Financial development and firm survival

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Posted on: 20 Apr 2010

Financial development and firm survival: Evidence from a panel of emerging Asian economies

Firm survival and financial development: Evidence from a panel of emerging Asian economies March 2010 Abstract Using a panel of five Asian economies - Indonesia, Korea, Malaysia, Singapore and Thailand - over the period 1995?2007 we analyze the links between firm survival and financial development. We find that traditionally used measures of financial develop- ment play an important role in influencing firm survival. When stock markets become larger or more liquid firms? survival chances improve. On the contrary, we show that higher levels of financial intermediation can increase firm failures. We also find that the beneficial effects of stock market development are more pronounced during the later years of our sample, while the adverse effects of bank intermediation have been reduced over time. Finally, large firms are more likely to benefit from developments in financial markets compared to small firms. Key words: Firm survival, Firm-specific characteristics, Financial development JEL: E44, D92, L20, O10 1 1 Introduction Does it matter for firm survival whether a country?s financial system is more or less devel- oped? The idea that the financial system has a central role to play in economic fluctuations is an old one (see Gertler (1988)). Following the seminal work of Goldsmith (1969), sev- eral empirical studies have documented the existence of a strong positive link between the functioning of the financial system and various aspects of economic activity such as invest- ment, employment and economic growth (see for instance, King and Levine (1993); Rajan and Zingales (1998) and Levine (2006)). These studies, however, remain largely silent about the role of financial development in firms? survival prospects. Such evidence is important for understanding the mechanism by which financial development affects survival and can better inform policy makers, especially in the context of emerging Asian economies that are undergoing periods of deregulation and redesign. The purpose of this paper is to provide, for the first time a systematic empirical anal- ysis of the impact of financial development on firm survival by looking at the direct effect of financial development indicators on firm survival after controlling for firm, industry and macroeconomic effects. Our empirical approach focuses on two of the most important aspects of financial development - banking development and stock market development. The moti- vation to do so stems from two important considerations. First, in the Asian region banks dominated the financial markets for many years, but recently Asian economies have become less bank centered and large strides were taken to develop equity and bond markets. Second, emerging East Asian economies are characterized by a highly volatile environment and high risk of bankruptcy making therefore the analysis of corporate failures very relevant.1 To this end we analyze the survival prospects of 3,272 firms from five Asian economies (Indonesia, Korea, Malaysia, Singapore and Thailand) that experience significant failure rates over the last decade. Corporate failures can be affected directly by the development of the financial system for a number of reasons. To begin with equity markets, at higher levels of equity development corporate failures should be reduced. Larger equity markets with greater liquidity reduce investment risk and the cost of accessing the paper market thereby providing a workable alternative to meet firms? external funding requirements.2 Therefore, gaining access to an alternative source for external financing can shield firms against failures, particularly when 1Compared to Western economies, emerging Asian countries experience significantly higher corporate failure rates: according to our dataset, failure rates in Indonesia, Korea, Malaysia, Singapore and Thailand are respectively 9%, 9%, 10%, 6% and 15%, compared to only 1.5% in the UK (Bridges and Guariglia (2008) and Go¨rg and Spaliara (2009)). 2 If there is a large volume of trading, it may be possible for brokers to spread their fixed costs more widely and thus reduce transactions costs. 1 banks decide to interrupt lines of credit. Moving to banking development, increased levels of bank lending might adversely affect corporate failures since emerging Asian markets are inherent to bank runs and therefore higher levels of banking intermediation could impede firms? performance and survival prospects. Furman and Stiglitz (1998) suggest that Asia?s dependence on banks was important for the 1997-98 financial crisis, while Beck et al. (2006) show that financial intermediary development can magnify the impact of macroeconomic shocks if there is limited access to external financial markets. In our paper we also recognize that the effect of institutional development to firm failures has evolved over time due to recent East Asian efforts to strengthen their financial markets. In addition, growth in stock markets and banks may not influence all firms in a proportional way. Therefore, we allow for the fact that firms of different sizes might respond to the growth of equity size, liquidity and banking intermediation disproportionately by measuring the indirect influence of these variables. The value added of the present paper is threefold. First, we consider a direct role for financial development in influencing business failures. In addition to the firm-specific and financial indicators previously considered, this study also considers the impact of different measures of financial development. This approach complements the existing empirical and theoretical literature on firm survival and borrowing constraints (see Zingales (1998); Bunn and Redwood (2003); Clementi and Hopenhayn (2006); Farinha and Santos (2006); Bridges and Guariglia (2008) and Go¨rg and Spaliara (2009)), which highlights the role of financial condition in firm survival. The second main contribution of the paper is that, using comparable multi-country data made up by firm-level panels, we are able to assess whether the financial development-survival nexus has changed over time since the recent developments in the Asian financial markets. The financial system in Asia has undergone significant changes and developments over the past decade and it may be possible that the role of financial development in firm survival has become more (or less) pronounced. The most prominent initiative towards the development of a regional financial market has been the establishment of the Asian Bond Fund, which was initiated in 2003 and extended in 2005. Finally, we are able to identify which firms are more likely to benefit from the financial development with respect to corporate failures. Intuitively, we do not expect all firms to be equally affected by financial development since large firms are able to tap financial markets, while small firms are more likely to be financially constrained and may be unable to access financial services due to significant fixed costs. Thus large firms may be better equipped to take advantage of developments in financial markets and consequently improve their per- formance. Attempts to identify groups of companies that are financially constrained using 2 criteria such as the firm size (Carpenter and Guariglia (2008) and Spaliara (2009)) or firm age (Guariglia (2008) and Spaliara (2009)) have been found to play an important role in various aspects of firm behavior (e.g investment and employment). Bridges and Guariglia (2008) found that financial constraints are important in firm survival but their effect can be mitigated with global engagement. In this paper we will test whether there is a differential effect of financial development indicators on the failure probabilities of small and large firms. The remainder of the paper is laid out as follows. Section 2 illustrates the baseline specification and econometric methodology. In Section 3 we describe our data and provide some summary statistics. Section 4 presents the empirical evidence. In section 5 we check the robustness of our findings. Section 6 concludes the paper. 2 Empirical methodology and baseline specification We use the theoretical analysis by Clementi and Hopenhayn (2006) as a starting point for our empirical analysis. In their model borrowing constraints affect firm survival and this generates a role for capital structure in an asymmetric information setup. In our empirical analysis we take on board these predictions and we also consider the effects of financial development on firm survival. In order to establish whether financial development changes firms? survival prospects, we model the determinants of firm survival and check whether the indicators of financial development are statistically significant determinants of firms? hazard of failure. We define a firm as failed in a given year when its company status is that of dead.3 Following the recent literature on firm survival (for example Go¨rg and Spaliara (2009) and Go¨rg and Bandick (2009)) our empirical models are estimated with the complementary log-log model (cloglog) which is equivalent to the discrete time version of the proportional hazard model. Given that our data are collected on a yearly basis, the cloglog model is more appropriate compared to the Cox model.4 Estimating the models with the proportional hazard model will allow us to capture the exact time of failure and the potential right censoring bias. The baseline proportional hazard of a firm failing at time t is formulated as: h(t) = h0(t)exp(??FD + ??X + ??Y + ??Z) (1) where h(t) is the rate at which firms fail at time t given that they have survived in 3Details on how this dummy is constructed are provided in the next section. Also note that we use the terms failure and survival interchangeably. 4 In addition, the cloglog model has the same assumptions on the coefficient vector a? as the continuous-time version of the proportional hazard model (Prentice and Gloeckler (1978)). 3 t ? 1, for a given number of covariates. h0(t) is the baseline hazard function at time t when all of the covariates are set to zero. To test whether firm exit is affected by country- level financial development, we include the term FD, which denotes the vector of financial development measures such as stock market size (Market Capitalization), the liquidity of the stock market (Stock Market Value Traded), the size of the banking system (Private Bank Credit) and the relative importance of deposit money banks (Bank Assets), respectively. X comprises a vector of financial variables assumed to capture the effect of financial health on the likelihood of survival. Y is a vector of firm-specific, industry-specific characteristics and macroeconomic control variables. Lastly, Z is a set of industry and country dummies that control for fixed effects across industries and for institutional differences between countries. To incorporate a role for finance in the survival model, as suggested by the theoretical model of Clementi and Hopenhayn (2006), vector X considers three dimensions of financial health from the balance sheet, namely leverage, profitability and collateral assets.5 We define leverage (LEV ERAGE), as total debt over total assets, to measure the firm?s overall indebtedness. Higher levels of existing debt are often associated with a poorer balance sheet, and thus firms with higher levels of debt face greater difficulties obtaining funds on the markets (see Zingales (1998) and Bougheas et al. (2006)). We expect therefore a positive relationship between leverage and the probability of failure. The profitability ratio (PROFITABILITY ) is defined as earnings before interest and taxes relative to total assets to measure a firm?s ability to generate profits. It is widely recognized that internal funds can serve as a buffer to absorb unexpected losses, reducing the probability of insolvency and, therefore, the expected bankruptcy cost (see Bunn and Redwood (2003); Bridges and Guariglia (2008) and Go¨rg and Spaliara (2009)). We therefore expect to find profitability to decrease the probability of failure. Collateral (COLLATERAL) is defined as tangible assets over total assets and proxies for the firm?s ability to pledge collateral for external finance. In the survival literature, access to collateral assets is very important since Farinha and Santos (2006) and Bridges and Guariglia (2008) document that firms with a larger fraction of tangibles in their balance sheets are more likely to survive. Accordingly, we expect firms with a high collateral ratio to experience lower probabilities of failure. Vector Y includes a choice of control variables guided by the existing empirical literature on the determinants of firm survival. According to Geroski (1995), a firm?s size plays an important role in determining firm failures. Small firms tend to be associated with the higher degree of information asymmetry and therefore are more at risk of failure than large firms (Dunne et al. (1998) and Clementi and Hopenhayn (2006)). Accordingly, we include firm 5Our firm-specific financial indicators are lagged one period to mitigate potential endogeneity concerns. 4 size (SIZE) defined as the logarithm of the firm?s real total assets. We also incorporate its square (SIZE2) to allow for non-linearities. Further, we introduce age (AGE) which measures the number of years a firm has been listed on the stock exchange. Firms with an established track record are less likely to fail than those that are younger because they are usually more able to withstand past economic and financial downturns and therefore face a smaller liquidation risk. This would be the case both for domestic and multinational firms as noted by Go¨rg and Strobl (2002). In vector Y we also control for the macroeconomic and industry-specific conditions by adding the GDP growth and the minimum efficient scale of the industry (MES). It should be noted that without controlling for GDP growth the impact of financial development on survival might simply reflect overall development and not something specific about the financial system. We add therefore the GDP growth to control for demand factors and we expect it to be negatively associated with the firm?s probability to fail.6 To control for the extent of economies of scale in the industry, we add MES measured as the log of median output in sector j. There is a consensus that attaining minimum efficient scale raises a firm?s survival prospects (Audretsch (1991)) and therefore we expect a negative relationship between MES and corporate failures. 3 Data and summary statistics The data for this paper are drawn from different sources including Thomson Financial Pri- mark, Zephyr, the Asian Development Bank and the World Bank. These are combined in a new way to cast light on the effect of financial development on the probability of failure in the Asian region. The data cover firms in five emerging Asian economies - Indonesia, Korea, Malaysia, Singapore and Thailand - over the period 1995-2007. 3.1 Firm-level data The Thomson Financial Primark database offers balance sheet and profit and loss accounts data for firms in the East Asian region. Our initial data set includes a total of 41,641 annual observations on 4,651 companies. We provide information on financial accounts and ratios for Asian firms operating in all sectors of the economy for the years 1995-2007. We use Zephyr, which is distributed by Bureau Van Dijk, to obtain data on mergers and acquisitions for the sampled firms. The Thomson Financial Primark database reports firms 6To check the robustness of our results we replaced GDP growth with the exchange rate, which measures the exchange rate environment. Our results, not reported here for brevity, remain unaffected. 5 as ?dead? but it may be possible that some firms could be recorded as ?dead? not because they failed but because they merged with another firm instead. Employing Zephyr we are able to identify and drop those firms that are mistakenly coded as ?dead? in our data. This will ensure that our dependent variable has been accurately constructed to capture firms that failed and did not exit the sample due to mergers and acquisitions. Following normal selection criteria used in the literature, we exclude companies that did not have complete records for all explanatory variables and firm-years with negative sales. To control for the potential influence of outliers, we exclude observations in the 0.5 percent from upper and lower tails of the distribution of the regression variables. Our sample contains data for 358 firms in Indonesia, 917 in Korea, 871 in Malaysia, 596 in Singapore and 530 in Thailand, a total of 3,272 firms. Finally, by allowing for both entry and exit, the panel has an unbalanced structure which helps mitigate potential selection and survivor bias. 3.2 Indicators of financial development Data on financial development indicators are taken form the World Development Indicators (WDI) and Beck et al. (2003). Annual data on GDP growth come from the Asian Devel- opment Bank. In line with the literature on financial development we use various aggregate indicators that proxy for financial development to ensure robustness.7 We use two indicators to capture the development of the stock market, which provides ?arms-length finance? (see Levine (2006)). We rely on both the size and the liquidity of the stock market. A larger mar- ket size (stock market capitalization/GDP) indicates that investors have confidence in the market?s ability to channel funds into the most efficient projects. Greater market liquidity (total stock market value traded/GDP) implies lower transactions costs and wider market participation. We employ two indicators to measure financial intermediary development. First, we consider the quantity of funds that is channeled through the banking system to investors in the private sector (private bank credit/GDP). This indicator shows the overall development in private banking system. According to Baltagi et al. (2008) this is the most important banking development indicator because it quantifies the extent to which new firms have opportunities to obtain bank finance. Second, we look at the total assets of domestic money deposits (bank assets/GDP). This indicator captures the overall size of the banking sector (see King and Levine (1993)). 7 In our main results we use financial development indicators averaged over the full period 1995-2007 to avoid significant informational loss. However, to address concerns regarding reverse causality we have re-estimated our models using financial development indicators in the initial year of our estimation period, 1995. The results, which are available upon request, remain unchanged. 6 3.3 Descriptive statistics Summary statistics for the firm-specific variables used in our empirical analysis are provided in Table 1. The figures are presented for all firms (column 1), those firms that failed (column 2) and those that are survivors (column 3). A final column reports the p-value of a test whether there is a significant difference between values for failing and surviving firms. On the basis of three different financial variables we find that failing firms are more leveraged, less profitable and less collateralized compared to survivors. This supports the notion put forward by a number of studies (see Zingales (1998); Bunn and Redwood (2003); Clementi and Hopenhayn (2006); Farinha and Santos (2006); Bridges and Guariglia (2008) and Go¨rg and Spaliara (2009)) that firms in bad financial shape are more likely to fail. We also observe that failing firms are smaller and younger than surviving firms. This is in line with the previous empirical and theoretical research, which shows that the probability of exit decreases with firm size and age (e.g Jovanovic (1982) and Clementi and Hopenhayn (2006)). These differences between sub-samples are statistically significant in all cases. Table 2 reports summary statistics for firm-specific and financial variables by country. We find that firms in Singapore and Malaysia maintain the lowest levels of leverage and the highest levels of profitability. In addition, Korean and Singapore firms are the most collateralized across the five countries included in this study. Finally, Indonesian firms display the largest values of size and Thai firms are older (i.e longer listed on the stock exchange). The evolution of financial development indicators over time is depicted in Figure 1.8 The upper panel refers to the indicators of stock market development, while the lower panel to indicators of banking development. We observe a sharp decline for both stock market indicators ( Market Capitalization and Market Value Traded) during the East Asian crisis in 1997-98, followed by a noticeably high growth in the post crisis period. Market Capitalization dropped substantially in 2002 but has grown rapidly after 2003. Market Value Traded has maintained levels between two and a half times higher than values in 1997-98, and it is growing steadily over time. This indicates that stock markets have become more liquid, which reflects the greater diversity of investors and the relative improvement in the trading environment due to faster settlement and more rapid dissemination of information. Finally, the banking development indicators (Private Bank Credit and Bank Assets) remained at elevated levels during the crisis, followed by a sharp decline in the subsequent years. Both indicators run up during the crisis and this was reflected in high leverage, or debt to equity in East Asian corporate sectors (see World Bank (1999)). It is clear, however, that after 2003 8These figures refer to the five economies included in the present study. 7 both indicators have been reduced substantially, which implies that Asian financial systems are noticeably less bank centered in the later years of our sample. All four indicators of financial development are summarized across countries in Table 3. The data reveal clear heterogeneity in financial development of the five economies used in this study. For instance, the average lowest values of stock market size and liquidity are shown for Indonesia. Malaysia and Singapore have the largest stock market capitalization, while Korea has the most liquid stock market followed by Singapore. According to Eichen- green (2004), the stock market is important in these economies because the authorities have aggressively promoted its development. With respect to the development of the banking sys- tem, we observe that bank intermediation is especially important in Malaysia and Thailand. Finally, Singapore experience the lowest average failure rate, while Malaysia and Thailand are characterized by the highest failure rates. Taken together these figures suggest that more market-oriented economies such as Singapore experience the lowest failure rates, while more bank-based economies, such as Thailand experience the highest failure rates. It remains to be seen, though, whether these preliminary findings continue to hold when we control for a number of factors which are known to play a role in firms? survival studies. In the sections that follow we test within a formal regression analysis framework whether financial development has a statistically significant influence on firms? survival prospects. 4 Results 4.1 Financial development and firm survival We begin our enquiry with a baseline model of business failure as shown in Equation (1). The probability of corporate failure is modeled as a function of the country-level financial development indicators, the firm-specific control variables, financial variables, industry char- acteristics and macroeconomic conditions. The predicted probability of exit, evaluated at the mean of the independent variables is 9.5%, which is close to the actual exit rates across countries reported in the summary statistics. Table 4 reports results for the baseline model, where the financial development indicators are used one by one in successive columns (1, 2, 3 and 4). The point estimates on measures of financial development suggest a robust relationship between firm survival and the devel- opment of the financial system. In columns 1 and 2 the coefficients on stock market size (Market Capitalization) and liquidity (Market V alue Traded) are negative and significant suggesting that larger and more efficient stock markets would reduce the incidence of busi- ness failures. These results suggest that in economies with more developed stock markets 8 firms are able to hedge, pool risk, and access an alternative source of external financing, raising their survival chances.9 Therefore, moving to a market-based system would provide the means to free Asian economies from excessive dependence on banking intermediation and to foster the development of a more diversified and efficient financial sector. In addi- tion, market-based economies are better in allocating resources to investment projects that promise the highest returns and therefore are able to facilitate more productive long-term investments (Levine (1991)). Finally, market-oriented systems are better in reducing asym- metric information (Hermes and Lensink (2000)). This may be due to the engagement of international rating agencies and local agencies, which can reduce information asymmetry in the capital markets. Moving to the banking development indicators, we observe that the coefficients on both Private Bank Credit and Bank Assets, which are shown in columns 3 and 4 respectively, are positive and significant at the one percent level. These findings suggest that firms? chances of failure are increasing in financial intermediary development. There are strong reasons to believe that increasing bank intermediation can lead to failures. The relatively short maturity of most bank loans means that a macroeconomic shock can generate a source of endogenous fragility due to the asset-liability mismatch. This may leave Asian corporations vulnerable to a disruptive credit crunch and the depreciation of the exchange rate can cause serious balance- sheet damage, in the worst case leading highly leveraged firms into bankruptcy 10. Beck et al. (2006) provide evidence of a magnifying role of financial intermediaries in the propagation of macroeconomic shocks in economies where firms have limited access to external finance. In addition, Farinha and Santos (2006) show that higher levels of bank debt are more likely to increase the incidence of corporate failures. Firm-specific financial indicators have the expected impact on firms? failure. In particu- lar, firms with high levels of LEV ERAGE face higher probabilities of failure compared to those with low leverage confirming previous reported empirical evidence (Zingales (1998); Farinha and Santos (2006); Bridges and Guariglia (2008) and Go¨rg and Spaliara (2009)). High levels of debt would increase moral hazard and adverse selection problems, and would lead to a higher probability of failure. PROFITABILITY enters with the expected neg- ative sign implying that an increase in profitability ratio lowers the hazard of failure. This result is consistent with previous findings that more profitable firms are less likely to fail ( Bunn and Redwood (2003); Bridges and Guariglia (2008) and Go¨rg and Spaliara (2009)). The coefficient on COLLATERAL attracts the expected negative sign and it has a highly 9As already noted, when financial markets develop both in terms of size and liquidity, it may be possible for brokers to spread their fixed costs more widely and thus reduce the thresholds that bar firms? entry to financial markets. 10This implication is consistent with Furman and Stiglitz (1998) as discussed in Eichengreen (2004). 9 significant impact on firms? failure prospects. Firms with high levels of tangible assets are able to pledge collateral and to obtain more external funding but also to pursue risk-shifting strategies (Bridges and Guariglia (2008) and Farinha and Santos (2006)). With respect to our firm-specific controls, the coefficients on SIZE and SIZE2 enter with the expected signs but only the latter is significant. The coefficient on firm AGE exerts a negative and significant impact on failure. This finding is in line with previous theoretical and empirical evidence which shows failure rates decrease with the firm?s track record (e.g Jovanovic (1982) and Clementi and Hopenhayn (2006)). Finally, the results on the MES and the GDP growth (GDP ) behave as conjectured. Firms operating in industries with high MES are more likely to survive, which is consistent with Audretsch (1991), whereas improved economic conditions reduce the probability of failures in line with Alvarez and Go¨rg (2009). 4.2 Evolution over time Having identified a direct relationship between financial development and firm survival, we now explore whether this linkage has evolved over time. Asian countries have sought to increase financial market development to avoid dependence on foreign capital as was the case in the 1990s around the time of the 1997-98 Asian crisis. In the post-crisis period, East Asian economies established a working group on financial market development and the priority was given to the development of stock and bond markets. This would provide the means to free Asian economies from excessive dependence on bank intermediation and to foster the development of a more diversified and efficient financial sector. Large strides have since been taken to improve the capital markets at the country and regional level. Perhaps the most prominent initiative has been the move towards a regional bond market with the establishment of the Asian Bond Fund, referred to as the ABF, in 2003 to purchase dollar denominated Asian government bond issues. This initiative was then extended in 2005, to an open fund with purchases of local currency government bond issues. It is therefore of primary interest to investigate whether the relationship between firm survival and financial development has been influenced by the recent East Asian efforts to strengthen their legal and financial systems and building secondary-market infrastructure. We should expect the creation of such pan-regional model to give boost to the integration of national markets. This will make investors more willing to participate in a security market that represents claims on a basket of regional bonds diversifying away idiosyncratic national risk (Eichengreen (2004)). In addition, according to Borensztein et al. (2008) bond markets grow together with the rest of the financial system (banking system and stock markets) and 10 thus all markets will benefit. In a context of a greater financial development, we anticipate the negative effect of banking development on survival to loose its relevance over time. Similarly, we would expect that the impact of stock market development on firms? failures to be more pronounced once the initiatives took place.11 In order to test this hypothesis, we interact the indicators of financial development with a time period dummy as follows: FD*Late and FD*(1 ? Late), where Late takes the value one in years 2003 to 2007, and zero for the years 1995 through 2002.12 Results for the evolution of financial development over time are reported in Table 5. We observe that the coefficients on Market Capitalization and Market V alue Traded reported in columns 1 and 2 are negative and highly significant only for the late period (2003?07) suggesting that stock market development is beneficial to firms? survival once the regional initiatives took place in 2003. The coefficients for both indicators during the pre- 2003 period are insignificant and quantitatively unimportant. These results lend support to our hypothesis that the relationship between firm survival and stock market development has been strengthened by the recent East Asian efforts to build secondary-market infrastructure. Coming to our banking development indicators, column 3 shows that the estimated pos- itive effect of Private Bank Credit on firm survival is confined only to the pre-2003 period. We observe a sign reversal for the later years of our sample, which suggests that the adverse effect of banking development on firm survival has declined over time, hindering firm sur- vival to a lower extent. Column 4 shows that the coefficient on Bank Assets is negative and highly significant for the later years of our sample, while positive but insignificant for the pre-2003 period. These results confirm our hypothesis that the adverse effects of banking development indicators became weaker during 2003-2007, which might reflect the reforms in the financial system that reduced Asian banks? inefficiencies. 4.3 The differentiated effect of firm size In this sub-section we test whether all firm types are equally affected by financial develop- ment. We use firm size as a sorting device because small firms are more likely to be associated with the higher degree of information asymmetry and therefore may find it difficult to access capital markets and benefit when financial development takes place. The importance of size in firms? real activities was emphasized in the empirical financing constraints literature. Size was employed as a criterion by Guariglia (2008) and Spaliara (2009) and is the key proxy 11A similar exercise was carried out by Guariglia and Poncet (2008) in order to test for the evolution of the finance-growth nexus in China. 12Our results were robust to defining the time period dummy Late equal to one in 2005 to 2007, in order to capture the second phase of the Asian Bond Fund. 11 for capital market access by manufacturing firms in Gertler and Gilchrist (1994) because small firms are more vulnerable to capital market imperfections and thus more likely to be financially constrained. In addition, there is evidence that the nexus between growth and financial development is related to firm size. Beck et al. (2005) find that the financial sys- tem affects the growth of small firms more severely across a wide set of both developed and developing economies. Consistent with this view, Guiso et al. (2004) show that financial development fosters the growth of small firms more than large firms in Italy. Given that our objective is to verify whether there is a differential effect of financial development on the failure probabilities of small and large firms, we interact our financial development indicators as follows: FD*Small and FD*(1?Small), where Small is a dummy variable equal to one if the firm?s real total assets are below the upper quartile of the size distribution, and zero otherwise. This exercise is based on the consideration that small firms tend to face greater asymmetric information problems and have therefore smaller chances of survival, as financing constraints become binding. Large firms, on the other hand, are likely to be less financially constrained and will be better equipped to take advantage of the development of the financial system. If this hypothesis were true, when banking development takes place, which was found to increase the incidence of firm failure, we should expect small firms to be more severely affected than large firms. On the other hand, in a market-based system, which is associated with a decrease in firm failures, we should expect improvements in financial services (in terms of size or liquidity of the stock market) to disproportionately help large firms. This is because large firms can access capital markets, while significant fixed costs may prevent small firms from accessing capital markets (Greenwood and Jovanovic (1990)). Therefore, when considering the banking system development we expect to find weaker effect on large firms? probabilities of failure: the coefficients and marginal effects associated with FD*(1? Small) should be smaller than those associated with FD*Small. The exact opposite pattern should be observed when stock market development takes place: the coefficients and marginal effects associated with FD*(1? Small) should be larger than those associated with FD*Small. Table 6 reports the estimated coefficients on the interacted financial system characteristics as well as our control variables. The results show that both market capitalization and market value traded (columns 1 and 2) are highly significant only for large firms, which do not face binding financing constraints and they are able to tap the capital markets, while they are insignificant for small firms. This result confirms our hypothesis that greater development in stock markets, both in terms of size and liquidity, shields only large firms against failures since they find it easier to access stock markets due to smaller information and transaction costs. Therefore, stock market development is particularly beneficial to large firms. 12 The coefficients on the banking development as shown in columns 3 and 4 are significant for both types of firms. We find that at higher levels of banking development (measured by private bank credit and bank assets respectively) both small and large firms? survival prospects are adversely affected but the coefficients on small firms are two times larger that of large firms. In addition, for both indicators these coefficients are significantly different from each other (p-values are 0.00 in both cases). This finding lends support to the story that higher levels of banking intermediation affect differently firms? survival prospects, with bank-dependent small firms being the most affected. 5 Robustness tests 5.1 Addressing concerns about endogeneity in our regressors Our empirical models include a set of firm-specific financial variables that may be endoge- nous. Therefore, one potential concern is that our results may be driven by endogeneity in our regressors. We address this issue by allowing the firm-specific variables to be endogenous and then instrumenting for them through a two-stage procedure. The results for the baseline model (shown in Table 4) are reported in Table 7. In addition, the results for partitioning our sample into different time periods and firm classes are shown in Tables 8 and 9, and these should be compared with Tables 5 and 6, respectively. We observe that in most cases the presented pattern of financial development indicators confirms our main findings. Stock market development would directly reduce firms? failure prospects and this finding is rel- evant to the later period of our sample and to large firms only. Financially intermediary development, on the other hand, would increase corporate failures and this is more potent for small firms and for the pre-2003 period. In addition, all control variables retain their signs and significance. We therefore conclude that the extent of endogeneity bias is very limited in our sample and our findings are robust to an instrumental variables technique. 5.2 Alternative cut-off points In our main empirical results, we used the 75th percentile as a cut-off value for small and large firms. In order to ensure that our results are not driven from the way that we divide our sample, we use the 50th percentile as an alternative cut-off point. Specifically, we classify small firms as those whose total assets are below the median of the distribution, and zero otherwise. We then re-estimate the model from Table 6 and report the results in Table 9. We find that large firms show the same sensitivity to stock market development, while small firms remain unaffected. In addition, we continue to observe that the coefficients on the 13 private banking development are significant for both types of firms, but with significantly higher values for small firms. The coefficients on the bank assets are significant for both small and large firms and the difference is not statistically significant. In summary, we can conclude that our main empirical results are robust to alternative cut-off values. 6 Conclusion Using a panel for five Asian economies - Indonesia, Korea, Malaysia, Singapore and Thailand - we find that country-level indicators of financial development have an important role to play in influencing firm survival. When stock markets develop, both in terms of size and liquidity, firms? survival chances improve. In other words, moving towards a more market- based system is likely to reduce the incidence of business failures. On the contrary, we show that greater banking intermediation can increase firm failures and we argue that bank-based systems in emerging Asian markets are inherent to bank runs and therefore could adversely affect firms? performance and survival prospects. When we consider whether the linkage between survival and financial development has evolved over time, we find that the beneficial effects of stock market development are more pronounced during the later years of our sample, while the adverse effects of bank interme- diation have been reduced over time. Finally, after separating firms into different categories using their size as sorting device we find that large firms would benefit the most from devel- opments in the stock market, while small firms are most severely affected from high levels of financial intermediation. This implies that not all firms are equally affected by financial development, reflecting the higher risk characteristics associated with small firms that are financially constrained and subject to greater information asymmetries. These results provide new evidence that the development of the financial system along with firms? financial condition can play a key role in determining corporate failures. It is therefore important for policymakers in Asia to promote the development of a sound banking system and well-functioning financial markets at the same time in order to improve firms? performance and survival prospects. 14 References Alvarez, R. and Go¨rg, H.: 2009, Multinationals and plant exit: Evidence from Chile, International Review of Economics and Finance 18, 45?51. Audretsch, D.: 1991, Firm survival and the technological regime, Review of Economics and Statistics 73, 441? 450. 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Bridges, S. and Guariglia, A.: 2008, Financial constraints, global engagement, and firm survival in the UK: Evidence from micro data, Scottish Journal of Political Economy 55, 444?464. Bunn, P. and Redwood, V.: 2003, Company accounts based modelling of business failures and the implica- tions for financial stability, Working Paper 210, Bank of England. Carpenter, R. and Guariglia, A.: 2008, Cash flow, investment, and investment opportunities: New tests using UK panel data, Journal of Banking and Finance 32, 1894?1906. Clementi, L. and Hopenhayn, H.: 2006, A theory of financing constraints and firm dynamics, Quarterly Journal of Economics 54, 229?265. Dunne, T., Roberts, M. and Samuelson, L.: 1998, Patterns of firm entry and exit in US manufacturing industries, Rand Journal of Economics, 19, 495?515. Eichengreen, B.: 2004, Financial development in Asia: the way forward, Institute of Southeast Asian Studies. Farinha, M. and Santos, J.: 2006, The survival of start-ups: Do their funding choices and bank relationships at birth matter?, Mimeo. Furman, J. and Stiglitz, J.: 1998, Economic crises: Evidence and insights from East Asia, Brookings Papers on Economic Activity 2, 1?136. Geroski, P.: 1995, What do we know about entry?, International Journal of Industrial Organization 13, 421? 440. Gertler, M.: 1988, Financial structure and aggregate economic activity: An overview, Journal of Money Credit and Banking 20, 559?588. Gertler, M. and Gilchrist, S.: 1994, Monetary policy, business cycles, and the behavior of small manufacturing firms, Quarterly Journal of Economics 109, 309?340. Goldsmith, R.: 1969, Financial Structure and Development, Yale University Press. Go¨rg, H. and Bandick, R.: 2009, Foreign acquisition, plant survival, and employment growth, Canadian Journal of Economics forthcoming . Go¨rg, H. and Spaliara, M.: 2009, Financial health, exports and firm survival: A comparison of British and French firms, Working Paper 7532, CEPR. 15 Go¨rg, H. and Strobl, E.: 2002, Multinational companies and indigenous development: An empirical analysis, European Economic Review 46, 1305?1322. Greenwood, J. and Jovanovic, B.: 1990, Financial development, growth, and the distribution of income, Journal of Political Economy 98, 1076?1107. Guariglia, A.: 2008, Internal financial constraints, external financial constraints, and investment choice: Evidence from a panel of UK firms, Journal of Banking and Finance 32, 1795?1809. Guariglia, A. and Poncet, S.: 2008, Could financial distortions be no impediment to economic growth after all? Evidence from China, Journal of Comparative Economics, 36, 633?657. Guiso, L., Sapienza, P. and Zingales, L.: 2004, Does local financial development matter?, Quarterly Journal of Economics 119, 929?969. Hermes, N. and Lensink, R.: 2000, Financial system development in transition economies, Journal of Banking and Finance 4, 507?524. Jovanovic, B.: 1982, Selection and evolution of industry, Econometrica 50, 3?37. King, R. and Levine, R.: 1993, Finance and growth: Schumpeter might be right, Quarterly Journal of Economics 108, 717?738. Levine, R.: 1991, Stock markets growth and tax policy, Journal of Finance 46, 1445?1465. Levine, R.: 2006, Finance and growth: Theory and evidence, in P. Aghion and S. Durlauf (eds), Handbook of Economic Growth, New York, Elsevier Science, North Holland. Prentice, R. and Gloeckler, L.: 1978, Regression analysis of grouped survival data with application to breast cancer data, Biometrics 34, 57?67. Rajan, R. and Zingales, L.: 1998, Financial dependence and growth, American Economic Review 88, 559? 586. Spaliara, M.: 2009, Do financial factors affect the capital-labour ratio? Evidence from UK firm-level data, Journal of Banking and Finance 33, 1932?1947. World Bank: 1999, Global economic propsects and developing countries, Annual publication, World Bank. Zingales, L.: 1998, Survival of the fittest or the fattest? Exit and financing in trucking industry, Journal of Finance 53, 905?938. 16 Table 1: Summary statistics for firm-specific variables used in the empirical models All firms Fail=1 Fail=0 Diff. (1) (2) (3) (4) Leverage 0.63 0.92 0.61 0.000 (0.81) (1.15) (0.76) Profitability 7.62 -8.91 9.36 0.000 (50.62) (64.42) (68.42) Collateral 0.72 0.59 0.73 0.000 (0.30) (0.37) (0.28) Size 14.04 14.01 14.38 0.000 (3.44) (3.38) ( 3.44) Age 14.25 12.88 14.39 0.000 (4.94) (5.06) (4.91) Observations 23,606 2,247 21,359 Notes: The table presents sample means. Standard deviations are reported in parentheses. The p-value of a test of the equality of means is reported. Fail is a dummy that equals 1 if the firm fails, and 0 otherwise. Leverage is measured as the firm?s total debt to assets ratio. Profitability is the ratio of the firm?s profits before interest and tax to its total assets. Collateral is defined as the ratio of the firm?s tangible assets over its total assets. Size is denoted by the log of real assets. Age measures the number of years a firm has been listed on the stock exchange. The time period is 1995-2007. Variables are measured in thousands of US dollars. Table 2: Summary statistics for firm-specific variables by country Leverage Profitability Collateral Size Age Observations (1) (2) (3) (4) (5) (6) Indonesia 0.72 4.87 0.71 18.9 15.33 2,705 (0.63) (30.20) (0.33) (2.15) (4.87) Korea 0.56 6.35 0.78 18.61 10.96 5,611 (0.49) (25.11) (0.27) (1.63) (4.48) Malaysia 0.51 7.42 0.67 12.09 15.53 7,357 (0.48) (31.50) (0.25) (1.66) (4.35) Singapore 0.48 10.11 0.74 11.29 14.87 3,956 (0.47) (31.07) (0.29) (1.90) (5.27) Thailand 0.61 5.77 0.69 14.45 15.78 4,333 (0.58) (27.55) (0.34) (1.78) (3.96) Notes: The table presents sample means. Standard deviations are reported in parentheses. Leverage is measured as the firm?s total debt to assets ratio. Profitability is the ratio of the firm?s profits before interest and tax to its total assets. Collateral is defined as the ratio of the firm?s tangible assets over its total assets. Size is denoted by the log of real assets. Age measures the number of years a firm has been listed on the stock exchange. The time period is 1995-2007. Variables are measured in thousands of US dollars. 17 Table 3: Summary statistics for development indicators and failure rates by country Market Capitalization Market Value Traded Private Bank Credit Bank Assets Failure (1) (2) (3) (4) Indonesia 0.25 0.10 0.31 0.42 0.09 (0.06) (0.04) (0.15) (0.08) (0.29) Korea 0.58 1.36 0.82 0.85 0.09 (0.23) (0.52) (0.14) (0.17) (0.28) Malaysia 1.62 0.66 1.31 1.32 0.10 (0.39) (0.42) (0.23) (0.15) (0.30) Singapore 1.62 0.98 1.02 1.21 0.06 (0.18) (0.48) (0.10) (0.12) (0.25) Thailand 0.53 0.37 1.14 1.26 0.15 (0.18) (0.16) (0.27) (0.24) (0.36) Notes: The table presents sample means. Standard deviations are reported in parentheses. Market Capitalization is defined as the ratio of stock market capitalization to GDP. Market V alue Traded is measured as the ratio of total stock market value traded to GDP. Private Bank Credit is given by the ratio of private bank credit to GDP. Bank Assets is calculated as the ratio of deposit-money bank domestic assets to GDP. Failure is the average rate of failure at the firm-level. 18 Table 4: Financial development and firm survival (1) (2) (3) (4)) Market Capitalization -0.230* (-1.90) Market V alue Traded -0.317*** (-4.39) Private Bank Credit 1.102*** (12.1) Bank Assets 1.017*** (7.00) Leverage 0.248*** 0.248*** 0.255*** 0.256*** (13.6) (13.5) (13.8) (13.6) Profitability -0.004*** -0.004*** -0.004*** -0.004*** (-9.70) (-9.72) (-9.31) (-9.89) Collateral -0.678*** -0.679*** -0.648*** -0.658*** (-8.91) (-8.90) (-8.61) (-8.00) Size -0.078 -0.076 -0.089 -0.092 (-1.23) (-1.20) (-1.43) (-1.48) Size2 0.004* 0.004* 0.004** 0.004*** (1.85) (1.80) (2.25) (3.57) Age -0.081*** -0.080*** -0.079*** -0.080*** (-15.4) (-15.4) (-15.4) (-15.9) GDP -0.027*** -0.021*** -0.015*** -0.037*** (-5.04) (-4.00) (-2.89) (-7.60) MES -0.025 -0.027 -0.022 -0.123 (-0.10) (-0.11) (-0.089) (-0.47) Observations 23606 23606 23606 23606 Log ? likelihood -6595 -6584 -6531 -6569 Notes: Proportional hazard model results are reported. The dependent variable is a dummy equal to one if the firm fails, and zero otherwise. All firm-specific variables are lagged one period. Robust z-statistics are presented in the parentheses. The following countries are included in the regressions: Indonesia, Korea, Malaysia, Singapore and Thailand. * significant at 10%; ** significant at 5%; *** significant at 1%. Country dummies and industry dummies are included in the model. Also see notes to Table 1. 19 Table 5: Evolution over time (1) (2) (3) (4) Market Capitalization*Late -1.156*** (-12.4) Market Capitalization*(1? Late) 0.065 (0.74) Market V alue Traded*Late -1.559*** (-15.6) Market V alue Traded*(1? Late) 0.036 (0.57) Private Bank Credit*Late -1.012*** (-8.14) Private Bank Credit*(1? Late) 0.345*** (3.54) Bank Assets*Late -1.294*** (8.16) Bank Assets*(1? Late) 0.009 (0.10) Leverage 0.227*** 0.218*** 0.223*** 0.225*** (11.4) (11.1) (11.1) (10.7) Profitability -0.004*** -0.004*** -0.004*** -0.004*** (-9.08) (-9.41) (-9.06) (-8.93) Collateral -0.740*** -0.752*** -0.723*** -0.731*** (-9.39) (-9.59) (-9.27) (-8.33) Size -0.030 0.024 -0.016 -0.096 (-0.47) (0.36) (-0.24) (-1.35) Size2 0.002 -0.000 0.001 0.001 (0.98) (-0.14) (0.69) (0.60) Age -0.081*** -0.080*** -0.080*** -0.079*** (-15.6) (-15.5) (-15.5) (-15.54) GDP -0.008 -0.012** -0.002 -0.004 (-1.60) (-2.37) (-0.48) (-0.83) MES -0.088 -0.094 -0.086 -0.183 (-0.35) (-0.38) (-0.34) (-0.70) Observations 23606 23606 23606 23606 Log ? likelihood -6240 -6210 -6208 -6210 Notes: Proportional hazard model results are reported. The dependent variable is a dummy equal to one if the firm fails, and zero otherwise. Late is a time period dummy that takes the value one in years 2003, 2004, 2005, 2006 and 2007, and zero for the years 1995 to 2002. All firm-specific variables are lagged one period. Robust z-statistics are presented in the parentheses. The following countries are included in the regressions: Indonesia, Korea, Malaysia, Singapore and Thailand. * significant at 10%; ** significant at 5%; *** significant at 1%. Country dummies and industry dummies are included in the model. Also see notes to Table 1. 20 Table 6: The differentiated effect of firm size (1) (2) (3) (4) Market Capitalization*Small -0.060 (-0.51) Market Capitalization*(1? Small) -1.094*** (-6.51) Market V alue Traded*Small -0.023 (-0.31) Market V alue Traded*(1? Small) -0.209** (-2.18) Private Bank Credit*Small 1.164*** (13.0) Private Bank Credit*(1? Small) 0.584*** (4.38) Bank Assets*Small 1.152*** (7.99) Bank Assets*(1? Small) 0.567*** (3.38) Leverage 0.249*** 0.254*** 0.257*** 0.255*** (13.5) (11.9) (13.6) (13.7) Profitability -0.004*** -0.004*** -0.004*** -0.004*** (-9.84) (-10.8) (-9.48) (-9.89) Collateral -0.667*** -0.532*** -0.651*** -0.644*** (-8.88) (-5.98) (-8.59) (-8.00) Size -0.191*** -0.084 -0.212*** -0.225 (-3.00) (-1.11) (-3.51) (-2.96) Size2 0.009*** 0.005* 0.010*** 0.010*** (4.35) (1.84) (2.18) (4.92) Age -0.080*** -0.080*** -0.079*** -0.079*** (-15.5) (-13.2) (-15.5) (-15.9) GDP -0.027*** -0.053*** -0.015*** -0.037*** (-4.97) (-10.9) (-2.84) (-7.60) MES -0.035 -0.210 -0.016 -0.122 (-0.14) (-0.72) (-0.064) (-0.46) Observations 23606 23606 23606 23606 Log ? likelihood -6562 -6581 -6511 -6559 Notes: Proportional hazard model results are reported. The dependent variable is a dummy equal to one if the firm fails, and zero otherwise. Small is a dummy variable equal to one if the firm?s real total assets are below the upper quartile of the size distribution, and zero otherwise. All firm-specific variables are lagged one period. Robust z-statistics are presented in the parentheses. The following countries are included in the regressions: Indonesia, Korea, Malaysia, Singapore and Thailand. * significant at 10%; ** significant at 5%; *** significant at 1%. Country dummies and industry dummies are included in the model. Also see notes to Table 1. 21 Table 7: Robustness: endogenous probit for the baseline models (1) (2) (3) (4) Market Capitalization -0.077 (-1.51) Market V alue Traded -0.123*** (-3.88) Private Bank Credit 0.586*** (10.4) Bank Assets 0.461*** (5.87) Leverage 0.194*** 0.194*** 0.199*** 0.198*** (11.5) (11.5) (11.8) (11.3) Profitability -0.005*** -0.005*** -0.005*** -0.006*** (-10.1) (-10.1) (-10.2) (-10.5) Collateral -0.348*** -0.349*** -0.338*** -0.342*** (-7.17) (-7.19) (-6.93) (-7.06) Size -0.080** -0.079** -0.089** -0.121*** (-2.24) (-2.23) (-2.52) (-3.13) Size2 0.004*** 0.003*** 0.004*** 0.005*** (2.97) (2.95) (3.43) (3.63) Age -0.040*** -0.040*** -0.039*** -0.042*** (-14.7) (-14.7) (-14.3) (-14.8) GDP -0.009*** -0.006* -0.004 -0.013*** (-2.89) (-1.93) (-1.24) (-4.40) MES -0.350*** -0.350*** -0.347*** -0.353*** (-7.21) (-7.21) (-7.15) (-6.90) Observations 23558 23558 23558 23558 Log ? likelihood -6571 -6562 -6510 -6003 Notes: Endogenous Probit regression results are reported. The dependent variable is a dummy equal to one if the firm fails, and zero otherwise. Leverage, profitability, collateral, size and size squared are instrumented using their lagged levels in t-1. Robust z-statistics are presented in the parentheses. The following countries are included in the regressions: Indonesia, Korea, Malaysia, Singapore and Thailand. * significant at 10%; ** significant at 5%; *** significant at 1%. Country dummies and industry dummies are included in the model. Also see notes to Table 1. 22 Table 8: Robustness: endogenous probit for the evolution over time (1) (2) (3) (4) Market Capitalization*Late -0.584*** (-10.5) Market Capitalization*(1-Late) 0.011 (0.21) Market V alue Traded*Late -0.697*** (-15.5) Market V alue Traded*(1? Late) 0.075** (2.25) Private Bank Credit*Late -0.535*** (-7.19) Private Bank Credit*(1? Late) 0.156*** (2.58) Bank Assets*Late -0.729*** (-7.67) Bank Assets*(1? Late) -0.062 (-0.74) Leverage 0.180*** 0.167*** 0.171*** 0.169*** (10.3) (9.65) (9.75) (9.69) Profitability -0.006*** -0.006*** -0.005*** -0.006*** (-10.0) (-10.0) (-9.96) (-9.72) Collateral -0.387*** -0.392*** -0.379*** -0.351*** (-7.65) (-7.76) (-7.50) (-6.59) Size -0.044 -0.009 -0.037 -0.072* (-1.20) (-0.25) (-1.01) (-1.80) Size2 0.002* 0.001 0.002 0.003** (1.90) (0.60) (1.61) (2.21) Age -0.041*** -0.041*** -0.040*** -0.044*** (-14.9) (-14.8) (-14.5) (-14.9) GDP 0.003 -0.003 0.005* 0.002 (0.91) (-1.13) (1.66) (0.77) MES -0.334*** -0.336*** -0.337*** -0.342*** (-6.75) (-6.74) (-6.78) (-6.54) Observations 23558 23558 23558 23558 Log ? likelihood -6228 -6207 -6196 -6209 Notes: Endogenous Probit regression results are reported. The dependent variable is a dummy equal to one if the firm fails, and zero otherwise. Late is a time period dummy that takes the value one in years 2003, 2004, 2005, 2006 and 2007, and zero for the years 1995 to 2002. Leverage, profitability, collateral,2s3ize and size squared are instrumented using their lagged levels in t-1. Robust z-statistics are presented in the parentheses. The following countries are included in the regressions: Indonesia, Korea, Malaysia, Singapore and Thailand. * significant at 10%; ** significant at 5%; *** significant at 1%. Country dummies and industry dummies are included in the model. Also see notes to Table 1. Table 9: Robustness: endogenous probit for the size effects (1) (2) (3) (4) Market Capitalization*Small 0.036 (0.70) Market Capitalization*(1? Small) -0.816*** (-8.99) Market V alue Traded*Small -0.069** (-1.98) Market V alue Traded*(1? Small) -0.210*** (-5.21) Private Bank Credit*Small 0.642*** (11.3) Private Bank Credit*(1? Small) 0.223*** (2.93) Bank Assets*Small 0.566*** (7.11) Bank Assets*(1? Small) 0.163* (1.84) Leverage 0.187*** 0.192*** 0.196*** 0.199*** (11.1) (11.4) (11.6) (11.8) Profitability -0.005*** -0.005*** -0.005*** -0.006*** (-10.4) (-10.2) (-10.2) (-10.4) Collateral -0.335*** -0.331*** -0.336*** -0.313*** (-6.89) (-6.76) (-6.89) (-6.08) Size -0.173*** -0.113*** -0.168*** -0.153*** (-4.73) (-3.08) (-4.54) (-3.74) Size2 0.008*** 0.005*** 0.008*** 0.006*** (6.35) (3.96) (6.05) (4.25) Age -0.040*** -0.040*** -0.039*** -0.042*** (-14.9) (-14.7) (-14.4) (-14.9) GDP -0.009*** -0.006** -0.004 -0.013*** (-2.82) (-2.00) (-1.23) (-4.36) MES -0.360*** -0.356*** -0.346*** -0.353*** (-7.37) (-7.31) (-7.13) (-6.91) Observations 23558 23558 23558 23558 Log ? likelihood -6506 -6555 -6478 -6198 Notes: Endogenous Probit regression results are reported. The dependent variable is a dummy equal to one if the firm fails, and zero otherwise. Leverage, profitability, collateral, size and size squared are instrumented using their lagged levels in t-2. Small is a dummy variable equal to one if the firm?s real tota2l a4ssets are below the upper quartile of the size distribution, and zero otherwise. Robust z-statistics are presented in the parentheses. The following countries are included in the regressions: Indonesia, Korea, Malaysia, Singapore and Thailand. * significant at 10%; ** significant at 5%; *** significant at 1%. Country dummies and industry dummies are included in the model. Also see notes to Table 1. Table 10: Robustness: alternative cut-off points (1) (2) (3) (4) Market Capitalization*Small -0.212 (-1.64) Market Capitalization*(1? Small) -0.249** (-2.02) Market V alue Traded*Small 0.078 (0.87) Market V alue Traded*(1? Small) -0.228** (-2.58) Private Bank Credit*Small 1.119*** (11.6) Private Bank Credit*(1? Small) 1.088*** (11.5) Bank Assets*Small 1.017*** (6.97) Bank Assets*(1? Small) 1.021*** (6.72) Leverage 0.249*** 0.252*** 0.256*** 0.255*** (13.6) (11.7) (13.7) (13.8) Profitability -0.004*** -0.005*** -0.004*** -0.004*** (-9.68) (-11.2) (-9.49) (-10.4) Collateral -0.682*** -0.565*** -0.652*** -0.614*** (-9.01) (-6.37) (-8.49) (-7.64) Size -0.063 0.022 -0.077 -0.275*** (-0.90) (0.29) (-1.15) (-3.84) Size2 0.003 0.001 0.004** 0.009*** (1.59) (0.58) (2.04) (3.98) Age -0.080*** -0.081*** -0.079*** -0.087*** (-15.4) (-13.3) (-15.4) (-15.9) GDP -0.027*** -0.052*** -0.015*** -0.037*** (-5.04) (-10.3) (-2.90) (-7.88) MES -0.028 -0.216 -0.024 -0.126 (-0.11) (-0.74) (-0.095) (-0.48) Observations 23606 23606 23606 23606 Log ? likelihood -6595 -6579 -6531 -6559 Notes: Proportional hazard model results are reported. The dependent variable is a dummy equal to one if the firm fails, and zero otherwise. Small is a dummy variable equal to one if the firm?s real total assets are below the median of the size distribution, and zero otherwise. All firm-specific variables are lagged one period. Robust z-statistics are presented in the parentheses. The following countries are included in the regressions: Indonesia, Korea, Malaysia, Singapore and Thailand. * significant at 10%; ** significant at 5%; *** significant at 1%. Country dummies and industry dummies are included in the model. Also see notes to Table 1. 25 Figure 1: Evolution of development indicators Evolution of Market Capitalization Evolution of Market Value Traded 1995 1997 1999 2001 2003 2005 2007 1995 1997 1999 2001 2003 2005 2007 Year Year Evolution of Private Bank Credit Evolution of Bank Deposits 1995 1997 1999 2001 2003 2005 2007 1995 1997 1999 2001 2003 2005 2007 Year Year 26 Private Bank Credit Market Capitalization .9 1 1.1 1.2 1.3 .8 .9 1 1.1 1.2 1.3 1 1.1Bank Deposits1.2 1.3 arket Value Traded.4 M.6 .8 1 1.2 1.4
Posted: 20 April 2010

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