Implicit Corruption in the Chinese Consumer Debt Industry? A Close Look at Recent Evidence

While many country’s bribery laws require an express quid pro quo—an agreement to exchange a specific benefit for a specific exercise of government power—in practice many corrupt relationships involve implicit quid pro quos, in which the private party provides something of value to government officials, and the government officials use their power to help their private benefactors, but there is never any express agreement, or even any direct connection between any individual official act and a particular benefit conferred by the private party. The context in which such implicit quid pro quos are most widely suspected and discussed is perhaps campaign finance in democracies, but such implicit quid pro quos can occur in many other contexts as well. It is often very difficult—not only for law enforcement agencies, but also for empirical researchers—to find sufficiently clear evidence of an implicit corrupt deal. Yet quantitative empirical researchers have been making important strides in using available data to detect evidence of hidden or implicit wrongdoing—an approach sometimes dubbed “forensic economics.”

A fascinating recent paper by Sumit Agarwal, Wenlan Qian, Amit Seru, and Jian Zhang (forthcoming in the Journal of Financial Economics) illustrates both the potential and limitations of this approach. The paper, entitled “Disguised Corruption: Evidence from Consumer Credit in China,” presents quantitative evidence of an implicit quid pro quo between a large Chinese bank and government officials who wield regulatory authority over the bank. The paper finds that the bank offers unusually favorable lending terms to government employees (the “quid”) and that in those provinces where this practice is more widespread, the bank receives more favorable treatment from governments (the “quo”). While this evidence alone cannot establish that there was an implicit exchange (the “pro”), the authors suggest that this is the most plausible explanation of the data.

The data is certainly susceptible to that interpretation, but there are other, more benign possibilities. I’ll first say a bit more about the main evidence the paper offers for an implicit quid pro quo, and then suggest (though not necessarily urge) a possible alternative explanation.

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Guest Post: The Infeasibility of Evidence-Based Evaluation of Transnational Anti-Bribery Laws

Kevin Davis, the Beller Family Professor of Business Law at New York University School of Law, contributes today’s guest post, based on his recent working paper.

Academics and policymakers enthusiastically endorse “evidence-based” policymaking, for obvious reasons. (After all, what is the alternative? Faith? Popularity contests?) But while evidence—including quantitative evidence—is often helpful, we must be mindful of the limits on what empirical analysis can tell us about important topics. Take the regulation of transnational bribery. Scholars and policymakers would like to know if the current regime—laws like the U.S. Foreign Corrupt Practices Act (FCPA) and U.K. Bribery Act, and international instruments like the OECD Anti-Bribery Convention—has “worked.” That is, have these instruments reduced bribery by the firms that they cover? And did those laws have additional, possibly undesirable collateral consequences, for example reducing investment in countries perceived to be corrupt?

The most sophisticated efforts to answer these questions (see, for example, here and here and here) essentially rely on what social scientists call “natural experiments.” First, the intervention (the law or policy change) of interest, which (in a borrowing from medical terminology) researchers call the “treatment.” Next, one must identify the population of interest—say, firms or countries—and an outcome of interest (such as the frequency of bribery or the level of investment). Then, the researcher identifies the subset of those entities that are affected by the intervention (for example, the firms that fall under the jurisdiction of the new anti-bribery law); this is the “treatment group.” The researcher also identifies another subset of entities—the “control group”—that appears otherwise similar to the treatment group, but did not receive the treatment (for example, a group of firms that are outside the jurisdiction of the new law). The big difference between a “controlled experiment” and a “natural experiment” is that in a controlled experiment the researcher can randomly choose which members of the population receive the treatment (for example by randomly selecting some patients to get a new drug and giving the other patients a placebo), but in a natural experiment, the assignment of the treatment is done not by the researcher, but by some “natural” process in the world. In trying to figure out the effect of an anti-corruption law, it generally is not feasible to conduct a controlled experiment: researchers can’t decide that these firms but not those firms, selected at random, will fall under the jurisdiction of an anti-bribery law. So the best that researchers can do is to rely on natural experiments and try to account as best they can for possible differences between the control group and the treatment group by including additional control variables in a multivariate regression.

Unfortunately, when it comes to studying the effects of transnational anti-bribery laws, these sorts of studies face several fundamental challenges, which are all too often overlooked or understated. Continue reading