Alcohol abuse is widely blamed for the very high rate of male mortality in Russia. I estimate a structural model of the demand for alcohol that incorporates two features of alcohol consumption, peer effects and habits. I use a kink in the policy regime of the excise tax on alcohol and regional variation in alcohol regulations to estimate a price elasticity of demand for alcohol. I find that peer influence and habits are critical determinants of the response of alcohol demand to price changes. The estimates imply that increases in alcohol prices would yield significant reductions in mortality. (JEL D12, H25, I12, L66, P23, P36)
We document the unequal enforcement of liberalization reform of business regulation across Russian regions with different governance institutions, which leads to unequal effects of liberalization. National liberalization laws were enforced more effectively in subnational regions with more transparent government, a more informed population, a higher concentration of industry, and stronger fiscal autonomy. As a result, liberalization had a substantial positive effect on the performance of small firms and the growth of the official small business sector in regions with stronger governance institutions. In contrast, in regions with weaker governance institutions, we observe no effect of reform and, in some cases, even a negative effect. (JEL: H10, K20, L50)
We study the long-run effects of Russia’s anti-alcohol campaign, which dramatically altered the relative supply of hard and light alcohol in the late 1980s. We find that this policy shifted young men’s long-run preferences from hard to light alcohol decades later, and we estimate the age at which consumers form their tastes. We show that the large beer market expansion in the late 1990s had similar effects on young consumers’ tastes, while older consumers’ tastes remained largely unchanged. We then link these long-run changes in alcohol consumption patterns to changes in male mortality. The shift from hard to light alcohol reduced incidences of binge drinking substantially, leading to fewer alcohol-related deaths. We conclude that the resulting large cohort differences in current alcohol consumption shares explain a significant part of the recent decrease in male mortality. Simulations suggest that mortality will continue to decrease by another 23 percent over the next 20 years due to persistent changes in consumer tastes. Program impact evaluations that focus only on contemporaneous effects can therefore severely underestimate the total effect of such public policies that change preferences for goods. (JEL D12, H25, H31, I12, I18, L66, P36)
It is commonplace that the data needed for econometric inference are not contained in a single source. In this paper we analyze the problem of parametric inference from combined individual-level data when data combination is based on personal and demographic identifiers such as name, age, or address. Our main question is the identification of the econometric model based on the combined data when the data do not contain exact individual identifiers and no parametric assumptions are imposed on the joint distribution of information that is common across the combined data set. We demonstrate the conditions on the observable marginal distributions of data in individual data sets that can and cannot guarantee identification of the parameters of interest. We also note that the data combination procedure is essential in a semiparametric setting such as ours. Provided that the (nonparametric) data combination procedure can only be defined in finite samples, we introduce a new notion of identification based on the concept of limits of statistical experiments. Our results apply to the setting where the individual data used for inferences are sensitive and their combination may lead to a substantial increase in the data sensitivity or lead to a "de-anonymization" of the previously "anonymized" information. We demonstrate that the point identification of an econometric model from combined data is incompatible with restrictions on the risk of individual disclosure. If the data combination procedure guarantees a bound on the risk of individual disclosure, then the information available from the combined data set allows one to identify the parameter of interest only partially, and the size of the identification region is inversely related to the upper bound guarantee for the disclosure risk. This result is new in the context of data combination as we notice that the quality of links that need to be used in the combined data to assure point identification may be much higher than the average link quality in the entire data set, and thus point inference requires the use of the 396 Komarova, Nekipelov, and Yakovlev Quantitative Economics 9 (2018) most sensitive subset of the data. Our results provide important insights into the ongoing discourse on the empirical analysis of merged administrative records as well as discussions on the "disclosive" nature of policies implemented by the datadriven companies (such as internet services companies and medical companies using individual patient records for policy decisions).
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