**Empirical Specification** To investigate the role of institution on the process of resource allocation, we map the measures of business climate from the ES to our census data. Since there is no firm-specific policy variable in our data, we first construct policy variables from the ES by taking the averages of survey responses of firms located in the same region. The underlying assumption of this approach is that policies vary across regions within a country, which is plausible since firms facing the same _de jure_ policy are likely to face different policy implementation across regions (Acemoglu and Dell, 2010; Hallward-Driemeier et al., 2010).<sup>26</sup> The estimation equation is then given by:

where _yisrc_ is firm-specific measure of distortion of firm _i_ in industry _s_ region _r_ and country _c_ . _Dc_ and _Ds_ are country and industry dummies, respectively. _xic_ is a vector of firm characteristics that may be correlated with distortions (age and size). The policy variables ( _BE_ ) are region-average of the respective measures of business environment. For example, ‘Finance’ measures the region-average of fraction of firms with no access to short-term finance. Corruption – the region-average of losses due to informal payments. Electricity - average of losses due to electrical outages in each region. Regulation – the region-average of the fraction of time that the senior managers spent dealing with government regulations.

The results of this regression are reported in Table 6. In a frictionless world, _TFPRsi_ should be equalized across all firms within industries, and we would expect all the coefficients of the policy variables to be not significant if these policies are orthogonal to other factors that may possibly affect _TFPR_ . To the extent there is a positive and significant coefficient of the policy variables, this can be taken as evidence that the business environment affects idiosyncratic distortions.

Although it cannot be interpreted as causal evidence, the results in table 6 shows that business environment indicators are found to be significantly correlated with firm-specific distortions.<sup>27</sup> In particular, our results show that lack of access to short-term finance is associated with a higher level of capital and output distortions, after controlling for firm and industry differences. Business regulations, measured as the fraction of time that the senior managers spent dealing with government regulations, is also positively correlated with capital and ‘output’ distortions, indicating that regulatory red-tape cause a higher capital and ‘output’ wedge. The effect of access to electricity on _τK_ is negative, suggesting that losses due to electricity generate a higher labor distortion relative to capital distortion. This result is puzzling because one would expect lack of electricity to affect capital more than labor. In addition, informal payments are associated with higher output distortions. Overall, and although there is a lack of variability in our data that can facilitate identification of the causal effects, the results suggest that the business environment is an important factor explaining misallocation in the sample countries.

Although we attempted to make connection between various business environment measures and resource misallocation, we are nonetheless aware that establishing causal relationship would require firm-specific policy variation, hence, this constitutes a profitable area for future research.

> 26By regressing firm-specific measures of business environment onto regional dummies, we find that the policies vary significantly across regions.

> 27Note that the number of observations decreases because the ES only covers establishments in the major regions of the sample countries. Thus, establishments located in small regions are not included in the analysis.

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