The full text on this page is automatically extracted from the file linked above and may contain errors and inconsistencies.
Federal Reserve Bank of Chicago Bank Lending, Financing Constraints and SME Investment Santiago Carbó-Valverde, Francisco Rodríguez-Fernández, and Gregory F. Udell WP 2008-04 BANK LENDING, FINANCING CONSTRAINTS AND SME INVESTMENT Santiago Carbó-Valverde* University of Granada and Federal Reserve Bank of Chicago scarbo@ugr.es Francisco Rodríguez-Fernández University of Granada franrod@ugr.es Gregory F. Udell Indiana University gudell@indiana.edu Abstract: SME investment opportunities depend on the level of financing constraints that firms face. Earlier research has mainly focused on the controversial argument that cash flow-investment correlations increase with the level of these constraints. We focus on bank loans rather than cash flow. Our results show that investment is sensitive to bank loans for unconstrained firms but not for constrained firms, and trade credit predicts investment, but only for constrained firms. We also find that unconstrained firms use bank loans to finance trade credit provided to other firms. Our results illustrate alternative mechanisms that firms employ both as borrowers and lenders (100 words). Keywords: SMEs, financing constraints, bank lending, trade credit, predictability. JEL classification: G21, D21, L26 * Corresponding author: Santiago Carbó Valverde (scarbo@ugr.es; svalverde@frbchi.org) Acknowledgements and disclaimer: Financial support from Junta de Andalucia, SEJ 693 (Excellence Groups) is acknowledged and appreciated by the authors. Santiago Carbó and Francisco Rodríguez also acknowledge financial support from MEC-FEDER, SEJ2005-04927. Discussion from Anjan Thakor and comments from Michael Koetter and other participants in the 44th Conference on Bank Structure and Competition held in Chicago in May 2008 are appreciated. We also thank comments from Sarayut Nathaphan, Alfredo Martín and other participants in the 2008 Midwest Finance Association and from Richard Rosen, Sumit Agarwal, Douglas Evanoff, Bob De Young, Marco Bassetto, Joseph Kaboski, Tara Rice and other participants in the research seminar at the Federal Reserve Bank of Chicago in March 2008. We also thank participants in the international IVIE-BBVA Workshop “Recent research in banking and finance” held in Valencia (Spain) in April 2008. The views in this paper are those of the authors and may not represent the views of the Federal Reserve Bank of Chicago or the Federal Reserve System. 1 1. INTRODUCTION The ability of firms to optimally exploit investment opportunities may crucially depend on the level of financial constraints that they face. SMEs may be particularly vulnerable because these firms are more opaque and thus susceptible to more credit rationing. Inquiry into the presence of financing constraints began in earnest with Fazzari et al. (1988) and their investigation of investment-cash flow sensitivity. However, this line of inquiry has been quite controversial. In particular, Kaplan and Zingales (1997 and 2000) have shown that correlation between investment and cash flow may not be a good indicator of financial constraints. In this paper we move this line of inquiry in a somewhat different direction. In some sense we look at the “dual” of the cash flow-investment sensitivity argument. Fazzari et al. (1988) argue that because financially constrained firms have limited access to external finance, their ability to exploit wealth-improving investment opportunities will be sensitive to their ability to finance these projects internally – that is, it will be sensitive to their cash flow. From an econometric perspective the cash flow variable may be problematic because, among other things, it may be correlated with omitted variables (e.g., Caballero and Leahy, 1996). To minimize these problems we take a more direct approach. Specifically, we instead focus on bank loans rather than cash flow in a sample of SMEs for whom bank loans are likely the least costly form of external finance. Our argument here is that just as the capital expenditures of less constrained firms are less likely to be sensitive to cash flow, they are more likely to be sensitive to bank loan funding. That is, unconstrained firms will utilize low cost bank loans to finance capital expenditures. In particular, we hypothesize that increased bank loan funding will (not) be associated with increased capital expenditures for unconstrained (constrained) firms. 2 The only other economically significant source of external funding for SMEs is trade credit, although it is generally considered to be more costly than bank loans (e.g., Petersen and Rajan, 1994, 1995). So, we also examine the sensitivity of investment to trade credit. Our investigation of trade credit will enable us to draw some inferences about the substitutability of trade credit and bank loans. In addition, we investigate the supply side of trade credit – in particular, whether unconstrained firms are more likely to extend trade credit (i.e., “invest in” trade credit) by using bank loans. We also extend the literature on financial constraints by examining predictability. That is, we go beyond just the estimated correlation (between bank loans and investment, between trade credit and investment, and between accounts receivable and bank loans) and investigate the casual links. By way of preview we find that investment is sensitive to bank loans for unconstrained firms – but not for constrained firms. We also find that trade credit predicts investment, but only for constrained firms. This suggests that constrained firms, whose access to bank loans is limited, resort to trade financing. Finally, we find that for unconstrained firms, bank loans cause accounts receivable – that is, unconstrained firms use bank loans to finance trade credit (i.e., invest in accounts receivable). The remainder of our paper is organized as follows: Section 2 discusses the relevant literature on investment and financing constraints and presents our hypotheses. Section 3 presents the empirical strategy, the data set and the methodology. Section 4 shows our results and Section 5 offers conclusions. 3 2. LITERATURE REVIEW AND HYPOTHESES 2.1. The literature on financing constraints, external finance and investment Firms depend on a variety of sources of financing, both internal and external. The relationships among these sources and their effects on investment, however, remain unclear in the literature. In the case of SMEs, bank loans and trade credit are the main two alternatives of external funding. Since bank lending may be the cheapest source of external funding (e.g. Petersen and Rajan 1994, 1995), access to bank lending may condition the demand for trade credit. Dependence on trade credit, arguably the most expensive source of credit and the degree of financial constraints will also depend on the internal source of funding from cash flow. The effects of bank loans on investment decisions have been mostly explored in cross-country studies. In particular, as predicted in the finance-growth literature, bank lending to firms may foster investment and growth. This finance-growth nexus has been presented both as an endogenous growth model whereby bank loans (and even trade credit) stimulate firm investment (Greenwood and Jovanovic, 1990; King and Levine, 1993; Galetovic, 1996, Fisman and Love, 2004) and from a monetary perspective, showing the response of lending by banks to changes in monetary policy and its effects on aggregate output or even the likely substitution of bank loans for trade credit during a monetary tightening (Gertler and Gilchrist, 1993; Calomiris et al., 1995; Oliner, and Rudebusch, 1996; Nielsen, 2002; Fukuda et al., 2006). Much of the previous literature on financing constraints has focused on cash flow-investment sensitivity. This literature has been embroiled in considerable controversy with two main opposing views. On one side, Fazzari et al. (1988, 2000) suggest that financing constraints increase with investment-cash flow sensitivity.1 On 1 See Caggese (2007) for a recent discussion of the literature. 4 the other side, Kaplan and Zingales (1997, 2000) suggest that investment-cash flow correlations are not necessarily monotonic in the degree of financing constraints. As an explanation for these controversial and conflicting results, Kaplan and Zingales (2000) suggest that unobserved changes in environmental conditions such as changes in firm investment criteria, changes in precautionary savings of firms that influence investment over time and changes in bank lending behavior are likely important. Hines and Thaler (1995) also suggests that firms may be conservative and that they only invest when they generate cash flow so that they prefer not to expand using external funding unless they are forced to so. An alternative strand of the literature on financing constraints has focused on the extent to which bank loans and trade credit are complements or substitutes. This strand of the literature might be especially applicable to SMEs. Some of these studies suggest that external financing is costly because of potential adverse selection in the market for capital. They argue that trade credit may play a critical role in lower funding costs and in reducing credit rationing. In particular, it may be more efficient for large, less informationally opaque vendors with relatively low cost access to the banking and capital markets to obtain external financing and advance trade credit (Myers and Majluf, 1984; Calomiris et al.,, 1995; Petersen and Rajan, 1997; Demirguç-Kunt and Maksimovic, 2001; Frank and Maksimovic, 2005). Cuñat (2007) shows that trade suppliers may have an advantage in enforcing noncollateralized debt contracts. This advantage allows suppliers to lend more than banks and to lend when their customers are rationed in the bank loan market. Trade credit also allows their customers to increase their leverage. Large trade creditors have also been shown to provide trade credit to firms experiencing idiosyncratic shocks or monetary policy shocks (Calomiris et al., 1995; Gropp and Boissay, 2007). Many hypotheses have been suggested to 5 explain why trade creditors might have an advantage over other lenders (specifically, banks) in providing credit to opaque firms.2 Among these arguments is the possibility that vendors may act as “relationship lenders” because they have unique proprietary information about their customers (McMillan and Woodruff, 1999; Uchida et al., 2007). Smith (1987) and Biais and Gollier (1997) argue that in the normal course of business a seller obtains information about the true state of a buyer's business that is not known to financial intermediaries. As noted by Demirguç-Kunt and Maksimovic (2001) the information on the buyer is potentially valuable and the seller acts on this information to extend credit to buyers on terms that they would not be able to receive from financial intermediaries. Similarly, it has been suggested that the information advantage that vendors may have over banks in funding opaque firms may imply a complementary use of trade credit and bank loans (Cook, 1999; Ono, 2001; García-Appendini, 2006). However, this argument does not necessarily contradict the view of bank loans are a cheaper substitute for trade credit (Meltzer, 1960; Brechling and Lipsey, 1963; Jaffee, 1971; Ramey, 1992; Marotta, 1996; Uesugi and Yamashiro 2004; Tsuruta, 2008). Interestingly, it is suggested that both views (substitutes and complements) can be reconciled when not only prices are considered but also whether firms are financially constrained or not (García Appendini, 2006). 2.2 Our Hypotheses Like this second strand of the literature on financing constraints, we focus on the two main sources of SME external financing: bank loans and trade credit. However, our approach also borrows from the first strand of the literature in that we also examine 2 For recent summaries of the literature on the comparative advantage of vendors as commercial lenders see Burkart et al. (2007), and Uchida et al. (2007). 6 investment sensitivity -except our focus is not on cash flow-investment sensitivity, but rather bank loan- and trade credit-investment sensitivity. In some sense, this can be viewed as the converse of the cash flow-investment sensitivity strand of the literature. That literature analyzes whether financially constrained firms who are denied full access to external credit markets link their investment decisions to available cash flow. We examine the flip side of this issue – whether these financially constrained firms can link their investment to either bank loans or trade credit. If financially constrained firms are linking their investment decision to cash flow, then they should not be linking their decision to bank loans (to which they are denied full access). If they are denied access to the bank loan market, they may turn to the trade credit market. So we also examine whether constrained firms link their investment to trade credit (i.e., trade creditinvestment sensitivity). In order to derive our hypotheses, we make several key assumptions. First of all, as in most of the previous literature we will assume that financing constraints are directly related to borrowing from banks so that a firm is considered to be financially constrained when the desired amount of lending is larger than the amount of lending that banks provide to that firm3. Second, as noted elsewhere in the literature we assume that firm financing and investment are dynamic and non-contemporaneous (Clementi and Hopenhayn, 2006). This allows us to examine the predictability/causality relationships as a primary tool in analyzing the link between bank loans and investment, and trade credit and investment. This is also interesting because the direction of predictability between many of these financing and investment variables has not been explored yet. 3 This is also the definition in studies classifying firms into constrained and unconstrained using survey data where firms are asked whether banks have denied them credit in a given period. In this context, an indication of financially constraint status is that a firm’s loan application is denied (Garmaise, 2008). Since we do not have information on loan applications we offer a novel way to classify firms into financially constrained and financially unconstrained. 7 We can now state our two main testable hypotheses: Hypothesis 1: Since the desired amount of loans exceeds the supplied amount of loans at constrained firms, loans will not predict/cause investment at constrained firms. Therefore the expected causality/predictability relationship between bank loans and investment should only be significant in the case of unconstrained SMEs. Hypothesis 2: Since constrained SMEs are not provided with the amount of loans that they need for investment, they have to rely on (more expensive) trade credit to finance their investment projects. Therefore, both the relative amount of accounts payable and the accounts payable turnover will cause/predict investment decisions at constrained SMEs. 3. EMPIRICAL METHODOLOGY 3.1. Empirical strategy and data Our empirical strategy involves three steps. First of all, cash-flow investment correlations are estimated as a benchmark to make our data and results comparable with previous research. The second empirical step in our analysis involves the identification of financially constrained firms. Under certain restrictive conditions, accounting ratios can be consistent proxies of firm financing constraints. However, it is likely that financial ratios are correlated among themselves and with other variables such as cash flow or sales growth which are relevant key and control variables in our dataset. Therefore, we rely on a direct estimation of the probability that a firm experience borrowing constraints from a so-called disequilibrium model. This methodology permits us to classify firms as constrained or unconstrained. The main estimations are then undertaken in the third step, using Granger predictability tests to test our hypotheses. 8 The data have been gathered from the Bureau-Van-Dijk Amadeus database and include annual information on 30,897 Spanish SMEs during 1994-2002. SMEs are defined as those with less than 250 employees4. All of the selected firms were active during the entire sample period. This balanced panel consists of 278,073 observations. In order to analyze the relationship between firm investment, bank loans and other internal and external sources of financing, two sets of variables are employed, one related to investment and financing decisions5 and the other to firm-level and environmental control variables. Table 1 contains the definitions and explanatory comments on the variables as well as their sample means. 3.2. Benchmark definitions: cash-flow investment correlations We begin by estimating cash flow-investment sensitivities to benchmark our analysis against the standard approach in the literature. We have the advantage of estimating these cash flow-investment correlations using a relatively homogeneous sample of firms, SMEs in Spain, in terms of financial structure and firm sizes. However, we also note that because most of our sample firms are unquoted, investment-cash flow sensitivities can be, to a certain extent, affected by non-optimizing behaviour by managers (Kaplan and Zingales, 2000). We use the approach offered in Bond and Meghir (1994) to estimate cash-flow investment correlations in unquoted firms. The methodology consists of an Euler equation6. In the general specification of the model, the current investment rate (Capital 4 This is the standard definition of SMEs according to the European Commission’s Recommendation 2003/361/EC. All SMEs in the sample are below 40 million of euros in total assets. 5 We focus on bank loans and trade credit as the external sources of funds for SMEs. There are other external sources (such as the deferred taxes and black market loan sharks) that have not been considered because of marginal importance and/or lack of data. 6 The Euler equation is a structural model, explicitly derived from a dynamic optimization problem under the assumption of symmetric, quadratic costs of adjustment. This has the advantage that, under the maintained structure, the model captures the influence of current expectations of future profitability on 9 expendituret / capitalt-1) is related to lagged values of the investment rate, a quadraticadjustment term for the investment rate, cash flow, sales growth and a quadratic adjustment term for bank debt (bank loans): Capital expendituret / capitalt-1 = α*(Capital expendituret-1 / capitalt-2) + β*( Capital expendituret / capitalt-1) 2+ χ*(Cash flowt/capitalt-1) (1) + δ*sales growtht + +γ*bank loanst2 In the Euler equation, the estimated value of the coefficient “χ” is interpreted as the cash-flow investment correlation. 3.3. Identifying constrained vs. unconstrained firms We employ a disequilibrium model (Maddala, 1983) consisting of two-reduced form equations: a demand equation for bank loans, and a supply equation that reflects the maximum amount of loans that banks are willing to lend on a collateral basis. A third equation is added as a transaction equation restricting the value of loans as a min equation of desired demand and loan supply. Similar empirical applications have been employed by Ogawa and Suzuki (2000) and Shikimi (2005) for Japan, Atanasova and Wilson (2004) for the United Kingdom and Carbó et al. (2006) for Spain. The loan demand equation ( Bank loansitd ), the loan supply equation ( Bank loansits ), and the transaction equation ( Bank loansit ) of firm i in period t are: Bank loansitd = β 0d + β1d ( Sales )itd + β 2d Cash flowit + β 3d ( Loan interest spread)it + β 4d log(GDP ) + uitd Bank loansits = β 0d + β1sTangible assetsit + β 2s Bank market power + β 3s Default riskit + β 4s log(GDP ) + uits (2) (3) current investment decisions. The Euler-equation model has the advantage of controlling for all expectational influences on the investment decision. 10 Bank loansit = Min( Bank loansitd , Bank loansits ) (4) The amount of bank loan demand is modelled as a function of the level or the expansion of firm activity (Sales), other sources of funding that are substitutes for bank loans (Cash flow), and the interest spread on bank loans (Loan interest spread) which is computed as the difference between the loan interest rate and the interbank interest rate7. The maximum amount of credit available to a firm is modelled as a function of the firm’s collateral (Tangible assets), the banks’ market power in the area where the firm operates (Banks’ market power) -our market power indicator is the Lerner index8- and a proxy for firm default risk (Default risk) which is defined as the ratio of operating profits over interest paid. All level variables are expressed in terms of ratios (of total assets) to reduce heteroscedasticity. As a consequence, the size (scale) effect of “total assets” in the demand function above is estimated as part of the constant term since the constant term is estimated as a coefficient of the reciprocal of total assets9. Both demand and supply equations contain log(GDP) to control for macroeconomic conditions across the regional markets where the SMEs operate10. The simultaneous equations system shown in (2), (3) and (4) is estimated as a switching regression model using a full information maximum likelihood (FIML) routine with fixed effects. The model allows us to compute the probability that loan demand exceed credit supply (Gersovitz, 1980) and, therefore, to classify the sample 7 The loan interest rate is computed as a ratio of loan expenses and bank loans outstanding. We implicitly assume that the year-end loan balance is roughly equal to the weighted average balance during the year. 8 See Cetorelli and Gambera (2001). The Lerner index is defined as the ratio “(price of total assets marginal costs of total assets)/price”. The price of total assets is directly computed from bank-level auxiliary data as the average ratio of “bank revenue/total assets” for the banks operating in a given region using the distribution of branches of banks in the different regions as the weighting factor. Marginal costs are also estimated from the auxiliary sample. 9 The constant term is them estimated as the parameter for “1/total assets” and, therefore, the estimated value of the coefficient of the estimated constant term or reciprocal of total assets is considerably large. 10 Since some of the variables are computed from regional data, errors are clustered by region since these variables would be equal or very similar across firms in the same region. 11 into constrained and unconstrained firms. Further details on this procedure are provided in Appendix A. 3.4. Testing the hypotheses: Granger predictability tests We use Granger predictability tests to study the relationships between different sources of financing and investment and among the financing measures. One, two and three lags (l) of the variables were employed since these relationships are not necessarily contemporary but likely to present long-term effects (Rosseau and Wachtel, 1998)11. Since our dataset consists of cross-section and time series firm-level observations, the causality/predictability includes fixed effects ( f ) in the regression. The empirical specification follows Holtz-Eakin et al. (1988) for Granger predictability with panel data. Considering N firms (i=1,…,N) and t time periods (t=1,…,T) and firmspecific fixed effects (fi). we will consider, for example, that “bank loans/total liabilities” will Granger-cause investment if two conditions are met: i) The bank loans ratio is statistically significant in a time-series regression of the firm investment rate: (Capital exp enditureit / capitalit-1 ) t = α 0 + + ∑ β (Capital exp enditure i ∑ γ ( Bank loans i it it / capitalit-1 )t -l / total liabilitiesit )t -l + ψ t f i + uit (5) ii) The investment rate variable is not significant when it is included in a time-series regression of the bank loans ratio: ( Bank loansit / total liabilitiesit ) t = α 0 + + ∑ β ( Bank loans i it ∑ γ (Capital exp enditure / capital i t / total liabilitiesit )t -l ) t-1 t -l + ψ t f i + uit (6) 11 An Augmented Dickey-Fuller (ADF) procedure is applied as a test for unit roots. First differencing the variables was sufficient to achieve stationarity. 12 If instead, the situation is reversed – so that the regressions is not significant while in the second set ∑β i ∑γ i in the first set of is significant, then investment Granger-predicts the bank loans ratio. Finally, if the added bank loans variable in equation (5) and the firm investment rate variable in equation (6) were both significant, there will be predictability in both directions and probably a third factor will be also explaining both the evolution of firm investment and bank loans. As control variables, the Granger equations incorporate Interbank interest rates, Cash flowt/capitalt-1, Sales growth and the Defaults in commercial paper. The statistical significance of the Granger test is measured using an F-test. The identification of the equation is easier when the individual effects and the lagged coefficients are stationary, so that the individual effects are eliminated. All variables are expressed in first-differences since standard Augmented-Dickey-Fuller tests suggest that first-differencing is sufficient to achieve stationarity. The estimation of hypothesis 1 requires running predictability tests between investment rates and bank loans. For hypothesis 2, the tests should relate “accounts payable/total liabilities” and investment rates. Importantly, in order to properly analyze these hypotheses, the Granger predictability equations are estimated separately for both constrained and unconstrained firms. The vector of instrumental variables that is available to identify the parameters of the equations in first differences includes various additional lags of the dependent and the explanatory variables in levels. A necessary condition for identification is that there are, at least, as many instrumental variables as right-hand side variables. The standard Sargan test for identification is employed. 13 4. MAIN RESULTS 4.1. Defining financially constrained firms The estimations of the FIML disequilibrium model that are employed to compute the probability that a given firm is financially constrained are shown in Table 2. All coefficients are found to be significant at 1% level excluding Default risk, which is not significant. As shown in Table 3, 33.90% of the firms in the sample are estimated to have experienced borrowing constraints during the period. These values remain very stable over time. Table 4 shows the mean values of the ratios of bank loans, investment and cash flow as well as the cash-flow investment correlations for SMEs of different sizes using the quartile distribution of firms by assets12 with the first quartile corresponding to the smallest firms and the fourth quartile to the largest firms in the sample13. The values are shown for both constrained and unconstrained firms according to the classification of the disequilibrium model. As for the bank loans ratio, constrained firms exhibit a slightly higher proportion of bank loans even if their access to bank financing is, at least partially, restricted. The lower ratio of bank loans for unconstrained firms is compensated by a higher cash flow generation. The latter suggests that lower cash flow generation may induce constrained firms to rely more on bank lending although their higher demand of loans is not completely satisfied. It is not surprising that constrained SMEs exhibit a significantly lower investment ratio (0.428) than unconstrained firms (0.507). The larger diversification of funding sources at unconstrained firms may also 12 The assets distribution and any other quartile distribution of firms according to assets in this study are undertaken on a yearly basis. This means that some firms may shift from a size category to any other size category over the sample period but this should not affect the economic significance of “size” in our main hypotheses tested. 13 Similar distributions using the number of employees as a criterion were also employed (not shown) and offered very similar results. These results are available upon request. 14 explain why they show, on average, lower cash flow-investment sensitivities (0.481) than their restricted counterparts (0.742). These correlations are the highest for the firms in the first quartile although they decrease for firms in the second and third quartiles and paradoxically increase again in the case of firms in the fourth quartile. As shown in Table 5, the sector breakdown reflects a significant degree of heterogeneity in financial ratios and estimated cash-flow investment sensitivities. While the percentage of constrained firms is the lowest in sectors such as “transport services” (21.31%) and “construction” (22.43%), other industries such as the “sale, maintenance and repair of motor vehicles” (41.75%) or “manufactures of textiles and dressing” (41.73%) show a higher percentage of constrained firms within the sample. Interestingly, some of the highest levels of firm investment are found in sectors suffering significant borrowing constraints such as “sales, maintenance and repair of motor vehicles”, “hotels and restaurants” or “computer and related activities”. 4.2. Granger predictability tests Table 6 and 7 show the detailed results of the Ganger predictability tests for unconstrained and constrained firms respectively. For simplicity, only the one-lag results are shown while Appendix B summarizes the results of all Granger-causality tests for 1 up to 3 lags. The values from the Sargan test for overidentifying restrictions suggest that the instruments employed are valid. Since the coefficients are shown as log-differences of the variables, they can be interpreted as marginal effects. The results shown in equations (1) and (2) in Table 6 suggest that bank loans Granger-predicts investment but, investment does not Granger predict bank loans at unconstrained firms14. In equation (2), where investment is the dependent variable, other 14 In the case of some constrained firms, short term capital investments may be more important than longterm capital investments. As a robustness check for the relative importance of short-term investment 15 explanatory factors are also significant and exhibit the expected signs. In particular, interest rates and the level of defaults in commercial paper are found to be negatively and significantly related to investment while sales growth has a positive effect. The last tests for unconstrained firms relate the payables turnover and “accounts payable to total assets” to investment. Results for these tests are shown in equations (3) to (6) in Table 6. None of the relationships among these variables were found to be significant. The six equations are then estimated for constrained firms in Table 7. First of all, equations (1) and (2) in Table 7 reveal that there is not any predictability relationship between investment and bank loans at constrained firms consistent with hypothesis 1. However, unlike unconstrained firms, the payables turnover and “accounts payable/total liabilities” are found to predict investment at constrained firms which, in turn, supports hypothesis 2. These results also imply that trade credit seems to be a substitute for bank lending in funding investment projects. For robustness purposes, hypothesis 2 is also tested on a sub-sample with no bank loans on their balance-sheets (2426 firms). This sub-sample includes fully-constrained firms. In this sub-sample, the payables turnover and “accounts payable/total liabilities” are also found to predict investment rates. This additional result may imply that the sensitivity of trade credit to investment at unconstrained firms may be irrespective of the level of these financial constraints. 4.3. Exploring the role of unconstrained firms as lenders Considering the significant differences in the sensitivity of loans to investment between constrained and unconstrained firms, we also investigated whether unconstrained SMEs may be more willing to extend trade credit to other firms. Since decisions at constrained firms, we alternatively tested the sensitivity of loans and trade credit to net working capital as an alternative to our reported results using total capital expenses to compute the investment variable. The results obtained using working capital are very similar and they are available upon request. 16 they get at least as much lending as they desire, bank loans at unconstrained firms may predict not only investment but also the capacity of the unconstrained firm to extend trade credit (i.e. accounts receivables). Equations (1) to (4) in Table 8 test the relationship between the bank loans and the inclination of an unconstrained firm to extend trade credit at unconstrained firms. For robustness purposes the capacity to extend (and to demand) trade credit has been estimated using both definitions based on the value of the accounts (receivable or payable) and their turnover. While neither the receivables turnover nor the ratio “accounts receivable/total assets” seem to predict the bank loans ratio – as shown in equations (1) and (3) - there appears to be predictability in the other direction –as shown in equations (2) and (4). In particular, the bank loans ratio has a significant impact on both measures of receivables turnover –as the bank loans ratio increases in one period firms tend to be inclined to extend more trade credit in the next period. Equations (5) to (8) in Table 8 replicate these Granger-predictability tests for constrained firms. The results show that the bank loans ratio is not found to predict receivables turnover and “accounts receivables/total assets” at constrained firms.15 5. CONCLUSIONS This paper employs a new approach to investigate the mechanisms that SMEs employ to finance their investment projects depending on whether they are financially constrained or not. The paper also illustrates how easier access to bank lending may encourage unconstrained firms to extend trade credit to other firms. Unlike the main strand of the previous literature in this area, the approach in this paper relies on predictability/causality tests and does not look primarily at cash flow-investment 15 These results appear to be consistent with Cuñat (2007) who finds that trade creditors are willing to lend more than banks when customers are rationed in the bank loan market. 17 sensitivity, but rather at bank loan- and trade credit-investment sensitivity. This can be viewed as the converse of the cash flow-investment sensitivity approach. Specifically, we investigate how financially constrained firms link their investment to these external sources of credit and how this may differ from unconstrained firms. In this regard, we contribute to the broader debate on financial constraints and investment behaviour by offering an alternative the approach in Fazzari et al. (1988). These relationships are tested on a sample of 30,897 Spanish SMEs during 19942002. The results suggest that constrained firms with restricted access to the bank loan market may turn to the trade credit market to exploit their investment opportunities. Unconstrained firms, however, turn to the bank loan market. Additionally, we analyze the supply side of the trade credit market by testing whether the extension of trade credit is sensitive to bank lending. We find a significant sensitivity of the extension of trade credit to bank lending at unconstrained firms which suggests that these firms may act as “lenders” due to their easier access to a less costly source of funding (bank loans). These results may help explain the (important) role of trade credit in alleviating borrowing constraints, in a country (Spain) where we estimate that around one third of the SMEs face significant financing constraints. These results also illustrate the role of unconstrained firms as lenders and suggest that they may exploit informational benefits from customer relationships and their access to low cost bank funding. This can be interpreted as complementing findings elsewhere in the literature that firms extend trade credit to help alleviate problems related to monetary policy shocks and idiosyncratic firm shocks. 18 REFERENCES Atanasova, C.V. and N. Wilson. 2004. Disequilibrium in the UK Corporate Loan Market, Journal of Banking and Finance 28: 595-614. Biais, B. and C. Gollier. 1997. Trade credit and credit rationing, Review of Financial Studies 10: 903–938. Burkart, M., T. Ellingsen, and M. Giannetti. 2007. What You Sell Is What You Lend? Explaining Trade Credit Contracts, working paper, Stockholm School of Economics. Bond, S., and C. Meghir. 1994. Dynamic investment models and the firm’s financial policy, Review of Economic Studies 61:197-222. Brechling, F. and R. Lipsey. 1963. Trade Credit and Monetary Policy, Economic Journal 73:618-641. Caballero, R. J. and J. V. Leahy. 1996. Fixed Costs: The Demise of Marginal q, NBER Working Papers 5508. National Bureau of Economic Research. Calomiris, C., Himmelberg, C. and P. Wachtel. 1995. Commercial Paper, Corporate Finance and the Business Cycle: A Microeconomic Perspective, Carnegie-Rochester Conference Series on Public Policy 42: 203-50. Caggese, A. 2007. Testing financing constraints on firm investment using variable capital, Journal of Financial Economics 86: 683-723. 19 Carbó, S., Rodríguez, F. y G. Udell. 2006. Bank market power and SME financing constraints, Working Paper 237/2006. Funcas, Madrid. Cetorelli, N. and M. Gambera. 2001. Banking Market Structure, Financial Dependence and Growth: International Evidence from Industry Data, Journal of Finance 56: 617648. Clementi, G. L. and H.A. Hopenhayn .2006. A Theory of Financing Constraints and Firm Dynamics, Quarterly Journal of Economics 121: 229-266 Cook, L. 1999. Trade credit and bank finance: Financing small firms in Russia, Journal of Business Venturing 14: 493-518. Cuñat, V. 2007, Trade Credit: Suppliers as Debt Collectors and Insurance Providers, Review of Financial Studies 20: 491-527. Demirguç-Kunt, A. and V. Maksimovic. 2001. Firms as financial intermediaries: evidence from trade credit data, Policy Research Working Paper 2696. The World Bank. Fazzari, S.M., Hubbard, R.G. and B.C. Petersen. 1988. Financing constraints and corporate investment. Brooking Papers on Economic Activity 1: 141-206. Fazzari, S. M., Hubbard, R. G. and B. C. Petersen. 2000. Financing Constraints and 20 Corporate Investment: Response to Kaplan and Zingales, NBER Working Papers 5462. National Bureau of Economic Research. Fisman, R. and I. Love. 2003. Trade Credit, Financial Intermediary Development, and Industry Growth, Journal of Finance 58: 353-374. Frank, M.. Z., and V. Maksimovic. 2005. Trade Credit, Collateral and Adverse Selection, working paper, University of Maryland. Fukuda, S., Kasuya, M. and K. Akashi. 2006. The Role of Trade Credit for Small Firms: An Implication from Japan’s Banking Crisis, Bank of Japan Working Paper Series, 06E18. Bank of Japan. Galetovic, A. 1996. Specialization, intermediation and growth, Journal of Monetary Economics 38: 549-59. García-Appendini, M. E. 2006. Signalling in the Credit Markets: The Case of Trade Credit, mimeo, Universitat Pompeu Fabra. Garmaise, M.J. 2008. Production in Entrepreneurial Firms: The Effects of Financial Constraints on Labor and Capital, Review of Financial Studies 21: 543-577. Gersovitz, M. 1980. On classification probabilities for the disequilibrium model, Journal of Econometrics 14: 239–246. 21 Gertler, M., and S. Gilchrist. 1993. The Role of Credit Market Imperfections in the Monetary Transmission Mechanism: Arguments and Evidence, Scandinavian Journal of Economics 95: 43-64. Greenwood, J. and B. Jovanovic. 1990. Financial development, growth, and the distribution of income, Journal of Political Economy 98: 1076–107. Gropp, R. and F. Boissay. 2007. Trade Credit Defaults and Liquidity Provision by Firms, ECB working paper 753/07. European Central Bank. Hines, J.R. Jr. and R. Thaler. 1995. The flypaper effect, Journal of Economic Perspectives 9: 217-226. Holtz-Eakin D., Newey W. and H. Rosen. 1988. Estimating vector autoregressions with panel data, Econometrica 56: 1371–95. Jaffee, D. 1971. Credit Rationing and the Commercial Loan Market, John Wiley and Sons, Inc. New York. King R. and R. Levine. 1993. Finance and growth: Schumpeter might be right, Quarterly Journal of Economics 108: 717–37. Kaplan, S. and L. Zingales. 1997. Do Financing Constraints Explain why Investment is Correlated with Cash Flow?, Quarterly Journal of Economics 112: 169-215. 22 Kaplan, S., and L. Zingales, 2000, Investment-Cash Flow Sensitivities are not Valid Measures of Financing Constraints, The Quarterly Journal of Economics 115: 707-712. Maddala, G. S. 1983. Limited-Dependent and Qualitative Variables in Econometrics. Cambridge University Press, Cambridge, MA. Marotta, G. 1996. Does Trade Credit Redistribution Thwart Monetary Policy? Evidence from Italy, mimeo. Meltzer, A. 1960. Mercantile Credit Monetary Policy, and Size of Firms, The Review of Economics and Statistics 42:.429-437. McMillan, John and Woodruff, Christopher. 1999. Interfirm Relationships and Informal Credit in Vietnam, Quarterly Journal of Economics 98, 1285-1320. Miwa, Y., J.M. Ramseyer. 2005. Trade Credit, Bank Loans, and Monitoring: Evidence from Japan, University of Tokyo Working Paper cf381. University of Tokyo. Myers, S. C. and N. S. Majluf. 1984. Corporate financing and investment decisions when firms have information that investors do not have, Journal of Financial Economics 13: 187-221. Nielsen, J.H., 2002. Trade Credit and the Bank Lending Channel. Journal of Money, Credit, and Banking 34: 226-253. 23 Oliner, S., and G. Rudebusch. 1996. Monetary Policy and Credit Constraints: Evidence from the Composition of External Finance: Comment, American Economic Review 86: 300-309. Ogawa, K., K. Suzuki. 2000. Uncertainty and investment: some evidence from the panel data of Japanese manufacturing firms, Japanese Economic Review 51: 170-192. Ono, M. 2001. Determinants of Trade Credit in the Japanese Manufacturing Sector, Journal of the Japanese and International Economies 15: 160-177. Petersen, M. A. and R.G. Rajan. 1994. The benefits of lending relationships: Evidence from small business data, Journal of Finance 49: 3-37 Petersen, M.A. and R.G. Rajan. 1995. The effect of credit market competition on firmcreditor relationships, Quarterly Journal of Economics 111: 407-443. Petersen, M. A. and R.G. Rajan. 1997. Trade Credit: Theories and Evidence, Review of Financial Studies 10: 661-691. Ramey, V., 1992. The Source of Fluctuations in Money. Evidence from Trade Credit, Journal of Monetary Economics 30:171-193. Rousseau, P.L. and P. Wachtel. 1998. Financial intermediation and economic performance: historical evidence from five industrialized countries, Journal of Money, Credit and Banking 30: 657-678. 24 Shikimi, M. 2005. Do Firms Benefit from Multiple Banking Relationships? Evidence from Small and Medium-Sized Firms in Japan, HU-STAT Discussion Paper Series, nº70, Hitotsubashi University Research Unit for Statistical Analysis in Social Sciences. Smith, J. K. 1987. Trade Credit and Information Asymmetry, Journal of Finance 42: 863-872. Tsuruta, D. 2008. Bank Information Monopoly and Trade Credit: Does Only Bank Have Information?, Applied Economics, 40: 981-996. Uchida, H., Udell, G. F. and W. Watanabe. 2006. Are trade creditors relationship lenders?, RIETI Discussion Paper Series 06-E-026. Research Institute of Economy, Trade and Industry. Uesugi, I. and G. M. Yamashiro. 2004. How Trade Credit Differs from Loans: Evidence from Japanese Trading Companies, REITI Discussion Paper Series 04-E-028. Research Institute of Economy, Trade and Industry. 25 TABLE 1. VARIABLES: DEFINITION AND SAMPLE MEANS VARIABLE MAIN INVESTMENT VARIABLE Capital expendituret / capitalt-1 VARIABLES RELATED TO FINANCING DECISIONS Bank loans Banks loans/total liabilities Receivables turnover Accounts receivable/total assets Payables turnover Accounts payable / total liabilities FIRM-LEVEL AND ENVIRONMENTAL CONTROL VARIABLES Total assets Tangible assets Cash flow Cash flowt/ capitalt-1 Sales Sales growth Interbank interest rates Loan interest spread Default risk Banks ’market power Defaults on commercial paper Log (GDP) DEFINITION MEAN The ratio of total capital expenditures at end-year relative to the total amount of capital at the beginning of the year is our investment variable (Kaplan and Zingales, 1997; Fazzari et al., 2000). 0.33601 Outstanding amount of loans in the liability side of firm’s balance sheet (thousand of euros) A ratio that reflects bank-leverage, the relevance of bank loans as a source of external finance. Computed by dividing the total sales of the firm in year t by the average of the “accounts receivable” between the end of year t and the end of year t-1. A high ratio suggests a combination of tight credit terms to the firm’s customers and an aggressive collections policy. A low ratio suggests that the firm is offering loose credit terms to its customers and/or that the firm has a weak collections policy. These loose credit terms could either reflect an optimal risk/return trade-off between increased sales volume and increased credit risk – or, weak risk management on the part of the firm. It indicates the relative amount of accounts receivable in the assets portfolio. It shows the actual extent to which the firm extends trade credit. Computed by dividing the total costs of the goods sold by the firm in year t by the average of the “accounts payable” between the end of year t and the end of year t-1. This ratio is a short-term liquidity measure used to quantify the rate at which a company pays off its suppliers. Because accounts payable are a source of credit to the firm, the payables turnover proxies for the maturity of this source of credit. It reflects the importance of trade credit relative to other sources of financing. Total assets on firm’s balance sheet (thousand of euros) Fixed assets on firm’s balance sheet (thousand of euros). This is considered as proxy of collateral. Net income plus depreciation plus changes in deferred taxes. This ratio is defined as cash flow in relative terms to the proportion of capital at the end of the previous year (Kaplan and Zingales, 1997, 2000; Fazzari et al., 2000) Total sales during the year (thousand of euros) Sales growth offers another alternative measure of firm financing constraints. It has been employed as a measure of investment opportunities and current cash-flows, which are expected to reduce borrowing constraints and as an indicator of financial distress for constrained firms (Fazzari et al., 2000, Lamont et al., 2001). The three-month interbank deposit rate, obtained from the Bank of Spain, and computed as the average monthly rate over the year. This interest rate controls for the costs of external financing. A shock to interest rates may affect both bank lending and trade credit (Nielsen, 2002; Fukuda et al., 2006). This spread is defined as the difference between loan interest rates and interbank rates. The loan interest rate is computed as a ratio of loan expenses and bank loans outstanding. We implicitly assume that the year-end loan balance is roughly equal to the weighted average balance during the year. This risk variable is defined as the ratio of operating profits to interest paid. A proxy for operating risk showing how many times interest paid are covered by operating profits. Bank market power is measured estimating the Lerner index (%). This index defined as the ratio “(price of total assets - marginal costs of total assets)/price”. Marginal costs are estimated from a translog cost function with a single output (total assets) and three inputs (deposits, labor and physical capital) using two stage least squares and bank fixed effects (Cetorelli and Gambera, 2001). This is a regional measure of the growth in defaults on commercial paper in the region where the firm operates. It provides a control for trade credit quality. This is the only business default rate available at the regional level. Logarithm of regional GDP in the region where the firm is located 26 5,531.6 0.20785 6.2365 0.17532 8.02354 0.30451 9,832.6 1,466.9 1,899.4 0.41220 18,621.6 0.4721 0.07952 0.01320 4.25660 22.3620 0.0236 5.23374 TABLE 2. ESTIMATED PARAMETERS OF THE DISEQUILIBRIUM MODEL. Switching regression model estimated by full information maximum likelihood (FIML) with fixed effects p-values in parenthesis Standard errors are clustered at the regional level Demand for bank loans Sales/total assets(t-1) Cash-flow/total assets(t-1) Loan interest spread Log(GDP) Coefficient 0.6509*** (0.000) -2.2918*** (0.000) -1.4678*** (0.000) 0.0232** (0.018) Std. Error 0.01 0.08 0.04 0.11 Supply of bank loans Tangible fixed assets/total assets(t-1) Banks’ market power Default risk Log(GDP) Reciprocal of total assets in the loan demand equation Reciprocal of total assets in the loan supply equation S.D. of demand equation S.D. of supply equation Correlation coefficient Log likelihood Observations Number of firms 2.4367*** (0.000) -0.9812*** (0.002) 0.000042 (0.831) -0.0886** (0.014) 0.01 0.01 0.01 0.09 340228.0*** 1156.15 (0.000) 211297.2*** 2170.12 (0.000) 1.5322*** 0.01 (0.000) 0.4688*** 0.01 (0.000) 0.6749*** 0.07 (0.000) 158955 278.073 30.897 * Statistically significant at 10% level ** Statistically significant at 5% level *** Statistically significant at 1% level 27 TABLE 3. PERCENTAGE OF BORROWING CONSTRAINED FIRMS Time Entire period (1994-2002) 1994 1995 1996 1997 1998 1999 2000 2001 2002 % 33,90 34,62 31,88 34,22 32,30 34,25 34,93 35,16 34,14 33,60 28 TABLE 4. FINANCING CONSTRAINTS, BANK LOANS, INVESTMENT AND CASH FLOW: BREAKDOWN BY SAMPLE ASSETS QUARTILES ALL SMEs Constrained Bank loans/total liabilities Capital expendituret/ capitalt-1 Cash flowt/ capitalt-1 Cash flow-investment correlation 0.227 0.206 0.309 0.346 0.428 0.507 0.742 0.481 FIRST QUARTILE Constrained Bank loans/total liabilities Capital expendituret/ capitalt-1 Cash flowt/ capitalt-1 Cash flow-investment correlation Unconstrained 0.227 0.202 0.289 0.320 0.401 0.488 0.568 0.483 THIRD QUARTILE Constrained Bank loans/total liabilities Capital expendituret/ capitalt-1 Cash flowt/ capitalt-1 Cash flow-investment correlation Unconstrained 0.212 0.151 0.267 0.304 0.316 0.541 0.911 0.844 SECOND QUARTILE Constrained Bank loans/total liabilities Capital expendituret/ capitalt-1 Cash flowt/ capitalt-1 Cash flow-investment correlation Unconstrained Unconstrained 0.249 0.221 0.344 0.336 0.467 0.516 0.415 0.353 FOURTH QUARTILE Constrained Unconstrained Bank loans/total liabilities 0.202 Capital expendituret/ capitalt-1 0.355 Cash flowt/ capitalt-1 0.559 Cash flow-investment correlation 0.427 * Differences in means are significant at the 5% level or lower. 0.200 0.399 0.538 0.356 Differences in means: constrained vs. unconstrained (p-values) 0.128 0.041* 0.039* 0.002* Differences in means: constrained vs. unconstrained (p-values) 0.006* 0.010* 0.011* 0.041* Differences in means: constrained vs. unconstrained (p-values) 0.013* 0.009* 0.005* 0.007* Differences in means: constrained vs. unconstrained (p-values) 0.021* 0.132 0.011* 0.016* Differences in means: constrained vs. unconstrained (p-values) 0.225 0.081* 0.033* 0.016* 29 TABLE 5. FINANCING CONSTRAINTS, BANK LOANS, INVESTMENT AND CASH FLOW: BREAKDOWN BY SECTOR Sector % borrowing constrained firms Bank loans/total liabilities Capital expendituret / capitalt-1 Cash flowt/ capitalt-1 Cash flowinvestment correlation MANUFACTURES OF FOOD PRODUCTS AND BEVERAGES 26,29 0.208 0.348 0.523 0.421 MANUFACTURES OF TEXTILES AND DRESSING 41,73 0.243 0.287 0.341 0.404 39,00 0.237 0.312 0.346 0.599 35,29 0.232 0.302 0.382 0.577 25,22 0.199 0.336 0.588 0.503 34,89 0.236 0.301 0.411 0.437 24,36 22,43 0.218 0.240 0.351 0.353 0.603 0.449 0.557 0.634 41,75 0.246 0.328 0.423 0.614 39,85 48,43 21,31 30,46 32,14 37,44 0.237 0.251 0.197 0.207 0.213 0.220 0.303 0.317 0.303 0.298 0.304 0.331 0.351 0.488 0.538 0.403 0.416 0.374 0.329 0.555 0.329 0.346 0.530 0.587 30,36 0.204 0.311 0.412 0.431 33,33 0.211 0.321 0.502 0.488 MANUFACTURES OF WOOD, PAPER, PRINTING AND RECORDED MEDIA PRODUCTS MANUFACTURES OF CHEMICAL, PLASTIC, MINERAL AND METAL PRODUCTS MANUFACTURES OF MACHINERY AND EQUIPMENT AND TRASNSPORT VEHICLES MANUFACTURES OF FURNITURE AND RECYCLING ELECTRICITY, GAS AND WATER SUPPLY CONSTRUCTION SALE, MAINTENANCE AND REPAIR OF MOTOR VEHICLES WHOLESALE TRADE AND COMISSION TRADE HOTELS AND RESTAURANTS TRANSPORT SERVICES REAL STATE ACTIVITIES RENTING OF MACHINERY AND EQUIPMENT COMPUTER AND RELATED ACTIVITIES OTHER RETAIL TRADE PRODUCTS AND SERVICES OTHER 30 TABLE 6. UNCONSTRAINED FIRMS: PANEL DATA GRANGER PREDICTABILITY TESTS. FIRM FINANCING AND INVESTMENT (FULL EQUATIONS) (1994-2002) 2SLS with instrumental variables. (95% significance level) (p-values in parentheses) Standard errors are clustered at the regional level Constant Dependent variablet-1 (Capital expendituret/ capitalt-1) t-1 (1) (2) (3) Bank loans/total liabilities Capital expendituret/ capitalt-1 Capital expendituret/ capitalt-1 0.01879* (0.014) 0.02681* (0.031) 0.6828 (0.119) 0.03822* (0.018) 0.03644* (0.029) 0.03744* (0.026) 0.02559* (0.035) - - (4) (5) (6) Payables turnover Capital expendituret/ capitalt-1 Accounts payable/ total liabilities -0.02154* (0.038) -0.01151* (0.036) 0.03448 (0.226) 0.03259* (0.021) 0.02154* (0.026) - - - - - - - - - - - - Bank loans/total liabilities t-1 - Receivables turnover t-1 (Accounts receivable/ total assets) t-1 - 0.22148** (0.002) - - - - - - - 0.12364 (0.237) - Payables turnover t-1 - - - - -0.01250* (0.027) 0.02319 (0.126) 0.0326 (0.213) -0.01977* (0.043) 0.092 -0.01854* (0.019) 0.4921** (0.005) 0.01241* (0.043) -0.01882* (0.031) 0.002 -0.01481* (0.022) 0.26142** (0.003) 0.01029* (0.037) -0.01633* (0.029) 0.045 -0.01264 (0.188) -0.30234 (0.221) -0.02314 (0.186) -0.03140* (0.025) 0.058 0.11457 (0.416) -0.01328** (0.009) 0.22594** (0.008) 0.00985* (0.036) -0.01884* (0.022) 0.034 0.149 0.187 0.123 0.202 (Accounts payable/ total liabilities) t-1 Interbank interest rates Cash flowt/ capitalt-1 Sales growth Defaults in commercial paper F-test for overall significance (p-value) - Sargan test (p-value) 0.01871* (0.031) -0.03325* (0.022) 0.02663 (0.441) -0.02256 (0.230) -0.25481 (0.137) -0.02114 (0.238) -0.01477* (0.021) 0.067 0.152 0.129 * significantly different from zero at 5% level ** significantly different from zero at 1% level 31 TABLE 7. CONSTRAINED FIRMS: PANEL DATA GRANGER PREDICTABILITY TESTS. FIRM FINANCING AND INVESTMENT (FULL EQUATIONS) (1994-2002) 2SLS with instrumental variables. (95% significance level) (p-values in parentheses) Standard errors are clustered at the regional level Constant Dependent variablet-1 (Capital expendituret/ capitalt-1) t-1 (1) (2) (7) (8) (9) (10) Bank loans/total liabilities Capital expendituret/ capitalt-1 Capital expendituret/ capitalt-1 Payables turnover Capital expendituret/ capitalt-1 Accounts payable/ total liabilities 0.01359* (0.026) 0.03308* (0.012) 0.34290 (0.193) 0.02639* (0.011) 0.02541* (0.023) 0.04118* (0.031) 0.02661* (0.041) 0.04339* (0.016) 0.01772* (0.031) - - -0.02881* (0.021) -0.06224* (0.011) 0.01238 (0.267) 0.02663* (0.028) -0.05544* (0.022) 0.01091 (0.347) Bank loans/total liabilities t-1 - Receivables turnover t-1 (Accounts receivable/ total assets) t-1 - 0.12088 (0.210) - - - - - Payables turnover t-1 - - 0.53652** (0.003) - (Accounts payable/ total liabilities) t-1 Interbank interest rates Cash flowt/ capitalt-1 Sales growth Defaults in commercial paper F-test for overall significance (p-value) Sargan test (p-value) - - - - - - - - - - - - - 0.61178** (0.002) -0.01880* (0.031) 0.25447** (0.004) 0.01541* (0.032) -0.01771* (0.031) 0.003 0.121 -0.03348 (0.352) -0.3351 (0.371) -0.00256 (0.215) -0.02270* (0.033) 0.079 0.186 - - - - -0.01358** (0.020) 0.03626 (0.139) 0.01841 (0.151) -0.02150* (0.022) 0.061 0.136 -0.03539* (0.029) 0.50244** (0.004) 0.01327* (0.034) -0.02366 (0.033) 0.003 0.151 -0.01788* (0.018) 0.28440** (0.004) 0.01661* (0.041) -0.02644* (0.014) 0.003 0.191 -0.02504 (0.142) -0.42115 (0.336) -0.00343 (0.533) -0.05607* (0.035) 0.052 0.158 - * significantly different from zero at 5% level ** significantly different from zero at 1% level 32 TABLE 8. THE ROLE OF UNCONSTRAINED FIRMS AS LENDERS: PANEL DATA GRANGER PREDICTABILITY TESTS (FULL EQUATIONS) (1994-2002) 2SLS with instrumental variables. (95% significance level) (p-values in parentheses) Standard errors are clustered at the regional level UNCONSTRAINED FIRMS CONSTRAINED FIRMS (1) (2) (3) (4) (5) (6) (7) (8) Bank loans/total liabilities Receivables turnover Bank loans/total liabilities Accounts receivable/ total assets Bank loans/total liabilities Receivables turnover Bank loans/total liabilities Accounts receivable/ total assets 0.04669* (0.022) 0.02344* (0.027) -0.24200* (0.039) 0.25879* (0.014) 0.03274* (0.031) 0.02482* (0.031) -0.03370 (0.131) 0.02355* (0.022) (Capital expendituret/ capitalt-1) t-1 - - - - 0.03228* (0.031) 0.03025* (0.022) -0.35571* (0.042) 0.20152* (0.011) 0.05158* (0.021) 0.02361* (0.018) -0.07226* (0.036) 0.01554* (0.013) Bank loans/total liabilities t-1 - 0.85987** (0.001) - 0.63685** (0.003) - - - - Receivables turnover t-1 0.02360 (0.288) - - - - 0.23590 (0.129) - 0.16307 (0.258) (Accounts receivable/ total assets) t-1 - - 0.02057 (0.441) - 0.01058 (0.328) - - - Payables turnover t-1 - - - - - - 0.01025 (0.352) - - - - - Constant Dependent variablet-1 (Accounts payable/ total liabilities) t-1 - - - - -0.01327* (0.025) 0.01807 (0.139) 0.02448 (0.321) -0.02018* (0.032) 0.02328 (0.126) 0.02699 (0.126) 0.01255* (0.040) -0.01233** (0.005) -0.01217* (0.021) 0.01397 (0.103) 0.03658 (0.423) -0.01641* (0.040) 0.03391 (0.266) 0.02152 (0.243) 0.01650* (0.032) -0.01399** (0.002) F-test for overall significance (p-value) 0.049 0.003 0.061 0.004 Sargan test (p-value) 0.151 0.132 0.125 0.246 Interbank interest rates Cash flowt/ capitalt-1 Sales growth Defaults in commercial paper - - - - -0.01458* (0.026) 0.01743 (0.164) 0.02148 (0.258) -0.0315* (0.048) 0.058 0.03147 (0.390) 0.02170 (0.153) 0.01436* (0.031) -0.01662 (0.003) 0.044 -0.01380* (0.016) 0.01746 (0.302) 0.04473 (0.250) -0.24733 (0.031) 0.058 0.06781 (0.134) 0.01583 (0.393) 0.02669* (0.022) -0.01844* (0.017) 0.048 * significantly different from zero at 5% level ** significantly different from zero at 1% level 33 APPENDIX A: COMPUTING PROBABILITIES FROM DISEQUILIBRIUM MODEL OF FIRM FINANCING CONSTRAINTS THE According to the results from the disequilibrium model in section 4.1., a firm is defined as financially constrained in year t if the probability that the desired amount of bank credit in year t exceeds the maximum amount of credit available in the same year is greater than 0.5. Following Gersovitz (1980), the probability that firm will face a financial constraint in year is derived as follows: X d β d − X its β s Pr(loanitd > loanits ) = Pr( X itd β d + uitd > X its β s + uits ) = Φ it σ (A1) where X itd and X its denote the variables that determine firms’ loan demand and the maximum amount of credit available to firms, respectively. The error terms are assumed to be distributed normally, σ 2 = var(uitd − uits ) , and Φ (.) is a standard normal distribution function. Since E (loanitd ) = X itd β d and E (loanits ) = X its β s , Pr(loanitd > loanits ) > 0.5 , if and only if E (loanitd ) > E (loanits ) . 34 APPENDIX B. SUMMARY OF PANEL DATA GRANGER PREDICTABILITY TESTS (1-3 LAGS). FIRM FINANCING AND INVESTMENT: UNCONSTRAINED FIRMS VS. CONSTRAINED FIRMS. (1994-2002) 2SLS with instrumental variables. (95% significance level) Standard errors are clustered at the regional level UNCONSTRAINED FIRMS “Bank loans/total liabilities” predicts “Capital expendituret / capitalt-1” Lags (l) F test 1 10.13 YES 2 11.25 YES 3 7.80 YES “Bank loans/total liabilities” predicts “Receivables turnover” Lags (l) F test 1 11.02 YES 2 4.26 YES YES 3 6.89 “Bank loans/total liabilities” predicts “Account receivables/total assets” Lags (l) F test 1 11.02 YES 2 4.26 YES NO 3 0.89 “Payables turnover” predicts “Capital expendituret / capitalt-1” Lags (l) F test 1 0.21 NO NO 2 0.16 NO 3 0.08 “Accounts payable/total liabilities” predicts “Capital expendituret / capitalt-1” Lags (l) F test 1 0.32 NO NO 2 0.11 NO 3 0.02 “Capital expendituret / capitalt-1”predicts “Bank loans/total liabilities” Lags (l) F test NO 1 0.02 NO 2 0.09 NO 3 0.71 “Receivables turnover” predicts “Bank loans/total liabilities” Lags (l) F test NO 1 0.11 NO 2 0.17 NO 3 0.10 “Account receivables/total assets” predicts “Bank loans/total liabilities” Lags (l) F test NO 1 0.12 NO 2 0.14 NO 3 0.16 “Capital expendituret / capitalt-1” predicts “Payables turnover” lags (l) F test NO 1 0.09 NO 2 0.07 NO 3 0.17 “Capital expendituret / capitalt-1” predicts “Accounts payable/ total liabilities” lags (l) F test NO 1 0.06 NO 2 0.04 NO 3 0.14 CONSTRAINED FIRMS “Bank loans/total liabilities” predicts “Capital expendituret / capitalt-1” Lags (l) F test NO 1 0.21 NO 2 0.08 NO 3 0.16 “Bank loans/total liabilities” predicts “Receivables turnover” Lags (l) F test NO 1 0.08 NO 2 0.09 NO 3 0.11 “Bank loans/total liabilities” predicts “Account receivables/total assets” Lags (l) F test NO 1 0.02 NO 2 0.06 NO 3 0.09 “Payables turnover ” predicts “Capital expendituret / capitalt-1” Lags (l) F test 1 9.59 YES 2 11.42 YES YES 3 6.27 “Accounts payable/total liabilities” predicts “Capital expendituret / capitalt-1” Lags (l) F test 1 11.16 YES 2 8.19 YES NO 3 0.05 “Capital expendituret / capitalt-1” predicts “Bank loans/total liabilities” lags (l) F test NO 1 0.03 NO 2 0.07 NO 3 0.09 “Account receivables/total assets” predicts “Bank loans/total liabilities” lags (l) F test NO 1 0.08 NO 2 0.09 NO 3 0.12 “Account receivables/total assets” predicts “Bank loans/total liabilities” lags (l) F test NO 1 0.06 NO 2 0.05 NO 3 0.04 “Capital expendituret / capitalt-1” predicts “Payables turnover/ total liabilities” lags (l) F test NO 1 0.07 NO 2 0.05 NO 3 0.03 “Capital expendituret / capitalt-1” predicts “Accounts payable/ total liabilities” lags (l) F test NO 1 0.03 NO 2 0.04 NO 3 0.06 35 Working Paper Series A series of research studies on regional economic issues relating to the Seventh Federal Reserve District, and on financial and economic topics. Firm-Specific Capital, Nominal Rigidities and the Business Cycle David Altig, Lawrence J. Christiano, Martin Eichenbaum and Jesper Linde WP-05-01 Do Returns to Schooling Differ by Race and Ethnicity? Lisa Barrow and Cecilia Elena Rouse WP-05-02 Derivatives and Systemic Risk: Netting, Collateral, and Closeout Robert R. Bliss and George G. Kaufman WP-05-03 Risk Overhang and Loan Portfolio Decisions Robert DeYoung, Anne Gron and Andrew Winton WP-05-04 Characterizations in a random record model with a non-identically distributed initial record Gadi Barlevy and H. N. Nagaraja WP-05-05 Price discovery in a market under stress: the U.S. Treasury market in fall 1998 Craig H. Furfine and Eli M. Remolona WP-05-06 Politics and Efficiency of Separating Capital and Ordinary Government Budgets Marco Bassetto with Thomas J. Sargent WP-05-07 Rigid Prices: Evidence from U.S. Scanner Data Jeffrey R. Campbell and Benjamin Eden WP-05-08 Entrepreneurship, Frictions, and Wealth Marco Cagetti and Mariacristina De Nardi WP-05-09 Wealth inequality: data and models Marco Cagetti and Mariacristina De Nardi WP-05-10 What Determines Bilateral Trade Flows? Marianne Baxter and Michael A. Kouparitsas WP-05-11 Intergenerational Economic Mobility in the U.S., 1940 to 2000 Daniel Aaronson and Bhashkar Mazumder WP-05-12 Differential Mortality, Uncertain Medical Expenses, and the Saving of Elderly Singles Mariacristina De Nardi, Eric French, and John Bailey Jones WP-05-13 Fixed Term Employment Contracts in an Equilibrium Search Model Fernando Alvarez and Marcelo Veracierto WP-05-14 1 Working Paper Series (continued) Causality, Causality, Causality: The View of Education Inputs and Outputs from Economics Lisa Barrow and Cecilia Elena Rouse WP-05-15 Competition in Large Markets Jeffrey R. Campbell WP-05-16 Why Do Firms Go Public? Evidence from the Banking Industry Richard J. Rosen, Scott B. Smart and Chad J. Zutter WP-05-17 Clustering of Auto Supplier Plants in the U.S.: GMM Spatial Logit for Large Samples Thomas Klier and Daniel P. McMillen WP-05-18 Why are Immigrants’ Incarceration Rates So Low? Evidence on Selective Immigration, Deterrence, and Deportation Kristin F. Butcher and Anne Morrison Piehl WP-05-19 Constructing the Chicago Fed Income Based Economic Index – Consumer Price Index: Inflation Experiences by Demographic Group: 1983-2005 Leslie McGranahan and Anna Paulson WP-05-20 Universal Access, Cost Recovery, and Payment Services Sujit Chakravorti, Jeffery W. Gunther, and Robert R. Moore WP-05-21 Supplier Switching and Outsourcing Yukako Ono and Victor Stango WP-05-22 Do Enclaves Matter in Immigrants’ Self-Employment Decision? Maude Toussaint-Comeau WP-05-23 The Changing Pattern of Wage Growth for Low Skilled Workers Eric French, Bhashkar Mazumder and Christopher Taber WP-05-24 U.S. Corporate and Bank Insolvency Regimes: An Economic Comparison and Evaluation Robert R. Bliss and George G. Kaufman WP-06-01 Redistribution, Taxes, and the Median Voter Marco Bassetto and Jess Benhabib WP-06-02 Identification of Search Models with Initial Condition Problems Gadi Barlevy and H. N. Nagaraja WP-06-03 Tax Riots Marco Bassetto and Christopher Phelan WP-06-04 The Tradeoff between Mortgage Prepayments and Tax-Deferred Retirement Savings Gene Amromin, Jennifer Huang,and Clemens Sialm WP-06-05 2 Working Paper Series (continued) Why are safeguards needed in a trade agreement? Meredith A. Crowley WP-06-06 Taxation, Entrepreneurship, and Wealth Marco Cagetti and Mariacristina De Nardi WP-06-07 A New Social Compact: How University Engagement Can Fuel Innovation Laura Melle, Larry Isaak, and Richard Mattoon WP-06-08 Mergers and Risk Craig H. Furfine and Richard J. Rosen WP-06-09 Two Flaws in Business Cycle Accounting Lawrence J. Christiano and Joshua M. Davis WP-06-10 Do Consumers Choose the Right Credit Contracts? Sumit Agarwal, Souphala Chomsisengphet, Chunlin Liu, and Nicholas S. Souleles WP-06-11 Chronicles of a Deflation Unforetold François R. Velde WP-06-12 Female Offenders Use of Social Welfare Programs Before and After Jail and Prison: Does Prison Cause Welfare Dependency? Kristin F. Butcher and Robert J. LaLonde Eat or Be Eaten: A Theory of Mergers and Firm Size Gary Gorton, Matthias Kahl, and Richard Rosen Do Bonds Span Volatility Risk in the U.S. Treasury Market? A Specification Test for Affine Term Structure Models Torben G. Andersen and Luca Benzoni WP-06-13 WP-06-14 WP-06-15 Transforming Payment Choices by Doubling Fees on the Illinois Tollway Gene Amromin, Carrie Jankowski, and Richard D. Porter WP-06-16 How Did the 2003 Dividend Tax Cut Affect Stock Prices? Gene Amromin, Paul Harrison, and Steven Sharpe WP-06-17 Will Writing and Bequest Motives: Early 20th Century Irish Evidence Leslie McGranahan WP-06-18 How Professional Forecasters View Shocks to GDP Spencer D. Krane WP-06-19 Evolving Agglomeration in the U.S. auto supplier industry Thomas Klier and Daniel P. McMillen WP-06-20 3 Working Paper Series (continued) Mortality, Mass-Layoffs, and Career Outcomes: An Analysis using Administrative Data Daniel Sullivan and Till von Wachter The Agreement on Subsidies and Countervailing Measures: Tying One’s Hand through the WTO. Meredith A. Crowley WP-06-21 WP-06-22 How Did Schooling Laws Improve Long-Term Health and Lower Mortality? Bhashkar Mazumder WP-06-23 Manufacturing Plants’ Use of Temporary Workers: An Analysis Using Census Micro Data Yukako Ono and Daniel Sullivan WP-06-24 What Can We Learn about Financial Access from U.S. Immigrants? Una Okonkwo Osili and Anna Paulson WP-06-25 Bank Imputed Interest Rates: Unbiased Estimates of Offered Rates? Evren Ors and Tara Rice WP-06-26 Welfare Implications of the Transition to High Household Debt Jeffrey R. Campbell and Zvi Hercowitz WP-06-27 Last-In First-Out Oligopoly Dynamics Jaap H. Abbring and Jeffrey R. Campbell WP-06-28 Oligopoly Dynamics with Barriers to Entry Jaap H. Abbring and Jeffrey R. Campbell WP-06-29 Risk Taking and the Quality of Informal Insurance: Gambling and Remittances in Thailand Douglas L. Miller and Anna L. Paulson WP-07-01 Fast Micro and Slow Macro: Can Aggregation Explain the Persistence of Inflation? Filippo Altissimo, Benoît Mojon, and Paolo Zaffaroni WP-07-02 Assessing a Decade of Interstate Bank Branching Christian Johnson and Tara Rice WP-07-03 Debit Card and Cash Usage: A Cross-Country Analysis Gene Amromin and Sujit Chakravorti WP-07-04 The Age of Reason: Financial Decisions Over the Lifecycle Sumit Agarwal, John C. Driscoll, Xavier Gabaix, and David Laibson WP-07-05 Information Acquisition in Financial Markets: a Correction Gadi Barlevy and Pietro Veronesi WP-07-06 Monetary Policy, Output Composition and the Great Moderation Benoît Mojon WP-07-07 4 Working Paper Series (continued) Estate Taxation, Entrepreneurship, and Wealth Marco Cagetti and Mariacristina De Nardi WP-07-08 Conflict of Interest and Certification in the U.S. IPO Market Luca Benzoni and Carola Schenone WP-07-09 The Reaction of Consumer Spending and Debt to Tax Rebates – Evidence from Consumer Credit Data Sumit Agarwal, Chunlin Liu, and Nicholas S. Souleles WP-07-10 Portfolio Choice over the Life-Cycle when the Stock and Labor Markets are Cointegrated Luca Benzoni, Pierre Collin-Dufresne, and Robert S. Goldstein WP-07-11 Nonparametric Analysis of Intergenerational Income Mobility with Application to the United States Debopam Bhattacharya and Bhashkar Mazumder WP-07-12 How the Credit Channel Works: Differentiating the Bank Lending Channel and the Balance Sheet Channel Lamont K. Black and Richard J. Rosen WP-07-13 Labor Market Transitions and Self-Employment Ellen R. Rissman WP-07-14 First-Time Home Buyers and Residential Investment Volatility Jonas D.M. Fisher and Martin Gervais WP-07-15 Establishments Dynamics and Matching Frictions in Classical Competitive Equilibrium Marcelo Veracierto WP-07-16 Technology’s Edge: The Educational Benefits of Computer-Aided Instruction Lisa Barrow, Lisa Markman, and Cecilia Elena Rouse WP-07-17 The Widow’s Offering: Inheritance, Family Structure, and the Charitable Gifts of Women Leslie McGranahan WP-07-18 Demand Volatility and the Lag between the Growth of Temporary and Permanent Employment Sainan Jin, Yukako Ono, and Qinghua Zhang WP-07-19 A Conversation with 590 Nascent Entrepreneurs Jeffrey R. Campbell and Mariacristina De Nardi WP-07-20 Cyclical Dumping and US Antidumping Protection: 1980-2001 Meredith A. Crowley WP-07-21 The Effects of Maternal Fasting During Ramadan on Birth and Adult Outcomes Douglas Almond and Bhashkar Mazumder WP-07-22 5 Working Paper Series (continued) The Consumption Response to Minimum Wage Increases Daniel Aaronson, Sumit Agarwal, and Eric French WP-07-23 The Impact of Mexican Immigrants on U.S. Wage Structure Maude Toussaint-Comeau WP-07-24 A Leverage-based Model of Speculative Bubbles Gadi Barlevy WP-08-01 Displacement, Asymmetric Information and Heterogeneous Human Capital Luojia Hu and Christopher Taber WP-08-02 BankCaR (Bank Capital-at-Risk): A credit risk model for US commercial bank charge-offs Jon Frye and Eduard Pelz WP-08-03 Bank Lending, Financing Constraints and SME Investment Santiago Carbó-Valverde, Francisco Rodríguez-Fernández, and Gregory F. Udell WP-08-04 6
@FedFRASER