Essays on Online Labor Markets in Low-Income Countries

Remote-work technologies have fundamentally reshaped labor markets, creating new opportunities for workers in low- and middle-income countries (LMICs) to access high-quality jobs previously beyond their reach. This shift will profoundly change how people acquire skills, access jobs, and organize within the labor market over the coming decades. In this dissertation, I investigate frictions that impede workers’ access to digital job opportunities and skill acquisition in the online labor market.

In the first chapter, joint with Hamna Ahmed and Zunia Tirmazee, we study entry barriers for workers in online labor markets. Hundreds of millions of workers in low- and middle-income countries seek digital jobs online but face entry barriers without an established reputation. Novice workers could offset this by lowering initial wages, yet few do. Our baseline survey points to two explanations: workers believe employers interpret low wages as low quality signals and are uncertain about their own abilities. We conduct two field experiments on a leading global freelancing platform to examine how these beliefs shape worker outcomes. In the demand-side experiment, we randomize wage offers by novice workers to 703 jobs and find that employers respond favorably to lower wage offers from novices, contrary to what workers believe. In the supply-side experiment with 481 novice workers, we randomly provide them with accurate information about employer responses and their performance. Correcting workers’ beliefs increases their willingness to lower wages. Consistent with reputation models, effects are driven by high-ability novices with high returns to reputation once these frictions are removed. Simulations show that without external intervention, worker learning about employer responses is slow and costly. Our findings highlight that information interventions can help workers in developing countries overcome reputation barriers and accelerate talent discovery in online labor markets.

In the second chapter, joint with Hamna Ahmed, Zunia Tirmazee, and Emma Zhang, we study how to incentivize digital skills investment among young women in low-income countries, a key prerequisite for participation in online labor markets. In particular, we study the role of intra-household payment and information targeting on the effectiveness of a conditional cash transfer (CCT) program using a randomized control trial. The program aims to boost completion of a digital skills training program among young females in urban Pakistan. Fixing the incentive size and daughters’ knowledge about it, we cross randomize (1) the payment split between parents and daughters and (2) whether parents receive information about the daughters’ incentive. We find that under asymmetric information about the CCT, incentivizing parents leads to a 103% increase in training completion compared to incentivizing daughters. When both parents and daughters know about the CCT, completion rates do not vary by the incentive split, consistent with the efficient collective household model. Our results suggest that in this parent-child context, incomplete information sharing is the main barrier to the optimal incentive targeting, instead of bargaining frictions on the future payment.