1 / 54

BANK LENDING, FINANCING CONSTRAINTS AND SME INVESTMENT

BANK LENDING, FINANCING CONSTRAINTS AND SME INVESTMENT. Santiago Carbó-Valverde (U. Granada and FRB Chicago*) Gregory Udell (Indiana University) Francisco Rodriguez-Fernandez (U. Granada).

morela
Download Presentation

BANK LENDING, FINANCING CONSTRAINTS AND SME INVESTMENT

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. BANK LENDING, FINANCING CONSTRAINTS AND SME INVESTMENT Santiago Carbó-Valverde (U. Granada and FRB Chicago*) Gregory Udell (Indiana University) Francisco Rodriguez-Fernandez (U. Granada) * The views in this paper are those of the author and may not represent the views of the Federal Reserve Bank of Chicago or the Federal Reserve System.

  2. MOTIVATION • The ability of firms to optimally exploit investment opportunities may crucially depend on the level of financial constraints that they face. • A controversial line of inquiry: • Fazzari et al. (1988): investment-cash flow sensitivities are related to financing constraints. • Zingales (1997 and 2000): correlation between investment and cash flow may not be a good indicator of financial constraints. 2

  3. We move the line of inquiry in a somewhat different direction: • From an econometric perspective the cash flow variable may be problematic because it may be correlated with omitted variables (e.g., Caballero and Leahy, 1996) (e.g. conservatism, attitudes towards investment,…). • We take a more direct approach. We 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. 3

  4. Basic definitions and assumptions for this study: • Firms are considered to be financially constrained when their loan demand exceeds the amount of credit that banks will lend to that firm. • We focus on bank loans. This is considered in the literature as the least costly external source of financing for SMEs. Trade credit is considered as the main (although more expensive) alternative. 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. 4

  5. SUMMARY • INTRODUCTION THE LITERATURE ON FINANCING CONSTRAINTS, EXTERNAL FINANCE AND INVESTMENT 2. LITERATURE REVIEW AND HYPOTHESES OUR HYPOTHESES BENCHMARK DEFINTIONS: CASH FLOW-INVESTMENT CORRELATIONS 3. EMPIRICAL METHODOLOGY EMPIRICAL STRATEGY AND DATA IDENTIFYING CONSTRAINED VS. UNCONSTRAINED FIRMS TESTING THE HYPOTHESES: GRANGER PREDICTABILITY TESTS 4. MAIN RESULTS DEFINING FINANCIALLY CONSTRAINED FIRMS GRANGER PREDICTABILITY TESTS 5. CONCLUSIONS ALTERNATIVE SPECIFICATIONS: A SWITCHING REGRESSION MODEL OF INVESTMENT WITH TWO-REGIMES 5

  6. 1. INTRODUCTION • Our argument: • As less constrained firms’ investment are less likely to be sensitive to cash flow, they are more likely to be sensitive to bank loan funding. • We also examine the sensitivity of investment to trade credit (the economically significant alternative source of funding). 6

  7. Some additional considerations: • Unconstrained firms will utilize low cost bank loans to finance capital expenditures. • We hypothesize that increased bank loan funding will (not) be associated with increased capital expenditures for unconstrained (constrained) firms. • Our investigation of trade credit will enable us to draw some inferences about the substitutability of trade credit and bank loans. 7

  8. From a methodological point of view, we also extend the literature on financial constraints by examining Granger predictability. • We investigate the predictability relationships between: • bank loans and capital expenditures. • trade credit and capital expenditures. 8

  9. PREVIEW OF RESULTS: • Investment is sensitive to bank loans for unconstrained firms –but not for constrained firms. • For constrained firms only, bank loans predict investment but not vice versa. • Trade credit causes investment, but only for constrained firms. This suggests that constrained firms, whose access to bank loans is limited, resort to trade credit. 9

  10. 2. LITERATURE REVIEW AND HYPOTHESES 2.1. The literature on financing constraints, external finance and investment • The relationships among the sources of external financing and their effects on investment remain unclear in the literature. • A GENERAL ASSUMPTION: Bank loans are considered to be the least costly source of external funding and access to bank lending may condition the demand for trade credit. (e.g., Petersen and Rajan 1994, 1995; Biais and Gollier, 1997; Clementi and Hopenhayn, 2006; Caggese, 2007). 10

  11. 2. LITERATURE REVIEW AND HYPOTHESES TWO MAIN STRANDS OF THE LITERATURE Cash flow-investment sensitivities Bank loans and trade credit as complements or substitutes 11

  12. The cash flow-investment sensitivity debate: a considerable controversy with two main opposing views. • Fazzari et al. (1988, 2000) suggest that financing constraints increase with investment-cash flow sensitivity. • On the other side, Kaplan and Zingales (1997, 2000) suggest that investment-cash flow correlations are not necessarily monotonic in the degree of financing constraints. 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 to be important. 12

  13. Another alternative explanation on the economic significance of cash flow-investment correlations: Hines and Thaler (1995) suggest that firms may be conservative and they only invest when they generate cash flow so that they prefer not to expand using external funding unless they are forced to so. 13

  14. A second strand of the literature has focused on examining whether bank loans and trade credit are complements or substitutes: • There are studies argue that 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). These firms may play a key role in lower funding costs and reducing credit rationing. 14

  15. Large trade creditors have also been shown to provide trade credit to firms experiencing idiosyncratic shocks or monetary policy shocks (Calomiris et al., 1995; Cook, 1999; Ono, 2001; Gropp and Boissay, 2007). • Trade creditors (vendors) may act as “relationship lenders” because they have unique proprietary information about their customers (McMillan and Woodruff, 1999; Uchida et al., 2007). 15

  16. Interestingly, it is suggested that both views (substitutes and complements) can be reconciled when taking into account whether firms are financially constrained or not (García Appendini, 2006). • For example, trade credit is a substitute of bank loans when firms are financially constrained. However, at unconstrained firms, both can be used at the same time and, thus, they can be complements. 16

  17. 2.2 Hypotheses discussion: • If cash flow predicts investment at financially constrained firms, then bank loans (to which they are denied full access) will not be predicting capital expenditures. If they are denied access to the bank loan market, they may turn to the trade credit market. • Therefore, we also examine whether trade credit predicts investment at constrained firms (i.e., trade credit-investment sensitivity). 17

  18. Assumptions: • Financing constraints are directly related to borrowing from banks so that a firm is considered to be financially constrained when the desired amount of loans is larger than the amount that banks effectively provide to that firm. • Firm financing and investment are dynamic and non-contemporaneous (Clementi and Hopenhayn, 2006). This allows us to examine the predictability relationships as a primary tool in analyzing the link between bank loans and investment, and trade credit and investment. 18

  19. Hypothesis 1: Since the desired amount of loans exceeds the supplied amount of loans at constrained firms, loans will not predict investment at constrained firms.Therefore the expected predictability relationship between bank loans and investment may 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. 19

  20. 3. EMPIRICAL METHODOLOGY 3.1. Empirical strategy and data • Our empirical strategy involves three steps • STEP 1: cash-flow investment correlations are estimated as a benchmark to make our data and results comparable with previous research. • STEP 2: identification of financially constrained firms. It is likely that financial ratios –as proxies of financing constraints- 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 experiences borrowing constraints from a so-called disequilibrium model. This methodology permits us to classify firms as constrained or unconstrained. • STEP 3: MAIN ESTIMATIONS: Granger predictability to test our hypotheses. 20

  21. Step 1. AUXILIARY ESTIMATION: Cash flow-investment correlations EMPIRICAL STRATEGY Step 2. AUXILIARY ESTIMATION: Identifying constrained firms: a disequilibrium model Step 3. Granger predictability tests 21

  22. Source: Bureau-Van-Dijk Amadeus database. • Annual information on 30,897 Spanish SMEs during 1994-2002. • 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 employees. • All of the selected firms where active during the entire sample period. The balanced panel sums up to 278,073 observations. • Variables definition: TABLE 1. 22

  23. TABLE 1. VARIABLES: DEFINITION AND SAMPLE MEANS (I) 23

  24. TABLE 1. VARIABLES: DEFINITION AND SAMPLE MEANS (II) 24

  25. 3.2. Benchmark definitions: cash-flow investment correlations • STEP 1. Cash flow investment correlations: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 equation. In the general specification of the model, the current investment rate (Capital expendituret / capitalt-1) is related to lagged values of the investment rate, a quadratic-adjustment term of investment rate, cash flow, sales growth and a quadratic adjustment term for bank debt (bank loans): 25

  26. 3.3. Identifying constrained vs. unconstrained firms • STEP 2. Financing constraints:from Maddala (1983) we employ a disequilibrium model with two-reduced form equations: • A demand equation for bank loans (equation 2). • A supply equation that reflects the maximum amount of loans that banks are willing to lend on a collateral basis (equation 3). • A third equation is added as a transaction equation restricting the value of loans as a min equation of loan demand and loan supply (equation 4). • Estimated parameters: Table 2. 26

  27. 27

  28. 28

  29. The simultaneous equations system 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 availability (Gersovitz, 1980) and, therefore, to classify the sample into constrained and unconstrained firms. 29

  30. 30

  31. STEP 3. Granger predictability tests:Our main results are for one lag (year). 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). • 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 firm-specific fixed effects (fi). we will consider, for example, that “bank loans/total liabilities” will Granger-cause investment if two conditions are met: 31

  32. i) The bank loan ratio is statistically significant in a time-series regression of the firm investment rate: ii) The investment rate variable is not significant when it is included in a time-series regression of the bank loans ratio: 32

  33. 4. MAIN RESULTS 4.1. Defining financially constrained firms • The estimated probabilities that loan demand exceeds loan supply (in the disequilibrium model) are shown in Table 3. • 33.90% of the firms are shown to be financially constrained. 33

  34. 34

  35. 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 assets: • 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 loans although their higher demand of loans is not completely satisfied. 35

  36. Table 4 (contd.): • 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 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. 36

  37. 37

  38. 38

  39. Table 5: • The sector breakdown reflects a significant degree of heterogeneity in financial ratios and estimated cash-flow investment sensitivities. • 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”. 39

  40. 40

  41. 4.2. Granger predictability tests Table 6: • The bank loans ratio Granger-predicts investment rates but, investment rates do not Granger predict bank loans at unconstrained firms. • 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. 41

  42. 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) 42

  43. Table 7: • 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. 43

  44. TABLE 7. 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) 44

  45. 4.3. Alternative specifications: a switching regression model of investment with two-regimes • We test the robustness of our results to alternative specifications. Some recent empirical studies (such as Schiantarelli and Hu, 1998; Hovakimian and Titman, 2006; or Almeida and Capello, 2007) have suggested the convenience of estimating a switching regression model with endogenous sample separation, assuming that there are two different investment regimes within the sample. • While the disequilibrium model permits to directly classify firms into financially constrained and unconstrained, the two-regimes method does not provide an ex-post classification but permits to estimate the likelihood that constrained and unconstrained firms demonstrate a statistically significant different investment behaviour within the model.

  46. The two-regimes switching regression model is composed of the following system of three equations that are estimated simultaneously:

  47. In one regime investment may be more sensitive to the availability of internal funds (cash-flow) than in the other regime. This allows us to test if –controlling for the sensitivity of investment to cash flow- the bank loans to total liabilities ratio affect investment for constrained (regime 1) and unconstrained (regime 2) firms. • In Equations (9) and (10), Xit are the determinants of corporate investment and Zit are the determinants of a firm’s likelihood of being in the first or the second regime. at time t.β1, β2, and γ are vectors of parameters, is a latent variable measuring the tendency or the likelihood of being in the first or second regime, and u1, u2, and ε are residuals. Firms are not fixed in one regime. A transfer between the regimes occurs if y* reaches a certain unobservable “threshold” value. This is important since a firm’s financial status may change over time, leading to a significant change in its investment behavior

  48. Our specification of vectors Xit and Zit is similar to Schiantarelli and Hu (1998) and Hovakimian and Titman (2006). As Xi we use the variables “cash flowt/ capitalt-1”, “interbank interest rates” and a measure of assets tangibility (“tangible assetst/ total assetst-1”). This vector also includes “loans/total liabilities” and “accounts payable/total liabilities” since we aim to test the sensitivity of these external sources of funding to investment at constrained (regime 1) and unconstrained firms (regime 2). The Zitin the regime-selection regression –equation (10)- includes size (log of total assets)-, sales growth, assets tangibility (“tangible assetst/ total assetst-1”) and a measure of financial slack (“cash and liquid securitiest / total assets t-1”).

  49. The results of the two-regimes switching regression model are shown in Table 8. Panels A and B correspond to the regime regression and selection equations, respectively, when “loans/total liabilities” is included among the explanatory variables. Panels C and D correspond to the regime regression and selection equations, respectively, when “accounts payable/total liabilities” is included among the explanatory variables. • Consistently with our previous estimations, “loans/total liabilities” is only positively and significantly related to the investment ratio in the case of unconstrained firms while “accounts payable/total liabilities” only affects the investment ratio positively and significantly for constrained firms.

More Related