Giuseppe Arcuri

Nadine Levratto

New firms' bankruptcy: does local banking market matter?

Keywords: Probability of bankruptcy, new firms, multilevel model, local banking structure JEL codes: C26, C30, M13, R11

This paper investigates the role of local context, with regard to the effect of local financial development and banking concentration, on a new firm's probability of bankruptcy. Our empirical setting is based on the Logit Multilevel Model that better allows the treatment of data referring to different levels of aggregation (firm and local variables) applied to new firms located in Italian provinces. We find that a higher level of financial development in a province decreases the likelihood of a new firm's bankruptcy. This result is robust considering a 2SLS regression in which we use instruments for the local financial development and for the concentration of bank branches. In addition, our estimations suggest that the effect of local financial development and bank concentration is shaped by size. Local financial development is particularly significant for small start-ups, which traditionally suffer from great difficulty in accessing credit, whereas local banking concentration reduces the probability of bankruptcy for large, new firms.

Introduction

Since the work of [START_REF] Guiso | Does local financial development matter?[END_REF], there has been a renewed interest in the differences at the local level of financial development affecting a firm's financial activities.

While it appears well stated that local financial development and, in general, institutional features of the local context, shape the financial decisions of firms [START_REF] Cariola | The Influence of Local Institutional Differences on the Capital Structure of SMEs: Evidence from Italy[END_REF][START_REF] Deloof | Local Financial Development and the Trade Credit Policy of Italian SMEs[END_REF][START_REF] Deloof | Local banking development and the use of debt financing by start-up firms[END_REF], with particular regards to small and medium-sized enterprises (SMEs), it is a first-order problem to investigate how the differences in the local market can affect the SMEs' quality of life in the short-run and their capability to access growth opportunities and operate for long-term success. In particular, local financial development can provide valuable support at the time firms are more fragile, as in their early stages. New and young companies are the primary source of job creation in economies [START_REF] Haltiwanger | Who Creates Jobs? Small versus Large versus Young[END_REF], contributing to economic dynamism by injecting competition into markets and spurring innovation [START_REF] Wiens | The Importance of Young Firms for Economic Growth, 13th of September[END_REF]. At the same time, these firms are the most financially vulnerable in the market. This weakness leads to questioning the role played by the financial system and, more precisely, by banks, in the local economic activity and as a driver of the performance of local firms. This research addresses this question focusing on new firms' survival, one among many proxies describing the robustness of the companies. The analysis is centred on the role of local financial markets as determinants of firm bankruptcy. Considering that small and young firms are mainly hit by strong difficulties in the take-off years, we consider that the postcreation period is the moment where the local financial context provides the more valuable support to new firms. As new firm, we mean a newly incorporated company, independent from any group, not related to any industrial spin-off, and operating in market sectors. The objective of this paper is thus to investigate whether the local financial market influences new firm bankruptcies to enhance our understanding of the drivers of this failure, to explore potential areas of interventions and to transform business failures into learning opportunities for future improvements in entrepreneurship.

The novelty of this paper results from several features: the emphasis put on the relationship between insolvency and the organization of the local credit market, the sample of companies considered in the empirical analysis based on new firms, and the estimation technique used. Examining the sources of the regional disparities in the probability of 3 corporate bankruptcy, it rapidly appears that the structure of the local debt market matters. A strand of the literature shows that credit rationing and institutional features vary across regions [START_REF] Bonnet | Financial Constraints on New Firms: Looking for Regional Disparities[END_REF][START_REF] Andriani | Is Acting Prosocially Beneficial for the Credit Market?[END_REF][START_REF] Andriani | Institutional Conformity and Regional Credit Market Failures: Evidence from the Italian Industrial Districts[END_REF], suggesting that the regional dimension is more important when companies are small [START_REF] Bonnet | Entrepreneurship motives, Entrepreneurial Orientation and Duration of New French Firms[END_REF].

Our paper is in accordance with the strand of literature [START_REF] Glauben | Agriculture on the move: Exploring regional differences in farm exit rates in Western Germany[END_REF][START_REF] Fotopoulos | Location and survival of new entry[END_REF][START_REF] Buehler | The geographic determinants of bankruptcy: evidence from Switzerland[END_REF] finding that a firm's bankruptcy is shaped by differences in the local context where the firms are based. The difference from the previous research originates from the restriction of the field of our analysis to new firms, which, according to many research studies on bankruptcy, are more likely to exit from the market than other firms [START_REF] Kale | Business failures: liabilities of newness, adolescence, and smallness[END_REF][START_REF] Thornhill | Comprendre l'échec: mortalité organisationnelle et approche fondée sur les ressources[END_REF] and are very vulnerable to the macroeconomic environment [START_REF] Petersen | The effect of credit market competition on lending relationships[END_REF][START_REF] Di | The changing structure of local credit markets: Are small businesses special[END_REF]. Another novelty of this paper is due to the estimation technique used. Our empirical setting is based on the logit multilevel model, which has never been used in bankruptcy studies. This novelty allows us to consider the hierarchical structure of the data and to better consider the effect of local variables.

The analysis is based on a unique sample covering all firms incorporated in Italy between 2008 and 2012. Italy represents an interesting case for studying this question, since it is characterized by cross-regional differences although all the regions are subject to the same formal institutions such as rules of law, constitution, civil, and criminal codes [START_REF] Andriani | Institutional Conformity and Regional Credit Market Failures: Evidence from the Italian Industrial Districts[END_REF][START_REF] Guiso | Does local financial development matter?[END_REF]). The results suggest that a higher level of local financial development decreases new firms' bankruptcy likelihood, particularly in the case of small firms, whereas concentration in the local banking market reduces the probability of bankruptcy of large, new firms.

The remainder of the paper is organized as follows. First, the literary review about firm bankruptcy, local financial development and bank concentration is presented. This presentation is followed by a description of the model, the sample, the variables employed, and their descriptive statistics. Next, the empirical results are reported. Finally, the main findings are synthesized, and considerations for future research are offered.

Literary review and hypotheses

Financial distress, bankruptcy and general firm exits from the market have been the theme of several research studies in recent years. Beginning with the pioneering work of on firm-specific features, searching to predict insolvency through the application of several statistical methods on economic and accounting data. The focus of this area of the accounting and finance literature has typically considered only the internal features of a company (financial and non-financial information) to assess its likelihood of failure. Only very recently, a small number of studies analysed the influence of institutional features of the local context to understand the exit behaviour across geographical regions [START_REF] Fotopoulos | Location and survival of new entry[END_REF][START_REF] Glauben | Agriculture on the move: Exploring regional differences in farm exit rates in Western Germany[END_REF], Buehler et al. 2010). These papers suggest that there is a link between a firm's bankruptcy risk and its geographic location. Our contribution differs from previous studies, because it focuses on the role of the local banking market in determining the access to credit and its consequence on new firms' survival.

The idea that the financial sector has the potential to influence patterns of innovation and growth dates to [START_REF] Schumpeter | The Theory of Economic Development. An Inquiry into Profits, Capital Credit, Interest, and the Business Cycle[END_REF], who argued that the services provided by financial intermediaries are essential for technological innovation and economic development. In the 1990s, beginning with the studies by King and Levine (1993a, 1993b, 1993c), a new body of literature has provided empirical evidence about a positive relation between the level of development achieved by the banking system and the growth rates of real variables (percapita GDP, per-capita productivity, value added of individual industrial sectors, and sales by individual firms).

Considering the effect of financial development at the micro level, and the local dimension of the bank credit market, another part of the literature, however, documents that distance matters in the provisions of funds, particularly for small firms [START_REF] Petersen | Does Distance Still Matter? The Information Revolution in Small Business Lending[END_REF]. [START_REF] Guiso | Does local financial development matter?[END_REF] emphasize the importance of finance at the local level, defining local financial development as the "ease with which subjects in need of external funds can access them and the premium they have to pay for these funds" and "enables a more efficient allocation of capital reducing borrowing and financing constraints". A well-developed financial system at the local level can thus facilitate the ability of a company to gain access to external financing, providing cheaper financing to worthy companies [START_REF] Guiso | Does local financial development matter?[END_REF].

In general, it is suggested that banks operating locally have more knowledge and control over local firms and entrepreneurs [START_REF] Alessandrini | A ''possibilist'' approach to local financial systems and regional development: The Italian experience[END_REF]. Consequently, local small businesses are very sensitive to the behaviour of local banks or branches.

The previous empirical findings demonstrate that local financial development is positively related to growth [START_REF] Guiso | Does local financial development matter?[END_REF][START_REF] Gagliardi | Financial development and the growth of cooperative firms[END_REF], enhances the probability of individuals starting their own businesses, favours the entry of new firms [START_REF] Guiso | Does local financial development matter?[END_REF] and affects firm's financial activities in different fields. It is suggested that, in more financially developed areas inside a country, firms use more debt [START_REF] Cariola | The Influence of Local Institutional Differences on the Capital Structure of SMEs: Evidence from Italy[END_REF]) and more trade credit [START_REF] Deloof | Local Financial Development and the Trade Credit Policy of Italian SMEs[END_REF]. These features strongly affect the financial decisions of new firms [START_REF] Deloof | Local banking development and the use of debt financing by start-up firms[END_REF]. A greater availability of bank credit with a higher level of post-entry growth for new firms should thus result in a lower risk of bankruptcy. In contrast, financial constraints are likely to be more severe in the presence of a poorly developed financial system.

Consistent with these considerations, we formulate our first hypothesis:

H1a: a higher level of local financial development reduces new firm's probability of bankruptcy.

The availability and cost of bank loans is crucial for many small businesses because they often do not have other possibilities for external funding [START_REF] Berger | The economics of small business finance: The roles of private equity and debt markets in the financial growth cycle[END_REF][START_REF] Miller | Small-Business Financing after the Financial Crisis: Lessons from the Literature[END_REF]. The evidence provided by [START_REF] Titman | The determinants of capital structure choice[END_REF] indicates that small firms tend to use significantly more short-term financing than large firms. This difference in financing practices may reflect the high transaction costs that small firms confront when they issue long-term debt or equity.

If small firms find it more difficult to access financial services due to greater information and transaction costs, the financial development that ameliorates these frictions will exert a particularly positive impact on small firms [START_REF] Cestone | Anticompetitive Financial Contracting: The Design of Financial Claims[END_REF]White 2003, Guiso et al 2004). Finally, bank debt represents a critical source of external financing for new firms (e.g., [START_REF] Bates | Financing small business creation: The case of Chinese and Korean immigrant entrepreneurs[END_REF][START_REF] Cassar | The financing of business start-ups[END_REF][START_REF] Robb | The capital structure decisions of new firms[END_REF][START_REF] Hanssens | Underexplored issues in entrepreneurial finance[END_REF]. The results of these empirical findings could suggest that local financial development may influence the extensive margin by allowing new small firms to access financial services and thus reduce their risk of bankruptcy.

Consistent with these considerations, we formulate the following hypothesis.

H1b: the effect of local financial development on new firms' probability of bankruptcy is stronger for small firms.

In addition to the relationship between the local financial development and the risk of insolvency, the literature also considers how banking concentration affects businesses.

Several scholars emphasize the need to disentangle the relationship between concentration and credit market functioning.

According to [START_REF] Petersen | The effect of credit market competition on lending relationships[END_REF], two opposite perspectives are available. The Structure-Conduct-Performance paradigm states a positive relationship between the level of concentration and the interest rates. A higher concentration deters firm creation, limits economic growth, and causes a higher rate of unemployment [START_REF] Black | Entrepreneurship and Bank Credit Availability[END_REF]Strahan, 2002, Cetorelli and[START_REF] Cetorelli | Finance as a Barrier to Entry: Bank Competition and Industry Structure in Local U.S. Markets[END_REF]. All these factors could lead to a higher risk of failure. An alternative perspective is supported by the information approach. This perspective argues that a higher concentration deters banks from developing information systems, leads them to prefer long-term customer relationships that grant them an advantage originating from the accumulation of private information about potential borrowers who, in turn, have better access to credit [START_REF] Dell'ariccia | Lending Booms and Lending Standards[END_REF].

The favourable effects of concentration on the survival of firms are more evident when firms are very young. In fact, considering the case of young firms, [START_REF] Petersen | The effect of credit market competition on lending relationships[END_REF] note that, when a firm is young, the potential for future cash flows may be high, while current cash flows are low. A monopolistic lender may be willing to subsidize such firms with cheap loans because the lender can extract rents later when the firms' cash flows become high. This finding means that a monopolistic bank may financially support firms with the objective of exploiting rents from eventually successful borrowers. When a bank adopts this kind of strategy, it has the objective of maintaining the lending relationships in the future, certain that the firm will not be attracted by rival banks. In contrast, in a competitive credit market, banks cannot expect to share the future firm's surplus and may be forced to charge a premium to cover the riskiness of young or distressed firms.

However, this effect is strictly linked to this specific context; therefore, our second hypothesis is inspired by research showing the advantages resulting from a more intense concentration in the local banking market.

H2a: a higher local banking concentration reduces bankruptcy probability for new firms.

However, it is important to note that firm size can shape the previous relationship.

Indeed, two major papers by [START_REF] Beck | Financial and Legal Constraints to Firm Growth: Does Firm Size Matter[END_REF] and Bonaccorsi di Patti and Gobbi (2001) find that competition in the bank market has a different effect on the credit volume of small and medium size enterprises that traditionally suffer from greater difficulty in accessing credit, compared to the impact on large firms. More recently, the stronger effect of competition on smaller companies is documented by [START_REF] Sääskilahti | Local bank competition and small business lending after the onset of the financial crisis[END_REF] who proposes an empirical analysis of the relationship between the competitive environment and changes in lending during the crisis, comparing Lerner and Herfindahl indices. He concludes there is superior sensitivity of smaller companies.

This finding leads us to hypothesize:

H2b: The influence of concentration on the probability of new firms' bankruptcy is higher for smaller companies.

Method

Sample and data

Our dataset is derived from various sources. Data on the local banking market are from the Bank of Italy; data on economic development, population, and crime rates in the 103 Italian provinces are provided by the Italian National Institute of Statistics (ISTAT). Firms' data are extracted from the Orbis database, compiled by Bureau Van Dijk (BvD), which is a great resource for company data. The database contains the financial statements of privately held and publicly traded global firms, including more than 1 million Italian firms.

Firms needed to satisfy different requirements to be part of our sample. First, we included all firms that were legally incorporated in Italy in the years from 2008 to 2012, to avoid certain events in a specific year of incorporation driving our estimations. Second, with the objective of only considering real new firms not born from industrial spinoffs, we only consider stand-alone companies with at least 1 employee and fewer than 50 employees. We also exclude firms having a previous company name. We use these criteria with the objective of excluding ghost firms (that often exist only for fiscal reasons) and companies that are unlikely to be new firms. Third, we excluded public-owned firms because these firms' policies may be influenced by regulatory issues; we also excluded firms operating in different sectors (agriculture, financial and insurance activities, real estate activities, public administration, education, social services and human health services) because they may be subjected to particular failure regimes. Fourth, we excluded observations for which the total assets are less than 2,500 euros, which is the minimum equity requirement to found a firm in Italy. Fifth, we excluded from the dataset all firms whose status was unknown, inactive or dissolved and did not request official bankruptcy procedures. Finally, we only selected firms for which all information needed to calculate our variables is available. The final sample includes 94,418 firms.

Model and Variables

Since our dependent variable is a firm's probability of bankruptcy and our data refer to various levels of aggregation, we study the different sources of variability by means of the Logit Multilevel Model.

Companies operate in a socio-economic context, which significantly affects the performance of business processes [START_REF] Audretsch | Location: A Neglected Determinant of Firm Growth[END_REF]Dohse 2007, Garsaa and[START_REF] Garsaa | Does the employment growth rate depend on the local context? An analysis of French establishments over the 2004-2010 period[END_REF].

This finding is highlighted, as apparently weak ties between the organization and external parties can have a relevant impact on competitiveness and business performance but also on institutional structures and entrepreneurial purposes. In other words, firms located in the same territory share the same external environment; consequently, they are likely to be more similar to each other than firms operating in other geographical areas. From an econometric perspective, the most important effect of this similarity is that the assumption of independence of standard error is violated. This problem is resolved by the multilevel approach, which provides efficient estimates of coefficients since it controls for spatial dependence and correct standard errors of variables. Specifically, whereas standard logit regression has an overall mean coefficient, the logit multilevel model considers, in addition, group-level variance explicitly through the incorporation of random coefficients.

The model allows the simultaneous consideration of individual variables (

, where h is the number of covariates and i is the firm located in the j-th province) and local variables that represent a 'higher level' ( where k is the number of local covariates and j the province). An econometric specification of the Logit Multilevel model can be written as the logistic function of the general model with a continuous dependent variable [START_REF] Snijders | Multilevel Analysis[END_REF]:

Pr 1 α ∑ ∑ (1)
where F(_) is the logistic cumulative distribution function, and , are the so called second and first level residuals, normally distributed with variance and . In particular, represents the difference between the j-province and the total average. Table 1 provides the definitions of the variables used to test our model. The dependent variable used in the empirical model is the Probability of bankruptcy, a dummy variable that takes value 1 if a new firm requested an official bankruptcy procedure and 0 if it is normally operating. We focus on companies that have undertaken an official juridical procedure because of permanent financial distress to a maximum of 2 years after incorporation, because new firms that survive over the second year after incorporation are more likely to generate revenue and remain on the market. To check the robustness of our results, we also consider the probability of bankruptcy for 1 and 3 years after incorporation.

We exclude firms with temporary financial problems or companies that have voluntarily chosen liquidation for economic opportunity, mergers or acquisition. Firms whose status was unknown or dissolved without precision were dropped from the sample. Regarding the local variables, it is worth specifying that we consider the "local" unit, the province (NUT3 code), similar to what is done by the large majority of empirical works based on Italy (Guiso et al. 2004, Deloof and[START_REF] Deloof | Local Financial Development and the Trade Credit Policy of Italian SMEs[END_REF] and because, citing [START_REF] Guiso | Does local financial development matter?[END_REF]: "According to the Italian Antitrust authority the "relevant market" in banking for antitrust purposes is the province, a geographic entity very similar to a US county. This is also the definition the Central Bank used until 1990 to decide whether to authorize the opening of new branches."

Defining financial development is a challenging task [START_REF] Giovannini | Financial Markets, Banks, and Growth: Disentangling the links[END_REF]). Among the diverse indicators in use, we measure local financial development (FinDev) by Private Credit/GDP. This measure captures the amount of credit channelled through financial intermediaries to the private sector, and it has been used in several cross-country and within country studies on financial development [START_REF] Rajan | Financial Dependence and Growth[END_REF]Kendall 2009). [START_REF] Levine | Financial intermediation and growth: Causality and causes[END_REF] show that Private Credit/GDP is a suitable predictor of economic growth. Moreover, considering that the local banking market is related to local crime (Bonaccorsi di Patti 2009), we included in the analysis a proxy of criminality. Financial contracts require trust, which is negatively affected by crime. However, the lending relationship between banks and the firm also requires trust [START_REF] Fisman | Trade credit, financial intermediary development, and industry growth[END_REF]. The variable Crime, as a proxy of the business climate, is a measure that is based on the average number of extortion crimes reported by police to the judicial authority per 1000 inhabitants at the province level over the period considered. In addition, we include GDPpercapita as a measure of macroeconomic conditions in the different provinces defined as GDP per thousand inhabitants.

In accordance with the general literature on bankruptcy, the analysis includes the main firm's internal features at the first level of the model. Table 1 also displays our control variables at the first level. We include firm Size as proxy for firm creditworthiness. We do not add firm's age in our model because the sample only contains new companies, but we add year fixed effects dummy variables to control for specific events that could occur in the year of incorporation. The model considers the financial structure and debt maturity of the firm.

The variables LtdTa and StdTa explain debt maturity. We also consider the value of tangible assets introducing a variable named Tangibility, which measures the capacity to provide collateral and, consequently, obtain financing to restructure the business. Similarly, it is essential to consider the role of the variable Intangible (i.e., intellectual resources: trademarks, patents and licenses) because these kinds of assets are more likely to form the basis for competitive advantage and growth. We also consider the return on assets (ROA), a measure of firm's profitability, which allows us to understand how profitable a company's assets are in generating revenue. We also include the variable WCTA as a measure of a firm's internal financing. Another essential element to consider when assessing firms' creditworthiness is the vulnerability of such debt. In fact, certain companies may be characterized by similar levels of indebtedness while presenting different degrees of vulnerability. Hence, it is important to consider the ability to generate sufficient income to cover the cost of debt. Therefore, in our model, we add a debt sustainability variable,

Interestcov. We include also the variable DifferentTaxShield, to understand the influence of different tax regimes and different amounts of amortization on the probability of bankruptcy.

The model considers other explanatory variables to control for additional non-financial characteristics of the firms, expected to be relevant in determining their bankruptcy. In our study, we include information about ownership structure with two dummy variables:

Majority_sh takes value 1 if there is a shareholder owning more than 50% of the firms and 0 otherwise, and Sole_pr that takes value 1 for firms owned and run by a unique shareholder and 0 otherwise. Industry dummies are included to capture industry-specific unobserved characteristics. Moreover, since the centre-north of Italy is more developed than the south and to explain the possibility that a firm's location influences its financial decisions, we include the dummies North and South to capture the location of a firm in a specific Italian macro-area.

Descriptive statistics and Empirical Results

The descriptive statistics concerning local and companies' variables are presented in Table 3, separately for active and bankrupt firms. The t tests for mean comparison for each variable is also presented. The variable FinDev has a negative sign at the 1% level of statistical significance.

Since the dependent variable is a dummy taking value 1 if the firm is in the default status and 0 otherwise, the negative sign of FinDev means that a firm incorporated in a province with a higher level of financial development has a lower probability to go bankrupt in the first years of its life. This empirical finding confirms our hypothesis H1a that a higher level of local financial development reduces new firms' probability of bankruptcy. This effect of local financial development on firm's probability of default is consistent with previous findings about local financial development in the literature.

Local financial development is positively related to growth [START_REF] Guiso | Does local financial development matter?[END_REF][START_REF] Gagliardi | Financial development and the growth of cooperative firms[END_REF]) and affects firm's financial activities in different fields. In more financially developed areas inside a country, firms use more debt [START_REF] Cariola | The Influence of Local Institutional Differences on the Capital Structure of SMEs: Evidence from Italy[END_REF]), more trade credit [START_REF] Deloof | Local Financial Development and the Trade Credit Policy of Italian SMEs[END_REF] and these features strongly affect financial decisions of start-ups [START_REF] Deloof | Local banking development and the use of debt financing by start-up firms[END_REF]. A greater availability of bank credit brings thus new firms to have a higher probability of survival and a greater potential to grow.

To test our hypothesis H1b concerning small firms, we divide our sample of firms into two groups depending on the size: small firms and large firms. To identify these groups, we split our population considering the distribution of the variable Size and composed two subsamples (above and below the median value). The results for Small and Large new firms are displayed in column 2 and 3, respectively. It is worth noting that the magnitude of FinDev coefficient declines, in absolute value, as we move from small firms to large ones, moving from -6.578 to -3.646; it is statistically significant at 1% and 5% for the subsample of small and large start-ups, respectively. This empirical finding confirms our hypothesis H1b that the effect of local financial development on new firms' bankruptcy is stronger for small new firms 2 . This finding is consistent with previous findings on financial development and different firm's performance according to the size. If small firms find it more difficult to access financial services due to greater information and transaction costs, then financial development that ameliorates these frictions can exert a particularly positive impact on small firms [START_REF] Cestone | Anticompetitive Financial Contracting: The Design of Financial Claims[END_REF]White 2003, Guiso et al 2004) more strongly reducing their probability to exit from the market.

The variable describing concentration in the local banking market HHI is not significant in the estimations concerning our whole sample. This finding means that, considering the total sample of Italian new firms, our hypotheses H2a and H2b are not confirmed since HHI is significant only for the subsample of large firms. This result is consistent with the standard flight-to-quality of credit from smaller (and relatively opaquer) firms to larger (and relatively more transparent) ones because of negative shocks hitting the banking sector over the studied period. The economic turmoil that hit the Italian economy after Lehman's collapse induced a contraction of credit supply [START_REF] Albertazzi | Credit supply, flight to quality and evergreening: an analysis of bank-firm relationships after Lehman[END_REF]European Central Bank, 2014) that particularly concerned small and more opaque firms, for which a long-term relationship with their main bank has been the most effective means of overcoming financial constraints [START_REF] Arnaudo | Should I stay or should I go? Firms' mobility across banks in the aftermath of financial turmoil[END_REF]. This bank-borrower relationship is more likely when the credit market is more concentrated so that, for smaller companies the flight to quality effect overpassed the advantages resulting from concentration. Consequently, our results exhibit no correlation between the rate of bankruptcy and the Herdindahl Hirschman index for this this size class, whereas larger companies continue to benefit from a strong customer relationship. The negative and statistically significant sign associated with HHI for large firms, is consistent with our expectations; concentration in the bank market reduces the probability of bankruptcy of new firms. In accordance with [START_REF] Petersen | The effect of credit market competition on lending relationships[END_REF], a bank operating in a concentrated market may offer more credit and at lower rates to young firms than may a bank operating in a competitive market. The other local variables, Crime and GDPpercapita, show no statistical significance in the model.

Concerning individual firm's feature at the first level, it is possible to argue that all variables have the intended sign in estimation.

Firm Size enters with a negative sign at the 1% level of significance; therefore, larger companies would encounter a lower probability of bankruptcy. This result confirms the previous empirical findings on size on firm performance [START_REF] Hurst | What Do Small Businesses Do?[END_REF][START_REF] Fort | How Firms Respond to Business Cycles: The Role of Firm Age and Firm Size[END_REF]). Short-term debt is associated with a positive sign at the 1% level of significance. This finding confirms our expectations that new firms have limited cash flows and low profits and rely more heavily on short-term debt finance and therefore, are most likely to be subject to financial distress [START_REF] Titman | The determinants of capital structure choice[END_REF] and financial restrictions [START_REF] Fazzari | Working Capital and Fixed Investment: New Evidence on Financing Constraints The[END_REF][START_REF] Whited | Debt, Liquidity Constraints, and Corporate Investment: Evidence from Panel Data[END_REF]. ROA enters, as expected, with a negative sign at the 1% level of statistical significance, indicating that more profitable companies encounter a lower bankruptcy risk. The estimated coefficient of the variable Tangibility is negative at the 1% level of significance. The proportion of tangible fixed assets in the total of all assets is confirmed as a measure of the capacity to provide collateral and, consequently, obtain financing to restructure the business.

DifferentTaxShield enters the regression with a negative sign at the 1% significance level, indicating that growing firms that are subject to higher levels of amortizations and taxes are less subject to financial distress and bankruptcy risk. The coefficient of (WCTA) has a negative sign in the estimation with a significance at 10%, indicating that a higher level of working capital helps the internal financing of a firm's activity, reducing its probability of exit from the market. The dummy variable Majority_sh enters with a negative sign at the 1% level, suggesting that, for firms with an alignment of interests in more concentrated ownership, the probability of financial instability and bankruptcy is reduced. The variables Ltd, Intangible, Interestcov and Sole_propr show no statistical significance, suggesting that long-term debt, the equipment of intangible assets, sustainability of debt and fully concentrated ownership do not appear to affect the probability of new firms' bankruptcy. The regressions are controlled for Italian macro-area, year of incorporation and industry fixed effects to avoid that specific issues would drive our estimations.

Robustness checks

A potential problem with the previous findings is that the observed effect that 5. Moreover, columns 2 and 3 of Table 5 show the estimations obtained when we modify our dependent variable that corresponds to the probability of bankruptcy over a period of 2 years after the year of incorporation. In particular, we want to investigate the effect of the local banking market (local financial development and local banking concentration) in influencing the probability of a firm's bankruptcy 1 and 3 year after incorporation. These regressions show that the effect of the local banking market is not relevant in the first year of a firm's life and that it does not appear to affect the probability of bankruptcy, since the coefficients of FinDev and HHI are not significant.

The regression in column 1 of

Conversely, if we consider bankruptcy over a period of 3 years after incorporation, the effect of local financial development is stronger in reducing a new firm's bankruptcy, since the coefficient associated with the variable FinDev extends from -4.700 to -6.004.

In the third year of life, the concentration in the local banking market also has a relevant role in reducing a new firm's bankruptcy, since the coefficient associated with HHI is negative and statistically significant. This finding means that the effect of the local banking market is relevant as the bank intends to be present in a firm's financial structure, and this effect is increasingly stronger over time.

Conclusions

The empirical investigation undertaken in this research targets estimating the impact of local financial development and bank concentration on new firms' bankruptcy.

Local financial development appears to play a role in shaping bankruptcy risk, since it reduces the probability of bankruptcy of new firms. Local financial development is positively related to growth and affects firm's financial activities in different fields. In more financially developed areas inside a country, firms use more debt, and this feature strongly affects the financial decisions of new firms. A greater availability of bank credit provides new firms with more potential to grow and survive. This effect is stronger for small new firms. The reason underlying this topic is that, if small firms find it more difficult to access financial services due to greater information and transaction costs, financial development that ameliorates these frictions can exert a particularly positive impact on small firms.

Furthermore, our results suggest that local banking concentration reduces the probability of bankruptcy only for large, new firms. A bank operating in a more concentrated market may financially support new firms with the objective of exploiting rents from eventually successful borrowers. When a bank adopts this kind of strategy, it has the objective of maintaining lending relationships in the future, certain of the fact that the firm will not be attracted to rival banks.

In terms of policy, a first indication offered by the current research is that the regulation of the bank sector at the local level plays a key role in a firm's early stage life, and a more stable financing relationship could represent an advantage for newly established firms. Second, agencies supporting business creation should define specific criteria in the selection of investment projects and the subsequent attribution of credit to create a stable lending relationship.

A limit of our study is represented by the observation that we consider the probability of bankruptcy over the early years after start-ups. We found that the effect of local financial development and bank concentration is relevant as the bank pursues a presence in a firm's financial structure, and this effect is stronger over time; however, we cannot obtain further evidence about the direction of this relationship in future years.

Furthermore, it would be interesting to investigate the relative importance of local features in a study that includes a sample of firms operating in different countries, to understand the level of heterogeneity in insolvency and its determinants across European countries and regions.

Notes 1. To measure the size of a firm, different variables could be used, such as the number of employees, total assets and turnover. However, the accounting data on "turnover" are more reliable than those on total number of employees reported in the balance sheets, and there are less missing data.

2. The different effect of Financial Development and HHI for small and medium new firms is confirmed by adding, for the whole sample, an interaction between

Figure 1

 1 Figure1displays the magnitude of our variable FinDev across Italian provinces.

Figure

  Figure 1 -Level of Financial Development (Average Values 2008-2012)

  local financial development and banking concentration have on a firm's bankruptcy may actually reflect omitted factors that affect both the local banking market and firms' performance, such as the local economic development. This finding means that estimations could suffer omitted variable bias. To ascertain the effect that the local banking market has on a firm's bankruptcy, we use exogenous determinants of the degree of banking development as instruments in 2SLS regressions. In accordance with Guiso et al. (2004) and Deloof and La Rocca (2014), we use measures of the local supply of credit in 1936 as determinants of the local banking development in the 2000s. While local banking structures in 1936 were largely determined by factors unrelated to local economic development, a new banking law in 1936 severely constrained the growth of the banking system. Since this law affected certain types of banks more than others and the type of banks in the system differed across regions, the law created significant local differences in banking development that may persist to the present day. Consistent with this argument, Guiso et al. (2004) find that local banking development in 1936 is strongly correlated with the current local banking market, but it is only weakly correlated with contemporary local economic development. First, we identify five measures of banking development in 1936 that significantly affect the current local banking development: the number of bank branches and banks in the province, the total number of mutual banks in the province, and the number of banks and bank branches over the population in the region in which a firm is located. The results of our robustness tests are displayed in Table

Table 1 -

 1 Variables' names and definition

	Explained variable	Probability of bankruptcy up to 2 years after incorporation
	Explanatory variables
	Local variables
	FinDev	Private Credit/Gross Domestic Production
	HHI	number of branches of bank i number of total branches
	Crime	Number of extortions / Thousands inhabitants
	GdpPerCapita	Gross Domestic Production / Thousands inhabitants
	Firm's variables
	Size	Logarithm of Total Assets
	StdTa	Short term Debt / Total Assets
	LtdTa	Long term Debt / Total Assets
	Tangibility	Tangible Assets / Total Assets
	Intangible	Intangible Assets/ Total Assets
	ROA	Ebit / Total Assets
	WCTA	Working Capital / Total Assets
	Interestcov	Ebitda / Interest paid
	DifferentTaxShield	(Ebitda-Ebit)/ Total Assets
	Majority_sh	Dummy variable= 1 if there's a majority shareholder
	Sole_propr	Dummy variable=1 if there's a unique shareholder

Table 2 .

 2 Table 2 provides a description for the bankruptcy ratio of new firms calculated in our sample. As shown by Table 2, our sample is well-balanced, since the default ratios among firms born in the different years of the analysis, are similar. Description of the bankruptcy ratio for new firms

		(1)	(2)	(3)	(4)
		Number of new firms	Bankrupted firms up to 1 year	Bankrupted firms up to 2 years	Bankrupted firms up to 3 years
	Year of incorporation
	2008	22630	135	546	1150
			(0.60%)	(2.41%)	(5.08%)
	2009	16984	126	508	1028
			(0.74%)	(2.99)	(6.05%)
	2010	21637	44	443	1247
			(0.20%)	(2.05%)	(5.76%)
	2011	14940	41	389	996
			(0.27%)	(2.60%)	(6.67%)
	2012	17927	78	578	1175
			(0.44%)	(3.22)	(6.55%)
	Total	94418

Table 3 -

 3 Descriptive statistics null hypothesis is true, the logistic regression can be used instead of a mixed model.In our results, the test has the value is 141.17 and it is significant at 1% level. This result supports the use of Logit Multilevel Model and consequently, the intercepts related to the different clusters should be treated as a group by group variant coefficients. Table4shows the results obtained when the Logit Multilevel Model is augmented through a set of province and individual variables. Province level regressors inserted in the model are FinDev, HHI, Crime and provincial GDPpercapita. At the firm level we include Size, They have already been presented.Column 1 in Table4shows estimations obtained for our sample of new firms.Focusing on the specific objective of the paper it is worth discussing the empiric findings about how provincial features, and specifically local financial development and bank concentration (second level variables) affect new firms' bankruptcy.

		Active firms (n=91654)		Bankrupt firms (n=2464)	t-test Mean
	Variable	Mean	Median	STD dev	Mean	Median	STD dev	Comparison
	FinDev	0.034	0.113	0.034	0.026	0.114	0.037	8.061***
	HHI	0.100	0.094	0.041	0.101	0.096	0.040	-1.182
	Crime	10.338	9.725	4.152	10.332	9.575	4.321	0.073
	GDPpercapita	22.617	21.966	9.822	22.575	21.966	9,530	0.208

the

Table 4 :

 4 Empirical results

		(1)	(2)	(3)
		Whole sample	Small new firms sub-	Large new firms
			group sample	sub-group sample
	Local Variables (2nd level)
	FinDev	-4.700***	-6.578***	-3.646**
		(1.436)	(1.610)	(1.433)
	HHI	-1.241	-0.794	-2.592**
		(0.769)	(0.879)	(1.190)
	GDPperCapita	-0.00221	-0.00414	-0.000946
		(0.00341)	(0.00424)	(0.00445)
	Crime	0.00348	0.00558	-9.78e-05
		(0.0124)	(0.0142)	(0.0156)
	Firm's variable (1st level)
	Size	-0.158***	-0.142***	-0.146***
		(0.0176)	(0.0380)	(0.0407)
	Stdta	0.554***	0.669***	0.320
		(0.145)	(0.191)	(0.222)
	Ltdta	-0.241	-0.177	-0.0970
		(0.188)	(0.281)	(0.253)
	Tangibility	-0.730***	-0.290*	-1.273***
		(0.130)	(0.170)	(0.210)
	Intangible	-0.0379	0.374**	-0.548**
		(0.148)	(0.189)	(0.248)
	ROA	-0.556***	-0.432***	-1.978***
		(0.0430)	(0.0467)	(0.146)
	WCTA	-0.118*	0.0909	-0.318***
		(0.0651)	(0.0852)	(0.101)
	Interestcov	-3.34e-06	-1.65e-05**	1.76e-06
		(2.65e-06)	(7.78e-06)	(1.69e-06)
	DifferentTaxShield	-0.510***	-0.496***	-2.017***
		(0.158)	(0.166)	(0.626)
	Majority_sh	-0.146***	-0.263***	0.0345
		(0.0464)	(0.0596)	(0.0749)
	Sole_pr	0.0446	-0.110	0.255***
		(0.0561)	(0.0763)	(0.0846)
	Year of incorporation FE	YES	YES	YES
	Sector FE	YES	YES	YES
	North/South FE	YES	YES	YES
	Constant	-2.479***	-2.524***	-2.420***
		(0.218)	(0.280)	(0.352)
	Variance
	Firms	3.29	3.29	3.29
	Province	0.051	0.055	0.037
	LR test	58.70***	27.33***	8.73***
	Log-likelihood	-11049.287	-6335.5944	-4632.3457
	Observations	94,418	47,209	47,209
	Number of groups	103	103	103

  Table5is based on 2SLS estimation in which we use certain instrumental variables for FinDev and HHI. The results fully confirm our previous findings; a higher level of financial development at the province level reduces the probability of bankruptcy for new firms. The magnitude of the variable's coefficients is highly different, for multilevel logit, the coefficient of explanatory variables does not correspond to the marginal effect on the dependent variable but is the effect on the Logit function. In contrast, in the 2SLS regression, the coefficient is the marginal effect on the dependent variable, because we run a regression with instruments without restrictions on the distribution of the dependent variable (linear probability model)4 . Standard errors are clustered by province level.

		Table 5: Robustness checks
		(1)	(2)	(3)
		2sls	Bankruptcy up to 1	Bankruptcy up to 3
			year	years
	Local variables (2 nd level)
	FinDev	-0.0904**	-3.563	-6.004***
		(0.0391)	(2.363)	(1.436)
	HHI	-0.0132	0.347	-1.340**
		(0.0862)	(1.446)	(0.626)
	GDPperCapita	-9.20e-05	0.000340	0.000415
		(6.82e-05)	(0.00654)	(0.00276)
	Crime	0.000117	0.0336	0.00373
		(0.000286)	(0.0235)	(0.0109)
	Firm's variables (1 st level)
	Size	-0.00355***	-0.172***	-0.127***
		(0.000621)	(0.0425)	(0.0118)
	Stdta	0.0119***	1.078***	0.480***
		(0.00404)	(0.307)	(0.100)
	Ltdta	-0.00643*	-0.932*	-0.318**
		(0.00336)	(0.537)	(0.127)
	Tangibility	-0.0188***	-0.510*	-0.753***
		(0.00369)	(0.293)	(0.0879)
	Intangible	-0.00644*	-0.445	0.0741
		(0.00385)	(0.372)	(0.100)
	ROA	-0.0364***	-0.506***	-0.607***
		(0.00468)	(0.0623)	(0.0363)
	WCTA	-0.00104	-0.511***	-0.133***
		(0.00167)	(0.153)	(0.0441)
	Interestcov	1.04e-08	-6.30e-06**	-3.02e-06
		(1.72e-08)	(2.61e-06)	(1.94e-06)
	DifferentTaxShield	-0.00140*	-0.953	-0.442***
		(0.000749)	(0.589)	(0.0965)
	Year of incorporation FE	YES	YES	YES
	Sector FE	YES	YES	YES
	North/South FE	YES	YES	YES
	Constant	0.0523***	-4.309***	-1.912***
		(0.0103)	(0.439)	(0.179)
	Variance
	Firm		3.29	3.29
	Province		0.107	0.059
	LR Test		15.55***	168.29***
	Log-likelihood		-2575.936	-20531.5
	Observations	94,418	94,418	94,418
	R-squared	0.009
	Number of groups		103	103
		21

Financial Development/HHI and a dummy variable =1 if Size has a value below its median. The results are available upon request.

The VIF test, reported in the appendix, suggests that there are no multicollinearity problems since all variables have a VIF value below 5.

In our empirical setting, the two levels are as follows: firms and province.

The first empirical step when using the multilevel approach is running the empty model.


Gabriel Gomes
email: gabriel.gomes@outlook.fr.

Emmanuel Hache
email: emmanuel.hache@ifpen.fr.

Valérie Mignon
email: valerie.mignon@parisnanterre.fr.

Anthony Paris
email: anthony.paris@ifpen.fr.

Cécile Couharde

Anne-Laure De

On the current account -biofuels link in emerging and developing countries: do oil price fluctuations matter? *

Keywords: JEL Classification: Q16, Q43, F32, C23 Biofuels, Oil, Current account, Panel smooth transition regression

Many developed countries promote the use of biofuels for environmental concerns, leading to a rise in the price of agricultural commodities utilized in their production. Such environmental policies have major effects on the economy of emerging and developing countries whose activity is highly dependent on agricultural commodities involved in biofuel production. This paper tackles this issue by examining the price impact of biofuels on the current account for a panel of 16 developing and emerging countries, and the potential nonlinear effect exerted by the price of oil on this relationship. Relying on the estimation of panel smooth-transition regression models, we show that positive shocks in the price of biofuels lead to a current-account improvement for agricultural commodity exporters and producers only when the price of oil is below a certain threshold. When the price of oil exceeds this threshold, fluctuations in the price of biofuels no longer affect the current account. These findings illustrate that a rise in the price of oil exerts a negative effect on the trade balance of commodity exporters which are also oil importers, dampening the biofuel price impact on the current-account position.

Introduction

For the past two decades, a strong interest has emerged in favor of the integration of renewable energies in the electricity mix and in the transportation sector. This constitutes a major concern for developed economies as well as for developing and emerging countries in order to ensure energy transition policies, to fight against climate change and reduce Greenhouse Gas Emissions. Implementing renewable energies is all the more relevant because they allow the country to earn double dividends, as their diffusion de facto reduces the volume of imported fossil fuels in parallel of environmental objectives [START_REF] Criqui | European climate-energy security nexus: A model based scenario analysis[END_REF]. Along these lines, the use of biofuels is encouraged in developed countries and in emerging economies such as Brazil,1 China and India for environmental concerns, as well as for promoting energy security, agricultural opportunities and economic growth. For instance, the European Union introduced a blending target of biofuels in petroleum products in [START_REF] Chinn | Medium-term determinants of current accounts in industrial and developing countries: an empirical exploration[END_REF]the Renewable Fuel Standard program (2005) combined with the Energy Independence and Security act of 2007 (36 billion gallons of biofuels by 2020) allowed the establishment of mandatory target of biofuels utilization in the United States' transportation sector. However, such environmental policies may cause externalities or adverse effects on the economy of emerging and developing countries whose activity is highly dependent on agricultural commodities used in biofuel production. Aiming at investigating those topical issues, this paper analyzes the price impact of biofuels on the economy of such countries, focusing on the current account.

These various aforementioned concerns have lead to a sharp rise in biofuel production since the mid-2000s. As shown in Figure 1, while biofuel production was on average around 30 thousand barrels from 2001 to 2005, it started to take off in 2006 with a production that has increased more than ten-fold compared to the beginning of the first half of the 2000s. First-generation biofuels being produced with agricultural commodities (animal fats, starch, sugar and vegetable oil),2 this dynamics has been accompanied by an increase in the price of those raw materials (see Figure 1). 3 This rise in the price of agricultural commodities may exert important effects on the current account of emerging and developing countries. Regarding countries exporting agricultural commodities, the effect may be not clear cut at a first sight. Indeed, the direct effect may be positive on the current account if the price increase is sufficient to compensate the potential decrease in the quantity of exported agricultural commodities. However, the commodity price increase is detrimental for domestic consumption which, in turn, negatively affects economic activity and the current account.

By focusing here on the current account, we fall into the spirit of the oil -macroeconomy literature. Indeed, it is well known that oil-exporting countries experiment large current account improvements following a sharp rise in oil prices (see [START_REF] Allegret | Current accounts and oil price fluctuations in oil-exporting countries: The role of financial development[END_REF] and the references therein). In other words, for such countries, oil windfalls constitute a key source of foreign exchange and income. The price of oil is also a key element behind agricultural commodity prices (see [START_REF] Paris | The Effect of Biofuels on the Link between Oil and Agricultural Commodity Prices: A Smooth Transition Cointegration Approach[END_REF] and the references therein, and Figure 1). Shocks in the price of oil spill over agricultural production costs which comprise fertilizer and fuel [START_REF] Baffes | More on the energy/nonenergy price link[END_REF][START_REF] Baffes | More on the energy/nonenergy price link[END_REF][START_REF] Berument | The relative effects of crude oil price and exchange rate on petroleum product prices: Evidence from a set of Northern Mediterranean countries[END_REF], thus decreasing supply. On the consumer side, the impact can be either negative or positive. On the one hand, positive shocks in the price of oil have a negative impact on demand if in their food purchasing decisions, households account for price changes in other goods among those oil products [START_REF] Gohin | The long-run impact of energy prices on world agricultural markets: The role of macro-economic linkages[END_REF]. In this case, oil and agricultural commodity prices would have a negative relationship. On the other hand, a positive link between agricultural commodity demand and oil prices is likely to occur through the development of biofuels: due to the substitution effect between fuel and biofuel, a rise in the price of oil could lead to an increase in the demand for biofuel [START_REF] Ciaian | Interdependencies in the energy-bioenergyfood price systems: A cointegration analysis[END_REF]. In this context, oil and agricultural commodity prices would be positively related.

The preceding arguments show that important links exist between the price of agricultural raw materials used in biofuel production, the price of oil and the current account of emerging and developing countries exporting or importing agricultural commodities. While the impacts caused by biofuel production development are likely in 2007-2008 triggered several articles about co-movements between commodity prices, subsidies policy in the agricultural sector and economic development based on biofuels production policy (see, e.g., [START_REF] Thompson | The Agricultural Ethics of Biofuels: The Food vs. Fuel Debate[END_REF] and the references therein). Moreover, in addition to biofuels, the upward dynamics of agricultural commodity prices during the 2000s comes from a combination of demand and supply shocks. On the demand side, strong economic growth in developing and emerging countries (especially China) has played a positive impact on the global call for commodities [START_REF] Abbott | Recent Global Food Price Shocks: Causes, Consequences and Lessons for African Governments and Donors[END_REF][START_REF] Abbott | What's Driving Food Prices in 2011?[END_REF]. On the supply side, adverse local agro-climatic conditions (temperature and precipitation) in major producing countries (OECD, 2008) negatively affected the volume of commodities available in the market. to be highly significant on the economy of such countries, the literature on this topic is very scarce. 4 This paper aims at filling this gap by examining the price impact of biofuels, through the price of its agricultural inputs, on the current account for a panel of 16 countries-9 developing and 7 emerging economies-which are either exporters, producers or importers of agricultural commodities used in biofuel production. As stressed above, due to the links existing between the price of agricultural commodities, the price of oil and the current account, the biofuels-current account nexus is likely to depend on the dynamics in the oil market. Indeed, for a country exporting (resp. importing) agricultural commodities used in biofuel production but importing (resp. exporting) crude oil, a high price of oil could strengthen (resp. weaken) the effect of biofuel prices on the current account via the link between oil and agricultural prices. However, this high oil price could affect negatively this biofuel price effect with an increase in the country's import spending for crude oil.

Acknowledging this major role played by the price of oil, we account for such nonlinearities by estimating a panel smooth-transition regression (PSTR) model. In this type of modeling, the price impact of biofuels on the current account varies, depending on the value of another observable variable, i.e., the price of oil. Specifically, the observations in the panel are divided into two homogeneous groups or "regimes"high oil price and low oil price regimes-, with different coefficients depending on the regimes. Regression coefficients are allowed to change gradually when moving from one group to another: PSTR is a regime-switching model where the transition from one state to the other is smooth rather than discrete. To our best knowledge, this paper is the first to address the price impact of biofuels on the current account for such countries by accounting for nonlinearities exerted by the price of oil.

Estimating PSTR models over the 2000-2014 period for emerging and developing countries classified into three groups-exporters, producers, importers of agricultural commodities used for biofuel production-our results can be summarized as follows. We show that, overall, a rise in the biofuel price tends to improve the current-account position for exporting and producing countries. However, this biofuel price impact is nonlinear, depending on the level reached by the price of oil.

For low values of the price of oil, a 10% increase in the price of biofuels significantly improves the current account by around 2%. When the price of oil exceeds the threshold of 56 US dollars per barrel for producers and 45 US dollars for exporters, changes in the price of biofuels no longer impact the current account. These findings indicate that an oil price increase negatively affects the trade balance of agricultural commodity exporters which are also oil importers, dampening the biofuel price impact on the current-account position.

The rest of the paper is organized as follows. Section 2 provides some stylized facts regarding the links between agricultural commodity and oil prices, and their evolution. Section 3 describes the data and methodology. Section 4 presents our findings and Section 5 concludes the paper.

Some stylized facts

Let us provide a first insight regarding the links between agricultural commodity prices and the price of oil, and their evolution along with the development of biofuel production. To this end, we consider monthly price series ranging from January 1980 to June 2016. All agricultural commodity and oil price series are taken from IMF.5 Table 1 reports the correlations of some agricultural commodity price series with the price of oil, all series being expressed in first-logarithmic difference. As shown, correlations are quite low over the whole period, the highest value being equal to 16% for palm oil. These results indicate that the links between agricultural commodity and oil prices are not very strong on the full sample. As stressed above, the development of biofuel production has been particularly important since the mid-2000s and not accounting for this dynamics may mask important evolutions in the link between our series of interest. Indeed, the rise in biofuel production may have intensified the relation between agricultural commodity and oil prices.

To simply illustrate the hypothesis of a stronger link between agricultural commodities and oil prices since the development of biofuel production, we also calculate the previous correlations over two subperiods, i.e. before and after 2006 as this year corresponds to the date of the major take-off in biofuel production worldwide. As shown in Table 1, correlations over the first subperiod are very weak, and even slightly negative for some commodities. Clearly, the links between agricultural commodities and oil were very tiny. These findings are in sharp contrast with those obtained after 2006. All correlations have strongly increased, the most impressive rises being observed for palm oil, rapeseed oil, and soybean oil.

This preliminary analysis based on correlation coefficients reveals that the links between agricultural commodity prices and the price of oil have sharply increased with the development of biofuel production. To complement this first investigation, Figure 2 reports rolling correlations (calculated for a three-year window) between the price of oil and three agricultural commodity prices, namely soybean oil, palm oil and corn prices (price series being expressed in their first-logarithmic difference).6 While these correlations followed a declining trend during the first mid-2000s, the dynamics evolves in the opposite sense after, with values reaching very high levelsthis pattern being thus observed for commodities used both for biodiesel and ethanol.

Data and methodology

For our estimations, we rely on annual data over the 2000-2014 period. The dependent variable is the current account to GDP ratio, extracted from WDI (World Development Indicators, World Bank). Turning to the explanatory and control variables, we consider usual current-account determinants (see below, subsection 3.2) in addition to our biofuel feedstock price index whose calculation is described below. 7 As stressed above, we acknowledge that this current account -biofuel price relationship may vary depending on the price of oil. The latter is defined as the simple average (in logarithm) of Dated Brent, West Texas Intermediate and the Dubai Fateh spot prices, and is extracted from IMF (International Financial Statistics, IFS). Source: authors' calculations based on price data extracted from IMF.

Aggregated biofuel price and panel of countries

As first-generation biofuels are produced from agricultural commodities, the first step consists in selecting those raw materials. In a second step, we have to identify for each retained commodity which emerging and developing countries are producers, exporters and/or importers. This leads us to select the following 10 commodities used in biofuel production: sugar cane, sugar beet, corn, soybean oil, palm oil, wheat, sorghum, cassava, rapeseed oil, and sunflower oil.8 Turning to the panel of countries (see Table 2), we consider as producer (resp. exporter, importer) a country which produces (resp. exports, imports) at least one of the commodities listed above. From the selected 10 commodities, we construct an aggregate biofuel price index based on the weight of each commodity in the volume of biofuel production. Let us now briefly describe the construction of the biofuel price index.

First, we have to identify the countries which are major players in biofuel production.

Based on data from the U.S. Energy Information Administration, we select a sample of 14 countries and regions, representing between 97% and 100% of the world ethanol and biodiesel production depending on the considered year.9 Second, we rely on annual reports of the U.S. Department of Agriculture (USDA) to specify the quantity of each commodity used in the production of biofuels in each selected country for each year. Third, we aggregate these data to determine the total quantity of each agricultural commodity used in the annual production of biofuels at a world level and, in turn, their corresponding share. Fourth, based on these weights, we construct a price index for one ton of agricultural input the biofuel production.10 Finally, we calculate our aggregate price index from these data and the world price of each agricultural commodity-the latter being computed on the basis of the prices of the three main producers of each commodity.11 The evolution of this biofuel feedstock price index is displayed on Figure 3.

Current-account determinants

Based on the previous literature,12 we rely on the usual current-account determinants: the net foreign asset (NFA) position expressed as percentage of GDP, the ratio of exports plus imports of goods and nonfactor services to GDP as a proxy of openness, dependency ratio expressed as the ratio of dependent population (below 15 and above 65) to the working age population (between 15 and 64), terms of trade (in logarithm) defined as the ratio of export prices to import prices, GDP per capita, adjusted by PPP exchange rates, relative to the United States, the ratio of M2 to GDP used as an indicator of financial depth, and the population growth rate. All these variables are taken from WDI.

PSTR specification

To assess the potential nonlinear effect exerted by the price of oil on the biofuel price -current account relationship, we rely on the PSTR methodology proposed by [START_REF] Gonzalez | Panel Smooth Transition Regression Models[END_REF]. According to the PSTR specification, current-account regression coefficients are allowed to change across countries and with time, depending on the price of oil. The observations are divided in-say-two regimes delimited by a threshold reached by the oil price, with estimated coefficients that vary depending on the considered regime. The change in the estimated value of coefficients is smooth and gradual, as PSTR models are regime-switching models in which the transition from one state to the other is smooth rather than discrete. Thanks to these specificities, PSTR models allow us to account for sufficient heterogeneity in view of the diversity of our sample of countries.

Let CA i,t denote the current account in percent of GDP in country i at time t. The PSTR specification is given by:

CA i,t = α i + β 0 ∆B t + β 1 ∆B t × F (P t ; γ, c) + φ X i,t + i,t (1)
for i = 1, ..., N , N being the number of countries, and t = 1, ..., T . α i stands for country fixed effects, ∆B t denotes the biofuel price index expressed in first logarithmic difference, P t is the price of oil expressed in logarithm that acts as a transition variable, F is a transition function, X i,t is a vector of control variables, and i,t is an independent and identically distributed error term. To assess the impact of the price of oil on the biofuel price -current account relationship, we consider that only the biofuel price varies according to the level reached by the price of oil.

The transition function F is bounded between 0 and 1, and is expressed as:

F (P t ; γ, c) = 1 + exp -γ m l=1 (P t -c l ) -1
(2) γ (γ > 0) denotes the slope parameter and c l , l = 1, ..., m (c 1 ≤ c 2 ≤ ... ≤ c m ), are the threshold parameters. From an empirical point of view and as mentioned by [START_REF] Gonzalez | Panel Smooth Transition Regression Models[END_REF], it is sufficient to consider only the cases of m = 1 (logistic) or m = 2 (quadratic logistic) to capture the nonlinearities due to regime switching.13

Depending on the value reached by the price of oil, the link between the currentaccount position and the biofuel price is given by a continuum of parameters, namely β 0 in the first regime (when F (.) = 0) and β 0 + β 1 in the second regime (when F (.) = 1). In other words, depending on the level of the price of oil, a biofuel price change has a different effect-that varies across countries and over time-on the current account dynamics.

Following the methodology used in the time series context, [START_REF] Gonzalez | Panel Smooth Transition Regression Models[END_REF] suggest a three step strategy to apply PSTR models: (i) specification, (ii) estimation, (iii) evaluation and choice of the number r of regimes. The identification step aims at testing for homogeneity against the PSTR alternative and at selecting (i) between the logistic and logistic quadratic specification of the transition function-i.e., the appropriate order of m-and (ii) the transition variable as the one that minimizes the associated p-value. Then, if the nonlinearity hypothesis is retained, nonlinear least squares are used in the estimation step to obtain the parameter estimates once the data have been demeaned (see [START_REF] Hansen | Threshold effects in non-dynamic panels: Estimation, testing, and inference[END_REF][START_REF] Gonzalez | Panel Smooth Transition Regression Models[END_REF]. Finally, various misspecification tests are applied in the third step to check the validity of the estimated PSTR model and determine the number of regimes r.

Results

We start by testing the linearity hypothesis in Equation (1) using the [START_REF] Gonzalez | Panel Smooth Transition Regression Models[END_REF] test with the price of oil (in logarithm) as the tran-sition variable. Results are reported in Table 3 for the following panels of countries whose composition is given in Table 2: the whole panel including our 16 considered countries, the panel of 11 producing countries, the sample of 7 exporting countries, and the panel of 12 importing countries. 3 indicate that the null of linearity is rejected in favor of the alternative of logistic PSTR specification for all panels except importing countries. The latter result regarding importers may be related to the policies implemented in some of those importing economies.

Results in Table

In Asia (China, India, and Indonesia for example), governments introduced specific measures after the 2007-2008 peak in commodity prices in order to protect domestic markets from inflation pressures. In China, a mix of temporary economic tools regarding import tariffs or the cancellation of VAT rebate on exports for specific agricultural products was implemented until 2007 to stabilize the domestic market [START_REF] Jones | Policy Responses in Emerging Economies to International Agricultural Commodity Price Surges[END_REF]. As an illustration, soybeans' import tariff was divided by 3 and the 13% rebate on ethanol, soybeans and grains exports was eliminated. In India, import tariffs for maize (resp. vegetable oil) reduced from 50% to zero (resp. 80% to zero) till 2007. Indonesia applied the same kind of fiscal tools regarding soybeans (reduction of import tariffs from 10% to zero) and palm oil (restriction of exports). All those measures could have limited the current-account deterioration with the reduction of international price pressures in the domestic market, thus explaining the absence of nonlinearities on the biofuel price impact on the current account in importing countries.14

Turning to the three other panels, in the whole sample as well as in commodityproducing and -exporting countries, fluctuations in the price of biofuels impact the current account differently, depending on the level reached by the price of oil. Let us now proceed to the estimation of the PSTR models to investigate this property more deeply. Table 5 reports the estimation of our PSTR model (Equation (1)) using the price of oil as the transition variable for the three panels of countries for which the null hypothesis of linearity has been rejected. 15Let us first briefly comment the results concerning the control variables. For the whole sample, the effect of (lagged) NFA to GDP ratio (N F A(-1)) on the current account is negative. Countries displaying large net foreign asset positions are able to undergo long-lasting trade deficits while remaining solvent. This characteristic may thus explain the negative link between net foreign asset and current-account positions. Population (P OP ) positively affects the current account, while the dependency ratio has a negative impact. As recalled by [START_REF] Allegret | Current accounts and oil price fluctuations in oil-exporting countries: The role of financial development[END_REF], this result could be related to the life-cycle hypothesis: a rise in the dependency ratio tends to exert a negative effect on aggregate domestic saving, affecting in turn negatively the current-account position.

Consistent with the Harberger-Laursen-Metzler effect (see [START_REF] Bouakez | Terms of trade and current account fluctuations: The Harberger-Laursen-Metzler effect revisited[END_REF], we find that terms of trade (T OT ) and current account are positively linked: if income increases more than spending following an improvement in terms of trade, the current account will automatically improve. Openness has a negative influence on the current account for exporting countries. Given that our sample of exporters mainly contains emerging countries, this result is in line with those generally obtained in the literature for such economies [START_REF] Chinn | Medium-term determinants of current accounts in industrial and developing countries: an empirical exploration[END_REF][START_REF] Chakravorty | Food for Fuel: The Effect of the U.S. Biofuel Mandate on Poverty in India[END_REF][START_REF] Allegret | Current accounts and oil price fluctuations in oil-exporting countries: The role of financial development[END_REF]. The underlying idea is that openness lifts trade barriers favoring flows of goods and services and foreign direct investments, making those countries more attractive to foreign capital and increasing opportunities. Consequently, the relationship between openness and the current account is negatively signed.

It is worth mentioning that GDP per capita, adjusted by PPP exchange rates, relative to the United States (GDP P P P ) is never significant. This finding is in accordance with Chinn andPrasad (2003), Cheung, Furceri, and[START_REF] Chakravorty | Food for Fuel: The Effect of the U.S. Biofuel Mandate on Poverty in India[END_REF] and [START_REF] Allegret | Current accounts and oil price fluctuations in oil-exporting countries: The role of financial development[END_REF], and can be explained through the stage of economic development of our countries relative to the United States. Indeed, some countries are at early stages of development with a corresponding negative effect on the current account, while others have reached higher levels of development with an associated positive impact on the current-account position. On the whole, the coefficient of the variable is found to be non-significant due to the compensation of negative and positive effects. Finally, our findings show that financial depth, proxied by the ratio M 2/GDP (M 2), has no significant effect on the current-account position. This result is not surprising given our panel of countries, which are economies characterized by a weak developed financial system.

Let us now turn to our main variable of interest, namely the price of biofuels. Our PSTR estimations show that, overall, the current-account position is positively affected by the price of biofuels. 16 The intensity of such impact is nonlinear, depending on the level reached by the price of oil. For the three samples of countries, when the price of oil is low (Regime 1), the biofuel price impact is positive with a coefficient amounting to about 0.20. In other words, a 10% increase in the price of biofuels leads to a current-account improvement of 2%. As expected, this effect is higher and more significant for exporting countries than for producing economies. Indeed, exporters' current account is directly affected by our biofuel price index via the price of agricultural commodities exported. Given the construction of our producers' panel-which mainly includes exporters-we obtain a similar, but weaker effect for this group of countries. The biofuel impact on the current account differs in the second regime. Indeed, in this regime characterized by a price of oil higher than 60 US dollars per barrel for the whole sample, fluctuations in the price of biofuels no longer affect the current-account position. The threshold c after which the price of biofuels has no significant effect varies across groups, being equal to 56 US dollars per barrel for producers and 45 US dollars per barrel for exporters.

A possible explanation for these results is the following. For the group of exporting countries, when the price of oil is low, positive variations in the price of commodities used in biofuel production translate into an improvement in terms of trade which, in turn, positively impact the current account. When the price of oil tends to increase, it exerts a negative impact on the trade balance of exporters which are crude oil importers, therefore weakening the effect exerted by the biofuel price on the currentaccount position: a rise in the price of oil tends to dampen the biofuel price effect through the increase in the corresponding country's import spending for crude oil (see Table 6 for an illustration). Turning to the group of producing countries, which comprises oil exporters (such as Mexico, Nigeria, Sudan to name a few), positive variations in the price of biofuels increase the trade-off between biofuel and oil when the price of oil is low. Higher prices of biofuels increase the demand for oil, thus benefiting oil exporters. As a result, the impact on the current account is positive.

Considering finally the whole sample, as it includes both mechanisms, the biofuel price impact on the current account is weakened, and the threshold oil price value is higher.

For the sake of completeness, Figure 4 displays the transition functions.17 As shown, Regime 2 occurred more often during the covered period, especially for exporting countries. It is worth noticing that, for our three estimated models, Regime 1 corresponds to the 2001-2005 period when biofuel commodities had a low correlation with the price of oil.

Conclusion

Assessing the impact of fluctuations in the price of biofuels on the economy of emerging and developing countries is worthy of investigation. Indeed, many developed economies promote the use of biofuels for environmental concerns and to ensure energy security, leading a to sharp rise in their production or imports since the mid-2000s. First-generation biofuels being produced from agricultural commodities, this huge increase in biofuel production has been accompanied by an acute rise in the price of those raw materials. As a result, such development of biofuels is likely to generate externalities and adverse effects on the economy of emerging and developing countries whose activity strongly depends on agricultural commodities involved in the biofuel production process.

The present paper tackles this issue by considering a panel of 16 developing and emerging countries which either produce, export or import agricultural commodities used in biofuel production. Following the oil-macroeconomy literature, we focus on the current-account position of the considered countries as the latter is likely to be affected by the sharp rise in the price of the involved agricultural commodities.

Acknowledging that oil is a key input in agricultural production processes, changes in its price obviously affect agricultural commodities prices. We specifically account for this characteristic by investigating whether the biofuel price-current account relationship depends on the value reached by the price of oil. To this end, we rely on the estimation of a panel smooth-transition regression model in which the biofuel price-current account nexus is allowed to vary depending on whether the price of oil is low or high.

Considering the 2000-2014 period, our findings show that a rise in the biofuel price tends to improve the current-account position for agricultural commodity-producing and -exporting countries. However, this impact is nonlinear, depending on the level reached by the price of oil. Specifically, we find that for low values of the price of oil-i.e., below 60 US dollars per barrel for our whole panel of countries-, a 10% increase in the price of biofuels leads to a significant current-account improvement of about 2%. When the price of oil increases to exceed 56 US dollars per barrel for producers and 45 US dollars for exporters, fluctuations in the price of biofuels no more significantly affect the current account. These findings illustrate that a rise in the price of oil exerts a negative effect on the trade balance of commodity exporters which are also oil importers, dampening the biofuel price impact on the current-account position.

On the whole, our findings put forward the importance of accounting for the effect of the price of oil in designing policies to promote the use of biofuels. In particular, while an increase in the biofuel price is benefit for commodity-exporting countries in a low oil price regime, it is no more the case in high oil price states. With regard to the "food versus fuel" debate, sharp increase in the price of biofuels coupled with strong rise in the price of oil are likely to exert important detrimental effects on the economy of agricultural commodity-exporting countries.

Figure 1 :

 1 Figure 1: Biofuel production, crude oil price and food price index

Figure

  Figure 2: Rolling correlations

Figure

  Figure 3: Biofuel feedstock price index evolution

Figure

  Figure 4: Transition functions

Table 1 :

 1 Correlations between agricultural commodity and oil price series This table reports correlations between agricultural commodity and oil price seriesexpressed in first-logarithmic difference. Source: authors' calculations based on IMF data.

		Corn	Wheat	Soybean oil	Palm oil	Sugar cane
	1980.02-2016.06	0.0443	0.0820	0.1331	0.1601	0.0167
	1980.02-2005.12	-0.1146	-0.0380	-0.0502	0.0277	0.0031
	2006.01-2016.06	0.3112	0.2437	0.5383	0.4504	0.0829
		Sorghum Sugar beet Rapeseed oil Sunflower oil
	1980.02-2016.06	0.0291	0.0404	0.0758	0.0968
	1980.02-2005.12	-0.0886	0.0115	-0.0458	-0.0360
	2006.01-2016.06	0.2260	0.1237	0.5059	0.2819
	Note:

Table 2

 2

		: Panel of countries
	Producer	Exporter	Importer
	Congo	Argentina	Algeria
	Nigeria	Brazil	Bangladesh
	Pakistan	China	Egypt
	Sudan	India	Ethiopia
	Argentina Indonesia	Iran
	Brazil	Mexico	Pakistan
	China	Thailand	Sudan
	India		China
	Indonesia		India
	Mexico		Indonesia
	Thailand		Mexico
			Thailand

Note: In italics: developing country; otherwise: emerging country. Emerging: G20 countries or countries in the upper-middle income group classification from the World Bank (GNI per capita between $4,036 and $12,475); Developing: otherwise.

Table 3

 3

	: Linearity tests (p-values)
		LM	F	LR
	Whole sample	0.02** 0.03** 0.02**
	Producing countries 0.05*	0.08*	0.05*
	Exporting countries 0.04** 0.06* 0.04**
	Importing countries	0.26	0.30	0.26
	Note: This table reports the results of Lagrange multiplier (LM), F-type (F) and likelihood ratio

(LR) tests for linearity. Null hypothesis: linear model. Alternative hypothesis: PSTR model with two regimes (r=1). *** (resp. **, *): rejection of the null hypothesis at the 1% (resp. 5%, 10%) significance level.

Table 4 :

 4 Transmission of world commodity prices to the domestic market: Elasticity of price transmission(2003-2006 and 2003-2009)

		2003-2006 2003-2009
	China, soybean	0.95	0.38
	India, soybean	1.40	0.56
	Indonesia, wheat	3.05	0.46
	Source: Jones and Kwiecinski (2010).

Table 5 :

 5 PSTR estimation

		Whole sample	Producing countries	Exporting countries
		Regime 1 Regime 2 Regime 1 Regime 2 Regime 1 Regime 2
	∆B	0.19***	-0.18**	0.21**	-0.20*	0.24***	-0.22**
	N F A(-1)	-0.12***		-0.01		-0.01
	Openness	0.01		0		-0.11*
	Dependency	-0.17*		0.05		0
	T OT	0.11***		0.09*		0
	GDP P P P	-0.02		0.02		-0.05
	M 2	0.09		0.10		-0.05
	P OP	0.15***		0.13**		0.05
	c	4.09 (60$)	4.03 (56$)	3.81 (45$)
	γ	5.92		7.70		3.36
	Note: This table reports the estimation of PSTR models (Equation (1)). *** (resp. **, *) denotes
		significance at the 1% (resp. 5% and 10%) level.

Table 6 :

 6 Oil and petroleum products' balances, in million barrels per day(2004- 2014) This table reports the mean oil balance and the mean petroleum products' balance over the 2004-2014 period. A positive (resp. negative) sign indicates that the concerned country is importer (resp. exporter) over the considered period. Source: ENERDATA.

		Oil	Petroleum products
	Argentina -0.1121	-0.0393
	Brazil	0.1473	0.2272
	China	4.5562	0.4541
	India	2.3620	-0.6389
	Indonesia	0.4613	0.4094
	Mexico	-1.1697	0.4091
	Thailand	0.6653	-0.1329
	Note:

Brazil launched the Proalcool program in 1975 just after the first oil shock. This program triggered an acceleration in the use of ethanol for the transportation sector and innovations on flex-fuel engines for the car industry.

Typical first-generation biofuels are sugarcane ethanol, starch-based or 'corn' ethanol, biodiesel and Pure Plant Oil (PPO). The feedstock for producing first-generation biofuels either consists of sugar, starch and oil bearing crops or animal fats that, in most cases, can also be used as food and feed or consists of food residues (IEA, 2010).

In particular, the "food versus fuel" debate that followed the large increase in commodity prices

Indeed, most of the studies dealing with developing and emerging countries have been concerned with the impact of current and targeted domestic biofuel production on land or agricultural commodities' availability, on water resources required for cultivation and on food prices (see, e.g.,[START_REF] Khanna | Meeting Biofuels Targets: Implications for Land Use, Greenhouse Gas Emissions and Nitrogen Use in Illinois[END_REF][START_REF] Yang | Life-cycle analysis on biodiesel production from microalgae: Water footprint and nutrients balance[END_REF][START_REF] Khanna | Measuring Indirect Land Use Change with Biofuels: Implications for Policy[END_REF]. The main exception is[START_REF] Chakravorty | Food for Fuel: The Effect of the U.S. Biofuel Mandate on Poverty in India[END_REF] who have addressed the impact of US biofuel mandate on poverty in India.

The crude oil price index is the simple average of Dated Brent, West Texas Intermediate, and the Dubai Fateh spot prices.

The figures for the other commodities considered display similar patterns.

As (i) the biofuel feedstock price index can be calculated only at the yearly frequency, and (ii) data on the current-account position for our panel of countries are available only at the same frequency, this explains why we use annual data in our empirical analysis.

While emerging and developing countries are not major actors on rapeseed oil and sunflower oil markets, we include those commodities in our analysis as they enter significantly in the biofuel production process.

These countries are the following: Argentina, Australia, Brazil, Canada, China, Colombia, Guatemala, Indonesia, Malaysia, Peru, Philippines, Thailand, United States, and the EU28.

As an illustration, one ton of input in 2010 was composed of sugar cane for 69.95%, corn for 24.18%, sugar beet for 1.70%, rapeseed oil for 1.25%, wheat for 1.13%, soybean oil for 1.04%, palm oil for 0.46%, cassava for 0.25%, sunflower oil for 0.026%, and sorghum for 0.024%.

In doing so, we account for disparities in agricultural prices that may be caused by protection of some domestic markets or high transportation costs.

See Calderon, Chong, and Loayza (2002),[START_REF] Chinn | Medium-term determinants of current accounts in industrial and developing countries: an empirical exploration[END_REF],[START_REF] Gruber | Explaining the global pattern of current account imbalances[END_REF],[START_REF] Calderon | Current Account Deficits in Africa: Stylized Facts and Basic Determinants[END_REF],[START_REF] Chinn | Current account balances, financial development and institutions: Assaying the world "saving glut[END_REF],[START_REF] Chinn | Current account balances, financial development and institutions: Assaying the world "saving glut[END_REF],[START_REF] Brissimis | Current account determinants and external sustainability in periods of structural change[END_REF] Vasardani (2012), Cheung, Furceri, and[START_REF] Chakravorty | Food for Fuel: The Effect of the U.S. Biofuel Mandate on Poverty in India[END_REF] and[START_REF] Allegret | Current accounts and oil price fluctuations in oil-exporting countries: The role of financial development[END_REF] among others.

Note that the PSTR model can be extended to r regimes, with r > 2 (see[START_REF] Gonzalez | Panel Smooth Transition Regression Models[END_REF].

As illustrated in Table4, elasticities of international price transmission have indeed strongly decreased across the two considered periods reaching values less than unity.

To save space, results of the corresponding misspecification tests are not reported, but are available upon request to the authors. All the estimated models displayed in Table5have successfully passed the tests (parameter constancy, no remaining heterogeneity).

It is worth mentioning that we have also estimated our model lagging the price of biofuels. The results (available upon request to the authors) were very similar, highlighting the robustness of our findings to endogeneity issues.

It should be mentioned that when representing these functions, the x axis generally reports the values of the transition variable. Here, the x axis refers to the year of the corresponding values.

* This research has been supported by the French Energy Council, Paris, France.