Damien Cubizol
email: cubizol@gate.cnrs.fr

Sylvie Démurger

Gilles Dufrénot

Aurélien Eyquem

Jérôme Héricourt

Sandra Poncet

Tavis Barr

Emmanuel Bougna

Olivier Brossard

Christian De Peretti

Carl Grekou

Jie Jiang

Carl Lin

Alexis Penot

Stefano Ugolini

Unbalanced credit distribution in emerging economies and FDI

Keywords: Financial intermediation, Foreign Direct Investment, emerging economies, privatization JEL codes: F21, G20, L30, O10 Unbalanced JEL Classification: F21, G20, L30, O10 Financial intermediation, Foreign Direct Investment, emerging economies, privatization

This empirical study shows that an increasing credit distribution to State-Owned Enterprises (SOEs), to the detriment of private firms, slows the increase in inward FDI in emerging economies. The first approach is global and dynamic; it relies on GMM and Bayesian techniques, utilizing a sample of 40 emerging countries over the period 1988-2008. Then, a sectoral approach is implemented for 1992-2012 and strengthens the negative effect on inward FDI (but the difference of financial dependence between sectors does not have clear effects). Finally, certain policy actions can improve the allocation of domestic and foreign capital in emerging economies.

Introduction

In the majority of emerging economies, the share of SOEs decreased at different speeds or remained constant (first stylized fact) during the last thirty years. However, the share of credit to SOEs over credit to all firms increased during some periods of their liberalization (second stylized fact 1 ); therefore, the banking system favored SOEs to the detriment of private firms at a certain stage of the development or transition process of some countries. By financing more SOEs, this credit bias has created some distortions in the access to credit between public and private firms. This phenomenon has been well developed for former Soviet countries in [START_REF] Perotti | Bank lending in transition economies[END_REF], [START_REF] Anderson | Transition banking: financial development of central and eastern europe[END_REF], [START_REF] Kornai | Understanding the soft budget constraint[END_REF] and [START_REF] Megginson | The economics of bank privatization[END_REF]; and for China in [START_REF] Boyreau-Debray | Financial intermediation and growth -Chinese style[END_REF], [START_REF] Dollar | Das (Wasted) Kapital: Firm Ownership and Investment Efficiency in China[END_REF], [START_REF] Héricourt | FDI and credit constraints: Firm-level evidence from China[END_REF] and [START_REF] Song | Growing like china[END_REF]. 2 Our study extends the presence of the credit bias in favor of SOEs to other emerging economies and analyzes its effect on inward FDI. Indeed, although private firms can also use alternative access to credit in emerging economies 3 , their formal access to credit remains constrained compared to SOEs during some periods of their development. Moreover, according to [START_REF] Harrison | Does direct foreign investment affect domestic credit constraints?[END_REF], [START_REF] Du | Finance and Firm Export in China[END_REF], [START_REF] Alfaro | Fdi, productivity and financial development[END_REF], and [START_REF] Desbordes | The effects of financial development on foreign direct investment[END_REF], FDI are also financed by the local country and not only by the parent country. Thus, by financing more SOEs to the detriment of private firms, local financial intermediation can modify the decision of a foreign private investor to expatriate production, acquire shares of a company, or conduct a 1 Table 1 gives some examples from the data. 2 For China see also [START_REF] Boyreau-Debray | Pitfalls of a State-Dominated Financial System: The Case of China[END_REF] and [START_REF] Poncet | Financial constraints in China: Firm-level evidence[END_REF]. 3 See [START_REF] Krugman | Will China break? The New York Times[END_REF], [START_REF] Claessens | Shadow banking: economics and policy[END_REF], [START_REF] Acharya | The growth of a shadow banking system in emerging markets: Evidence from india[END_REF], [START_REF] Li | Shadow banking in China: expanding scale, evolving structure[END_REF] and [START_REF] Funke | Monetary policy transmission in China: A DSGE model with parallel shadow banking and interest rate control[END_REF].

1 merger or joint venture. 4The paper uses two databases with two different methodologies to study this effect of the credit bias toward SOEs on FDI entries. First, a global and dynamic study with 40 emerging countries; second, a sectoral analysis based on the work of [START_REF] Rajan | Financial Dependence and Growth[END_REF] that gives more accurate results and links them to financial dependence. The conclusion is a slowdown of the increase in inward FDI when the credit bias toward SOEs rises. Given the heterogeneity in the degree of increase in inward FDI between emerging economies (see Figure 1), some factors may have slowed the increase in FDI entries in some countries; the results claim that the increase in inward FDI could have been higher in the countries where the credit bias in favor of SOEs was strong. Thus, the paper particularly deepens the analysis of the causal impact of local financial development on inward FDI [START_REF] Harrison | Does direct foreign investment affect domestic credit constraints?[END_REF][START_REF] Alfaro | Fdi, productivity and financial development[END_REF][START_REF] Desbordes | The effects of financial development on foreign direct investment[END_REF]. These two empirical studies also complete the literature on the soft budget constraints of SOEs described above and extend it to emerging economies.

Concerning the origins of the credit bias in favor of SOEs, the literature provides different explanations. In former Soviet states that experienced a transition process, the bias largely came from an incentive of State-Owned Banks (SOBs) to fund former debtors (SOEs) for future repayments [START_REF] Perotti | Bank lending in transition economies[END_REF]. Another explanation is the distortions of the lending rate in the credit market. Indeed, in China, the household savings deposit rate remains low to provide cheap credit to SOEs, whereas private firms face a higher lending rate [START_REF] Aglietta | La voie chinoise, capitalisme et empire[END_REF][START_REF] Song | Growing (with Capital Controls) like China[END_REF][START_REF] Funke | Monetary policy transmission in China: A DSGE model with parallel shadow banking and interest rate control[END_REF]. High bank concentration by SOBs and lending policies determined by the government to support SOEs also play a significant role in the credit misallocation [START_REF] Anderson | Transition banking: financial development of central and eastern europe[END_REF][START_REF] Boyreau-Debray | Financial intermediation and growth -Chinese style[END_REF][START_REF] Dobson | The Contradiction in China's Gradualist Banking Reforms[END_REF][START_REF] Cull | Formal finance and trade credit during China's transition[END_REF][START_REF] Firth | Leverage and investment under a state-owned bank lending environment: Evidence from China[END_REF]. La [START_REF] Porta | Government ownership of banks[END_REF], [START_REF] Sapienza | The effects of government ownership on bank lending[END_REF] and [START_REF] Khwaja | Do Lenders Favor Politically Connected Firms? Rent Provision in an Emerging Financial Market[END_REF] note a link to corruption because SOBs remain controlled by politicians, with personal and political incentives (e.g., hiring political partners and bailing out low-performing firms, see [START_REF] Firth | Leverage and investment under a state-owned bank lending environment: Evidence from China[END_REF]. Finally, another explanation is a method for the government to ensure tax revenues through SOEs, particularly during fiscal decentralization and privatization in emerging economies (for China, see [START_REF] Gordon | Taxation and economic growth in china[END_REF].

The first empirical study is detailed in Section 2, and Section 3 develops the sectoral analysis. The last section suggests policy actions to reduce the credit bias toward SOEs and its negative effects.

A global approach 2.1 Data and main variables

Before adopting a sectoral approach, the first model comprises a dynamic yearly study (which also captures the agglomeration effect of inward FDI, see [START_REF] Cheng | What are the determinants of the location of foreign direct investment? the chinese experience[END_REF][START_REF] Cheng | What are the determinants of the location of foreign direct investment? the chinese experience[END_REF][START_REF] Kinoshita | Why does fdi go where it goes? new evidence from the transition economies[END_REF][START_REF] Kinoshita | Why does fdi go where it goes? new evidence from the transition economies[END_REF] of the 40 main emerging countries. It covers the period 1988-2008 to include the privatization of former Soviet states, some ASEAN and South American countries, and China. 5 None of these countries experienced a nationalization of the economy: the share of SOEs decreased significantly in the transition economies, more slowly in other emerging economies, or remained constant.

Inward FDI :

Inward FDI have been a strong factor of growth in emerging economies [START_REF] Borensztein | How does foreign direct investment affect economic growth[END_REF][START_REF] De Mello | Foreign direct investment-led growth: evidence from time series and panel data[END_REF]. The increase in inward FDI started more than thirty years ago with the opening of the capital account of emerging economies, and the speed and magnitude of the entries varied across regions and countries (Figure 1 below). The variable FDI in the first global and dynamic study represents the stocks of inward FDI in percentage of GDP, and the data source is UNCTAD Statistics. A lag of the dependent variable is included in the first dynamic model (see Subsection 2.2) to capture the agglomeration effect of inward FDI [START_REF] Cheng | What are the determinants of the location of foreign direct investment? the chinese experience[END_REF][START_REF] Kinoshita | Why does fdi go where it goes? new evidence from the transition economies[END_REF]. Our study analyzes the effect of a credit distortion in favor of SOEs on inward FDI. Indeed, according to [START_REF] Harrison | Does direct foreign investment affect domestic credit constraints?[END_REF], [START_REF] Du | Finance and Firm Export in China[END_REF], [START_REF] Alfaro | Fdi, productivity and financial development[END_REF], and [START_REF] Desbordes | The effects of financial development on foreign direct investment[END_REF], FDI are also financed by the local country and not only by the parent country. Thus, by financing more SOEs to the detriment of private firms, local financial intermediation can modify the decision of a foreign private investor to expatriate production, acquire shares of a company, or conduct a merger or joint venture. 6 Note that there is a strong heterogeneity in the degree of increase in inward FDI between emerging countries in the sample (see Figure 1). Some factors may thus have slowed the increase in FDI entries in some countries, and the results of both global/dynamic and sectoral approaches claim that the increase in inward FDI could have been higher in the countries where the credit bias in favor of SOEs was strong. 5 The sample includes Argentina, Bangladesh, Brazil, Bulgaria, Chile, China, Colombia, the Czech Republic, Egypt, Estonia, Greece, Hong Kong, Hungary, India, Indonesia, Iran, Latvia, Lithuania, Malaysia, Mexico, Morocco, Nigeria, Pakistan, Peru, the Philippines, Poland, Romania, Russia, Singapore, Slovakia, Slovenia, South Africa, South Korea, Sri Lanka, Thailand, Turkey, Ukraine, the United Arab Emirates, Venezuela and Vietnam. These countries are or were classified by international institutions as emerging markets during the sample period; Hong Kong, Singapore and South Korea were considered emerging markets at the beginning of the sample period but were then classified as developed countries. The Commonwealth of Independent States (CIS) is not included in this sample because insufficient data are available for privatization. In all of the countries in the sample, the share of SOEs decreased, at different speeds, or remained constant. There was no nationalization across time, and a credit bias toward SOEs appeared within certain periods of time (an increasing ratio of credit to SOEs over credit to SOEs and the private sector).

6 The misallocation of capital and privatization:

Because it is the crucial issue of the study, the regressor for the misallocation of capital is added (variable crpubpriv, see Table 3). It is the share of credit to SOEs against the total credits to the private sector and SOEs (Source: International Financial Statistics, 2013, IMF). We consider that there is a credit bias toward SOEs when this ratio increases whereas the share of SOEs in GDP decreases or remains constant.

For all countries in the sample, the share of SOEs decreases at different speeds or remains constant, but there is no nationalization across time. The crpubpriv variable increases during some periods, meaning that a credit bias toward SOEs appears. All the factors at the origin of this credit bias are described at the end of the introduction; and Table 1 gives some examples of its presence in some countries of the sample. Table 1: Some data on the credit bias toward SOEs

It is initially assumed that the decision to lend more to SOEs is a strategy by the government, as in [START_REF] Boyreau-Debray | Financial intermediation and growth -Chinese style[END_REF], Boyreau-Debray and Wei (2005), and Gonzalez-Garcia and Grigoli (2013).

Thus, the first estimations consider that crpubpriv is exogenous. This assumption is not realistic for all emerging countries but corresponds to the situation of credit distortions in China now and in former Soviet states in the late 1990s. In these cases, it is a choice by the government or SOB governance to favor SOEs.7

Then, estimations are performed with instruments for crpubpriv, which are the lags of the share of SOEs.

Indeed, if there were no credit bias toward SOEs, crpubpriv would follow the past privatization process of firms (and the latter is unlikely to be correlated to the entries of FDI). In the sources from which the data are extracted, firms are considered SOEs when a state, a government or a public authority holds more than 50.01% of the firms' shares.

Some estimations are also performed with an interaction term to include privatization: crpubpriv ×soe is added to crpubpriv. The share of SOEs (variable soe, see Table 3) is also instrumented to ensure the exogenous process of privatization in the estimations. 8 The low correlation between crpubpriv and soe is checked to avoid biases in the estimations with the interaction term (Figures 2 and3). This low correlation confirms the existence of soft budget constraints for SOEs: otherwise, crpubpriv would follow soe. The presence of the credit bias can also be confirmed by a significant correlation between crpubpriv and the share of SOBs. As explained in the literature (see the introduction), SOBs can provide more credit to SOEs, either for rational or political incentives. Explanatory and control variables for FDI :

The usual macroeconomic control variables are used as regressors for the estimations of FDI, i.e., GDP and the real exchange rate. Then, because there are many potential explanatory variables for FDI in the literature, their correct determinants are chosen with Bayesian model averaging. By generating additional estimations, this method determines the probability that a variable explains the dependent variable of a specific database. This technique is described in Appendix B, and we present only the results in this section.

The potential explanatory variables from the literature are the lending rate [START_REF] Gastanaga | Host Country Reforms and FDI Inflows: How Much Difference do they Make?[END_REF],

wages [START_REF] Blonigen | A Review of the Empirical Literature on FDI Determinants[END_REF][START_REF] Braconier | Multinational enterprises and wage costs: vertical FDI revisited[END_REF][START_REF] Dooley | Direct investment, rising real wages and the absorption of excess labor in the periphery[END_REF], TFP growth [START_REF] Wei | Productivity spillovers from R&D, exports and FDI in China's manufacturing sector[END_REF][START_REF] Bénassy-Quéré | Institutional Determinants of Foreign Direct Investment[END_REF], FDI costs (trade, administrative and business costs; see [START_REF] Gastanaga | Host Country Reforms and FDI Inflows: How Much Difference do they Make?[END_REF][START_REF] Bénassy-Quéré | Institutional Determinants of Foreign Direct Investment[END_REF], capital controls, corruption [START_REF] Bénassy-Quéré | Institutional Determinants of Foreign Direct Investment[END_REF] and an index for the legal system and the level of property rights [START_REF] Bénassy-Quéré | Institutional Determinants of Foreign Direct Investment[END_REF]. We also add the share of capital in production and savings (variables in Table 2).

According to the first results of Bayesian model averaging (in Appendix B) , TFP growth is not selected as an explanatory variable for this specific sample but TFP level is selected. Moreover, capital controls (cp), FDI costs (fdicsts) and the index for the legal system and the level of property rights (lleg) are also not selected by Bayesian simulations as explanatory variables for this sample.9 However, in some GMM estimations, the lleg and fdicsts variables are significant.

Estimations with and without correlated variables (such as GDP or savings) are performed to reduce multicollinearity, and the GMM estimator reduces endogeneity issues (particularly for corruption) with internal instruments. As described above, we only use external instruments for the variables linked to privatization (soe) and the credit bias toward SOEs (crpubpriv ). Finally, in this study, adding too many dummies considerably raises the number of instruments, and some dummies are often dropped due to collinearity. Thus, for some estimations, time dummies are only set for the 2007-2008 contagion of the U.S. financial collapse.

Observations:

The soe and lleg variables have considerable data gaps. Indeed, no studies include a yearly ratio of SOEs in the period 1988-2008 for the main emerging countries, and the few databases with information are limited to the most recent seven years (e.g., Bankscope). Therefore, for the soe variable, the sample has been completed by taking values from various studies and sources (see Table 3), and interpolation is necessary to have enough observations. Different interpolation techniques are used (linear, log-linear, Catmull-Rom Spline or Cardinal Spline interpolations), and errors are limited because, apart from the former Soviet states, the privatization process was gradual and linear in the countries in the sample.

Concerning lleg, the Fraser Institute is the only source that gives a time dimension (1985, 1990, 1995, 2000, 2005 and 2010). Hence, interpolations are also performed for lleg to increase the sample size. 10The variables, data and statistics are summarized in Tables 2, 3 and8. Only the two variables soe and lleg are interpolated.

The model

The first approach is global with a dynamic model. In the first stage, because there are many potential explanatory variables for inward FDI in the literature, their correct determinants are chosen with Bayesian model averaging (see Appendix B). This method allows for additional estimations to determinate the probability that a variable is included in the model. Then, to avoid the endogenous biases on the key variable crpubpriv explained above, the GMM estimator from [START_REF] Arellano | Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations[END_REF] is used, with the addition of external instruments. The number of countries is sufficient to have more robust results

with the Arellano and Bond GMM estimator than with the usual OLS or panel regression [START_REF] Bond | GMM Estimation of Empirical Growth Models[END_REF][START_REF] Soto | System GMM Estimation With A Small Sample[END_REF]. Because the GMM estimator needs a small period of time compared to the number of countries N (to avoid instrument proliferation, see [START_REF] Roodman | A Note on the Theme of Too Many Instruments[END_REF], a variable value at one period is the average of the value of two years (thus, T=10).

A difference GMM is more convenient for an analysis with a small number of countries [START_REF] Mileva | Using Arellano Bond Dynamic Panel GMM Estimators in Stata[END_REF] because System GMM uses more instruments than the difference GMM does (the instruments are the levels and first differences in the lags of explanatory variables for System GMM, whereas the instruments are only the levels for difference GMM). Indeed, with this number of countries (N=40), there would be more instruments than countries with a System GMM, which would generate invalid results, overfit the endogenous variables and weaken the Hansen test [START_REF] Roodman | A Note on the Theme of Too Many Instruments[END_REF]. Another advantage of the first difference is in addressing the problem of omitted variables with panel data. However, although a difference GMM removes the unit root and individual specific/unobserved effects, it considerably reduces the size of the sample by magnifying gaps in unbalanced panels, which is why a GMM with Forward Orthogonal Deviation (GMM FOD) is applied to overcome this issue. The latter is "an alternative to differencing proposed by [START_REF] Arellano | Another look at the instrumental variable estimation of error-components models[END_REF] that preserves the sample size in panels with gaps" (Roodman, 2006, p. 1). Instead of subtracting the observation at t-1 from that at t, it subtracts the average of all future available observations for a variable. Only the last observation for each individual is not computed, thus minimizing data loss. Via the Helmert transformation, the observations at time t become the following:

X * i,t = c t X i,t - 1 T -t (X i,t+1 + ... + X i,T ) , t = 1, ..., T -1, (1)
with c t = T -t
T -t+1 introduced by Arellano and Bover to equalize the variances. In GMM FOD, unlike in difference GMM, lagged observations are valid as instruments because they do not enter the formula.

The good performance of this estimator is detailed in the literature [START_REF] Arellano | Another look at the instrumental variable estimation of error-components models[END_REF][START_REF] Bond | GMM Estimation of Empirical Growth Models[END_REF][START_REF] Roodman | How to Do xtabond2: An Introduction to difference and system GMM in Stata[END_REF][START_REF] Hayakawa | First Difference or Forward Orthogonal Deviation-Which Transformation Should be Used in Dynamic Panel Data Models?: A Simulation Study[END_REF]. Indeed, all GMM estimators remain invariant for any transformation that removes individual effects, provided that the transformation matrix is upper triangular and with the use of available instruments, as is true for forward orthogonal deviation [START_REF] Arellano | Another look at the instrumental variable estimation of error-components models[END_REF]. 11 The model can be written as follows: with countries i = 1, ..., 40 and periods t = 1, ..., 10. A variable's value at one period is the average of the value of two years (X(i, 1) = X(i,1988)+X(i,1989) 2 , X(i, 2) = X(i,1990)+X(i,1991) 2 , ..., X(i, 10) =

y i,t = αy i,t-1 +β n t=0 X i,t-n +ϕcrpubpriv i,
X(i,2007)+X(i,2008) 2
). y i,t (y i,t-1 ) represents the dependent (lagged) variable for each country at each period t (the stock of inward FDI), which also captures the agglomeration effect of inward FDI [START_REF] Cheng | What are the determinants of the location of foreign direct investment? the chinese experience[END_REF][START_REF] Kinoshita | Why does fdi go where it goes? new evidence from the transition economies[END_REF] -"credit to government" (IFS). crpubpriv crpub/(crpub+crpriv) Ratio of credit to SOEs over credit to SOEs and the private sector 1 In the sources where the data is extracted, firms and banks are considered SOEs and SOBs respectively when a state, a government or a public authority holds more than 50.01% of the firms or banks' shares.

Results

The results of the first approach are summarized in Table 4. Some elements are verified to attest to the robustness of the estimations. First, the stationarity of each variable is checked even if it is not a crucial issue with a xtabond2 procedure. To tackle multicollinearity, correlation tests are performed, and other estimations are conducted without correlated variables such as GDP (as in [START_REF] Bénassy-Quéré | Institutional Determinants of Foreign Direct Investment[END_REF].

Then, to avoid biased estimations, the coefficient value of the dependent variable lag is, for most of the estimations, between the coefficient value in OLS and fixed effects estimations [START_REF] Arellano | Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations[END_REF]. 12 Moreover, the Arellano and Bond test ensures that there is no second-order autocorrelation of residuals' first difference. To ensure the robustness of the coefficients, their standard deviations are often largely below the coefficients' values (approximately 20-40 %). Regarding instruments, their number is always less than N, and their exogeneity is controlled by Hansen statistics. 13 Finally, the results are strengthened by other robustness checks: bootstrapping, quantile estimations (given the heterogeneity in inward FDI between emerging economies), estimations for different periods, and the addition of explanatory variables or interaction terms; see Table 4 and the end of this subsection.

Results

Robustness checks

Significance Coefficient Significance Coefficient

Table 4: The effect of the credit bias on inward FDI and robustness checks

12 When the ordinary least squares estimator is used, there is some correlation between the explanatory variables and the error term due to individual effects, creating an upward bias. Concerning the within estimator, when the number of periods is limited, the dependent lagged variable is correlated with the fixed effects in the error term, leading to a downward bias [START_REF] Arellano | Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations[END_REF].

13 Most of the estimations verify that the values of the Hansen test are neither too high nor too low and are not biased by too many instruments, although there is no precise rule (Ruud, 2000, p. 515;[START_REF] Roodman | A Note on the Theme of Too Many Instruments[END_REF]. Moreover, it is preferable to reduce the number of instruments as much as possible when the latter is close to the number of individuals because doing so considerably lowers the average bias in the two-step estimate of the parameters [START_REF] Windmeijer | A finite sample correction for the variance of linear efficient two-step GMM estimators[END_REF]. In the literature and in the STATA xtabond2 procedure, the number of instruments becomes critical when it is equal to or greater than the number of individuals in the panel (which could be true in these estimations if the System GMM was applied because it has more instruments than GMM FOD or difference GMM). The effect on inward FDI:

It is initially assumed that the share of credit to SOEs (variable crpubpriv ) is exogenous. 14 crpubpriv is quite significant, whether some variables are added or removed (e.g., wages, FDI costs, the share of capital in production, GDP, TFP and the real effective exchange rate; see Table 5). The coefficient of crpubpriv is negative: although there is likely endogeneity, there is a clear negative effect of the credit bias toward SOEs on inward FDI (estimations 1 to 4 in Table 5).

Then, crpubpriv is instrumented by lags of the share of SOEs. The negative effect of the credit bias is still significant when endogeneity is more controlled (estimations (5), ( 6) and ( 7) in Table 5). An interaction with soe is also added to include privatization in some estimations. As explained in Subsection 2.1, there is no correlation between crpubpriv and soe that could bias the estimations. There is an underlying 14 See Subsection 2.1 for justifications.

first step in which crpubpriv and soe are estimated by the external instruments, whose predicted values are substituted for crpubpriv and soe in the next GMM, OLS and fixed effects. This first estimation is equivalent to the first step of the ivreg STATA procedure. In the new estimations with the interaction term, crpubpriv ×soe, crpubpriv is also significant with a negative coefficient, regardless of whether other explanatory variables are removed (estimations ( 2) and ( 4)) in Table 5). Regarding the tests, the instruments used in the model are validated by Hansen's test of overidentifying restrictions, and there is no second-order autocorrelation with the residuals (Arellano-Bond test). Moreover, the value of crpubpriv 's coefficient is close to the OLS and fixed effects values; see Table 4, and more complete robustness checks are available upon request.

Considering the low number of countries, bootstrap estimations ensure that the error margins are robust. Some other estimations are performed with SOEs directly affiliated with local governments accounted for in credit amounts to the public sector; the negative effect on inward FDI is still present with these new values (Table 4). This effect is consolidated by estimations with T=5 instead of T=10, which yield quite similar results.

Quantile estimations:

Given the heterogeneity in the degree of increase in inward FDI between emerging economies (see Figure 1), the distributional impact of the credit bias in favor of SOEs on inward FDI is analyzed by using quantile regressions for panel data [START_REF] Baker | Qregpd: Stata module to perform quantile regression for panel data[END_REF]. In Figure 5, the quantile treatment effects estimates are clearly negative until close to the 85% of the FDI distribution, then slightly below zero, and again clearly negative at the end of the FDI distribution. The responses are quite heterogeneous along the distribution: (i) the lower 25% of the inward FDI distribution is generally highly responsive to credit market distortions that constrain private firms (variable crpubpriv ) but with a wide confidence interval (ii) the magnitude of the negative effect of crpubpriv on FDI is generally lower for the upper part of the distribution (except above 92%) but the confidence interval is tighter. Given the results, this first global and dynamic approach extends the notion of credit bias in favor of SOEs (developed in the literature) to other emerging economies (not only former Soviet states and China) and shows that it slows the increase in inward FDI. Therefore, this result is also in line with [START_REF] Feldstein | Aspects of global economic intergration: Outlook for the future[END_REF], [START_REF] Harrison | Does direct foreign investment affect domestic credit constraints?[END_REF], [START_REF] Du | Finance and Firm Export in China[END_REF], [START_REF] Alfaro | Fdi, productivity and financial development[END_REF] and [START_REF] Desbordes | The effects of financial development on foreign direct investment[END_REF], who explain that the local financial system and its development are also determinants of inward FDI. Moreover, the analysis deepens the link between inward FDI and local credit constraints: the increase in inward FDI can also be slowed by an unbalanced local credit distribution between private and public firms, and is not only a means to overcome local credit constraints [START_REF] Héricourt | FDI and credit constraints: Firm-level evidence from China[END_REF][START_REF] Ju | Domestic Institutions and the Bypass Effect of Financial Globalization[END_REF][START_REF] Poncet | Financial constraints in China: Firm-level evidence[END_REF]. However, at this step and considering the error margins and the few observations, it is difficult to precisely quantify the slowdown of inward FDI due to the increase in crpubpriv. For this reason, the next analysis relies on a sectoral database and another estimator. The results strengthen the previous global and dynamic approach.

A sectoral approach and financial dependence

This sectoral approach makes it possible to capture more specific and richer firms' effects, knowing that the ownership structure and dependence on external finance widely vary between sectors. Moreover, by utilizing a sectoral database over the period 1992-2012, some estimations reach approximately 600 observations versus 200 in the global approach, providing more accuracy to the quantification of the effect of the credit bias on inward FDI.

The model and data

The model follows the methodology developed by [START_REF] Rajan | Financial Dependence and Growth[END_REF], which enables correcting for country and industry characteristics and reduces omitted variables or endogeneity issues. This approach relies on the interaction of the key variable, the credit bias, with an index of sectoral financial dependence, which is independent of time and country. The estimated model is as follows:

F DI i,j,t = αT B i,j,t + µZ i,t + βcrpubpriv i,t, ∧instru.L.soei,t \instru.L.soei,t + ϕcrpubpriv i,t × soe i,t,instru.L.soei,t (3)
+γcrpubpriv i,t × f idep j + u i + v j + η t + Cste
with Z i,t = [gdpr; r; lleg; f dicsts] and u i , v j , η t , dummies for dimensions i (country), j (sector) and t (time).

The estimations concern manufacturing sectors (ISIC revision 2 classification). F DI i,j,t is the dependent variable: the amount of inward FDI stock by country, sector and time (from CEIC database) relative to GDP (aggregated GDP by country and time). The trade balance T B i,j,t is a control variable by country, sector and time (from [START_REF] Nicita | Trade, Production, and Protection Database, 1976-2004[END_REF]. Z i,t are control and explanatory variables by country and time, which were previously described in Section 2 and Tables 2 and3. crpubpriv i,t is the key variable proxy for the credit bias, i.e., the amount of credit afforded to SOEs relative to credit to SOEs and private firms, by time and country. f idep j represents financial dependence for each sector, independent of time and country. 15 The data (Table 6) come from [START_REF] Rajan | Financial Dependence and Growth[END_REF]; only financial dependence on external finance is considered because the study focuses on credit distribution. 15 Similar to the previous global study, the privatization of SOEs is also considered through an interaction between crpubpriv i,t and soe i,t (by country and time); instru.L.soe i,t is the external instrument, that is, lags of soe i,t [START_REF] Andreyeva | Company Performance in Ukraine: What Governs its Success[END_REF][START_REF] Hanousek | Origin and Concentration: Corporate Ownership, Control and Performance[END_REF]2009)

Results

We can observe that the crpubpriv variable is significant only when instrumented (crpubpriv instru.L.soe in Table 7); it emphasizes the endogeneity of crpubpriv for inward FDI (see Subsection 2.1). The key variables crpubpriv and crpubpriv instru.L.soe are also significant if other explanatory variables are added or removed (estimations ( 2), ( 3), ( 4) and ( 6)). When technically possible, bootstrap estimations are implemented and provide substantial robustness. Dummies do not appear in Table 7 but have a significant effect, particularly in 1999-2002 and 2007-2011 (during financial crises) in Central and Eastern Europe countries (that is, where privatization was the largest) and for some sectors (particularly for ISIC codes 332, 341, 342, 355, 369 and 381). An interesting point is that the effect of the key variable crpubpriv is robust to the addition of dummies by sector and time, which strengthens the causality link. However, the

Policy implications

This section proposes policy actions to reduce the credit bias toward SOEs and its negative effect on FDI.

First, the problem may obviously be solved at its source by directly reducing the over-financing of SOEs. The first option would be, in countries that experienced a transition process, to restructure SOEs' debts to reduce the incentive for SOBs to fund former debtors for future repayments (see the end of the introduction). [START_REF] Begg | Enterprise debt and economic transformation (Financial restructuring of the state sector in Central and Eastern Europe)[END_REF] and [START_REF] Perotti | Bank lending in transition economies[END_REF] mention that the government should write off banks' bad loans from SOEs. Indeed, such debt raises and concentrates financial risk and instability, in addition to hindering investment in private firms. [START_REF] Calvo | Credit Markets, Credibility, and Economic Transformation[END_REF] argue that a substitution of these claims in the banks' balance sheets with long-term government bonds could be a solution. Furthermore, the scourge of corruption reduces inward FDI in some estimations in this work, and is above all one cause of the credit bias of SOBs (in favor of SOEs).17 The fight against corruption is not a new element and is already in progress in these emerging economies, but this study adds a new important consequence if the issue of corruption is underestimated.

Second, because it has been demonstrated in the literature that inward FDI can improve the efficiency of the local credit market [START_REF] Héricourt | FDI and credit constraints: Firm-level evidence from China[END_REF][START_REF] Ju | Domestic Institutions and the Bypass Effect of Financial Globalization[END_REF][START_REF] Poncet | Financial constraints in China: Firm-level evidence[END_REF], administrative and business barriers must be reduced. It is even more important according to this paper's results in which the increase in inward FDI is already slowed by a better access to credit for SOEs compared to private firms. Thus, the loosening of protectionism measures that are already in place in specific sectors (for example in China; see [START_REF] Branstetter | China's Embrace of Globalization[END_REF] could both offset the negative consequences of the credit bias and support private firms to bypass domestic credit constraints.

Third, in addition to reducing the credit bias at its origin or alleviate administrative and business barriers, it is necessary to slow privatization. Indeed, although the over-financing of SOEs is high in China, the privatization catch-up was done step by step compared to other countries. That process allowed China to avoid or reduce many issues encountered by former Soviet states and others (e.g., underdevelopment of supporting institutions, huge capital flight, government favors, public and private indebtedness, and the Russian crisis; for more details see [START_REF] Roland | On the Speed and Sequencing of Privatization and Restructuring[END_REF][START_REF] Havrylyshyn | Privatization in transition countries: Lessons of the first decade[END_REF][START_REF] Havrylyshyn | Privatization in transition countries: Lessons of the first decade[END_REF]. Moreover, if the credit bias cannot be reduced and if SOBs still have an incentive to fund former debtors (SOEs) for future repayments, a gradual privatization will reduce the losses in credit distribution to private firms each year. This consequence will allow supplementary time for future SOE repayments and adjustments, thus providing more credit opportunities for future new private firms.

Finally, one approach for the government and some banks to lend more to certain types of firms is to control lending rates [START_REF] Aglietta | La voie chinoise, capitalisme et empire[END_REF][START_REF] Song | Growing (with Capital Controls) like China[END_REF], and Funke et al., 2015 for China). Hence, the necessity to liberalize interest rates, bearing in mind that doing so will most likely result in an increase in lending rate volatility (which could occur in China; see [START_REF] Feyzioglu | Interest Rate Liberalization in China[END_REF].

Conclusion

Both global and sectoral approaches show that while the share of SOEs decreases or remains constant, an increasing credit distribution to SOEs compared to private firms slows the increase in inward FDI in emerging economies. This misallocation of capital is a crucial issue because the credit bias toward SOEs continues to occur in certain emerging economies, particularly in China, Russia, United Arab Emirates, Venezuela and Vietnam. 18 The implications of this study are twofold. First, many policy actions can be established to improve capital allocation, either by reducing the over-financing of SOEs at its source or by limiting the negative consequences. Second, the analysis completes two fields of the literature. On the one hand, it extends the presence of the credit bias in favor of SOEs to other emerging economies (not only former Soviet states and China; see [START_REF] Perotti | Bank lending in transition economies[END_REF][START_REF] Boyreau-Debray | Financial intermediation and growth -Chinese style[END_REF][START_REF] Dollar | Das (Wasted) Kapital: Firm Ownership and Investment Efficiency in China[END_REF][START_REF] Poncet | Financial constraints in China: Firm-level evidence[END_REF][START_REF] Song | Growing like china[END_REF] and analyzes its effect on inward FDI. On the other hand, the paper strengthens the role of local financial intermediation as a determinant of inward FDI in the host country [START_REF] Feldstein | Aspects of global economic intergration: Outlook for the future[END_REF][START_REF] Harrison | Does direct foreign investment affect domestic credit constraints?[END_REF][START_REF] Alfaro | Fdi, productivity and financial development[END_REF][START_REF] Desbordes | The effects of financial development on foreign direct investment[END_REF].

The two empirical studies in this article suggest future research directions. An interesting point would be, if the data are obtainable, to extend this study to Commonwealth of Independent States because these countries experience a substantial privatization program. Therefore, the magnitude and the consequences of the credit bias toward SOEs would be strong. Then, we can also expect a negative effect on outward FDI; such analysis would require another model including determinants of outward FDI in the foreign countries.

18 Source: International Financial Statistics (2013, IMF). 0.5 threshold in the PIP rank are selected, or the variables that, according to the literature, still seem crucial. 22 Therefore, GDP and the real effective exchange rate are included as control variables. Then, in addition to explanatory variables with a P IP > 0.5 (r, w and corru), the lag of inward FDI, savings, investments, GDP and the real exchange rate are also included as fundamentals. The legal environment, TFP level and FDI costs (lleg, tfpl and fdicsts) are included in some estimations because they can be significant despite the results below. Moreover, because the focus of the study is the bias toward public sector, all of the associated variables (crpubpriv, crpubpriv ×soe and instrumentation (lags of soe)) are added to the previous fundamentals for each estimation. There is a quite good certainty concerning coefficients' signs (see "Cond. Pos. Sign" in Figure 7), which broadly corresponds to the literature, to the expected results and to the results. 22 Indeed, the BMA regression from y = α + βX + cannot fully reflect the GMM FOD estimations presented in this paper; primarily because of the addition of variables for the credit misallocation, privatization and instruments. Therefore, not to omit some determinants, variables with a PIP close to 0.5, or those that are often used in the literature, are also considered. 1 Such differences between the minimum and maximum of the real GDP are due to large differences in terms of development and population size (the presence of very small countries in the first periods (like Estonia) and very large countries in the last periods (like China)).

2 509.835 is the mean of the lending rate on 1991-1992 during the war in Slovenia. The database has been cleaned of other more extreme values. 3 All sectoral variables are divided by GDP; variables of the global database are also used as control and explanatory variables for the sectoral approach (see Subsection 3.1).

Figure 6 in

 6 Appendix A illustrates the mechanism. Inward FDI -BRICMS, Indonesia, South Korea and Turkey (a) (Stocks in % GDP) Inward FDI -Asian countries (b) (Stocks in % GDP) Inward FDI -South America countries (c) (Stocks in % GDP) Inward FDI -African and Middle East countries (d) (Stocks in % GDP) Inward FDI -Eastern Europe countries (1) (e) (Stocks in % GDP) Inward FDI -Eastern Europe countries (2) (f) (Stocks in % GDP)

Figure 1 :

 1 Figure 1: Inward FDI stocks

Figure 3 :

 3 Figure 3: Credit to SOEs over total credit (to SOEs and private sector) and the share of SOEs

Figure 4 :

 4 Figure 4: Credit to SOEs over total credit (to SOEs and private sector) and the share of SOBs

Figure 5 :

 5 Figure 5: Quantile estimations -the effect of the credit bias (crpubpriv ) on inward FDI

Figure 7 :

 7 Figure 7: BMA results -inward FDI

  t, ∧instru.L.soei,t \instru.L.soei,t +γcrpubpriv i,t ×soe i,t,instru.L.soei,t +u i +v t (2)

  . X i,t-n the set of explanatory lagged variables, u i the specific individual effect for each country and v t time dummies. E[x i,t-n (u i + v i,t )] = 0 is valid for each country, and the Roodman hypotheses (2006) for the application of Arellano and Bond estimator are verified. The misallocation of capital is captured by crpubpriv (instrumented or not by instru.L.soe i,t ), which also interacts with privatization (crpubpriv i,t × soe). The key variables are described above, summarized in Tables2 and 3, and the descriptive statistics are in Table8in Appendix C.

	name	definition	sources
	dependent variable:
	fdiin	Inward FDI stock (/GDP)	UNCTAD Statistics
	explanatory variables:
	gdpr	Real GDP (billions U.S. $,	World Bank
		divided by deflator (index 2005=100))
	reer	Real effective exchange rate	Bank of International Settlements,
		base 2010=100	World Bank, and author's calculations
	r	Interest rate (%, lending rate)	International Monetary Fund and World Bank
	rw	Word interest rate (mean of	OECD
		U.S. short-and long-term interest rates)
	w	Wages (U.S.$, monthly average)	ILOSTAT Database
			(International Labour Organisation)
	ky	Share of capital in production	World Bank
	s	Gross savings (/GDP)	World Bank
	tfpg	TFP growth (%)	The Conference Board Data and World Bank
	tfpl	TFP level (U.S. index = 1)	Penn World Table
	cp	Capital controls	The Chinn-Ito index (KAOPEN,
		(financial openness)	Chinn and Ito, 2006),
			revised and updated to 2012
	fdicsts	Trade costs, and administrative	World Development Indicators
		and business fees (/GDP)	(Trade costs Dataset, the World Bank)
			and author's calculations on bilateral data
	corru	Corruption index	The Corruption Perceptions Index
		0 (high corruption)	of Transparency International
		to 10 (low corruption)
	lleg	Legal system and property rights	The Fraser Institute Database
		0 (low quality) to 10 (high quality)

Table 2 :

 2 Variables

	name	definition	sources
	privatization and
	the misallocation
	of capital:
	soe	Share of SOEs 1 in GDP	Kowalski et al. (2013, Source:
			Bureau Van Dijk Databases)
			Estrin et al. (2009, Source:
			European Bank for Reconstruction
			and Development Transition Reports),
			Ramamurti (1999), and
			China Statistical Yearbook.
	sob (only in the	Share of SOBs 1	Barth et al. (2001, Source:
	correlation table,	(share of assets and liabilities	versions 2000 and 2003 of the
	see Figures 2 and 4) owned by the government)	Bank Regulation and Supervision
			Survey of the World Bank),
			La Porta et al. (2002, various sources),
			Gonzalez-Garcia and Grigoli (2013,
			Source: Bank Regulation and
			Supervision Survey of the World Bank),
			and Almanac of China's
			Finance and Banking.
	crpriv	Domestic credit to the private sector	International Financial Statistics
		(/GDP)	(2013, IMF, line "claims on
			private sector")
	crpub	Domestic credit to SOEs (/GDP):	International Financial Statistics
			(2013, IMF, line "claims on
			public non-financial corporations");
			when the latter is not available for a country:
			"credit to SOEs and government"
			(World Bank and St. Louis Fed Stats)

Table 3 :

 3 Variables (2)

  GMM fod GMM fod GMM fod GMM fod GMM fod GMM fod GMM fod

		(1)	(2)	(3)	(4)	(5)	(6)	(7)
		fdiin	fdiin	fdiin	fdiin	fdiin	fdiin	fdiin
	L.fdiin	0.798*** 0.590*** 0.750*** 0.533*** 0.632*** 0.603***	0.417*
		[0.046]	[0.055]	[0.058]	[0.077]	[0.041]	[0.056]	[0.244]
	gdpr			-0.226**	0.096	0.274*	0.227*
				[0.106]	[0.091]	[0.144]	[0.116]
	reer			0.120	0.411*** -0.455*	-0.595**
				[0.234]	[0.142]	[0.255]	[0.257]
	ky	-0.319	-1.965** -1.760** -5.681*** -4.597*** -3.857***	-2.382
		[0.851]	[0.801]	[0.830]	[0.923]	[1.266]	[1.163]	[1.702]
	r	0.002	0.002	0.001	-0.002	-0.007**	-0.006*	-0.002
		[0.002]	[0.001]	[0.002]	[0.002]	[0.003]	[0.003]	[0.007]
	w	-0.113**	-0.232					-0.550***
		[0.053]	[0.141]					[0.176]
	crpubpriv	-0.082**	-0.204* -0.155*** -0.294***
		[0.041]	[0.112]	[0.053]	[0.072]
	crpubpriv×soe instru.L.soe		-0.079***		-0.072*
			[0.027]		[0.036]
	crpubpriv instru.L.soe					-0.175** -0.391*** -0.654***
						[0.071]	[0.091]	[0.213]
	lleg	-0.204	0.222	0.558**	0.647** 1.032***	0.246	0.979
		[0.283]	[0.230]	[0.244]	[0.241]	[0.310]	[0.216]	[0.659]
	tfpl						1.029** 2.330***
							[0.394]	[0.830]
	fdicsts	0.006	-0.007	0.002	-0.029**	-0.022	-0.006	-0.035
		[0.007]	[0.015]	[0.013]	[0.011]	[0.015]	[0.018]	[0.022]
	L.corru	0.084	0.039	0.041	-0.203***		0.263**	0.142
		[0.057]	[0.041]	[0.039]	[0.044]	[0.053]	[0.108]	[0.087]
	time dummies	yes	yes	yes	yes	yes	yes	yes
	Observations	168	112	202	134	104	102	91
	Number of countries	35	32	38	36	29	28	26
	Sargan statistic	22.25	15.06	12	12.26	16.65	19.53	14.31
	p-value of Sargan statistic	0.0348	0.238	0.151	0.907	0.276	0.242	0.575
	Hansen statistic	16.58	20.34	10.15	17.75	18.84	19.19	8.353
	p-value of Hansen statistic	0.166	0.0610	0.255	0.604	0.171	0.259	0.938
	Arellano-Bond test for AR(1) -2.858	-2.556	-2.242	-2.619	-1.346	-1.621	-2.448
	p-value AR1	0.00427	0.0106	0.0249	0.00882	0.178	0.105	0.0144
	Arellano-Bond test for AR(2) -1.618	-1.451	-0.965	-1.675	-0.991	-0.966	-0.152
	p-value AR2	0.106	0.147	0.335	0.0939	0.321	0.334	0.880
		Standard errors in brackets
		*** p<0.01, ** p<0.05, * p<0.1

Table 5 :

 5 The effect on inward FDI -GMM with Forward Orthogonal Deviation

  . The external instrument is used or not used with crpubpriv i,t and soe i,t to ensure exogeneity because the latter are considered either exogenous or endogenous in the literature (see Subsection 2.1). Finally, country, sector and time dummies control for omitted variables and characteristics for each dimension; in addition, the robustness is ensured by adding interactions between dummies (for example time × sector to correct for all sectoral effects). Estimations

	are performed on a panel of eight countries, restricted by sectoral data availability, that experienced in
	the last 25 years a credit bias toward SOEs within certain periods of time (China, Indonesia, Romania,
	Slovenia, South Africa, South Korea and Turkey). 16
			Manufacturing
	Sector	Financial	Sector	Financial
	dependence		dependence
	311 Food products		0.14	355 Rubber products	0.23
	313 Beverages		0.08	356 Plastic products	1.14
	314 Tobacco		-0.45	361 Pottery. china. earthenware	-0.15
	321 Textile		0.4	362 Glass and products	0.53
	322 Apparel		0.03	369 Other non-metallic	0.06
	323 Leather products		-0.14	mineral products
	324 Footwear		-0.08	371 Iron and steel	0.09
	331 Wood products,		0.28	372 Non-ferrous metals	0.01
	except furniture			381 Fabricated metal products	0.24
	332 Furniture		0.24	382 Machinery	0.45
	341 Paper and products		0.18	383 Electric machinery	0.77
	342 Printing and publishing	0.2	384 Transport equipment	0.31
	351 Industrial chemicals		0.21	385 Professional and	0.96
	352 Other chemicals		0.22	scientific equipment
	353 Petroleum refineries		0.04	390 Other manufactured products	0.47
	354 Miscellaneous petroleum	0.33
	and coal products

Table 6 :

 6 Sectors' financial dependence -

	Source: Rajan and Zingales (1998)

Table 8 :

 8 Descriptive statistics

Figure 6 in Appendix A illustrates the mechanism.

According to Boyreau-Debray (2003, p. 4), "the state banks have been asked to channel savings to the loss-making state-owned enterprises".

Indeed, although soe can be considered exogenous in most cases because it is the direct result of the government's privatization policy to change ownership or sell shares, an endogenous bias can also appear[START_REF] Megginson | The economics of bank privatization[END_REF]. Thus, in the literature, we can find studies that both control and do not control for these issues[START_REF] Estrin | Effects of Privatization and Ownership in Transition Economies[END_REF]. Therefore, the lags of soe are used as instruments for soe, as is true in the literature[START_REF] Andreyeva | Company Performance in Ukraine: What Governs its Success[END_REF][START_REF] Hanousek | Origin and Concentration: Corporate Ownership, Control and Performance[END_REF] 2009). Note that the general context of liberalization and deregulation also seems to be a quite good instrument for soe, however, it is correlated with the entries of FDI.

The variable of capital controls cp is the Chinn-Ito index, which is a broad index of financial liberalization including many types of capital flows. Thus, this index can not explain the specific pattern of inward FDI.

The other variables have observations for nearly each period and country, except wages and corruption (available for 1992-2008), FDI costs and the real effective exchange rate(available for 1994-2008). Countries such as Brazil, Bulgaria, Iran, Romania, Russia, Ukraine, the United Arab Emirates, Venezuela and Vietnam have sizeable data gaps.

In addition, forward orthogonal deviation preserves the orthogonality among the transformed errors; indeed, if the original individual specific effects are not autocorrelated and have constant variance, then the same is true for the transformed errors[START_REF] Arellano | Another look at the instrumental variable estimation of error-components models[END_REF]. With this choice of ct in Equation1, it is assured that X i,t are not only independent but also identically distributed, both before and after the transformation.

Note that financial dependence is calculated by[START_REF] Rajan | Financial Dependence and Growth[END_REF] and often used in the literature as a global sectoral index, independent of country and time. It is based on a large sample of U.S. firms (from the Compustat database).The key assumption is that the U.S. financial market is the most liquid in the world, thus, the measure of sectors financial dependence in the U.S. captures the specific needs of credit for each sector (so independently of countries). Moreover, the difference of financial dependence between sectors is the interesting point, but not the level of financial dependence of each sector. The authors also specify that the calculation of this index is based on the 1980s and corresponds to the emerging economies' product life cycle in the 1990s.

Brazil, India Russia, and some Central and Eastern Europe countries are not in the sample because of a lack of available sectoral data. South Korea was considered an emerging market at the beginning of the sample period but was then classified as a developed country.

See Wei (2000),[START_REF] Wei | Negative Alchemy? Corruption, Composition of Capital Flows, and Currency Crises[END_REF],Gelos and[START_REF] Gelos | Transparency and International Investor Behavior[END_REF] 2005),[START_REF] Papaioannou | What drives international bank flows? Politics, institutions and other determinants[END_REF], and[START_REF] Branstetter | China's Embrace of Globalization[END_REF] for the link between corruption and capital flows; and La[START_REF] Porta | Government ownership of banks[END_REF],[START_REF] Sapienza | The effects of government ownership on bank lending[END_REF],[START_REF] Khwaja | Do Lenders Favor Politically Connected Firms? Rent Provision in an Emerging Financial Market[END_REF], and[START_REF] Firth | Leverage and investment under a state-owned bank lending environment: Evidence from China[END_REF] for the effect of corruption on financial intermediation during privatization.

The number of draws (iterations) limits the quality and duration of MCMC approximation to the actual posterior distribution. Because the first set of iterations does not draw models with high posterior model probabilities, this first set of iterations (the "burn-ins") is not included in the computation of results.

This work has been partly carried out within financial support from Région Rhônes-Alpes (Explo'ra Doc)

interaction between financial dependence and the credit distortion (crpubpriv i,t × f idep j ) is not robust to changes of explanatory variables; it means that the difference of financial dependence between sectors has no effects.

These results are consistent with those of the first global approach. The negative effect of the credit bias on inward FDI is more accurate in the sectoral approach: a one percentage point increase in the credit afforded to SOEs to the detriment of private firms is associated with a slowdown of inward FDI by approximately 1.8% to 3.1%. Similar to the GMM results, these sectoral estimations deepen the causality between local credit constraints and FDI described the literature. They also emphasize local funding as a FDI determinant. The figure shows that: (i) inward FDI can be financed by the local credit market [START_REF] Feldstein | Aspects of global economic intergration: Outlook for the future[END_REF][START_REF] Harrison | Does direct foreign investment affect domestic credit constraints?[END_REF][START_REF] Du | Finance and Firm Export in China[END_REF][START_REF] Alfaro | Fdi, productivity and financial development[END_REF][START_REF] Desbordes | The effects of financial development on foreign direct investment[END_REF]; (ii) thus, inward FDI can be slowed if SOEs are more financed to the detriment of private firms (it is the focus of this article); (iii) inward FDI are also financed by the foreign country and are a means for private firms to bypass local credit constraints [START_REF] Héricourt | FDI and credit constraints: Firm-level evidence from China[END_REF][START_REF] Ju | Domestic Institutions and the Bypass Effect of Financial Globalization[END_REF][START_REF] Poncet | Financial constraints in China: Firm-level evidence[END_REF].

Appendix B: Bayesian meaning average and the determination of fundamentals

A problem appears when there are too many potential explanatory variables X i for a dependent variable y, a situation that pertains to this study for the determinants of inward FDI. The models and combinations of explanatory variables from the literature are numerous (see Subsection 2.1), which can skew the study of a specific variable and causality. Bayesian Model Averaging (BMA) addresses model uncertainty and allows determining which variables X i ∈ X should be included in the model and their importance. This methodology is based on a linear model structure with all potential explanatory variables and a normal iid error term with variance σ 2 (y = α + βX + , ∼ N (0, σ 2 I)). Then, BMA creates a weighted average over estimated models for all combinations of X, meaning 2 K combinations of variables (inward FDI fundamentals) for K potential explanatory variables, i.e., 2 K models. Let p(y|M i , X) denote the marginal likelihood of the model (the probability of the data given the model M i ). The posterior model probability, p(M i |y, X), is proportional to p(y|M i , X) times a prior model probability p(M i ):

For further details on the theoretical framework, see [START_REF] Hoeting | Bayesian model averaging: a tutorial[END_REF] and Sala-I-Martin et al. (2004).

In this paper, the study is based on panel data following the methodology of [START_REF] Feldkircher | Bms and the fixed effects estimator -a tutorial[END_REF] and [START_REF] Moral-Benito | Determinants of Economic Growth: A Bayesian Panel Data Approach[END_REF]. As is often true for BMA, and because of uncertainty among a variety of potential determinants, priors are set to be uniform. In other words, the prior expected model size is K/2 with a common prior model probability. 19 Markov Chain Monte Carlo (MCMC) sampling and birth-death methods are used, as in [START_REF] Fernández | Model uncertainty in cross-country growth regressions[END_REF]. 20 The number of "burn-ins" and draws (iterations) does not significantly change the results, but a minimum of 5000 "burn-ins" and 10000 draws is set. 21 The hyperparameter "g" is fixed at g = max(N, K 2 ) as in [START_REF] Fernández | Model uncertainty in cross-country growth regressions[END_REF]. 13 potential explanatory variables are selected for inward FDI (L.fdiin, gdpr, reer, r, ky, s, w, tfpl, corru, fdicsts, cp, and lleg); thus, the BMA routine obtains 2 12 models, i.e. 4096 models, each of them defined by a different combination of FDI fundamentals, and by a probability of being the "true" model (therefore, the prior expected model size is 6).

BMA results: The posterior inclusion probability of each variable is given by the "PIP" column in Figure 7. "Post Mean" is the coefficients averaged over all models (including a zero coefficient when the variable was not contained), "Post SD" the corresponding standard deviations and "Cond. Pos.

Sign" the probability that the coefficient's sign is positive. All coefficients with a PIP above 0.5 are selected, as is usually true for uniform priors. In addition to the above, control variables just below the 19 Indeed, for K potential explanatory variables, there are 2 K possible variable combinations. Therefore, the common prior model probability is p(M i ) = 2 -K for a uniform prior model. Hence, a prior expected model size of

is anticipated. 20 MCMC is used when there are many covariates, and birth-death sampler is the standard model sampler used in most BMA routines. These methods are well developed in [START_REF] Zeugner | Bayesian Model Averaging with BMS[END_REF].

Appendix C: Descriptive statistics