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Small Business Economics

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28.06.2018

The impact of business-support services on firm performance: a meta-analysis

verfasst von: Túlio A. Cravo, Caio Piza

Erschienen in: Small Business Economics | Ausgabe 3/2019

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Abstract

Interventions designed to support small and medium enterprises (SMEs) are popular among policy makers, given the role SMEs play in job creation around the world. Significant resources from governments and international organizations are directed to business-support interventions in low and middle-income countries (LMICs) based on the assumption that market failures and institutional constraints impede SME growth. SME business-support interventions in LMICs most often relate to formalization, business environment, exports, clusters, training, technical assistance, access to credit, and innovation. This paper reviews and summarizes 40 rigorous evaluations of SME-support services in LMICs and presents evidence to inform policy debates pertaining to SMEs and business-support services. We present evidence that business-support interventions improve firm performance and create jobs. However, little is known about which interventions work best for SMEs and why. More rigorous impact evaluations are needed to fill the knowledge gap in the field.

Vorheriger Artikel On the productive efficiency of Australian businesses: firm size and age class effects

Nächster Artikel The economic and fiscal benefits of guarantee banks in Germany

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The most common criteria used to classify SMEs are based on an employment threshold. For instance, the European Union, the World Bank (see, for example, the Enterprise Survey website www.enterprisesurveys.org), Beck et al. (2005), and many papers included in this review adopt 250 employees as a cut-off to classify SMEs. However, there is no common definition, and countries also adopt other employment thresholds, revenue, or capital stock to define SMEs. This paper considers cut-offs used by the SME support service programs evaluated by papers included in the review.

Bauchet and Morduch (2013) argue that there are significant differences between SMEs and microenterprises. For instance, they state that unlike microcredit, which can be used to finance consumption needs, SME finance is targeted to entrepreneurs with skills and management capacity and to support investments. SME borrowers need capital in larger amounts than is typical of microcredit.

Bruhn and McKenzie (2013) review several experimental and quasi-experimental evaluations to investigate the impact of regulatory changes aimed at reducing bureaucratic barriers to SME formalization and growth.

Such indirect interventions are similar to what Syverson (2011) calls “external drivers” or “environmental factors” underlying a firm’s productivity. Syverson (2011) argues that governments can influence elements of the market environment and induce business to take actions to raise their productivity.

Although La Porta and Shleifer (2014) argue that by far the greatest perceived obstacle by informal firms is lack of access to finance, the link between access to finance and registration may not be causal as banks may evaluate other aspects such as organized accounting and formal human capital of entrepreneurs before granting credit.

In fact, there are interventions that are targeted at formal enterprises only, such as subsidized credit lines. Thus, it is possible that after formalizing, some firms may end up being served by different interventions.

Most studies included in the meta-analysis presented in this paper are evaluations of public interventions.

Heim et al. (2016) provide evidence of the impact of European Commission aid on firm survival rates. The results indicate that restructuring aid decisions of the European Commission between 2003 and 2012 increased survival rate and improve the financial viability of firms. The European Commission considers rescue and restructuring (R&R) aid as key policy tool to support firms in difficulty, aiming at avoiding their dissolution.

It is argued that some firms consider interest rates available at commercial banks as too high. This would affect the pool of bank’s clients, as most borrowers would be risk prone. One way of dealing with the adverse selection problem would be to lower the interest rates. The reluctance of banks to do so themselves opens space to government intervention.

Like the papers included in this review, we do not try to provide a specific and precise definition of local agglomeration. For more detail discussion on this see Altenburg and Meyer-Stamer (1999) and Martin and Sunley (2003).

The task of providing a detailed account about economic theory and small business is beyond the scope of this paper. You (1995) provides a lengthy discussion about small business in economic theory that can guide further discussions on SME support and economic theory.

Further, the European Union and the World Bank (see, for instance, the Enterprise Survey website www.enterprisesurveys.org) adopt 250 employees as a cut-off to classify SMEs.

Though the literature recommends that synthesis is informed by the theory of change embedded in the design of an intervention (see Hombrados and Waddington 2012), our focus extends beyond the outcomes directly anticipated by an intervention to also include unanticipated outcomes.

Unfortunately, we were not able to find detailed documentation for each study included in the review. The lack of this documentation limits our knowledge and understanding of the interventions tested.

The search strategy did not involve specific search of papers published in French (e.g., snowballing and internet searches). Nevertheless, papers in French identified through the search of electronic databases were screened.

For instance, a paper by Grimm and Paffhausen 2015) study a similar issue but focus only on employment outcome. Their search was conducted after 1990, and only one paper from prior to the year 2000 (Fretwell et al. 1999) was found. This paper would not qualify for this review, as it is designed to assess active labor policy in general (not SMEs specifically) and also includes assessment of self-employment, which is not covered by this review. This is an indication that going back in time would generate an enormous number of abstracts to be reviewed and would likely return few, if any, SME impact evaluations.

As discussed in Duvendack et al. (2012), there is no consensus on whether meta-analysis should be performed for quasi-experimental studies. In this review, we use meta-analysis to provide the “big picture” of the impact of interventions aimed at SMEs. However, given the challenges in practice and decisions made, we argue that these results should be treated with care.

Alternatively, we could have computed a weighted average of two separate coefficients.

Variance of (a + b) = var.(a) + var.(b) – 2 Cov(a,b), assuming Cov(a,b) = 0 is a conservative assumption, as it implies lower precision of overall effects unless the covariance is negative. On average, we expect the covariance across studies to be close to zero. We also believe this is a reasonable assumption, because according to these studies, the number of firms taking up different treatments is not high. Given the restricted overlap between different treatments, we do not believe there is reason to worry about high correlation between firms participating in different interventions. It is important to clarify that by doing this, we are not averaging across outcomes, but instead, across different ES for a given outcome.

In other words, we did not combine estimates obtained for firms only receiving matching grants with estimates for firms receiving a package of interventions (for example, matching grants and technical assistance).

We report forest plot and heterogeneity measures, such as the chi-squared test for heterogeneity (which captures within-study variance), the I-squared statistic, which we interpret as the proportion of total variance across the observed effects explained by between-study variance, and τ^2 (tau-squared), an estimate for the variance of the “true effect size” (see Borenstein et al. 2009). Borenstein et al. (2009, p.118) argue that “I-squared is a descriptive statistic and not an estimate for any underlying quantity.”

Figure D.1 in the online appendix reports forest plots dropping studies with ES that are outliers. The results with the full set of observations are similar (see Piza et al. 2016).

The decision to report overall effect for different interventions was also made in a systematic review that covered the impact of interventions aimed at improving children’s enrollment in primary and secondary schools. See Petrosino et al. 2012.

We tested a quadratic specification for the variable size. The coefficients for the quadratic term are very often negative, suggesting a concave relationship between firm size and firm performance. Because the number of studies is relatively small, the estimates are imprecisely estimated and are available upon request.

Studies were conducted in different countries, years, and scales, as some used administrative data and other small-scale RCTs.

See footnote 16.

We used the metabias command in Stata.

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Titel: The impact of business-support services on firm performance: a meta-analysis
verfasst von: Túlio A. Cravo
Caio Piza
Publikationsdatum: 28.06.2018
Verlag: Springer US
Erschienen in: Small Business Economics / Ausgabe 3/2019
Print ISSN: 0921-898X
Elektronische ISSN: 1573-0913
DOI: https://doi.org/10.1007/s11187-018-0065-x

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