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We empirically investigate the relationship between corruption and growth using a firm-level dataset that is unique in scale, covering almost 88,000 firms across 141 economies in 2006–20, with wide-ranging corruption experiences. The scale and detail of our data allow us to explore the corruption-growth relationship at a very local level, within industries in a relatively narrow geography. We report three empirical regularities. First, firms that make zero informal payments tend to grow slower than bribers. Second, this result is driven by non-bribers in high-corruption countries. Third, among bribers, growth is decreasing in the amount of informal payments—in both high- and low-corruption countries. We suggest that this set of results may be reconciled with a simple model in which endogenously determined higher bribe rates lead to lower growth, while non-bribers are often excluded entirely from growth opportunities in high-corruption settings. [ABSTRACT FROM AUTHOR]

We use eBay data to investigate how within-U.S. trade is influenced by differences in socioeconomic characteristics. States' similarity in cultural characteristics (ethnicity, religious affiliations, and political behavior) predicts online trade; cultural similarity similarly predicts trade within product categories. The culture-trade relationship is stronger for transactions with sellers who lack extensive reputations or certification, suggesting that consumers infer seller trustworthiness from cultural similarity. There is no correlation between cultural similarity and buyer satisfaction, suggesting that perceived differences in trustworthiness are not validated in actual transactions. This paper was accepted by Alfonso Gambardella, business strategy. Supplemental Material: The data files and online appendix are available at https://doi.org/10.1287/mnsc.2023.4681. [ABSTRACT FROM AUTHOR]

We report a laboratory experiment that enables us to study systematically the substantive and procedural rationality of decision making under uncertainty. By using novel graphical representations of budget sets over bundles of state-contingent commodities, we generate a very rich data set well-suited to studying behavior at the level of the individual subject. We test the data for consistency with the maximization hypothesis, and we recover underlying preferences using both nonparametric and parametric methods. We find that individual behaviors are complex and highly heterogeneous. In spite of this heterogeneity, we identify ‘prototypical’ heuristics that inform subjects’ decision rules. To account for these heuristics, we propose a type-mixture model based on Expected Utility Theory employing only combinations of three heuristics which correspond to the behavior of individuals who are infinitely risk averse, risk neutral, and expected utility maximizers with intermediate risk aversion. The decision rules of the type-mixture model accord well with the large-scale features of the data at the level of the individual subject.

We report a laboratory experiment that enables us to distinguish preferences for altruism (concerning trade-offs between own payoffs and the payoffs of others) from social preferences (concerning trade-offs between the payoffs of others). By using graphical representations of three-person Dictator Games that vary the relative prices of giving,we generate a very rich data set well-suited to studying behavior at the level of the individual subject. We attempt to recover subjects’underlying preferences by estimating a constant elasticity of substitution (CES) model that represents altruistic and social preferences. We find that both social preferences and preferences for altruism are highly heterogeneous, ranging from utilitarian to Rawlsian. In spite of this heterogeneity across subjects, there exists a strong positive within subject correlation between the efficiency-equity trade-offs made in altruistic and social preferences.

This paper reports an experimental test of individual preferences for giving. We use graphical representations of modified Dictator Games that vary the price of giving. This generates a very rich data set well-suited to studying behavior at the level of the individual subject. We test the data for consistency with preference maximization, and we recover underlying preferences and forecast behavior using both nonparametric and parametric methods. Our results emphasize that classical demand theory can account surprisingly well for behaviors observed in the laboratory and that individual preferences for giving are highly heterogeneous, ranging from utilitarian to Rawlsian to perfectly selfish.

We examine the correlation between gender and bureaucratic corruption using two distinct datasets, from Italy and from China. In each case, we find that women are far less likely to be investigated for corruption than men. In our Italian data, female procurement officials are 22% less likely than men to be investigated for corruption by enforcement authorities; in China, female prefectural leaders are 81% less likely to be arrested for corruption than men. While these represent correlations (rather than definitive causal effects), both are very robust relationships, which survive the inclusion of fine-grained individual and geographic controls, and based on Oster's (2019. "Unobservable Selection and Coefficient Stability: Theory and Evidence," 37 Journal of Business & Economic Statistics 187–204.) test unlikely to be driven by unobservables. Using data from a survey of Italian procurement officials, we present tentative evidence on mechanism: the gender gap is partly due to women acting more "defensively" in administering their duties. [ABSTRACT FROM AUTHOR]