of uncertainty, particularly uncertainty deriving from more predatory practices such as unconstrained corruption and weak property rights (Gehlbach 2008; Gehlbach and Keefer 2012).

Because RuFIGE does not include questions about corruption, these firm-level data were supplemented with regional level data collected from the RR BEEPS. This survey was conducted between August 2011 and June 2012 as part of the fifth round of the Business Environment and Enterprise Performance Survey (BEEPS), a joint initiative of the World Bank Group (WB) and the European Bank for Reconstruction and Development (EBRD). The main objective of the survey was to gain an understanding of firms’ perception of the environment in which they operate and it was the first BEEPS survey to provide representative, albeit small, sub-national samples. The survey covered 4,223 firms in 37 Russian regions.

Regional-level independent variables, which we discuss in detail below, were derived from the RR BEEPS data for three main reasons. First, because BEEPS was conducted several years before RuFIGE, the probability that the investment patterns we observe are driving corruption outcomes in the regions somewhat diminishes. Second, by using regional level data generated by a group of firms outside of our primary sample (RuFIGE), the probability that any relationship observed is due to the peculiarities of the RuFIGE sample also somewhat diminishes, and our regional variables data is independent of our firm-level data. Finally, we also note that using BEEPS measure provides a fuller and more complete measure of regional level variables, as it is representative for a wider range of sectors than RuFIGE.

An addition, we also use a measure of violent corporate raiding in Russian regions introduced in Kazun (2015) to capture uncertainty over property rights enforcement. This indicator is calculated using a publicly accessible database of complaints to the NGO “Business Against Corruption” from entrepreneurs claiming to have faced unlawful criminal prosecution ( _c.f._ <u>http://www.nocorruption.biz/?cat=6</u> _)_ . The data from the registry include information about the initiation of criminal cases, the types of business activities carried out by the business issuing the complaint, and the region in which it is located. We discuss both of these measures in greater depth below.

Further regional controls for our regressions were collected from data published by the Federal State Statistics Service of Russian Federation and by the Ministry of Regional Development of Russian Federation.

# **B. Hypotheses**

Drawing on the literature reviewed above, this paper aims to distinguish between the effect of regulatory burdens and regulatory uncertainty, on the one hand, and of different types of regulatory uncertainty, on the other. With respect to regulatory burden, the literature noted above generates a straightforward prediction: greater regulatory burdens are associated with greater transaction costs, which decrease incentives to invest. This suggests:

# **<u>Hypothesis 1: The greater the regulatory burden on firms, the less likely they are to invest.</u>**

Typically, the overall level of regulation in Russia has been measured using indicators such as the World Bank’s Doing Business indicator, which measures how difficult regulations make certain business activities ( _c.f._ Eiffert 2009). This measure was constructed using data from experts on statutory regulations; however, making it inappropriate for a study such as ours that is concerned primarily with practices related to _informal regulatory enforcement_ . Instead, here we concern ourselves with the level of corruption, which we argue likely represents an excessive, informal regulatory burden on firms. Such a burden can emerge either due to excessive regulatory 11