Data for: Demand for law and the security of property rights: The case of Post-soviet Russia



This is an Annotation for Transparent Inquiry (ATI) data project. The annotated article can be viewed on the publisher's website. Data Generation “Demand for Law and the Security of Property Rights: The Case of Russia” employs a mixed-methods approach, utilizing formal modeling, quantitative analysis, and qualitative analysis. Sampling for the quantitative data – an original survey of Russian firms – is discussed in Section 2 of the Online Appendix. The qualitative analysis is based on 90 semi-structured interviews with firms, lawyers, and private security agencies conducted in 2009; 20 semi-structured follow-up interviews with the original respondents conducted in 2014; 36 supplementary interviews conducted with business journalists, business association representatives, academics, and NGOs; and extensive analysis of English and Russian-language secondary materials, including reports and articles by journalists, scholars, NGOs, and government agencies. Additional details about the interview respondents can be found in Section 1 of the article’s Online Appendix. I was able to audio record and create full transcripts for 53 of the 90 interviews (59%), but given the sensitive nature of the research topic, for other interviews I relied on notes taken by me and/or a Russian-speaking RA during the interview. Data Analysis The quantitative analyses are used primarily to demonstrate that correlations between explanatory variables and outcomes variables are consistent with the theory presented in the article. The qualitative analyses are used for two purposes. First, they are used, particularly in the section “Evolving Property Security Strategies in Post-Soviet Russia,” for descriptive inference as one type of evidence demonstrating changes in the study’s dependent variable, the strategies that firms use to secure property. In later sections, they are also used for descriptive inference providing evidence of changes in the value of explanatory variables, such as consolidation of ownership in privatized firms, over time. Second, qualitative analyses are used for causal inference to elucidate causal mechanisms, particularly in the sections “Demand-Side Barriers: Exiting the Unofficial Economy,” “Effectiveness of Illegal Strategies: The Impact of Ownership Consolidation,” and “Coordination Dilemmas.” Logic of Annotation I have annotated all empirical claims other than those based on my original survey (the data and replication code for quantitative analyses are already available on Dataverse and discussed in detail in the article’s Online Appendix). I also did not annotate claims based on widely available data, such as the World Bank/EBRD Business Environment and Enterprise Survey (BEEPS). My annotations consist primarily of three types: Annotations to provide sources for descriptive statistics: In the article I cite a number of descriptive statistics, some based on surveys by other scholars, others based on administrative data such as caseloads. Where possible, I have provided source material for the exact tables, figures, or in-text passages on which I based my claims. In many cases, these sources are in Russian, in which case I provided translations to make evaluation of the claims feasible for non-Russian speakers. Annotations to provide context and original language versions of interview quotes: For interviews in which respondents allowed audio recording, facilitating the creation of full transcripts, I provided complete excerpts in Russian and in translation of the quotations and in some cases the conversation preceding and following the citation itself. In cases where I could not make audio recordings, my quotes are based on handwritten notes that I or an RA typed up the day of the interview. These cases are noted in the annotations themselves. Annotations to discuss credibility of a specific source or provide additional substantiation of a claim: For example, annotation #33 discusses a quote from a major Russian business tycoon and examines why this source is credible.
Date made available2018
PublisherQualitative Data Repository

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