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Labor Markets in Developing Countries

Paper Session

Saturday, Jan. 3, 2026 10:15 AM - 12:15 PM (EST)

Philadelphia Convention Center
Hosted By: American Economic Association
  • Chair: Seema Jayachandran, Princeton University

Employment Preferences of Favela Residents

Mayara Felix
,
Yale University
Ieda Matavelli
,
University of New South Wales
Beatriz Marcoje
,
Universidade Federal Fluminense
Maria Clara Rodrigues
,
Yale University

Abstract

Growing evidence shows that labor markets in developing countries have a dual nature: large informal sectors coexist with formal sectors where firms wield considerable labor market power. While the relationship between informality and market power is complex, we can elucidate key aspects of this interplay by carefully mapping worker preferences. This project conducts a novel survey of slum residents to generate such data. Young slum residents are especially relevant for this analysis because they frequently move between formal and informal jobs, facing both barriers and opportunities unique to high-informality settings. We map preferences for various job characteristics and directly ask about job aspirations among prime-age (18-30) residents of a large slum in Rio de Janeiro. We use open-text questions and discrete choice experiments to estimate how workers value different work arrangements, including various aspects of self-employment and informal wage work. Beyond providing direct evidence on worker preferences, these data can improve existing estimates of wage markdowns in high-informality contexts, where preference parameters rely on formal-sector data (e.g., Felix (2021), Sharma (2022, 2025)) or limited census data (e.g., Amodio, Medina, and Morlacco (2022)).

The Impact of AI on Developing Country Labor Markets

Lindsey Raymond
,
Microsoft Research
Garima Sharma
,
Northwestern University

Abstract

Generative AI tools are widely being adopted in developing countries—-in India, for example, 92% of knowledge workers, or workers working at a desk, report utilizing AI tools in their job every day (2024 Work Trend Index Annual Report). Leveraging cross-occupation variation in exposure to AI, and combining data from the universe of LinkedIn profiles, job postings, and all workers on a large online freelancing platform, this study will examine how generative AI is reshaping employment, wages, career progression, productivity, and the distribution of gains and losses across workers in India and other markets.

The Economic Consequences of Knowledge Hoarding

Luisa Cefala
,
University of California-Berkeley
Franck Irakoze
,
University of Burundi
Pedro Naso
,
Swedish University of Agricultural Sciences
Nicholas Swanson
,
Cornell University

Abstract

Social learning is an important source of knowledge diffusion in low-income countries. However, the highly localized character of many labor markets could inhibit social learning by generating incentives for individuals to hoard knowledge. This paper studies the impact of knowledge hoarding on the diffusion of profitable skills and technologies in rural Burundi and measures its aggregate and distributional consequences for the village economy. In a field experiment covering 223 villages (labor markets), we encourage workers skilled in high-return agricultural technologies to share their knowledge with unskilled individuals. We randomize at the local labor market level whether the unskilled worker is a competitor (i.e., someone from the same labor market) and whether the training is about a technology with rivalrous rents (row planting, which commands a wage premium in the labor market). We first establish that knowledge hoarding indeed reduces social learning. When incumbents are matched with an individual from the same labor market, knowledge transmission occurs only 3% of the time, but this figure reaches 43% if the unskilled worker is not a competitor. In contrast, transmission of a technology with nonrivalrous rents (composting) is high regardless of the unskilled worker's identity. Next, we show that knowledge hoarding creates winners and losers: By hoarding knowledge, incumbents earn 6% more, and the skilled equilibrium wage is 3% higher. In contrast, unskilled workers' earnings and farm output are 7% and 20% lower, respectively. Overall, knowledge hoarding reduces technology adoption by over 20%, suggesting substantial yield losses. Finally, our results suggest that fear of social sanction is a mechanism that sustains knowledge hoarding among the incumbents, highlighting how social ties can foster social learning but also inhibit it when knowledge diffusion threatens incumbents' rents.

Exposure and Spatial Choice: Evidence from Nairobi

Joshua Dean
,
University of Chicago
Gabriel Kreindler
,
Harvard University
Oluchi Mbonu
,
Harvard University

Abstract

Economic analysis of access to opportunities (jobs, education, healthcare, goods, etc.) often focuses on the role of prices, quality, and access costs. Building on research from psychology, marketing and neuroscience, we study the additional role of prior exposure with an option for shaping choices, in the context of urban mobility for work. In a sample of 800 casual workers in Nairobi, the median person commutes 7.8 km but has never been to half the neighborhoods within 75 minutes from where they live. To quantify pure preferences for familiar locations, defined as locations visited at least once in the past, we offer short-term employment and experimentally induce familiarity by training participants in either familiar or unfamiliar locations. Participants are willing to travel 3.5 km further or take a pay cut worth 22% of the median daily wage to avoid working in a location never visited before. This differential is fully offset after one visit to such an unfamiliar neighborhood. Individuals are also initially less likely to consider working in an unfamiliar neighborhood without prompting and a single visit closes half of this gap. Participants partially anticipate these effects. These effects persist for a different paid opportunity 2-4 months after the intervention, and participants report visiting training neighborhoods outside our study. Our results are consistent with unfamiliarity making people view neighborhoods more negatively, and we find little evidence that navigation costs or exploration risk can explain these results. Our results suggest that past exposure frictions are an important component of urban mobility costs in cities like Nairobi.
JEL Classifications
  • O1 - Economic Development
  • J4 - Particular Labor Markets