I am Pedro Pires. I am an Assistant Professor of Economics at the Nova School of Business and Economics and a member of the NOVAFRICA research center. I work on topics in behavioral, labor and development economics.
I received a Ph.D. in Economics from the University of California, Berkeley in 2023. Before that, I received a Masters in Economics in 2016 from the Sao Paulo School of Economics (FGV) and a BA in Economics in 2013 from the University of Sao Paulo (USP), Brazil.
Email me at pedro.m.pires [at] novasbe [dot] pt
Click here for my CV
Research
Working Papers
How Much Can You Make? Misprediction and Biased Memory in Gig Jobs [Draft]
Abstract: Flexibility is an increasingly prominent feature of many jobs. In the gig economy, workers can choose their work hours and face wages that vary across hours and weeks. This increased complexity adds challenges to predicting and understanding job outcomes. Incomplete information or behavioral biases can then lead to inaccurate beliefs about pay and labor supply. We test this hypothesis by collecting novel survey data on 454 delivery and ride share gig workers in the United States. Comparing gig workers’ beliefs with data on their actual job performance, we find they overestimate their predictions (43%) and their recalls (31%) of weekly pay, despite it being reported prominently in their earnings statements. Furthermore, gig workers underestimate expenses and overestimate hours worked. The results are consistent with selective recall: when forming and updating their beliefs in noisy environments, workers overweight past high-paying periods. We then examine how biased beliefs affect labor market decisions. We derive predictions from a behavioral labor supply model and test them. We conclude that job choices and labor supply decisions are likely influenced by mistaken beliefs in flexible gig jobs.
Can Laws Change Social Norms? Evidence from Domestic Work Formalization in Brazil [Draft]
Abstract: This paper examines Brazil’s Constitutional Amendment 72/2013, which expanded labor rights for domestic workers. The amendment raised formalization costs and lacked enforcement, with many provisions delayed until subsequent legislation passed in 2015. The period surrounding the reform was marked by intense media coverage and public debate, which we argue influenced social norms and reduced the acceptability of informal employment of domestic workers. Using an event study design with multiple control groups, we estimate a 2 to 5 percentage point increase in the formality rate, 4 to 7 percent higher real wages, improved minimum wage compliance, and reduced average weekly hours, alongside a decline in total domestic work employment. These findings support a social norms interpretation and do not align with conventional formalization models focused on enforcement and labor costs. We then employ triple-difference designs that leverage variation in factors expected to intensify norm effects. We find larger formalization effects for live-in workers (who have closer employer-employee ties) and in areas with higher domestic worker density or more frequent formalization events. Our findings suggest that labor regulations may affect formalization decisions by shifting social perceptions of informal employment.
Selected Work in Progress
Finding the Perfect Hire: Screening Strategies and Job Match Quality (with Priscila De Oliveira and Ben Scuderi)
Abstract: This paper examines how companies make recruitment decisions and how these decisions align with post-hiring productivity. We use a novel dataset that links proprietary data from a hiring platform in Brazil covering over 20 million job applications with matched employer-employee labor market data. We begin by analyzing how offer decisions relate to applicant characteristics assessing implicit weights placed on, for example, education, past experience, and demographics. We then evaluate how well firm offers predict post-hiring outcomes, including turnover, tenure, and salary growth. We then use machine learning models to directly predict productivity based on job applicant hiring characteristics. We identify possible inefficiencies in hiring practices by comparing firms’ actual offer decisions to our predictions from machine learning models. Finally, we investigate when these divergences occur, first exploring which characteristics correlate with them the most. We then discuss mechanisms such as stereotyping, discrimination, and complexity in decision-making.
Signaling Creativity: How Idea Contests Shape Employee Trajectories (with Priscila de Oliveira)
Abstract: This study examines the impact of firm-level innovation contests on employee outcomes, using novel data from a large company in Brazil linked to an employer-employee matched dataset. In innovation contests, workers propose changes to current firm practices. Ideas are then evaluated internally by a panel of specialists. We explore whether these contests allow workers in non-creative roles to signal creativity and how idea submission and acceptance influence career trajectories, including promotions, salary growth, and transitions to more creative roles within the firm.
How Recruiters Make Decisions: Unpacking the Drivers of Discrimination (with Priscila de Oliveira)
Behavioral Determinants of Consumer Reactions to Product Size Changes (with Lucio Wasserman)