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신성장 중소기업에 대한 정책자금 지원의 인과적 효과 분석

  • 우석진 명지대학교 경제학과 교수
  • 이기영 경기대학교 경제학과 교수
본 논문의 목적은 신성장 중소기업을 대상으로 집행되었던 정책자금의 인과적 효과를 추정하는 것이다. 정책자금의 인과적 효과를 추정하기 위한 식별전략으로 중소기업 진흥공단의 신성장기반 자금에 지원하였으나 탈락한 기업을 통제군으로 사용하였다. 기존연구들은 정책자금의 수혜 기업과 비수혜 기업을 비교하였다. 하지만, 이런 경우 처리군에 속한 기업과 통제군에 속한 기업 사이에 미관측 이질성이 존재하여 처리효과의 일치추정량을 얻을 수 없다. 본고에서는 동일한 정책자금에 지원한 기업들을 비교함으로써 미관측 이질성에서 발생하는 문제를 최소화 하였다. 또한, 자료에서 발생하는 생존편의를 최소화하기 위해 성과평가 모형에 기업의 생존여부를 명시적으로 고려하였다. 실증 분석에서는 중소기업진흥공단의 신성장기반 자금 수혜기업(2002년~2009년)에 대한 분석결과는 다음과 같은 흥미로운 점을 보여주고 있다. 먼저, 생존효과를 고려하지 않았을 경우 정책효과는 편의 있게 추정되었다. 둘째, 신성장기반 자금은 수혜 기업의 수익성과 안정성을 제고하는 효과가 있었다. 하지만, 사업의 목적인 기업의 성장성에는 통계적으로 유의한 영향을 미치지는 못했다. 셋째, 신성장기반 자금은 수혜 기업의 생존을 연장하는 역할을 하고 있었다.
신성장기반 자금,중소기업,인과효과,생존편의,재무적 성과

The Causal Effects of New Growth Funds on the Financial Performance of SMEs

  • Seokjin Woo
  • Kiyoung Lee
The purpose of this paper is to estimate the causal effects of the new growth funds (NGF) for Small and Medium Business Corporations (SBC) on the financial performances of the beneficiary SMEs. The recent studies by Kim (2005), KIPA (2006), KIET (2007), KSBI (2009) presented a series of evaluations on how public loans for SMEs had performed. They basically compared the beneficiary SMEs and the non-beneficiary SMEs to evaluate the effects of the public loan program. However, those studies showed many limitations. First, they failed to account for the heterogeneity of the SMEs by failing to reflect unobserved dimensions such as the management capability of CEOs. Even with a proper set of control variables, the beneficiary SMEs could be quite different from the nonbeneficiary SMEs. Second, the data used in the studies is limited to that of the SMEs whose financial data were readily available. This can be problematic as the sample selection based upon only survival can incur inconsistent policy effect because the survivor SMEs are more likely to be better performing ones. In short, the previous estimates carry the survival bias. Especially, if the survival rate of the beneficiary SMEs is significantly different from that of the non-beneficiary SMEs, the inconsistency problem can be further aggravated. As an alternative identification strategy, we narrowed down the scope of our data to the SMEs which had previously applied for the NGF but were excluded from a control group. As mentioned, the previous studies used the non-beneficiary SMEs as a control group. However, the beneficiary SMEs are different from the non-beneficiary SMEs in the areas that are unobserved as well as observed. The observed differentials can be controlled for in a parametric empirical specification. The unobserved heterogeniety is, however, still prevalent, and we need to take a different strategy to better identify it. To do so, we included in our samples only those SMEs which had applied for the same public loan program, that is the NGF. Evidently, some applicants were approved and some were rejected after a pre-announced screening process. We used the rejected SMEs as a control group. Because they were viewed as an under-performing group by the SBC standard, the estimated effect must be in favor of the beneficiary SMEs. That is, the comparison group should playa a key role in lowering the boundary for the NGF effects. If the estimated policy effect was indeed in favor of the control group, then we can conclude that the treated SMEs had performed poorly and that the NGF did not serve the purpose of the program. Furthermore, we explicitly took into consideration the survival of the SMEs in order to minimize the survival bias, which is chronic in this line of literature. The data showed that the average financial performances of the beneficiary SMEs were worse than that of the rejected SMEs if we ignore the survival patterns of the SMEs. The reason is that the NGF tends to extenuate the lives of the marginal SMEs whose performances were poor while the rejected SMEs ended up filing for bankruptcies after the rejection, leaving only the fittest among the rejected SMEs to survive. As a result, the average performance of the rejected SMEs turned out to be better than that of the approved SMEs. The survival bias might cause an underestimation of the policy effect. To further minimize the selection bias, we benchmarked the concept of Heckman’s two-stage estimation which uses the control function approach. In sum, in order to accurately evaluate the policy effect of the NGF of SBC we developed the difference-indifferences estimation by explicitly considering the survivals of SMEs. For our empirical analysis, we used a set of administrative data to reduce the measurement errors. Specifically, the data pool is comprised of the longitudinal data on the public loan information for the NGF and the financial data from the Korean Enterprise Data (KED) over the period of 2002 through 2009. We primarily focused on the financial ratios as outcome variables, representing profitability (return on asset and return on equity), growth (growth of sales and growth of operation profit), and stability (debt ratio and own-capital ratio). The following three empirical analyses were implemented. First, we calculated the simple difference-in-differences estimates without any covariates. Second, we estimated the usual difference-in-differences estimated while controlling for the observed characteristics of the SMEs. Third, we additionally controlled for the possibility of survival in the short or medium term. We presented the estimation results for the purpose of comparison. The empirical results show the following three interesting facts. First, we found a noticeable level of survival bias. Thus, without properly handling the survival bias of the SMEs, we are likely to end up with the policy effect biased downward. Second, the profitability and stability of the beneficiary SMEs turned out to be improved by the NGF even though the statistical significance depends upon models and measurements. Lastly, the NGF tended to prolong the lives of the beneficiary SMEs to a statistical significance margin.
New Growth Fund,SMEs,Causal Effects,Survival Bias,Financial Performance