Working Papers: (all working papers are available upon request)
Awards: Winner of John A. Howard/AMA Doctoral Dissertation Award, Winner of CESifo Distinguished Affiliate Award; Finalist for American Statistical Association (ASA) Dissertation Award; Winner of Best Paper Award at Discrimination and Diversity Workshop; Finalist for Routledge Inclusive Economics Award; Winner of Emerging Scholar Award
Presentations: Quantitative Marketing and Economics (QME) Conference, London
Insurance companies frequently use consumer attributes, such as gender or age, when setting a price for their services. Young male drivers, for example, are often charged more than young females for car insurance, as they are expected to be riskier. In 2019, California banned auto insurance companies from using information on gender in their pricing algorithms. I study how this ban affects the gender gap in prices, using a difference-in-differences strategy with older individuals and other states as control groups. I find that the ban reduced the gender gap in the insurance premiums paid by young drivers by around 55 percent, but it failed to eliminate it completely. My analysis of the pricing algorithm of a large insurance company in California indicates that algorithms are adjusted in a way that gender proxies receive larger weights after the policy. For instance, drivers using specific car models associated with young males were charged up to 22 percent more after the ban. My findings illustrate the limitations of anti-discrimination policies that impose group-blind pricing, with implications for the design of fairer regulations for algorithms.
Presentations: Digital Economics Paris Seminar Series (Online), UTD Bass FORMS Conference (Dallas, TX), Artificial Intelligence in Management Conference (Los Angeles, CA)
This paper studies the impact of generative AI technology on the demand for online freelancers, using a large dataset from a leading global freelancing platform. We focus on how the release of ChatGPT affects various freelance jobs that require different skills or software. Our findings indicate a 21 percent decrease following the ChatGPT introduction in the number of job posts for automation-prone jobs when compared to jobs requiring manual-intensive skills. We also find that the introduction of image-generating AI technologies led to a significant 17 percent decrease in the number of job posts related to image creation. Furthermore, we utilize prior evidence on AI exposure to different occupations and Google Trends to demonstrate that the more pronounced decline in freelancer demand within those specific occupations is related to their heightened exposure to AI technology, as well as higher general public awareness of ChatGPT's substitutability.
Presentations: Workshop on Discrimination and Inequalities on Online Markets, Paris
Freelancing platforms connecting millions of employers and freelancers worldwide have become enormously popular. These online platforms enable workers to supply their labor easily by reducing transaction and search costs. Although online platforms reduce certain costs by making global labor markets more accessible, some information asymmetries persist. Such informational frictions may disadvantage freelancers from developing countries, particularly when employers are from high-income countries. In this paper, I study the wage gap between freelancers from high-income and developing countries in an online freelancing platform and explain the mechanisms driving this gap. I find that freelancers from developing countries earn 22 percent less than freelancers from high-income countries after controlling for the job and country-specific characteristics. However, the penalty on wages decreases as contractors provide information about themselves over time. Experience, reputation scores, and more information on previous earnings or standardized test scores benefit freelancers from developing countries more. My findings have implications for the role of information in achieving fairer outcomes in digital platforms.
Online education technologies are becoming increasingly popular in higher education. One widely used method is recording lectures during face-to-face classes. Access to online lectures allows students to review the content, whereas knowing that the lecture recordings will be available online can decrease incentives to attend classes. Besides, lecture recordings can have heterogeneous impacts on specific groups of students. It can be helpful as a revision tool, particularly for non-native English speakers. In that sense, the overall effect of online education in the hybrid setting is not easy to predict. This paper studies the impact of online access to recorded lectures on student performance, attendance, and satisfaction. I use a difference-in-differences strategy by exploiting the staggered implementation of the recorded lecture system in different courses in a top UK university. I find that (i) online lectures decrease the performance gap between native and non-native speakers significantly, (ii) they increase the achievement of high-performer students and decrease it for low-performers and (iii) they reduce attendance to the main lectures and have no statistically significant impact on student satisfaction. My findings have significant implications for designing policies on access to distance learning technologies and overcoming learning disparities across different groups.
Work in Progress:
Do Online Food Delivery Platforms Help or Harm Restaurant Businesses?
Today, the notion of eating out is rapidly evolving with changing consumer eating habits. The advent of user-friendly food delivery platforms has enormously helped this transformation. and the global pandemic has given food delivery platforms an important boost. However, despite their rapid growth, the economics of these platforms and their impact on the restaurant business are still evolving. In this study, I focus on how the availability of food delivery platforms helps or harms the restaurant business. I use an extensive geospatial dataset containing 4 million different geographic features in the UK since 2013. This dataset allows me to observe all restaurant businesses, their location, capacity, and together with their neighborhood properties over time. I use a difference-in-difference strategy by exploiting the staggered entry of online food platforms in each postcode. I analyze how restaurant locations and store sizes have changed over time as an online food delivery company has started to serve in the same postcode as the restaurant.