Models 1.4 – 1.5 introduce one of the proxies for regulatory uncertainty–a measure of the variation in “bribe tax”. The variable is negative and statistically significant, as expected by **Hypothesis 2.a** , suggesting that unpredictable bribery is indeed likely to decrease the probability of investment by firms. This result comports with previous findings by Malesky and Samphantharak (2008) using firm-level micro-data from Cambodia and cross-national, macro designs used by Wei (1997) and Campos et al. (1999). It is also comports more broadly with the literature on regulatory uncertainty, which has almost always found that uncertainty in the enforcement of written regulations leads to declines in investment (Hallward-Driemeier and Pritchett 2015; Fabrizio 2012; Barrodale 2010; Engau and Hoffmann 2009; Lyons and Mayo 2005; Bittlingmayer 2001). Where our results differ, however, is that we examine not the difference between _de jure_ and _de facto_ written regulations, but the predictability of informal practices – specifically corruption – that are indelibly tied to them.

Similarly, Models 1.6-1.7 introduce our measure of raider attacks into our specification and the results indicate that raider attacks have a negative and significant effect on investment decisions. This finding indicates that as raider attacks increase, firms are less likely to invest and is consistent with **Hypothesis 2.b** , which suggests that a higher volume of raider attacks decreases the probability of investment due to uncertainty by firms about the sanctity of their property rights. Again, while this relationship comports with theoretical expectations ( _c.f._ North, Wallis, and Weingast 2009), this result is novel to the extent that we are unaware of any quantitative test of these expectations.

It is worth noting that in Models 1.5 and 1.7, we compare the effect of “bribe incidence” and “bribe tax” to our measures of regulatory uncertainty, variation in bribery and raider attacks, respectively. In both cases, our measures of regulatory uncertainty remain significant, while “bribe tax” and “bribe incidence” remain statistically insignificant at conventional levels.

Turning briefly to our control variables, it is worth noting that employment is a positive and statistically significant predictor of investment, with larger firms being more likely to invest. The effect of state involvement is more complex, however. On the one hand, the receipt of state orders by the firm is also a positive and statistically significant predictor of investment. On the other hand, while receipt of state support is a positive predictor of investment, it is not significant at conventional levels. Finally, state owned firms are less likely to have invested; this result holds at conventional levels <mark>In order to check the robustness of our results, we ran a number of alternative specifications. To check whether outliers may be driving our results, we tried specifications where we omitted Russia’s two largest (by population and economic output) regions, Moscow City and St. Petersburg. We also omitted Primorskii Krai, which is a serious outlier along the “bribe tax” variable. We also included additional regional level controls in our main specifications that account for budgetary transfers from the federal center and regional spending. Neither of these variables was significant at conventional levels and neither substantially altered our main results either substantively or statistically. We also controlled for regional mineral wealth by including a variable that captures the value added to regional GRP by mining activities (both oil and mineral). Again, the addition of this new control does not alter our main results.</mark><sup>15</sup>

> 15 All robustness results described here are available upon request.

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