Seminar: Ricardo Perez-Truglia, Berkeley Haas
Ricardo Perez-Truglia, Berkeley Haas
Ricardo Perez-Truglia is an Associate Professor at the Haas School of Business.
His research focuses on various topics, including income inequality, the gender pay gap, pay transparency, and tax compliance. Typically, he begins by formulating a hypothesis about how individuals make decisions or how a policy will affect them. To test those hypotheses, he collaborates with firms and governments. These collaborations often involve field experiments.
In 2020, Perez-Truglia was named a Sloan Research Fellow, an award that recognizes outstanding early-career faculty who have the potential to revolutionize their fields of study.
Perez-Truglia teaches Microeconomics for MBAs. In 2022, he was awarded the Earl F. Cheit Award for Excellence in MBA Teaching. He also works as a scholar for Amazon, where he conducts research to improve employee satisfaction, diversity, equity, and inclusion.
Perez-Truglia received his PhD in Economics from Harvard University in 2014. He grew up in the Ciudadela neighborhood near Buenos Aires. Ricardo and his wife (Marina) have three children: Alma, Lucas and Nicolas.
"What's My Employee Worth? The Effects of Salary Benchmarking"
While U.S. legislation prohibits employers from sharing information about their employees’ compensation with each other, companies are still allowed to acquire and use more aggregated data provided by third parties. Most medium and large firms report using this type of data to set salaries, a practice that is known as salary benchmarking. Despite their widespread use across occupations, there is no evidence on the effects of salary benchmarking. We provide a model that explains why firms are interested in salary benchmarking and makes predictions regarding the effects of the tool. Next, we measure the actual effects of these tools using administrative data from one of the leading providers of payroll services and salary benchmarks. The evidence suggests that salary benchmarking has a significant effect on pay setting and in a manner that is consistent with the predictions of the model. Our findings have implications for the study of labor markets and for ongoing policy debates.