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.

Crowdsourced evaluation is a promising method of evaluating engineering design attributes that require human input. The challenge is to correctly estimate scores using a massive and diverse crowd, particularly when only a small subset of evaluators has the expertise to give correct evaluations. Since averaging evaluations across all evaluators will result in an inaccurate crowd evaluation, this paper benchmarks a crowd consensus model that aims to identify experts such that their evaluations may be given more weight. Simulation results indicate this crowd consensus model outperforms averaging when it correctly identifies experts in the crowd, under the assumption that only experts have consistent evaluations. However, empirical results from a real human crowd indicate this assumption may not hold even on a simple engineering design evaluation task, as clusters of consistently wrong evaluators are shown to exist along with the cluster of experts. This suggests that both averaging evaluations and a crowd consensus model that relies only on evaluations may not be adequate for engineering design tasks, accordingly calling for further research into methods of finding experts within the crowd.

In 2014, when Ford Motor Co. announced that it would use aluminum instead of steel for the bed and cab of the 2015 F-150, the best-selling vehicle in the U.S., a new auto race began. Alcoa, a major global aluminum producer, spent $300 million to overhaul its Iowa factory and planned to spend tens of millions more on other U.S. manufacturing facilities to meet the demand generated by the Ford F-150 contract. 

Traditionally, the automotive industry preferred steel for car manufacture due to its strength, durability, and cost-effectiveness. However, aluminum's lighter weight offered significant advantages for improving fuel efficiency and reducing emissions in line with stricter new regulatory and environmental standards. Using aluminum in car body manufacture, for example, allowed more efficient, smaller auto engines to produce the same performance with increased miles per gallon, meeting rigorous fuel economy standards for U.S. vehicles. 

Still, its major contract with Ford for the production of the F-150 notwithstanding, Alcoa faced challenges in selling aluminum to automakers. Consumers perceived aluminum as more vulnerable than steel, and the steel industry was expected to fight back to preserve its top position in auto materials by promoting steel as tougher than aluminum. Additionally, aluminum cost more than steel and lacked steel's long history with automakers, who had tooled their production to steel components for generations. Finally, other new competitors had entered the race to replace steel in autos, including plastics and carbon-fiber composites. 

With these uncertainties in the market, forecasting future growth for aluminum in automobiles in the U.S. and overseas was difficult for Alcoa. Would other vehicle manufacturers follow Ford's lead? How much more should Alcoa invest to supply automotive aluminum? How could it convince consumers that its material was as tough as steel? 

Do store brands aid store loyalty by enhancing store differentiation or merely draw price-sensitive customers with little or no store loyalty? This paper seeks to answer this question by empirically investigating the relationship between store brand loyalty and store loyalty. First, we find a robust, monotonic, positive relationship between store brand loyalty and store loyalty by using multiple loyalty metrics and data from multiple retailers and by controlling for alternative factors that can influence store loyalty. Second, we take advantage of a natural experiment involving a store closure and find that the attrition in chain loyalty is lower for households with greater store brand loyalty prior to store closure. Together, our results are consistent with evidence for the store differentiation role of store brands.

In August 2011, investor Wilbur Ross, through his firm WL Ross & Co LLC, participated in a consortium that raised $1.6 billion to acquire a 35% stake in the Bank of Ireland, amidst skepticism about the viability of investing in an Irish bank post-economic collapse. Following its rapid growth in the late 1990s, Ireland faced a severe downturn from 2008, largely due to a real estate market collapse and the subsequent drying up of foreign capital, which left its banks on the brink of failure. Government interventions included a massive guarantee of bank deposits, the creation of the National Asset Management Agency (NAMA), and asset purchases at significant discounts, all aimed at stabilizing the financial sector.

However, with economy-threatening bank debts converted to government liabilities and limited currency devaluation options, Ireland ultimately sought a bailout from the European Union and the IMF. The resulting package of 85 billion euros required severe austerity measures from the Irish government. Despite these challenges, Ross, who believed in Ireland’s long-term recovery, viewed the Bank of Ireland as a strategic investment opportunity. By late 2013, the bank was nearing profitability, having returned significant funds to the state, and the share price increased markedly. Nonetheless, residual macroeconomic challenges, slow economic recovery, public scrutiny of bank management, and regulatory risks remained, posing ongoing threats to investment stability in the Irish banking sector.

Developed in partnership with UCD Michael Smurfit Graduate Business School and IE Business School