# **C. Methodology and estimates**

To test our hypotheses, we use a standard probit regression model with robust standard errors clustered at the regional level and the functional form: Yit = α + βXt + γRt+ δFit + εit, where Y is a dummy variable that takes the value 1 if firm _i_ in region _t_ made a capital investment between 2011 and 2013 and 0 otherwise drawn from the 2014 RUFIGE survey. X is one of the regional-level variables of interest (i.e. measures of regulatory burden or uncertainty) described above. Our primary regional control (R) is the log GRP per capita in 2008 and is meant to control for initial conditions in the regions. We use only this variable in order to present a more parsimonious model, although in additional models we check the robustness of our results by introducing other regional level controls.

Finally, F is a matrix of firm-level controls drawn from the RUFIGE survey. Firm-level controls (F) include log employment at the firm, dummy variables indicating whether the firm received state support or state orders, indicators of whether the firm has foreign or state ownership, an indicator of the type of locality the firm is located in (i.e. city, village, etc.), and a set of dummy variables indicating the firms primary sector of economic activity.

We present descriptive statistics for all variables used in our analysis in Table A3 in the Annex. Similarly, Table A4 provides information on the correlation for our regional-level variables of interest.

Before proceeding, we note again that in this particular paper we do not seek to make strong causal claims about the relationship between uncertainty and investment. This paper is instead meant to be exploratory and seeks to establish the presence of a correlation between these variables to serve as a stepping-stone for future work. We believe that identifying a strong correlation between variation in levels of corruption or inconsistent property rights enforcement, on the one hand, and investment behavior, on the other, is a valuable contribution in its own right. As noted in the introduction of this paper, precious few micro-level studies of variability in corruption have been conducted (Malesky and Samphantharak 2008; Wei 1997) and to our knowledge none exist using actual measures of state-abetted violence against firms. Even without the ability to make strong causal claims, the relationships examined in this paper are therefore interesting in their own right.

# **V. REGRESSION RESULTS AND DISCUSSION**

Table 1 presents the results of our main tests. For ease of interpretation, we present the marginal effects for each variable in the table rather than the beta coefficients. Models 1.1 – 1.3 present the results for **Hypothesis 1** and seem to imply little support for it. Both the “bribe incidence” variable, indicating the dispersion of corruption, and the “bribe tax” variable, indicating the average bribe paid, are not significant at conventional levels in any of the models. This suggests that absolute levels of corruption are not significantly related to the investment decisions of firms. This interpretation is consistent with the idea that corruption is regarded by firms as a tax, like any other, to be planned around (Mauro 1995). It is worth noting that the point estimate for bribe incidence is negative, which is consistent with the expected negative relationship between investment and regulatory burdens found in much of the literature (Eifert 2009; Djankov et al. 2006). Conversely, the estimate for bribe tax is unexpectedly (from the standpoint of existing work) positive, which indicates that an increase in the average level of bribes in a region is associated with higher levels of firm investment. These results should be interpreted cautiously, however, since they do not reach conventional levels of significance and the point estimates are unlikely to be correct.

16