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This paper examines the effects of credit availability on small firm survivability over the period 2004–2008 for non-publicly traded small enterprises. Using data from the 2003 Survey of Small Business Finances, we develop failure prediction models for a sample of small firms that were confirmed to have been in business as of December 2003, with particular attention to the impact of credit constraints. We find that credit constrained firms were significantly more likely to go out of business than non constrained firms. Moreover, credit constraint and credit access variables appear to be among the most important factors predicting which small U.S. firms went out of business during 2004–2008 even though an extensive set of firm, owner, and market characteristics were also included as explanatory factors.
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Senior Loan Officer Opinion Survey on Bank Lending Practices http://www.federalreserve.gov/boarddocs/SnloanSurvey/
More precisely, to be eligible for the survey, a firm had to have been in business at the end of 2003 and at the time of the interview which was conducted during 2004. For more information, see Mach and Wolken 2006.
Typically, balance sheet or income information is not available for small firms.
In addition to studies predicting failure or discontinuance, there are also studies of entry and exit rates among businesses. The entry-exit studies provide some useful insights into reasons underlying failure, even though such studies may be largely descriptive. See Hall ( 1992), p. 239, and also Dunne et al. ( 1988), Headd ( 2003), Hudson ( 1986), and Boden ( 2000).
There are ratio studies that claim to have examined small firms. However, most of these studies use publicly available information which generally includes firms that are publicly traded (or “quoted” or “listed” or “not private”). For example, Altman and Sabato ( 2007) wrote on failure prediction for SMEs. In their study, an SME is a firm that has less than $65 million in sales but is available on Compustat. Compustat in 2007 contained about 24,000 listings. There are approximately 24 million enterprises in the United States.
Credit quality or credit access may also be a symptom, rather than a cause if it is firm actions (risk taking, performance, etc.) that result in the credit constraints. On the other hand, credit constraints resulting from frozen credit markets or deterioration of the financial institution used by the firm may be causal. Future research on this topic will attempt to sort out the nature of the credit constraints and their effect on survivability.
See Holman and Fletcher ( 1989). They examine factors affecting failure rates and find that macroeconomic factors such as the money supply, real GNP, and real corporate profits are highly significant in predicting firm failure.
For more information on the construction of the NETS database, see Walls & Associates ( 2009). As new data becomes available (the next year), Walls and Associates reexamines and corrects, if necessary, previous years’ data as well. The results reported in this paper are for the NETs database as of 2009. Due to the dynamic nature of the database, results may be slightly different if the models were to be estimated with a different release of the NETs database even if the same time period were examined.
The models control for this possibility by including a variable on the owner’s age. See below.
For example, the correlation coefficient of total sales provided on the D&B DMI and total sales provided by the individual firms is 0.6 and the correlation coefficient of employment is similar.
A detailed interpretation of the credit rating can be found at http://www.dnb.com/about-dnb/15062603-1.html. The variable is a multi-tiered variable based both on the firm’s credit history and size. For our analysis, we construct a discrete variable with five categories: high, good, fair, limited, or not rated. Its value may change from year to year. This is separate from the credit score measure that ranks all firms from 1 to 100, with 1 being the most risky firm. The credit score is only available for 2003.
The ten firms that did not match up are still being investigated. The most likely explanation is that the D&B number was incorrect on either the SSBF sample control file or the NETS database.
Descriptive statistics and model estimates are based on the fully imputed data with standard errors adjusted to take into account the multiple imputations; they are also weighted to take into account sample design. Definitions of variables are summarized in Mach and Wolken ( 2011).
We constructed multiple measures of each of the components, but only include one of each such measures in the analysis. Summary statistics for the other measures are provided in Mach and Wolken ( 2011).
Additional specifications were estimated with fewer controls, or different combinations of variables. The estimated coefficients and odds ratios are robust to these other specifications. Results from these models can be found in Mach and Wolken ( 2011).
We include only one financial ratio of each type. The specific ratio chosen from each of the five types of financial ratios was the one that performed best in predicting that the firm was out of business.
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- Examining the Impact of Credit Access on Small Firm Survivability
Traci L. Mach
John D. Wolken
- Physica-Verlag HD
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