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Investment–cash flow sensitivities, credit rationing and financing constraints in small and medium-sized firms

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Abstract

The controversy on whether investment–cash flow sensitivity is a good indicator of financing constraints is still unsolved. We apply a comprehensive approach by cross-validating our analysis with both balance sheet and qualitative data on self-declared credit rationing and financing constraints on a sample of mainly small and medium-sized firms. Our qualitative information shows that (self-declared) credit rationing is (weakly) related to both traditional a priori factors—such as firm size, age and location—and lenders’ rational decisions taken on the basis of their credit risk models. We use the qualitative information on firms which were denied (additional) credit to provide evidence relevant to the investment–cash flow sensitivity debate. The evidence shows that self-declared credit rationing significantly discriminates between firms which possess or not such sensitivity, while a priori criteria do not. The same result does not apply when we consider the wider group of financially constrained firms (which do not seem to have a higher investment–cash flow sensitivity), supporting the more recent empirical evidence in this direction.

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Notes

  1. Findings which do not contradict FHP (1988) results are those of Bond and Meghir (1994), Whited (1992) and Hoshi et al. (1992).

  2. Empirical papers closely related to our are those of Cole (1998) and Sapienza (2002). Cole (1998) uses survey data to examine the likelihood of credit denial for small US firms, finding that firms without pre-existing relationships, younger firms and smaller firms are more likely to be denied credit. Sapienza (2002) documents that Italian firms with higher leverage and lower profitability are more likely to lose their credit lines. The difference of our approach from that of Cole (1998) is in the matching of qualitative and balance sheet data and the use of qualitative information on credit denial to shed light on the investment–cash flow sensitivity debate. The difference with respect to Sapienza (2002) is that our analysis is not limited to target banks’ and borrower banks’ prior to bank acquisition and the focus is the loss of credit lines while ours is on the more general issue of credit denial (without reference to the previous existence of credit lines).

  3. By financing constraints we mean a wedge between the cost of external and internal finance and by credit rationing the impossibility of obtaining (additional) finance from external sources.

  4. Our use of credit risk indicators is different from that of Cleary (1999). We use these variables as regressors in the estimate of the determinants of self-declared credit rationing and not as sorting criteria used to test the investment–cash flow sensitivity of firms with financing constraints.

  5. Consider that KZ (1997) have similar information for the fifth subgroup of firm-year observations which they define as undoubtedly financially constrained. In this group they include companies “in violation of debt covenants, cut out of the usual source of credit, renegotiating debt payment or forced to reduce investments for liquidity problem”. It is likely that some of these firms would fall into our credit-rationed subgroup. Since firm-year observations for these firms are too few, KZ do not test the investment–cash flow sensitivity on this specific subgroup.

  6. Credit denial implies that the supplier of credit is not available to provide additional finance at any (no matter how higher) interest rate and is therefore equivalent to saying that the price for external finance for the borrower approaches infinity.

  7. The survey has been previously known as the Mediocredito Centrale Survey. The related questionnaire is omitted for reasons of space but is available upon request.

  8. Size classes: 11–20; 21–50; 51–250; 251–500; more than 500. Macroareas: North East (Trentino Alto Adige, Veneto, Friuli Venezia Giulia and Emilia Romagna), North West (Piemonte, Valle d’Aosta, Lombardia and Liguria), Central Regions (Toscana, Umbria, Marche and Lazio), South and Isles (Abruzzo, Molise, Campania, Puglia, Basilicata, Calabria, Sicilia and Sardegna). Pavitt sectors: scale economies, specialised, traditional and high technology.

  9. All balance sheet data in the Capitalia Survey database are accurately checked. These data come from official sources: the CERVED database (first sample period) and the AIDA—Bureau Van Dijk database (last two sample periods) which collects from CERVED all balance sheets for the same firms. CERVED obtains the information from the Italian Chambers of Commerce and is currently the most authoritative and reliable source of information on Italian companies. Qualitative data from questionnaire are filled by a representative appointed by the firm collecting information from the relevant firm division. The questionnaire has a system of controls based on “long inconsistencies”, namely inconsistencies between answers to questions placed at a certain distance in the questionnaire. In case of inconsistent information the firm is subject to a second phone interview. Firms which do not provide reliable information after being recontacted are excluded from the sample. A supplementary list of 8,000 firms is built for each of the 3-year surveys in order to avoid that exclusions generated by missing answers or inaccuracies in the questionnaire may alter the sample design. Substitutions follow the criteria of consistency between the sample size and the population of the Universe.

  10. In order to eliminate the influence of extreme values we will follow in econometric estimates the procedure adopted by Cleary (1999) and winsorise the data according to the following rules: (i) return on equity (ROE) greater than 100% or lower than –20%; (ii) return on assets (ROA) greater than 30% or lower than −20%; (iii) ratio of total sales to total assets greater than 300% or lower than 20%; (iv) ratio of investment to net fixed assets greater than 50%; (v) ratio of total sales to net fixed assets grater than 400%; (vi) ratio of cash flow to net fixed assets grater than 50%; (vii) ratio of total debt to net fixed assets grater than 200%.

    Results presented in the next sections are nonetheless robust to the inclusion of outliers. Evidence on this point is available from the authors upon request.

  11. We adopt here the standard European Union (EU) classification which considers as small firms those below 50 employees, as medium firms those between 50 and 250 employees and as large firms those above 250 employees.

  12. This threshold of 15 employees identifies a discontinuity in firing costs determined by an Italian law (Law 300/1975) which establishes that workers fired by firms with more than 15 employees must be reintegrated in their workplace if a judge concludes that they have been fired without giusta causa (i.e. fair grounds). The same fair grounds rule cannot be applied to workers fired in firms with fewer than 15 employees.

  13. See Barniv and MacDonald (1999) for a detailed survey on the issue.

  14. See Dwyer et al. (2004).

  15. Although credit scoring has well-known disadvantages (see, for example, Saunders and Allen 2002), it remains the most effectively and widely used methodology for the evaluation of privately held firms’ risk profiles.

  16. Indicators tested are marked with an asterisk (*) in Table 13 in the Appendix.

  17. Shumway (2001) and Saretto (2004) reproduce both Altman (1984) and Zmijeski (1984) indicators, proposing different approaches for their estimations and applying them to different samples. For this reason we consider two indicators for Shumway (2001) (Shumway Altman and Shumway Zmijeski) and two for Saretto (2004) (Saretto Altman and Saretto Zmijeski).

  18. The Z-model implies that all the accounting ratios included in the function have positive coefficients and this is in fact true for the Altman (1984), Altman et al. (1979), and Saretto (2004) indicators. On the contrary Altman’s coefficients, as estimated by Shumway (2001), have negative signs and this explains the counterintuitive sign of the Shumway–Altman indicator in our logit estimates. The same is true for the negative sign of the Zmijeski-Up (unweighted probit) indicator, whose coefficients have positive sign.

  19. Some relevant examples of this are Banco di Napoli acquired by S. Paolo IMI, Banco di Sicilia acquired by Capitalia, and Banco di Sardegna acquired by Cassa di Risparmio di Reggio Emilia.

  20. Logit estimates for the desirecred subgroups including desirecred firms which are also in the highlyrationed and deniedcred subgroups have also been performed without significant changes in our findings. Results are omitted for reasons of space and are available from the authors upon request.

  21. Similar considerations are developed by Bonato et al. (1993).

  22. The importance of not using the entire 1992–2000 sample period when estimating with the quantitative approach the relationship between investment and cash flow for firms declaring financing constraints in the 1998–2000 period is emphasised by Schiantarelli (1996). Those author argues that studies which assign a firm to one group for the entire period are “neglecting the information that the financial constraints may be binding for the same firm in some years but not in others.”

  23. Firm output is significant when we omit Pavitt dummies from regressors and instrument while it becomes insignificant when we include it. This should imply that industry-specific controls absorb the imperfect competition result of the sales coefficient.

  24. The weak relationship between high-technology industries and financial constraints (with the exception of what was found in Tables 8 and 9) goes in the opposite direction with respect to standard literature findings (Berger and Udell 1998; Carpenter and Petersen 2002; Denis 2004). However, it must be considered that, in almost all Italian industries, innovation goes on through adoption of new processes and much more rarely via direct innovation. Furthermore, insider–outsider problems in the labour market and other rigidities reduce skill premia and the introduction of innovation when it requires substantial job restructuring (Brynjolfsson and Hitt 2000). The combination of these problems is such that the high-technology industry has a very small role. Based on these considerations we argue that the result on the Italian sample cannot lead to general conclusions applicable to other countries about the relationship between R&D investing and credit rationing.

  25. GMM estimates for the desirecred subgroups including desirecred firms which are also in the highlyrationed and deniedcred subgroups have also been performed without significant changes in our findings. Results are omitted and available from the authors upon request.

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  1. Universita Tor Vergata, Rome, Italy

    Leonardo Becchetti, Annalisa Castelli & Iftekhar Hasan

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  1. Leonardo Becchetti
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  2. Annalisa Castelli
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  3. Iftekhar Hasan
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Correspondence to Leonardo Becchetti.

Appendix

Appendix

Table 13 Credit risk measures mostly cited in the empirical literature on bankruptcy risk
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Becchetti, L., Castelli, A. & Hasan, I. Investment–cash flow sensitivities, credit rationing and financing constraints in small and medium-sized firms. Small Bus Econ 35, 467–497 (2010). https://doi.org/10.1007/s11187-008-9167-1

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