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Unemployment and Development

Author

Listed:
  • Ying Feng

    (University of California, San Diego)

  • David Lagakos

    (University of California, San Diego)

  • James E. Rauch

    (University of California, San Diego)

Abstract

This paper draws on household survey data from countries of all income levels to measure how average unemployment rates vary with income per capita. 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 firms hire in frictional labor markets, and output increases with ability. Countries differ 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. Quantitatively, the model accounts for around one third of the cross-country patterns we document.

Suggested Citation

  • Ying Feng & David Lagakos & James E. Rauch, 2018. "Unemployment and Development," Working Papers 2018-083, Human Capital and Economic Opportunity Working Group.
  • Handle: RePEc:hka:wpaper:2018-083
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    2. Piotr Denderski & Florian Sniekers, "undated". "Broadband Internet and the Self-Employment Rate: A Cross-Country Study on the Gig Economy," Discussion Papers in Economics 19/13, Division of Economics, School of Business, University of Leicester.
    3. Matteo Bobba & Luca Flabbi & Santiago Levy, 2022. "Labor Market Search, Informality, And Schooling Investments," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 63(1), pages 211-259, February.
    4. Camila Cisneros-Acevedo & Alessandro Ruggieri, 2022. "Firms, policies, informality, and the labour market," Discussion Papers 2022-11, University of Nottingham, GEP.
    5. Alexander Bick & David Lagakos & Hitoshi Tsujiyama & Nicola Fuchs-Schündeln, 2019. "Explaining Hours Worked Across and Within Countries: Income Effects vs. Taxes and Transfers," 2019 Meeting Papers 1363, Society for Economic Dynamics.
    6. Titan Alon & Minki Kim & David Lagakos & Mitchell VanVuren, 2020. "How Should Policy Responses to the COVID-19 Pandemic Differ in the Developing World?," NBER Working Papers 27273, National Bureau of Economic Research, Inc.
    7. Gu, Jiajia, 2021. "Financial intermediation and occupational choice," Journal of Economic Dynamics and Control, Elsevier, vol. 133(C).
    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.
    9. Denderski, Piotr & Sniekers, Florian, 2021. "Declining Search Frictions and Type-of-Employment Choice," Discussion Paper 2021-010, Tilburg University, Center for Economic Research.
    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.
    11. Poschke, Markus, 2019. "Wage Employment, Unemployment and Self-Employment across Countries," IZA Discussion Papers 12367, Institute of Labor Economics (IZA).
    12. 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.
    13. Xiao Ma & Alejandro Nakab & Daniela Vidart, 2021. "Human Capital Investment and Development: The Role of On-the-job Training," Working papers 2021-10, University of Connecticut, Department of Economics, revised Aug 2022.
    14. Ma, Xiao & Muendler, Marc-Andreas & Nakab, Alejandro, 2020. "Learning by Exporting and Wage Profiles: New Evidence from Brazil," MPRA Paper 109497, University Library of Munich, Germany, revised 31 Aug 2021.
    15. 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 Aug 2023.

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    More about this item

    Keywords

    unemployment; causal effects of education; ability effects; household survey data;
    All these keywords.

    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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