This approach is based on an extensive literature studying the correlation between the business environment and development outcomes. Most recently, Gennaioli et al. (2011) use business environment averages to explain sub-national variation in per capita income levels worldwide. Their study encompasses areas constituting more than 90% of global GDP and population, finding that the business environment is an important correlate of per capita incomes. Two productivity measures comprise our core analysis using the above specification estimated by OLS: output and value added per worker.

# **Does the business environment explain productivity differences across clusters of firms?**

Table 4 reports the estimations from the cluster specification (equation 1). Columns 1 and 2 report the main specification for our two measures of productivity. Columns 3-6 repeat the main specifications, showing robustness of results based on exclusion of fixed-effect vectors (dropping regional and sectoral fixed effects, respectively).

We find evidence supporting many of our hypotheses regarding the relationship between the business environment and productivity. In particular, we find a strong association between productivity and access to international markets: we estimate that output per worker in a hypothetical cluster with all firms participating in export markets is 50 percent higher than in a cluster with no such exposure. We regard this as evidence of the importance of connectivity to global knowledge flows and potential spillover effects from interaction with foreign firms.

Regarding incentives for productive entrepreneurship, competition with foreign firms in local markets is strongly correlated with higher productivity: those clusters reporting benefiting from higher levels of competition with foreign firms have significantly higher productivity. This effect may be driven by both a ‘pull’ factor—where firms find it easier to adopt closer-to-frontier technologies and practices learned from competitors—and a ‘push’ factor whereby firms with foreign competitors face pressure to innovate in order to remain competitive in local markets. Our results reflect within-cluster spillover effects of exporting and foreign competition on local firms. We also find productivity to be lower in clusters with a higher proportion of establishments with public ownership. Clusters with 100 percent government ownership would have 44% lower output per worker than a cluster with 0 percent public ownership, all else equal. Measures of crime and conflict drastically cut into industrial performance: for each one percent of lost sales due to crime, theft and/or disorder, firms can expect to experience nearly a ten percent loss in productivity. We presume that this quantitatively large result reflects the effects of crime and conflict both on local populations (whom firms depend on as buyers) and on the deterioration or destruction of local infrastructure typically seen in conflict-affected areas.

Regarding access to skills, clusters with higher rates of employee training show the highest estimated potential gain in productivity. We posit that the large coefficient on worker training may be due to local labor markets amplifying spillover effects: worker training and skills such business environment averages from a representative survey to industrial metrics based on a comprehensive and separate data collection instrument. See Aterido et al (2011).

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