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Declining Search Frictions, Unemployment and Growth

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  • Paolo Martellini
  • Guido Menzio

Abstract

Over the last century, unemployment, vacancy, job-finding and job-loss rates as well as the Beveridge curve have no trend. Yet, the last century has seen the development and diffusion of many information technologies—such as telephones, fax machines, computers, the Internet—which presumably have increased the efficiency of search in the labor market. We explain this phenomenon using a textbook search-theoretic model of the labor market. We show that there exists an equilibrium in which unemployment, vacancies, job-finding and job-loss rates are constant while the search technology improves over time if and only if firm-worker matches are heterogeneous in quality, the distribution of match qualities is Pareto, and the quality of a match is observed before the start of the employment relationship. Under these conditions, improvements in search lead to an increase in the rate at which workers meet firms and to a proportional decline in the probability that the quality of a firm-worker match is acceptable leading to a constant job-finding rate, unemployment, etc... Interestingly, under the same conditions, unemployment, vacancies, job-finding and job-loss rates are independent of the size of the labor market even in the presence of increasing returns to scale in search. While declining search frictions do not lower unemployment, they contribute to growth. The magnitude of the contribution depends on the thickness of the tail of the Pareto distribution. We present a simple strategy to measure the decline in search frictions and its contribution to growth. A rudimentary implementation of this strategy suggests that the decline in search frictions has been substantial, it has been caused by both improvements in the search technology and increasing returns to scale in the search process, and it has had a non-negligible impact on growth.

Suggested Citation

  • Paolo Martellini & Guido Menzio, 2018. "Declining Search Frictions, Unemployment and Growth," NBER Working Papers 24518, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:24518
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    Cited by:

    1. 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.
    2. Bhuller, Manudeep & Kostøl, Andreas & Vigtel, Trond Christian, 2019. "How Broadband Internet Affects Labor Market Matching," Memorandum 10/2019, Oslo University, Department of Economics.
    3. Edward Kung, 2020. "Innovation and Entrepreneurship in Housing," NBER Working Papers 26886, National Bureau of Economic Research, Inc.
    4. Avcioglu, Sahin & Karabay, Bilgehan, 2019. "Search efficiency, wage dynamics and welfare," Economic Modelling, Elsevier, vol. 83(C), pages 270-286.
    5. Edward Kung, 2020. "Innovation and Entrepreneurship in Housing," NBER Chapters, in: The Role of Innovation and Entrepreneurship in Economic Growth, National Bureau of Economic Research, Inc.
    6. Kevin Donovan & Will Jianyu Lu & Todd Schoellman, 2020. "Labor Market Dynamics and Development," Staff Report 596, Federal Reserve Bank of Minneapolis.

    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
    • O40 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity - - - General
    • R11 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Regional Economic Activity: Growth, Development, Environmental Issues, and Changes

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