IDEAS home Printed from https://ideas.repec.org/p/wbk/jbsgrp/30834024.html
   My bibliography  Save this paper

Does Automatization in Rich Countries hurt Developing Ones? Evidence from the US and Mexico

Author

Listed:
  • Artuc, Erhan
  • Christiaensen, Luc
  • Winkler, Hernan

Abstract

Following a couple of decades of offshoring, the fear today is of reshoring. Using administrative data on Mexican exports by municipality, sector and destination from 2004 to 2014, this paper investigates how local labor markets in Mexico that are more exposed to automation in the U.S. through trade fared in exports and employment outcomes. The results show that an increase of one robot per thousand workers in the U.S. — about twice the increase observed between 2004-2014 — lowers growth in exports per worker from Mexico to the U.S. by 6.7 percent. Higher exposure to U.S. automation did not affect wage employment, nor manufacturing wage employment overall. Yet, the latter is the result of two counteracting forces. Exposure to U.S. automation reduced manufacturing wage employment in areas where occupations were initially more susceptible to being automated; but exposure increased manufacturing wage employment in other areas. Finally, the analysis also finds negative impacts of exposure to local automation on local labor market outcomes.

Suggested Citation

  • Artuc, Erhan & Christiaensen, Luc & Winkler, Hernan, 2019. "Does Automatization in Rich Countries hurt Developing Ones? Evidence from the US and Mexico," Jobs Group Papers, Notes, and Guides 30834024, The World Bank.
    • Handle: RePEc:wbk:jbsgrp:30834024
    as

    Download full text from publisher

    File URL: http://documents.worldbank.org/curated/en/221101550152344701/Does-Automation-in-Rich-Countries-Hurt-Developing-Ones-Evidence-from-the-U-S-and-Mexico
    Download Restriction: no
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Carbonero, Francesco. & Ernst, Ekkehard & Weber, Enzo., 2018. "Robots worldwide the impact of automation on employment and trade," ILO Working Papers 995008793402676, International Labour Organization.
    2. Cilekoglu, Akin A. & Moreno, Rosina & Ramos, Raul, 2024. "The impact of robot adoption on global sourcing," Research Policy, Elsevier, vol. 53(3).
    3. Baert, Stijn, 2021. "The iceberg decomposition: A parsimonious way to map the health of labour markets," Economic Analysis and Policy, Elsevier, vol. 69(C), pages 350-365.
    4. Calì, Massimiliano & Presidente, Giorgio, 2021. "Robots For Economic Development," GLO Discussion Paper Series 942, Global Labor Organization (GLO).
    5. Lili Yan Ing & Gene Grossman & David Christian, 2022. "Digital Transformation:‘Development for All’?," Chapters, in: Lili Yan Ing & Dani Rodrik (ed.), New Normal, New Technologies, New Financing, chapter 7, pages 75-88, Economic Research Institute for ASEAN and East Asia (ERIA).
    6. Stemmler, Henry, 2023. "Automated Deindustrialization: How Global Robotization Affects Emerging Economies—Evidence from Brazil," World Development, Elsevier, vol. 171(C).
    7. Artuc, Erhan & Bastos, Paulo & Rijkers, Bob, 2023. "Robots, tasks, and trade," Journal of International Economics, Elsevier, vol. 145(C).
    8. Sang Hyun Park & Amelia U. Santos-Paulino & Claudia Trentini, . "Fourth Industrial Revolution and FDI from SMEs: The Case of the Republic of Korea," UNCTAD Transnational Corporations Journal, United Nations Conference on Trade and Development.
    9. Azmeh, Shamel & Nguyen, Huong & Kuhn, Marlene, 2022. "Automation and industrialisation through global value chains: North Africa in the German automotive wiring harness industry," Structural Change and Economic Dynamics, Elsevier, vol. 63(C), pages 125-138.
    10. Jurkat, Anne & Klump, Rainer & Schneider, Florian, 2023. "Robots and Wages: A Meta-Analysis," EconStor Preprints 274156, ZBW - Leibniz Information Centre for Economics.
    11. Diaz Pavez, Luis R. & Martínez-Zarzoso, Inmaculada, 2021. "The impact of local and foreign automation on labor market outcomes in emerging countries," University of Göttingen Working Papers in Economics 423, University of Goettingen, Department of Economics.
    12. Katherine Stapleton & Michael Webb, 2020. "Automation, trade and multinational activity: Micro evidence from Spain," CSAE Working Paper Series 2020-16, Centre for the Study of African Economies, University of Oxford.
    13. Guido Matias Cortes & Diego M. Morris, 2019. "Are Routine Jobs Moving South? Evidence from Changes in the Occupational Structure of Employment in the U.S. and Mexico," Working Paper series 19-15, Rimini Centre for Economic Analysis.
    14. Cali,Massimiliano & Presidente,Giorgio, 2021. "Automation and Manufacturing Performance in a Developing Country," Policy Research Working Paper Series 9653, The World Bank.
    15. Antonio Martins-Neto & Nanditha Mathew & Pierre Mohnen & Tania Treibich, 2024. "Is There Job Polarization in Developing Economies? A Review and Outlook," The World Bank Research Observer, World Bank, vol. 39(2), pages 259-288.
    16. Hidalgo, Camila & Micco, Alejandro, 2024. "Computerization, offshoring and trade: The effect on developing countries," World Development, Elsevier, vol. 180(C).
    17. Faber, Marius, 2020. "Robots and reshoring: Evidence from Mexican labor markets," Journal of International Economics, Elsevier, vol. 127(C).
    18. Luis Gerardo Hernández García, 2022. "Transport equipment network analysis: the value-added contribution," Journal of Economic Structures, Springer;Pan-Pacific Association of Input-Output Studies (PAPAIOS), vol. 11(1), pages 1-25, December.

    More about this item

    Keywords

    labor market impact; labor market indicator; local labor market; share of export; working age population; number of workers; labor market outcome; share of employment; impacts on employment; increased import competition; composition of employment; large metropolitan areas; production and export; share of wage; demand for worker; exposure to investments; adoption of ict; share of import; skilled labor force; trade and employment; high school graduate; economies of scale; tradable sector; automotive sector; comparative advantage; total employment; wage employment; estimate impact; trade partner; manufacturing jobs; instrumental variable; positive impact; export drive; high share; export growth; empirical evidence; consumption good; raw material; capital good; Labor migration; anecdotal evidence; weighting scheme; trade pattern; manufacturing wage; point estimate; empirical literature; employment growth; robustness check; exclusion restriction; informal sector; manufacturing sector; econometric model; total wage; external shock; production process; day laborer; family worker; economic sector; regional economy; oecd countries; investment pattern; foreign providers; home country; home countries; export share; literature review; export sector; emerging economy; domestic demand; unskilled worker; Learning and Innovation Credit; logistics cost; foreign supplier; employment impact; high-tech sector; econometric result; worker type; tax authorities; trade shock; world market; depreciation rate; demographic characteristic; market penetration; municipality level; data sample; technological frontier; tax authority; drive system; percent change; informal worker; unpaid worker; import category; manual occupations; local trade; basic metal; trading partner; substantial variation; instrument exposure; labor-intensive industry; net export; gravity model; policy shock; digital manufacturing; labor productivity; Emerging economies; trade datum; initial value; small municipality; weighted average; manufacturing industry; domestic industry; economic integration; car industry; trade variables; Trade Impact; trade links; total trade; worker increase; employment declines; employment trend; negative relationship; export data; employment outcome; intermediate input; employment rate; complementary good; theoretical model; open access; textile industry; industrial revolution; development policy; economic history; media outlet; average share; administrative datum; positive spillover; local area; monthly wage; technology frontier; unskilled job; upward pressure; job displacement; displaced worker;
    All these keywords.

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:wbk:jbsgrp:30834024. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Selome Assefa Hailemariam (email available below). General contact details of provider: https://edirc.repec.org/data/dvewbus.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.