Research & Projects

Abstract

In recent years, political and ideological associations ascribed to consumer products have become increasingly salient (e.g., Bud Light, Nike, Goya). While prior work has studied how such associations affect choice, the role of consumer misperceptions over political identity remains underexplored. This study investigates how mistaken beliefs about a product’s “Russianness” influenced consumer behavior during the 2022 escalation of the Russo-Ukrainian War. The unexpected conflict triggered widespread anti-Russian sentiment and government bans on products of Russian origin. Notably, these bans meant that any consumer boycotts of Russian-associated liquors (e.g., vodka or Russian-sounding brands) could not impact actual Russian products. Using retail liquor sales data from the Pennsylvania Liquor Control Board, voter registration data, and demographic data from the ACS (2021–2022), I estimate a Difference-in-Differences model to quantify the war’s impact on vodka sales and find sharp post-invasion declines in purchases of “Russian-sounding” vodkas, with some heterogeneity by consumer characteristics. I further estimate a nested logit model of demand and find that perceived Russian-ness association reduced sales by roughly 50%, an effect equivalent to more than doubling the price. These findings highlight the economic consequences of politically motivated misperceptions in consumer markets. 

Abstract

Extant perception-based measures of judicial corruption primarily rely either on experts whose direct experience in and ability to evaluate corruption in the judicial system is questionable (i.e., general country experts or business leaders) and/or surveys of ordinary citizens and businesses, only a small proportion of whom have any direct experience in the judicial system. Many fail to account for key differences among coders, or to distinguish among types of corrupt behavior, court types where it is prevalent, or geographic regions. Finally, they may miss key connections among issues, such as bribery and slow processing times. Thus, even though corruption is a highly heterogeneous phenomenon, we are often left with flat, homogeneous measures to evaluate it. This is problematic for policymakers who seek to strengthen the rule of law by reducing judicial corruption: existing measures provide only broad information that is difficult to translate into targeted reforms. Our project develops a new set of perception-based indicators of judicial corruption; in this very early draft, we begin developing our approach using original data from Uzbekistan, Kazakhstan, and Georgia. We apply factor analysis and item response theory models to data from interviews with practicing attorneys to assess levels of corruption and trust, and then turn to grounded theory to provide insights into conditions that shape them. Our mixed-method approach thus provides both cross-nationally comparable ratings with in-depth insights into factors that shape them. 

Abstract