Incrementality of SME Loan Guarantees
In many countries, loan guarantee programs are important elements of government policy with respect to small- and medium-sized enterprises (SMEs). If loan guarantee schemes are to be effective, a majority of firms obtaining assistance through such a scheme ought not to be able to obtain financing from existing sources: a property known as incrementality or additionality. This paper describes a new approach to measuring incrementality. This work uses a two-stage process to estimate the incrementality of loans made under the terms of the Canada Small Business Financing (CSBF) program. First, a logistic regression-based model of loan outcomes (essentially a credit-scoring model) is estimated based on a large representative sample of SMEs. The resulting model was consistent with prior expectations and exhibited high levels of goodness-of-fit. The model was then employed to classify a sample of firms that had received loans under the terms of the loan guarantee scheme. Incremental loans ought to be classified as “turndowns” by the model; hence the proportion of loan guarantee recipients that the model classified as turndowns is a direct measure of incrementality. For the CSBF loan guarantee program incrementality was estimated (with 95% confidence) as 74.8±9.0%. Copyright Springer 2007
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Volume (Year): 29 (2007)
Issue (Month): 1 (June)
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- Stiglitz, Joseph E & Weiss, Andrew, 1983. "Incentive Effects of Terminations: Applications to the Credit and Labor Markets," American Economic Review, American Economic Association, vol. 73(5), pages 912-27, December.
- Cowling, Marc & Mitchell, Peter, 2003. "Is the Small Firms Loan Guarantee Scheme Hazardous for Banks or Helpful to Small Business?," Small Business Economics, Springer, vol. 21(1), pages 63-71, August.
- Riding, Allan L. & HainesJR., George, 2001. "Loan guarantees: Costs of default and benefits to small firms," Journal of Business Venturing, Elsevier, vol. 16(6), pages 595-612, November.
- Bester, Helmut, 1985. "Screening vs. Rationing in Credit Markets with Imperfect Information," American Economic Review, American Economic Association, vol. 75(4), pages 850-55, September.
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