IDEAS home Printed from https://ideas.repec.org/p/anp/en2016/163.html

Location And Wages: The Contribution Of Firm And Worker Effects In Brazil

Author

Listed:
  • DIANA LÚCIA GONZAGA DA SILVA
  • CARLOS R. AZZONI

Abstract

The objective of this paper is to assess the contribution of unobservable firm and individual heterogeneity for the location effects on wages and for the variation of wages in Brazil. In the first stage we estimate the effects of location through a wage equation, controlling for observable worker characteristics and unobserved heterogeneity of workers and firms. In a second stage, the estimated location effects are regressed on the fixed effects of firms and workers. We use micro data panel for the period 1995-2008 (RAIS-Migra). We estimate the model proposed by Abowd et al. (1999) for the wage decomposition, to deal with multiple fixed effects in large databases matching workers and firms. One contribution of this paper is to deal with more controls than usual in this type of analysis. As for the literature on the Brazilian case, the simultaneous control for firm and worker effects is also an important contribution. The findings show that firm and worker effects account for a substantial variation of wages across individuals (93%) and for the variation in location effects across metropolitan areas (95%). In the first and second stages individual characteristics are more important than firm effect to explain wage differentials (individuals 91%, firms 80%) and location effects (92%, 41%). Controlling for all these effects, the “pure” agglomeration effects would amount to only 5%. Therefore, both effects account for substantial shares of the variation of real wages and location effects on wages in Brazil.
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Diana Lúcia Gonzaga Da Silva & Carlos R. Azzoni, 2018. "Location And Wages: The Contribution Of Firm And Worker Effects In Brazil," Anais do XLIV Encontro Nacional de Economia [Proceedings of the 44th Brazilian Economics Meeting] 163, ANPEC - Associação Nacional dos Centros de Pós-Graduação em Economia [Brazilian Association of Graduate Programs in Economics].
  • Handle: RePEc:anp:en2016:163
    as

    Download full text from publisher

    File URL: https://www.anpec.org.br/encontro/2016/submissao/files_I/i10-a0a9badf6ef818a139257a5e00d69873.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Mittag, Nikolas, 2012. "New methods to estimate models with large sets of fixed effects with an application to matched employer-employee data from Germany," FDZ Methodenreport 201201_en, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
    2. Martyn Andrews & Thorsten Schank & Richard Upward, 2006. "Practical fixed-effects estimation methods for the three-way error-components model," Stata Journal, StataCorp LLC, vol. 6(4), pages 461-481, December.
    3. Carlos R. Azzoni & Luciana M.S. Servo, 2002. "Education, cost of living and regional wage inequality in Brazil," Review of Economic Design, Springer;Society for Economic Design, vol. 81(2), pages 157-175, April.
    4. John M. Abowd & Robert H. Creecy & Francis Kramarz, 2002. "Computing Person and Firm Effects Using Linked Longitudinal Employer-Employee Data," Longitudinal Employer-Household Dynamics Technical Papers 2002-06, Center for Economic Studies, U.S. Census Bureau.
    5. repec:iab:iabfme:201201(en is not listed on IDEAS
    6. Leonard, J.S. & Van Audenrode, M., 1996. "Persistence of Firm and Individual Wage Components," Papers 9607, Laval - Recherche en Politique Economique.
    7. Combes, Pierre-Philippe & Duranton, Gilles & Gobillon, Laurent & Roux, Sébastien, 2012. "Sorting and local wage and skill distributions in France," Regional Science and Urban Economics, Elsevier, vol. 42(6), pages 913-930.
    8. Guimaraes, Paulo & Portugal, Pedro, 2009. "A Simple Feasible Alternative Procedure to Estimate Models with High-Dimensional Fixed Effects," IZA Discussion Papers 3935, Institute of Labor Economics (IZA).
    9. Combes, Pierre-Philippe & Duranton, Gilles & Gobillon, Laurent, 2008. "Spatial wage disparities: Sorting matters!," Journal of Urban Economics, Elsevier, vol. 63(2), pages 723-742, March.
    10. Daniel F. McCaffrey & J. R. Lockwood & Kata Mihaly & Tim R. Sass, 2012. "A review of Stata commands for fixed-effects estimation in normal linear models," Stata Journal, StataCorp LLC, vol. 12(3), pages 406-432, September.
    11. Diego Puga, 2010. "The Magnitude And Causes Of Agglomeration Economies," Journal of Regional Science, Wiley Blackwell, vol. 50(1), pages 203-219, February.
    12. Jacob A. Mincer, 1974. "Schooling and Earnings," NBER Chapters, in: Schooling, Experience, and Earnings, pages 41-63, National Bureau of Economic Research, Inc.
    13. John M. Abowd & Francis Kramarz & David N. Margolis, 1999. "High Wage Workers and High Wage Firms," Econometrica, Econometric Society, vol. 67(2), pages 251-334, March.
    14. Jacob A. Mincer, 1974. "Schooling, Experience, and Earnings," NBER Books, National Bureau of Economic Research, Inc, number minc74-1, January.
    15. Daniel Heuermann & Benedikt Halfdanarson & Jens Suedekum, 2010. "Human Capital Externalities and the Urban Wage Premium: Two Literatures and their Interrelations," Urban Studies, Urban Studies Journal Limited, vol. 47(4), pages 749-767, April.
    16. repec:hal:pseose:halshs-00754585 is not listed on IDEAS
    17. Paulo Guimarães & Pedro Portugal, 2010. "A simple feasible procedure to fit models with high-dimensional fixed effects," Stata Journal, StataCorp LLC, vol. 10(4), pages 628-649, December.
    18. Jacob A. Mincer, 1974. "Introduction to "Schooling, Experience, and Earnings"," NBER Chapters, in: Schooling, Experience, and Earnings, pages 1-4, National Bureau of Economic Research, Inc.
    19. Gaure, Simen, 2013. "OLS with multiple high dimensional category variables," Computational Statistics & Data Analysis, Elsevier, vol. 66(C), pages 8-18.
    20. Jacob A. Mincer, 1974. "Age and Experience Profiles of Earnings," NBER Chapters, in: Schooling, Experience, and Earnings, pages 64-82, National Bureau of Economic Research, Inc.
    21. Ricardo Freguglia & Naercio Menezes-Filho, 2012. "Inter-regional wage differentials with individual heterogeneity: evidence from Brazil," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 49(1), pages 17-34, August.
    22. Goux, Dominique & Maurin, Eric, 1999. "Persistence of Interindustry Wage Differentials: A Reexamination Using Matched Worker-Firm Panel Data," Journal of Labor Economics, University of Chicago Press, vol. 17(3), pages 492-533, July.
    23. Glaeser, Edward L & Mare, David C, 2001. "Cities and Skills," Journal of Labor Economics, University of Chicago Press, vol. 19(2), pages 316-342, April.
    24. repec:fth:stanho:e-94-11 is not listed on IDEAS
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Rodger Barros Antunes Campos & Carlos Roberto Azzoni, 2019. "Estimating the Intra-Urban Wage Premium," TD NEREUS 4-2019, Núcleo de Economia Regional e Urbana da Universidade de São Paulo (NEREUS).
    2. Michaela Fuchs & Anja Rossen & Antje Weyh & Gabriele Wydra‐Somaggio, 2021. "Where do women earn more than men? Explaining regional differences in the gender pay gap," Journal of Regional Science, Wiley Blackwell, vol. 61(5), pages 1065-1086, November.
    3. Stefan P. T. Groot & Henri L. F. Groot, 2020. "Estimating the Skill Bias in Agglomeration Externalities and Social Returns to Education: Evidence from Dutch Matched Worker-Firm Micro-Data," De Economist, Springer, vol. 168(1), pages 53-78, March.
    4. Edyta Laszkiewicz, 2016. "Determinants Of Hourly Wages Inequality In Selected European Metropolises. The Results From The Multilevel Modelling," Equilibrium. Quarterly Journal of Economics and Economic Policy, Institute of Economic Research, vol. 11(4), pages 853-869, December.
    5. Paulo Guimarães & Pedro Portugal & Sónia Torres, 2010. "The Sources of Wage Variation: An Analysis Using Matched Employer-Employee Data," Working Papers w201025, Banco de Portugal, Economics and Research Department.
    6. Torben Sørensen & Rune Vejlin, 2013. "The importance of worker, firm and match effects in the formation of wages," Empirical Economics, Springer, vol. 45(1), pages 435-464, August.
    7. Chloé Duvivier Duvivier & Mary-Françoise Renard & Shi Li, 2012. "Are workers close to cities paid higher non-agricultural wages in rural China?," CERDI Working papers halshs-00673698, HAL.
    8. Ana Maria Bonomi Barufi & Eduardo Amaral Haddad & Peter Nijkamp, 2016. "Industrial scope of agglomeration economies in Brazil," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 56(3), pages 707-755, May.
    9. Vera A. Adamchik & Thomas J. Hyclak, 2013. "The Evolution Of Regional Wage Differentials In A Transition Economy: Evidence From Poland," Global Journal of Business Research, The Institute for Business and Finance Research, vol. 7(5), pages 1-13.
    10. Mittag, Nikolas, 2016. "A Simple Method to Estimate Large Fixed Effects Models Applied to Wage Determinants and Matching," IZA Discussion Papers 10447, Institute of Labor Economics (IZA).
    11. Fuchs, Michaela & Rossen, Anja & Weyh, Antje & Wydra-Somaggio, Gabriele, 2019. "Why do women earn more than men in some regions? : Explaining regional differences in the gender pay gap in Germany," IAB-Discussion Paper 201911, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
    12. Öner, Özge, 2013. "RETURNS TO LOCATION IN RETAIL: Investigating the relevance of market size and regional hierarchy," Working Paper Series in Economics and Institutions of Innovation 336, Royal Institute of Technology, CESIS - Centre of Excellence for Science and Innovation Studies.
    13. Achyuta Adhvaryu & Anant Nyshadham & Jorge Tamayo, 2023. "Managerial Quality and Productivity Dynamics," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 90(4), pages 1569-1607.
    14. Charlie Karlsson & Börje Johansson & Roger R. Stough, 2012. "Introduction – Human Capital and Agglomeration," Chapters, in: Charlie Karlsson & Börje Johansson & Roger R. Stough (ed.), The Regional Economics of Knowledge and Talent, chapter 1, Edward Elgar Publishing.
    15. Ana Maria Bonomi Barufi, 2014. "Regional labour markets in Brazil: the role of skills and agglomeration economies," ERSA conference papers ersa14p1307, European Regional Science Association.
    16. Morchio, Iacopo & Moser, Christian, 2018. "The Gender Pay Gap: Micro Sources and Macro Consequences," MPRA Paper 99276, University Library of Munich, Germany, revised 24 Mar 2020.
    17. Graham, Daniel J. & Gibbons, Stephen, 2019. "Quantifying Wider Economic Impacts of agglomeration for transport appraisal: Existing evidence and future directions," Economics of Transportation, Elsevier, vol. 19(C), pages 1-1.
    18. Woodcock Simon D, 2010. "Heterogeneity and Learning in Labor Markets," The B.E. Journal of Economic Analysis & Policy, De Gruyter, vol. 10(1), pages 1-69, September.
    19. Niklas Engbom & Christian Moser, 2017. "Returns to Education through Access to Higher-Paying Firms: Evidence from US Matched Employer-Employee Data," American Economic Review, American Economic Association, vol. 107(5), pages 374-378, May.
    20. Florian Lehmer & Joachim Möller, 2010. "Interrelations between the urban wage premium and firm-size wage differentials: a microdata cohort analysis for Germany," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 45(1), pages 31-53, August.

    More about this item

    JEL classification:

    • J24 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Human Capital; Skills; Occupational Choice; Labor Productivity
    • J31 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs - - - Wage Level and Structure; Wage Differentials
    • R23 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Household Analysis - - - Regional Migration; Regional Labor Markets; Population
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

    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:anp:en2016:163. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: Rodrigo Zadra Armond (email available below). General contact details of provider: https://edirc.repec.org/data/anpecea.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.