector to one or more control rms. 14 5 Results

Baseline results

Table 2 presents our baseline results for the full sample. Controlling for country-sector-year xed eects and for conventional determinants of leverage, we nd that state ownership is negatively and signicantly correlated with corporate indebtedness. Column (1) shows that even when using a very low minimum ownership threshold of just 1 percent, we nd that state ownership decreases leverage by about 5 percentage points. This magnitude is substantial as the mean leverage in our data is 18.6 percent.

The eect is larger when we use higher thresholds for state ownership. In columns (2),

(3) and (4), we present the results for the 20, 50 and 99 percent thresholds, respectively. In column (5), we include dummies for a comprehensive set of ranges of state ownership and nd the eect monotonically increases from about 3 percentage points for state ownership below 20 percent to about 7 percentage points for state ownership above 99 percent. In all of the following regressions, we will use the dummy for the state's stake being above 20 percent as our main measure of state ownership. All our results are robust to using alternative thresholds.

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Throughout Table 2, we control for rm-level protability, eectively shutting down the possibility that state ownership not only impacts leverage directly but also indirectly through 14 The number of matched rms is lower in our year-by-year ATT estimates because data for the outcome variable may be missing for specic years (either for the treatment rm or for all control rms in the relevant country-sector-year cell) or because none of the available control rms are similar to the treated rms in terms of the matching covariates.

15 Appendix Figure A.1 shows that these results are also robust to excluding one industry, region or year at a time.

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Electronic copy available at: https://ssrn.com/abstract=4105153 protability. For example, state ownership may deter rm entry in the same sector and/or locality [START_REF] Brandt | Barriers to Entry and Regional Economic Growth in China[END_REF]. Such a protected position may then allow state rms to charge higher markups and become more protable [START_REF] Berkowitz | The Growth of Firms, Markets and Rents: Evidence from China[END_REF] which in turn may translate in either higher leverage (in line with trade-o and agency theories) or lower leverage (as pecking-order theory would predict). Yet, when we exclude the protability measure from our baseline regressions in Table 2, the estimated coecients for the state-ownership variables turn out to be very stable. This suggests that, on average in our global sample, protability is not an important mediator in the relationship between state ownership and leverage.

In Table 3, we explore the heterogeneity of the eect with respect to rm size. We consider our sub-samples of micro, small, medium-sized, large and super-large rms, as dened in Section 3.1.2. We nd that the average negative eect in the full sample is driven by all but the largest rms. The eect is greatest for micro and small rms, lesser for medium-sized rms, even less for large rms and negligible for super-large rms. As we have only around 75,000 super-large rms in our sample (compared to 3.5 million micro and small rms), the average eect is similar in magnitude to that for micro and small rms. In columns ( 7) and (8), we rerun regressions for the full sample weighting the observations by size. In column (7), where the weights are proportional to log total assets, the coecient on state ownership remains signicantly negative. In column (8), where we weight observations by linear total assets, the average eect is driven by super-large rms and is essentially zero.

Figure 4 presents the estimated marginal eects of state ownership on leverage at dierent points of the rm-size distribution (the horizontal axis is logarithmic). These estimates are based on a regression for the full sample where the state ownership dummy is interacted with the log of total assets and with the squared log of total assets. The eect is clearly negative and statistically signicant for rms with assets below EUR 1 billion, becomes zero or insignicant among rms with assets from EUR 1 to 10 billion, and is positive for rms with assets of EUR 10 billion or more. The positive eect for the largest rms is in line with previous studies, which mostly focus on listed rms that are usually very large.

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Importantly, Figure 4 reveals that such ndings based on only a subset of the very largest 16 In Appendix Table A.2, we focus on a global sample of the 100, 300 or 500 largest listed rms. For this select sample, we also nd a positive relationship between state ownership (of at least 20 percent) and leverage, although the coecient is only precisely estimated for the 100 largest listed rms in the years before the global nancial crisis.

Electronic copy available at: https://ssrn.com/abstract=4105153 rms cannot be extrapolated to the vast majority of smaller companies. For these rms, the eect of state ownership is actually negative, statistically signicant, and large in magnitude.

In Appendix Table A.4, we run similar regressions while interacting the state-owned dummy variable with one of three sectoral characteristics: the sector's external nance dependence (EFD), its liquidity needs and the sector's average asset tangibility. The results conrm the strong and robust negative relationship between state ownership and rm leverage. They also show, however, thatin sectors with a high external nance dependence (column 1) or with higher asset tangibility (column 3)this negative eect is attenuated.

Cross-country heterogeneity

Institutional quality

In Table 4, we explore cross-country heterogeneity in the quality of political and legal institutions. Each column includes our main independent variable (state ownership) and its interaction with a country-level measure of institutional quality. In all specications, the coecient on state ownership remains negative and statistically signicant, while the coecient on the interaction term is positive and signicant. The results indicate that the negative eect of state ownership on leverage is substantially stronger in economically less developed countries (column 1) and in countries with a weaker rule of law (column 2), more corruption (column 3), weaker insolvency regimes (column 4) and less investor protection (column 5).

The magnitudes are also large: if one country is 2.7 times as poor as another country, then the eect of state ownership on leverage is stronger by an additional 5 percentage points (similar to our average eect in the full sample). Likewise, reducing a country's corruption or strengthening its rule of law by one global standard deviation reduces the eect of state ownership on leverage by almost 3 percent (about half of the average eect). The magnitudes are similar for the indicators of insolvency resolution and the protection of minority investors.

Together these results reveal how underdeveloped institutional frameworks exacerbate creditmarket frictions related to state ownership.

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Ownership structure of the banking sector

In Table 5, we explore the role of the ownership structure of countries' banking systems. In addition to the state ownership dummy, we include its interactions with the share of state banks and the share of foreign banks in a country's banking system. Column (1) presents the results for the full sample. The coecient on state ownership remains negative and statistically signicant, and so are the coecients on the interaction terms.

First, we nd that the higher the share of state banks in the economy, the stronger the negative eect of state ownership on rm leverage. This result is clearly at odds with the conventional wisdom that state banks are more likely to lend to (all) state-owned rms. To investigate this further, columns (2)(6) break the sample down into dierent size categories.

We nd the eect for the full sample is driven by micro, small and medium-sized rms. That is, a larger presence of state banks further reduces the leverage of small and medium-sized state enterprises. At the same time, the coecient on the interaction term for larger rms is positive and signicant (in the case of large rms) or insignicant (in the case of super-large rms). This suggests that countries may use state banks to channel credit towards larger state-owned national champions and away from smaller state-owned rms.

Second, we nd that, in countries with a higher share of foreign banks, the negative eect of state ownership on leverage is also stronger. 17 This indicates that foreign banks provide nancial discipline and are more reluctant to lend to state-owned rms than their (private) domestic competitors are. As in the case of state banks, the eect is concentrated in the micro, small and medium-sized rm categories. It is these smaller state-owned enterprises that foreign banks feel less secure lending to, whereas this is not the case for larger (likely more transparent and publicly more supported) state-owned enterprises.

State ownership and the cost of debt

Table 3 shows that state ownership is associated with signicantly lower rm leverage across most of the rm-size distribution. The exceptions are large and super-large rms, where this negative eect is either very small (in the case of large rms) or entirely absent (in the 17 This is conrmed by the sample-split regressions reported in Appendix Table A.3, which show that the relationship between state ownership and rm leverage is systematically more negative in countries where foreign banks own larger segments of the banking sector. case of super-large rms). These baseline results, and the subsequent additional ndings, are consistent with the idea that creditors are concerned about the governance-related risks of lending to (smaller) state-owned enterprises. This should make creditors price in these risks by charging higher interest rates to (relatively small) state-owned rms. The results in Table 6 demonstrate precisely this. We nd that, in the sample as a whole, state ownership is associated with, on average, a 0.3 percentage point higher implied interest rate. This amounts to 3.5 percent of the median interest rate. Importantly, this positive eect is entirely concentrated among small and medium-sized state rms (column 2). In contrast, large and super-large rms appear to benet from state ownership in the form of lower borrowing costs.

Figure 5 shows the marginal eects of state ownership on borrowing costs along the rmsize distribution (the horizontal axis is again logarithmic). These estimations are based on column (1) of Table 6, with the state ownership dummy interacted with the log of total assets and the squared log of total assets. The gure shows clearly how state ownership makes debt more expensive for smaller state-owned enterprises but cheaper for larger ones.

The nding that the largest rms benet nancially from the implicit and explicit bailout guarantees associated with state ownership is in line with the existing literature [START_REF] Faccio | Political Connections and Corporate Bailouts[END_REF][START_REF] Iannotta | The Impact of Government Onwership on Bank Risk[END_REF]. Our contribution here is to demonstrate that the impact of state ownership on funding costs is in fact the opposite for the vast majority of smaller state-owned enterprises that have, as yet, not been studied in the corporate nance literature.

In Table 7, we investigate whether and how the structure of the banking system moderates the relationship between state ownership and corporate debt costs. Interestingly, the data show that there is no special role for state-owned banks (relative to private domestic banks) in determining the interest rates paid by state-owned rms. This indicates that the bailout guarantees associated with state ownership help to reduce the credit risk as perceived by privately owned and state-owned domestic banks alike. In contrast, we nd that a greater presence of foreign banks results in a higher cost of borrowing for state rms. Yet, in line with Table 5, this holds mainly for smaller state rms and much less so, or not at all, for large and super-large ones.

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Privatization and rm leverage

In this section, we study a sub-sample of rms that were privatized. This allows us to estimate the impact of state ownership on leverage by analyzing an ownership change within the same rm. We focus on privatized rms for which we have at least three years of data before and after the privatization year. There are about 900 such rms. Of these, about 700 are micro, small and medium-sized rms, while almost 200 are large and super-large rms.

Figure 6 shows the evolution of leverage before and after privatization separately for micro, small and medium-sized rms (solid line) versus large and super-large rms (dashed line).

For the latter, there is no change in leverage in the 10-year window around the privatization event. However, for the smaller rms, privatization is accompanied by a major increase in leverage around the year of privatization. Moreover, this increase is sustained for at least ve years after the change in ownership.

In Table 8, we report results from panel regressions where we include rm xed eects, country-year and sector-year xed eects, and our standard set of time-varying rm-level characteristics. We exclude the year of privatization from the analysis. The results are strikingly similar to our earlier baseline resultsin terms of the sign of the eect and its magnitude. In column (1), we present the results for the full sample. Here state ownership (that is, the pre-privatization dummy) has a negative and signicant impact on leverage.

The magnitude of the eect is 5 percentage points, exactly as in Table 2 where we compared leverage in state-owned and private rms while controlling for country-sector-year xed effects. Once we split the sample into size categories, we again nd that the average eect is driven by the micro, small and medium-sized rms (column 2). For these, the coecient is negative and signicant and the size of the eect is 6 percentage points. This is again very similar to the results in Table 3. For the large and super-large rms in column (3), there is no eect: the coecient is close to zero.

Next, we use a matching estimator to systematically compare privatized rms with similar rms that remained state-controlled. We condition on several key pre-treatment variables by matching exactly on country, two-digit sector and year. In addition, we match on rm size, tangibility, productivity, leverage (the outcome variable) and the ratio of total informal debt to total assets. We use genetic Mahalanobis distance matching and Appendix Figure A.2. presents the variance-standardized means between the treated and control observations for both the raw and the matched sample. For each year, we then estimate the ATT with 21 Electronic copy available at: https://ssrn.com/abstract=4105153 and without a correction for multiple covariates bias [START_REF] Abadie | Large Sample Properties of Matching Estimators for Average Treatment Eects[END_REF].

Table 9 and Figure 7 present these ATT estimates. The results are qualitatively and quantitatively similar to those of the cross-sectional regressions and the panel data analysis.

Relative to the matched control group, privatization increases leverage by about 5 percentage points. There is, however, one nuance: while the main increase in leverage takes place in the year of privatization (and the year after), there is also a clear (albeit not as steep) increase in leverage just beforehand. In the two years before privatization, rm leverage already rises by about 2 percentage points. As the preparation of privatization deals usually takes several years, this likely reects creditors' ex ante expectations of improved governance after privatization.

6 Conclusions

In this paper, we have studied the relationship between state ownership and leverage in 4 million rms around the world. While previous studies focused mostly on large and listed companies, our sample includes many micro, small and medium-sized rms. We show that the relationship between state ownership and leverage is indeed heterogeneous across rm sizes. While there is no robust impact of state ownership on leverage for large rms in our sample, we nd a strong negative eect of state ownership on leverage among micro, small and medium-sized rms. Controlling for country-sector-year xed eects and standard rmlevel determinants of leverage, state-owned rms have a 5 percentage points lower debt to assets ratio than their private peers. This is substantial: the average leverage in our data set is 18.6 percent. The eect is increasing in the degree of state ownership, but is signicant even if the state only has a small ownership stake. Finally, we nd similar eects on rms' costs of debt: while state ownership increases these costs for smaller state rms, it actually reduces external funding costs for large and super-large state-owned enterprises.

In addition to comparing state and private rms within the same countries, sectors and years, we also analyze the eect of state ownership on leverage within the same rms. We study the evolution of leverage in rms that underwent privatization and nd that privatiza- 18 In unreported regressions, we also nd a simultaneous decline in privatized rms' average debt costs of 3 percentage points. Because we only have data on the cost of debt for about half of the privatized rms, this sample is too small to perform sub-sample analyses by rm size.

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Electronic copy available at: https://ssrn.com/abstract=4105153 tion allows rms to lever up. Similar to our ndings from the cross-rm analysis, this eect is again driven by micro, small and medium-sized rms. The magnitude of the eect is also very similar: 5 percentage points.

The strong negative relationship between state ownership and corporate leverage likely reects the corporate governance risks of state ownership. Creditors may fear the state's intervention in rms' operations, and they may therefore be less willing to lend to such rms. Indeed, we nd the negative eects of state ownership on leverage are much stronger in countries with a weaker rule of law, control of corruption, protection of investors, and insolvency procedures. These results are consistent with the view that state ownership is especially costly in countries with weaker political and legal institutions.

Our results can also be seen in light of a recent literature that underlines the substantial misallocation of capital and labor across rmseven within narrowly dened industrial sectors and within the same country [START_REF] Hsieh | Misallocation and Manufacturing TFP in China and India[END_REF][START_REF] Restuccia | The Causes and Costs of Misallocation[END_REF].

State ownership can be an important source of such allocative ineciency and the resulting drag on total factor productivity [START_REF] Nigmatulina | Misallocation and State Ownership: Evidence from the Russian Sanctions[END_REF]. Our results highlight one mechanism through which state ownership can introduce distortions and resource misallocation: it interferes with the ability of all but the largest rms to access credit. The sample includes only firms that were privatized and for which we have data for at least three years before and three years after privatization. A privatization is a deal that was classified as "Acquisition" by Bureau van Dijk's Orbis Zephyr database (that is, the acquirer ended up with 50 percent or more of the target's equity).

Tables and Figures

The year of privatization is excluded from the analysis. In column 1, the sample includes all eligible firms. In column 2, the sample includes micro, small and medium-sized firms. In column 3, the sample includes large and super-large firms. The notes to Table 3 provide the definitions of these size categories. The explanatory variable of interest-Pre-privatization-is a dummy that takes the value '1' in the years before privatization and '0' in the years afterwards. Firm characteristics include firm size, tangibility and productivity. All regressions include firm fixed effects (FE) as well as interactive FE at the country × year level and the two-digit NACE 2 sector × year level. Standard errors are clustered at the firm level. Appendix Table A.1 contains all variable definitions and data sources.

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Electronic copy available at: https://ssrn.com/abstract=4105153 Notes: Standard errors are reported in parentheses. A privatization is a deal that was classified as "Acquisition" by Bureau van Dijk's Orbis Zephyr database (that is, the acquirer ended up with 50 percent or more of the equity of the target). Year T denotes the year of privatization. Years T-5 through T-1 denote the years before privatization while years T+1 through T+5 denote the years after privatization. The matched sample is obtained by genetic Mahalanobis distance matching (with one nearest neighbor) on firm size, tangibility, productivity, the ratio of total formal debt to total assets, and the ratio of total informal debt to total assets, averaged over years T-3, T-4, and T-5. We also force exact matching on country, two-digit NACE Rev. 2 industry and year. In the matched sample, Treated is the number of matched treated observations; Control is the number of matched controls. The dependent variable is firm leverage. Raw Diff. are raw differences based on simple dummy variable regressions on the whole sample. ATT and ATT b.a. are estimates of the average treatment effect on the treated (ATT) excluding and including a bias-adjustment term, respectively (Abadie and Imbens, 2011). In both cases, standard errors are computed following [START_REF] Abadie | Large Sample Properties of Matching Estimators for Average Treatment Eects[END_REF]. Γ * is the minimum value of parameter Γ ≥ 1, selected from a grid spaced by intervals of 0.05 length, such that in a sensitivity analysis à la [START_REF] Rosenbaum | Covariance Adjustment in Randomized Experiments and Observational Studies[END_REF] the Wilcoxon signed-rank tests associated with Γ * do not simultaneously reject the null hypothesis that the outcome variable is not different across the treated and control samples, for tests with α = .05 type I error. Appendix Table A.1 contains all variable definitions and data sources.

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Electronic copy available at: https://ssrn.com/abstract=4105153 A.1 contains all variable definitions and data sources.

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Electronic copy available at: https://ssrn.com/abstract=4105153 Notes: This figure reports the share of state-owned enterprises (SOEs) within each of five firm size categories: super-large (total assets above EUR 1 billion), large (total assets between EUR 1 billion and EUR 43 million), medium (total assets between EUR 10 EUR 43 million), small (total assets between EUR 2 and 10 million) and micro (total assets below EUR 2 million). A firm is classified by its size only once: in the year it first enters the dataset. Reported shares are averages over the years 2011-2019. SOEs are defined as firms with at least 20 percent state ownership. Appendix Table A.1 contains all variable definitions and data sources.

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Electronic copy available at: https://ssrn.com/abstract=4105153 The analysis is based on the regressions reported in Table 2, column 2, with the state-owned dummy interacted with the log of total assets and the squared log of total assets. Appendix Table A.1 contains all variable definitions and data sources.

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Electronic copy available at: https://ssrn.com/abstract=4105153 The analysis is based on the regressions reported in column 1 of Table 6 with the state-owned dummy interacted with the log of total assets and the squared log of total assets. Appendix Table A.1 contains all variable definitions and data sources.

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Electronic copy available at: https://ssrn.com/abstract=4105153 Notes: This figure reports average firm leverage before and after privatization. The sample includes only firms that were privatized and for which data are available for at least three years before and three years after privatization. A privatization is a deal that was classified as "Acquisition" by Bureau van Dijk's Orbis Zephyr database (that is, the acquirer ended up with 50 percent or more of the target's equity). Year 0 denotes the year of privatization. Years -5 through -1 are the years before privatization while the years 1 through 5 are the years after privatization. The solid line shows average firm leverage for micro, small and medium-sized enterprises (MSMEs), and the dashed line shows average firm leverage for large and super-large enterprises. The notes to Table 3 provide the definitions of these size categories. Appendix Table A.1 contains all variable definitions and data sources. A.1 contains all variable definitions and data sources.

A.3

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  10 Data on external nance dependence come from Rajan and Zingales (1998) and Duygan-Bump et al. (2015). Both measure rms' dependence on external nance as the proportion of capital expenditures not nanced with cash ow from operations. Positive values indicate that rms tend to issue debt or equity to nance investments, while negative values mean that rms al., 2015 and Aminadav and Papaioannou, 2020).

Figure 1 :

 1 Figure 1: State Ownership and Firm Leverage over Time

Figure 2 :

 2 Figure 2: Share of State-Owned Enterprises among All Enterprises, by Firm Size

Figure 3 :

 3 Figure 3: Variation in State Ownership and Firm Size

Figure 4 :

 4 Figure 4: Marginal Effects of State Ownership on Firm Leverage, by Firm Size

Figure 5 :

 5 Figure 5: Marginal Effects of State Ownership on the Cost of Debt, by Firm Size

Figure 6 :

 6 Figure 6: Firm Leverage Before and After Privatization

  This table reports OLS regressions where the dependent variable is firm leverage. In each column, the sample includes the N largest listed firms within the specified period, where N = {100, 300, 500}. Firm characteristics include firm size, tangibility, profitability and the non-debt tax shield. All regressions include interactive fixed effects (FE) at the country × two-digit NACE 2 sector × year level. Columns 1-3 report regressions for the period 2000-2019. Columns 4-6 report regressions for the period 2000-2008 (before the global financial crisis). Columns 7-9 report regressions for the period 2009-2019 (during and after the global financial crisis). Standard errors are clustered at the firm level. Appendix Table

Figure A. 1 :

 1 Figure A.1: State Ownership and Firm Leverage: Excluding One Industry/Region/Year at a Time

Figure A. 2 :

 2 Figure A.2: Covariate Balance and Genetic Mahalanobis Matching

  cover only manufacturing sectors. For this reason, the classication by[START_REF] Duygan-Bump | Financing Constraints and Unemployment: Evidence from the Great Recession[END_REF] is our preferred measure, while we use the estimates by[START_REF] Rajan | Financial Dependence and Growth[END_REF] for robustness checks. purchasing power parity rates. Our main institutional quality measures are the Rule of law and Control of corruption indices from the World Governance Indicators database; and the Resolving insolvency and Protecting minority investors scores from the World Bank's Doing Business report. Appendix TableA.1 contains all variable denitions.

	4 Empirical Methodology
	4.1 Cross-sectional analysis

Table 1 :

 1 Summary Statistics This table provides summary statistics for all variables used in the analysis. Panels A, B and C summarize the main characteristics of firm-, country-and industry-level variables, respectively. Appendix TableA.1 contains all variable definitions and data sources.

	Panel A: Firm-level Variables
	Variable	Observations Mean	SD	Median	Min.	Max.
	State ownership	19,651,734	0.814	8.384	0	0	100
	State-owned ≥ 20% State-owned ≥ 50% State-owned ≥ 99% State-owned [1%; 20%)	19,651,734 19,651,734 19,651,734 19,651,734	0.010 0.008 0.006 0.005	0.099 0.090 0.076 0.067	0 0 0 0	0 0 0 0	1 1 1 1
	State-owned [20%; 50%)	19,651,734	0.002	0.042	0	0	1
	State-owned [50%; 99%)	19,651,734	0.002	0.048	0	0	1
	Firm size	19,651,734	13.925	2.197	13.732	2.197	29.131
	Profitability	19,651,734	0.091	0.195	0.074	-2	2
	Tangibility	19,651,734	0.274	0.289	0.158	0	1
	Non-debt tax shield	19,651,734	0.043	0.056	0.027	0	1
	Firm leverage	19,651,734	0.186	0.249	0.072	0	2
	Cost of debt	9,847,218	0.087	0.106	0.054	0.006	0.823
	Panel B: Country-level Variables
	Variable	Countries	Mean	SD	Median	Min.	Max.
	State banks	87	0.152	0.196	0.056	0	1
	Foreign banks	86	0.341	0.323	0.22	0	1
	GDP per capita, PPP, log	87	10.049	0.868	10.224	7.729	11.655
	Rule of law	86	0.576	0.931	0.590	-1.823	2.100
	Control of corruption	86	0.538	1.023	0.356	-1.431	2.470
	Resolving insolvency	84	0.564	0.204	0.552	0	0.939
	Protecting minority investors 84	0.600	0.149	0.600	0.200	0.967
	Panel C: Industry-level Variables
	Variable	Industries	Mean	SD	Median	Min.	Max.
	External finance dependence	73	0.036	0.314	0.010	-0.960	0.670
	Liquidity needs	107	0.173	0.042	0.174	0.050	0.290
	Tangibility	403	0.257	0.188	0.207	0.000	0.855

Notes:

Table 2 :

 2 State Ownership and Firm Leverage

				Firm leverage
		(1)	(2)	(3)	(4)	(5)
	State-owned ≥ 1%	-0.048*** (0.001)
	State-owned ≥ 20%		-0.055*** (0.001)
	State-owned ≥ 50%			-0.060*** (0.001)
	State-owned ≥ 99%				-0.064*** (0.002)
	State-owned [1%; 20%)					-0.030***
						(0.001)
	State-owned [20%; 50%)					-0.031***
						(0.002)
	State-owned [50%; 99%)					-0.047***
						(0.002)
	State-owned [99%; 100%]					-0.067***
						(0.002)
	Firm size	0.011***	0.011***	0.011***	0.011***	0.011***
		(0.000)	(0.000)	(0.000)	(0.000)	(0.000)
	Profitability	-0.125***	-0.126***	-0.125***	-0.125***	-0.125***
		(0.000)	(0.000)	(0.000)	(0.000)	(0.000)
	Tangibility	0.184***	0.185***	0.185***	0.184***	0.185***
		(0.001)	(0.001)	(0.001)	(0.001)	(0.001)
	Non-debt tax shield	0.188***	0.187***	0.187***	0.186***	0.187***
		(0.002)	(0.002)	(0.002)	(0.002)	(0.002)
	Country × Sector × Year FE	Yes	Yes	Yes	Yes	Yes
	R-squared	0.214	0.214	0.214	0.214	0.214
	N observations	19,651,734 19,651,734 19,651,734 19,651,734 19,651,734
	N firms	3,976,881	3,976,881	3,976,881	3,976,881	3,976,881
	N countries	89	89	89	89	89

Table 3 :

 3 State Ownership and Firm Leverage: Firm-Size Heterogeneity This table reports OLS regressions where the dependent variable is firm leverage. In columns 1-6, the sample includes firms of the specified size category only and observations are not weighted. The firm-size categories are: super-large (total assets above EUR 1 billion, large (total assets between EUR 1 billion and EUR 43 million), medium-sized (total assets between EUR 10 and 43 million), small (total assets between EUR 2 and 10 million) and micro (total assets below EUR 2 million).MSMEs is a combination of micro, small and medium-sized firms. A firm is classified by its size only once: in the year it first enters the data set. In columns 7 and 8, the sample includes all firms. In column 7, observations are weighted by log of total assets and in column 8 by total assets. Firm characteristics include firm size, tangibility, profitability and non-debt tax shield. All regressions include interactive fixed effects (FE) at the country × two-digit NACE 2 sector × year level. Standard errors are clustered at the firm level. Appendix TableA.1 contains all variable definitions and data sources.

	All firms	(7) (8)	-0.052*** -0.007	(0.001) (0.008)	Yes Yes	Yes Yes	0.214 0.387	19.7M 19.7M	4.0M 4.0M	89 89
	Super-large	(6)	-0.005	(0.009)	Yes	Yes	0.380	75,625	8,433	68
	Large	(5)	-0.018***	(0.003)	Yes	Yes	0.286	737,270	96,179	89
	MSMEs	(4)	-0.062***	(0.001)	Yes	Yes	0.213	18.8M	3.9M	87
	Medium	(3)	-0.040***	(0.003)	Yes	Yes	0.227	1.3M	198,609	85
	Small	(2)	-0.062***	(0.003)	Yes	Yes	0.201	3.5M	582,095	76
	Micro	(1)	-0.063***	(0.002)	Yes	Yes	0.220	14.0M	3.1M	61
			State-owned ≥ 20%	Firm characteristics	Country×Sector×Year FE	R-squared	N observations	N firms	N countries	Notes:

Table 4 :

 4 State Ownership, Institutional Quality and Firm Leverage This table reports OLS regressions where the dependent variable is firm leverage. Firm characteristics include firm size, tangibility, profitability and non-debt tax shield. All regressions include interactive fixed effects (FE) at the country Standard errors are clustered at the firm level. Appendix TableA.1 contains all variable definitions and data sources.

	Firm leverage	(1) (2) (3) (4) (5)	State-owned ≥ 20% -0.521*** -0.074*** -0.071*** -0.139*** -0.113*** (0.021) (0.001) (0.001) (0.004) (0.006)	State-owned ≥ 20% × GDP per capita 0.045*** (0.002)	State-owned ≥ 20% × Rule of law 0.023*** (0.001)	State-owned ≥ 20% × Control of corruption 0.022*** (0.001)	State-owned ≥ 20% × Resolving insolvency 0.120*** (0.006)	State-owned ≥ 20% × Protecting investors 0.097*** (0.010)	Firm characteristics Yes Yes Yes Yes Yes	Country × Sector × Year FE Yes Yes Yes Yes Yes	R-squared 0.214 0.214 0.214 0.213 0.212	N observations 19.6M 19.5M 19.5M 19.2M 18.5M	N firms 4.0M 3.9M 3.9M 3.8M 3.7M	N countries 87 86 86 84 84	× year Notes: × two-digit NACE 2 sector level.

Table 7 :

 7 State Ownership, Structure of the Banking Sector and the Cost of Debt This table reports OLS regressions where the dependent variable is a firm's cost of debt. In column 1, the sample includes all firms while in columns 2-4, the sample includes firms of the specified size category. The notes to Table3provide the definitions of these size categories. Firm characteristics include firm size, tangibility, profitability and non-debt tax shield. All regressions include interactive fixed effects (FE) at the country × two-digit NACE 2 sector × year level. Standard errors are clustered at the firm level. Appendix TableA.1 contains all variable definitions and data sources.

		All firms	MSMEs	Large	Super-large
		(1)	(2)	(3)	(4)
	State-owned ≥ 20%	-0.005*** (0.001)	-0.004*** (0.001)	-0.014*** (0.002)	-0.010 (0.008)
	State-owned ≥ 20% × State banks	0.002 (0.003)	0.006 (0.004)	0.007 (0.005)	0.008 (0.015)
	State-owned ≥ 20% × Foreign banks	0.022*** (0.002)	0.023*** (0.003)	0.011* (0.006)	0.009 (0.013)
	Firm characteristics	Yes	Yes	Yes	Yes
	Country × Sector × Year FE	Yes	Yes	Yes	Yes
	R-squared	0.076	0.078	0.129	0.28
	N observations	7.0M	6.6M	355,851	35,866
	N firms	2.2M	2.1M	73,225	6,454
	N countries	85	81	85	61

Notes:

Table 8 :

 8 Privatization and Firm Leverage This table reports fixed effects panel data regressions where the dependent variable is firm leverage.

		All firms	MSMEs	(Super)Large
		(1)	(2)	(3)
	Pre-privatization	-0.050***	-0.061***	-0.006
		(0.010)	(0.011)	(0.025)
	Firm characteristics	Yes	Yes	Yes
	Country × Year FE Sector × Year FE Firm FE	Yes Yes Yes	Yes Yes Yes	Yes Yes Yes
	Within R-squared	0.042	0.043	0.010
	N observations	7,911	6,129	1,286
	N firms	920	727	164
	N countries	29	22	21

Notes:

Table 9 :

 9 Privatization and Firm Leverage: Average Treatment Effect on the Treated (ATT)

	Year	Treated	Control	Raw Diff.	ATT	ATT b.a.	Γ *
	T-5	373	334	-0.059***	0.001	-0.001	1.00
				(0.006)	(0.002)	(0.001)
	T-4	408	371	-0.051***	0.002	-0.002***	1.00
				(0.005)	(0.002)	(0.001)
	T-3	468	428	-0.051***	0.005***	0.002*	1.00
				(0.005)	(0.002)	(0.001)
	T-2	391	357	-0.046***	0.021***	0.018***	1.00
				(0.005)	(0.002)	(0.002)
	T-1	411	385	-0.042***	0.029***	0.026***	1.00
				(0.005)	(0.003)	(0.003)
	T	508	481	-0.015***	0.040***	0.038***	1.20
				(0.006)	(0.004)	(0.004)
	T+1	483	449	0.015**	0.069***	0.066***	1.60
				(0.006)	(0.005)	(0.005)
	T+2	451	417	0.023***	0.066***	0.064***	1.60
				(0.007)	(0.006)	(0.006)
	T+3	369	337	0.016**	0.076***	0.074***	1.75
				(0.007)	(0.006)	(0.006)
	T+4	316	287	0.023***	0.059***	0.058***	1.40
				(0.008)	(0.005)	(0.005)
	T+5	278	255	0.029***	0.064***	0.067***	1.20
				(0.009)	(0.006)	(0.006)

  Table A.2: State Ownership and Firm Leverage: Largest Public Firms

  TableA.3: State Ownership, Bank Ownership and Firm Leverage: Sample-Split Regressions This table summarizes the results of OLS regressions where the dependent variable is firm leverage. Each cell reports the results of a separate regression ran on a different sample of countries. The vertical and horizontal axis legends indicate the percentage of all banking assets owned by state banks (horizontal axis) and by foreign banks (vertical axis) in the countries of that cell. Firm characteristics include the log of total assets, tangibility, profitability and non-debt tax shield. All regressions include interactive fixed effects (FE) at the country × two-digit NACE 2 sector × year level. Standard errors are clustered at the firm level. Appendix TableA.1 contains all variable definitions and data sources.

					State bank share
				<= 10%	(10%; 50%]	>50%
			Coefficient	-0.049***	-0.031***	-0.012*
			SE	(0.003)	(0.004)	(0.007)
		<= 10%	N observations	5,824,114	419,985	55,761
			N firms	1,835,567	167,669	15,885
	Foreign bank share	(10%; 50%]	N countries Coefficient SE N observations N firms N countries	20 -0.037*** (0.004) 2,051,993 717,005 24	17 -0.067*** (0.003) 2,148,401 706,777 34	6 -0.109*** (0.007) 57,356 27,198 5
			Coefficient	-0.083***	-0.104***
			SE	(0.003)	(0.002)
		>50%	N observations	1,899,501	679,248	N/A
			N firms	616,062	226,874
			N countries	24	11

Notes:

  TableA.4: State Ownership and Firm Leverage: Cross-Industry Heterogeneity This table reports OLS regressions where the dependent variable is firm leverage. In columns 1 and 3, the sample includes all available firms. In column 2, the sample includes manufacturing firms only. EFD stands for external finance dependence. Firm characteristics include firm size, tangibility, profitability and non-debt tax shield. All regressions include interactive fixed effects (FE) at the country × two-digit NACE 2 sector × year level. Levels for EFD, liquidity needs and sector-level tangibility are absorbed in these fixed effects. Standard errors are clustered at the firm level. Appendix TableA.1 contains all variable definitions and data sources.

			Firm leverage
		(1)	(2)	(3)
	State-owned ≥ 20%	-0.060*** (0.001)	-0.059*** (0.020)	-0.065*** (0.002)
	State-owned ≥ 20% × EFD	0.035*** (0.005)
	State-owned ≥ 20% × Liquidity needs		0.082 (0.114)
	State-owned ≥ 20% × Tangibility (sector-level)			0.029*** (0.005)
	Firm characteristics	Yes	Yes	Yes
	Country × Sector × Year FE	Yes	Yes	Yes
	R-squared	0.214	0.207	0.219
	N observations	19.1M	1.6M	16.1M
	N firms	3.9M	285,324	3.3M
	N countries	89	80	89

Notes:

Electronic copy available at: https://ssrn.com/abstract=4105153

As our rm-level data use a four-digit NACE 2 sector classication, we rst establish concordance between manufacturing industries in ISIC rev. 2 and NACE 2 using concordance tables from UN Stats (see https://unstats.un.org/unsd/classications/Econ/isic). If one NACE 2 sector matches multiple ISIC rev. 2 sectors, then we use a simple mean of the corresponding ISIC rev. 2 sectors.

Electronic copy available at: https://ssrn.com/abstract=4105153 in a particular industry typically have cash ows that exceed their investments.

Notes: This table reports OLS regressions where the dependent variable is firm leverage. Column 5 uses bins where the omitted baseline category is all firms with no or less than 1% state ownership. All regressions include interactive fixed effects (FE) at the country × two-digit NACE 2 sector × year level. Standard errors are clustered at the firm level. Appendix TableA.1 contains all variable definitions and data sources.

sector × year level. Standard errors are clustered at the firm level. Appendix TableA.1 contains all variable definitions and data sources.

34

Electronic copy available at: https://ssrn.com/abstract=4105153 Notes: This table reports OLS regressions where the dependent variable is a firm's cost of debt. In column 1, the sample includes all firms while in columns 2-4, the sample includes firms of the specified size category. The notes to Table 3 provide the definitions of these size categories. Firm characteristics include firm size, tangibility, profitability and non-debt tax shield. All regressions include interactive fixed effects (FE) at the country × two-digit NACE 44

Electronic copy available at: https://ssrn.com/abstract=4105153 9. The dots correspond to annual ATT estimates including a bias-adjustment term. The whiskers represent 95 percent confidence intervals.

45

Electronic copy available at: https://ssrn.com/abstract=4105153 Id.

Appendices

State-owned [X%; Y%)

Dummy that is 1 if state ownership is above or equal to X% and below Y%, and 0 otherwise. Id.

Firm size

Total assets (log).

Id.

Profitability

Earnings before interest, taxes, depreciation, and amortization (EBITDA) to total assets.

Id.

Productivity

Operating revenue to total assets.

Id.

Tangibility (firm-level) Tangible fixed assets to total assets.

Id.

Non-debt tax shield Depreciation and amortization to total assets.

Id.

Firm leverage Total formal debt (loans from banks and outstanding bonds) to total assets.

Id.

Cost of debt

Total interest expenses to total formal debt (trimmed at the 5th and 95th percentiles).

Id.

State banks

Share of bank assets held by domestic state banks (50 percent or more state-owned).

Bank Regulation and Supervision Survey. Chinese data: Berger et al. ( 2009)

Foreign banks Share of bank assets held by foreign banks (50 percent or more foreign-owned).

Global Financial Development Database

GDP per capita Log of GDP per capita based on purchasing power parity (PPP) and constant 2017 dollars. World Development Indicators

Rule of law Index capturing perceptions of the extent to which agents have confidence in and abide by the rules of society, and in particular the quality of contract enforcement, property rights, the police and the courts, as well as the likelihood of crime and violence. The index is expressed in units of a standard normal distribution, with mean zero, standard deviation of one, and runs from approximately -2.5 to 2.5. Higher values indicate a stronger rule of law.

World Governance Indicators

A.1

Electronic copy available at: https://ssrn.com/abstract=4105153

Control of corruption Index capturing perceptions of the extent to which public power is exercised for private gain, including both petty and grand forms of corruption, as well as "capture" of the state by elites and private interests. The index is expressed in units of a standard normal distribution, with mean zero, standard deviation of one, and runs from approximately -2.5 to 2.5. Higher values correspond to better control of corruption (i.e. lower corruption).

Id.

Resolving insolvency

Index measuring the gap between an economy's performance and the regulatory best practice on the Resolving Insolvency Indicator components. The score ranges from 0 to 100, where 0 represents the worst regulatory performance and 100 the best.

World Bank Doing Business

Protecting minority investors Index measuring the gap between an economy's performance and the regulatory best practice on the Protecting Minority Investors Indicator components. The score ranges from 0 to 100, where 0 represents the worst regulatory performance and 100 the best.

Id.

External finance dependence Industry-level median proportion of capital expenditures not financed with cash flow from operations. Positive values mean firms must issue debt or equity to finance investments.

Negative values mean firms have cash flow exceeding their investments. Calculated using data from companies in Compustat for at least 10 years between 1980 and 1996. Industry is defined at the two-digit level.

Duygan- [START_REF] Duygan-Bump | Financing Constraints and Unemployment: Evidence from the Great Recession[END_REF] Liquidity needs Industry-level median ratio of inventories over annual sales. Calculated based on data from all manufacturing companies in Compustat during 1980-1989. Industry is defined at the four-digit level. [START_REF] Raddatz | Liquidity Needs and Vulnerability to Financial Underdevelopment[END_REF] Tangibility (sector-level) Industry-level median ratio of tangible fixed assets to total assets. Calculated based on data from all available US companies in Bureau van Dijk's Orbis during 2000-2019. Industry is defined at the four-digit level.

BvD Orbis

A.2