Thus, (1) tests if, within the same 2-digit sector, WBES respondents in 4-digit industries with more political connections report greater access to government land, industrial zones and credit than those in related 4-digit industries without connections. Overall, we observe about 80 4-digit subsectors with available information on the policy variables in 23 2-digit manufacturing sectors.<sup>31</sup> The results parallel the earlier NTM and subsidy findings. Table 6 shows that firms in politically-connected are more likely to report that they acquired land from the government; are located in an industrial zone; and obtained a bank loan. In only two cases, where politically-connected firms are identified on the basis of the political connectedness of the CEO, is the effect insignificant. The results are robust to controlling for 2-digit sector-specific effects, firm size and firm age. The specification is conservative, since controlling for firm size biases the impact of political connections downwards: connected firms are larger than unconnected firms in our sample and Table 6 shows that large firms are more likely to have access to land, industrial zones, and bank loans.<sup>32</sup> These effects are also economically significant. For example, the second column indicates that with each additional politically connected firm owner in the 4-digit sector, the probability of obtaining land from the government increases by 2.1 percentage points. Thus, assuming linearity, firms in sectors with 10 politically connected firm owners are 21 percentage points more likely to have obtained land from the government than firms in sectors without politically connected firm owners. Similarly, firms in sectors with five politically connected CEOs are 60 percentage points more likely to have obtained a bank loan than firms in sectors without politically connected CEOs.

# **Do connected firms disproportionately benefit from the enforcement of rules?**

Politically connected firms also used their connections to reduce their own regulatory burden and the threat of predatory behavior by government officials relative to the burden and threats faced by their competitors. To see this, we again employ the WBES data, which contains firms’ assessments of the burden imposed by various government policies and regulations. The WBES also asks firms for objective information, such as the time it takes to get a construction permit. Again, we supplement the WBES data with our information on the number of political connected per 4-digit manufacturing industries.

Following Hallward-Driemeier et al. (2010), we also examine within-industry variations of firm reports of the regulatory environment. If politically connected businessmen influence regulator behavior, their firms in a sector should report a lower regulatory burden; for instance, politically connected firms should report lower waiting times to obtain permits and more predictable regulatory behavior than firms in other sectors. At the same time, unconnected firms that compete with politically connected firms in the same sector should have the longest waiting times if connected businessmen use their influence to discriminate against their direct competitors. The (coefficient of) variation in

> 31 Our findings are generally stronger when we allow for comparisons of political connections across 4-digit sectors in different manufacturing industries (not including 2-digit sectors dummies).

> 32 The size and precision of the estimated coefficients increases if we drop firm size from the control variables (the coefficients are significant in five out of six cases). The results are available from the authors upon request. The estimated coefficients also increase if we drop the 2-digit sector dummies; however, in this case, the results are also based on comparing, for example, a 4-digit textile industry with a 4-digit base metals industry instead of restricting the comparison to two different 4-digit textile (base metals) industries within the textile (base metals) sector.

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