Summary statistics of business environment indicators at the establishment level for 2003 are presented in the first column of Table 3.<sup>10</sup> Of all sampled establishments, roughly 15 percent have at least 10 percent government ownership, 68 percent participate in export markets, 68 percent have and use internet, 41 percent have a formal training program, while only 21 percent report facing a foreign competitor. The mean interest rate on loans is around 14 percent, with an average share of investment financed externally of 33 percent. The average rate of losses from crime is low, under 1 percent of sales, and the mean share of electricity sourced from private generators is around 13 percent. In addition to these WBES indicators, the outcome variables based on the Census data are the log of output per worker (its mean across clusters is 9.22, or $10,071) and the log of value added per worker (its mean across clusters is 8.45, or $4,687).

Figure 5 presents evidence of large labor productivity heterogeneity across industrial clusters. Although it would be desirable to explore these dispersions based on establishment-level data at the most disaggregated product level while controlling for differences in quality and market power, the results are nonetheless suggestive. Across industrial clusters, the 90<sup>th</sup> percentile in output per worker is, on average, ten times that of the 10<sup>th</sup> percentile. The industrial sectors with the greatest dispersion in productivity are food/beverages/tobacco and non-metallic mineral products. Dispersion across locations is as prominent if not more so—with an average ratio of output per worker for the 90<sup>th</sup> relative to the 10<sup>th</sup> percentiles greater than 20 in the North Central and Central provinces. These numbers are likely an understatement of the actual firm-level dispersion in productivity, given that they are based on averages across firms within a given location, sector and size group. In principle, large dispersions in productivity for the same product highlight the enormous potential for catch-up productivity upgrading or exit by the less productive firms.

# **Methodology and empirical model**

The empirical specification to estimate the effects of business environment on productivity is given by: where ln(X/L)i,j,kis a generic productivity measure (in this case, output per worker) for an industrial cluster of firms in province _i,_ sector _j,_ of size class _k_ . _A_ is a vector of industry and location fixed effects to control for unobservable heterogeneity across clusters that varies by industry (due to, among others, different levels of technology and relative capital/labor intensity) or location (differential access to international markets, interaction density/information in cities, or location relative to conflict areas). εi,j,k is a randomly distributed disturbance term.<sup>11</sup>

> 10 The indicators listed in Table 3, except for foreign ownership and share of sales on credit, are used to construct the cluster averages that are then matched into the 2003 Census data.

> 11 We note that several existing studies argue for the exogeneity of cluster averages (as constructed) to firm-level outcomes such as employment growth or productivity. We expect this is also, if not more, the case when relating

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