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Unemployment and Development

Author

Listed:
  • Ying Feng

    (University of California, San Diego)

  • David Lagakos

    (University of California, San Diego)

  • James Rauch

    (UCSD)

Abstract
This paper draws on household survey data from countries of all income levels to measure how unemployment varies with income. We document that unemployment is increasing with GDP per capita. Furthermore, we show that this fact is accounted for almost entirely by low-educated workers, whose unemployment rates are strongly increasing in GDP per capita, rather than by high-educated workers, whose unemployment rates are not correlated with income. To interpret these facts, we build a model with workers of heterogeneous ability and two sectors: a traditional sector, in which self-employed workers produce output without reward for ability; and a modern sector, in which rms hire in frictional labor markets, and output increases with ability. Countries dier exogenously in the productivity level of the modern sector. The model predicts that as productivity rises, the traditional sector shrinks, as progressively less-able workers enter the modern sector, leading to a rise in overall unemployment and in the ratio of low-educated to high-educated unemployment rates. A calibrated version of the model accounts for some, but not all, of the cross-country patterns we document.

Suggested Citation

  • Ying Feng & David Lagakos & James Rauch, 2018. "Unemployment and Development," 2018 Meeting Papers 289, Society for Economic Dynamics.
    • Handle: RePEc:red:sed018:289
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    8. Anurag Banerjee & Parantap Basu & Elisa Keller, 2023. "Cross‐country disparities in skill premium and skill acquisition," Economic Inquiry, Western Economic Association International, vol. 61(1), pages 179-198, January.
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    10. Girum Abebe & A Stefano Caria & Marcel Fafchamps & Paolo Falco & Simon Franklin & Simon Quinn, 2021. "Anonymity or Distance? Job Search and Labour Market Exclusion in a Growing African City [Endogenous Stratification in Randomized Experiments]," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 88(3), pages 1279-1310.
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    14. Ufuk Bingöl & Fatih Ayhan, 2020. "The Impact of NEET and Labor Market Indicators on Human Development: A Panel Data Analysis for EU-28 Countries," Journal of Social Policy Conferences, Istanbul University, Faculty of Economics, vol. 0(79), pages 441-468, December.
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    16. Xiao Ma & Alejandro Nakab & Daniela Vidart, 2022. "How do Workers Learn? Theory and Evidence on the Roots of Lifecycle Human Capital Accumulation," Working papers 2022-11, University of Connecticut, Department of Economics, revised Dec 2024.

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    More about this item

    JEL classification:

    • E24 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Employment; Unemployment; Wages; Intergenerational Income Distribution; Aggregate Human Capital; Aggregate Labor Productivity
    • E26 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Informal Economy; Underground Economy
    • O11 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Macroeconomic Analyses of Economic Development
    • O41 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity - - - One, Two, and Multisector Growth Models

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