Publications

The Foreign Corrupt Practices Act (FCPA) of 1977 is a U.S. law designed to prevent companies and their representatives from influencing foreign officials through bribery. It has two main provisions: the anti-bribery provision and the accounting provision. The anti-bribery provision makes it illegal for companies to offer or give payments to foreign officials to secure business advantages. The accounting provision requires publicly traded companies to maintain accurate financial records and internal controls to prevent hiding of bribery.

The FCPA operates through the collaboration of the U.S. Department of Justice (DOJ) and the Securities and Exchange Commission (SEC). The DOJ handles criminal enforcement against those who willfully violate the anti-bribery provisions, while the SEC enforces civil penalties for accounting infractions.

Dilemmas facing the FCPA include jurisdictional challenges, as U.S. enforcement agencies sometimes face difficulties in prosecuting foreign entities or individuals. Additionally, differing national standards on corruption can lead to friction and unease among international companies. Critics argue that the FCPA can place U.S. businesses at a disadvantage in countries where bribery is a common practice. There are also concerns about the effectiveness of the FCPA in truly curbing global corruption and whether its enforcement disproportionately targets certain industries or companies.

We explore the in- and out- of sample robustness of tests for consumer choice inconsistencies based on parameter restrictions in parametric models, with a focus on tests proposed by Ketcham, Kuminoff and Powers (2015). We start by arguing that non-parametric alternatives are inherently conservative with respect to detecting mistakes (and one specific test proposed by KKP is incorrect). We then consider several proposed robustness checks of parametric models and argue that they do not separately identify misspecification and choice inconsistencies. We also show that, when implemented using a comprehensive goodness of fit measure, the Keane and Wolpin (2007) test of out of sample forecasting demonstrates that a model allowing for choice inconsistencies forecasts substantially better than one that does not. Finally, we explore the robustness of our 2011 results to alternative normative assumptions.