IDEAS home Printed from https://ideas.repec.org/a/bla/rgscpp/v13y2021i3p957-981.html
   My bibliography  Save this article

Spatial aggregation and resampling expansion of big surveys: An analysis of wage inequality

Author

Listed:
  • Beatriz Larraz
  • Jose M. Pavía
  • Marcos Herrera‐Gómez

Abstract

Income inequality is becoming a growing concern, worldwide, with wage inequality being the root cause of its recent escalation. With the aim of adding to the knowledge on this subject, this paper focuses on the spatial dimension of the problem, an aspect which has received less attention in the literature. We identify the determinants of inequality in wage distribution in Spain at a provincial level using the microdata of the Structure of Earnings Survey (N = 216,769) and estimate their impact from a spatial perspective. Spatial computation of wage concentrations, however, reduces the sample size to just 52 observations, leading to model challenges. To overcome this problem, we adopt a super‐population approach and, by exploiting the rich dataset available, increase the instances of the model variables by replicating the actual sampling design employed to collect the data. We apply a re‐sampling method that gives us the opportunity to test the impact of spatial dependence and recover provincial fixed effects. Our best model is a spatial‐lag of X fixed‐effect SLX model. We analyse the impact of workers' personal and employment characteristics, their workplace and their membership of a province on the concentration of wages. Our study finds that a more equitable distribution of wages could be brought about by reducing the impact of workers' training and level of responsibility on their wages and by the promotion of small and medium‐sized enterprises. These results highlight the importance of the labour and salary structure in the equitable development of societies. La desigualdad de ingresos se está convirtiendo en una preocupación creciente en todo el mundo debido a la desigualdad salarial como la causa principal de su aumento reciente. Con el fin de ampliar el conocimiento sobre este tema, este artículo se centra en la dimensión espacial del problema, que es un aspecto que ha recibido menos atención en la literatura. Se identificaron los determinantes de la desigualdad en la distribución de los salarios en España a nivel provincial mediante el uso de microdatos de la Encuesta de Estructura de los Ingresos (N = 216.769) y se estimó su impacto desde una perspectiva espacial. Sin embargo, el cálculo espacial de las concentraciones salariales reduce el tamaño de la muestra a sólo 52 observaciones, lo que plantea dificultades para la modelización. Para superar este problema, se adoptó un enfoque superpoblacional y, aprovechando el amplio conjunto de datos disponibles, aumentar las instancias de las variables del modelo replicando el diseño de muestreo real empleado para recoger los datos. Se aplicó un método de remuestreo que dio la oportunidad de probar el impacto de la dependencia espacial y recuperar los efectos fijos provinciales. El mejor modelo fue un modelo SLX de efecto fijo con desfase espacial de X. Se analizó el impacto de las características personales y laborales de los trabajadores, su lugar de trabajo y su pertenencia a una provincia en la concentración de los salarios. El estudio concluye que se podría lograr una distribución más equitativa de los salarios reduciendo el impacto de la formación y el nivel de responsabilidad de los trabajadores en sus salarios y mediante la promoción de empresas de tamaño pequeño y mediano. Estos resultados ponen de relieve la importancia de la estructura laboral y salarial en el desarrollo equitativo de las sociedades. 所得の不平等の問題は世界的に拡大しており、賃金格差がその最近の拡大の根本原因となっている。本稿では、この問題に関するさらなる知識を得るため、研究ではあまり注目されていない側面であるこの問題の空間的次元に焦点を当てる。Structure of Earnings Survey(N=216,769)のミクロデータを用いてスペインの県レベルでの賃金分配の不平等の決定要因を明らかにし、その影響を空間的視点から推計した。しかし、空間的な賃金の集中度の算出では、サンプルサイズはわずか52にまで減少し、モデルに関する問題がもたらされた。この問題を克服するために、超母集団アプローチを採用し、利用可能な豊富なデータセットを活用することにより、データ収集に用いた実際のサンプリング設計を複製することにより、モデルのインスタンス変数を増やした。空間依存性の影響を検証し、県の固定効果を回復する機会が得られる再サンプリング法を適用した。最適なモデルは、X固定効果SLXモデルの空間ラグである。我々は、労働者の個人特性および就業特性、労働者の職場、その県の住人であることが賃金の集中に及ぼす影響を分析した。この結果から、労働者の訓練と責務のレベルの賃金に対する影響を低減し、中小企業を促進することにより、より公平な賃金の分配がもたらされることが分かった。この結果は、社会の公平な発展における労働と給与の構造の重要性を強調している。

Suggested Citation

  • Beatriz Larraz & Jose M. Pavía & Marcos Herrera‐Gómez, 2021. "Spatial aggregation and resampling expansion of big surveys: An analysis of wage inequality," Regional Science Policy & Practice, Wiley Blackwell, vol. 13(3), pages 957-981, June.
  • Handle: RePEc:bla:rgscpp:v:13:y:2021:i:3:p:957-981
    DOI: 10.1111/rsp3.12333
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/rsp3.12333
    Download Restriction: no

    File URL: https://libkey.io/10.1111/rsp3.12333?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. José Manuel Pastor & Jose M. Pavía & Lorenzo Serrano & Emili Tortosa-Ausina, 2017. "Rich regions, poor regions and bank branch deregulation in Spain," Regional Studies, Taylor & Francis Journals, vol. 51(11), pages 1678-1694, November.
    2. Antonczyk, Dirk & Fitzenberger, Bernd & Sommerfeld, Katrin, 2010. "Rising wage inequality, the decline of collective bargaining, and the gender wage gap," Labour Economics, Elsevier, vol. 17(5), pages 835-847, October.
    3. David Castells-Quintana & Vicente Royuela, 2014. "Agglomeration, inequality and economic growth," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 52(2), pages 343-366, March.
    4. Debarsy, Nicolas & Ertur, Cem, 2010. "Testing for spatial autocorrelation in a fixed effects panel data model," Regional Science and Urban Economics, Elsevier, vol. 40(6), pages 453-470, November.
    5. Florax, Raymond & Folmer, Henk, 1992. "Specification and estimation of spatial linear regression models : Monte Carlo evaluation of pre-test estimators," Regional Science and Urban Economics, Elsevier, vol. 22(3), pages 405-432, September.
    6. Kevin M. Murphy & Robert H. Topel, 2016. "Human Capital Investment, Inequality and Economic Growth," NBER Working Papers 21841, National Bureau of Economic Research, Inc.
    7. David H. Autor & Frank Levy & Richard J. Murnane, 2003. "The skill content of recent technological change: an empirical exploration," Proceedings, Federal Reserve Bank of San Francisco, issue Nov.
    8. Andrés Rodríguez‐Pose & Vassilis Tselios, 2009. "Education And Income Inequality In The Regions Of The European Union," Journal of Regional Science, Wiley Blackwell, vol. 49(3), pages 411-437, August.
    9. Hausman, Jerry, 2015. "Specification tests in econometrics," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 38(2), pages 112-134.
    10. M. Hashem Pesaran, 2021. "General diagnostic tests for cross-sectional dependence in panels," Empirical Economics, Springer, vol. 60(1), pages 13-50, January.
    11. Richard Florida & Charlotta Mellander, 2016. "The Geography of Inequality: Difference and Determinants of Wage and Income Inequality across US Metros," Regional Studies, Taylor & Francis Journals, vol. 50(1), pages 79-92, January.
    12. Eliana Cardoso, 1993. "Cyclical variations of earnings inequality in Brazil," Brazilian Journal of Political Economy, Center of Political Economy, vol. 13(4).
    13. David H. Autor & Lawrence F. Katz & Melissa S. Kearney, 2006. "The Polarization of the U.S. Labor Market," American Economic Review, American Economic Association, vol. 96(2), pages 189-194, May.
    14. Vicente Royuela & Paolo Veneri & Raul Ramos, 2019. "The short-run relationship between inequality and growth: evidence from OECD regions during the Great Recession," Regional Studies, Taylor & Francis Journals, vol. 53(4), pages 574-586, April.
    15. Erling Barth & Alex Bryson & James C. Davis & Richard Freeman, 2016. "It's Where You Work: Increases in the Dispersion of Earnings across Establishments and Individuals in the United States," Journal of Labor Economics, University of Chicago Press, vol. 34(S2), pages 67-97.
    16. Lars Osberg, 2017. "On the Limitations of Some Current Usages of the Gini Index," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 63(3), pages 574-584, September.
    17. Rubens Penha Cysne, 2009. "On the Positive Correlation between Income Inequality and Unemployment," The Review of Economics and Statistics, MIT Press, vol. 91(1), pages 218-226, February.
    18. Solmaria Halleck Vega & J. Paul Elhorst, 2015. "The Slx Model," Journal of Regional Science, Wiley Blackwell, vol. 55(3), pages 339-363, June.
    19. H. Naci Mocan, 1999. "Structural Unemployment, Cyclical Unemployment, and Income Inequality," The Review of Economics and Statistics, MIT Press, vol. 81(1), pages 122-134, February.
    20. László Mátyás & Patrick Sevestre (ed.), 2008. "The Econometrics of Panel Data," Advanced Studies in Theoretical and Applied Econometrics, Springer, number 978-3-540-75892-1, July-Dece.
    21. Daron Acemoglu, 1998. "Why Do New Technologies Complement Skills? Directed Technical Change and Wage Inequality," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 113(4), pages 1055-1089.
    22. Nicolas Herault & Francisco Azpitarte, 2016. "Understanding Changes in the Distribution and Redistribution of Income: A Unifying Decomposition Framework," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 62(2), pages 266-282, June.
    23. Jose M. Pavía & Francisco G. Morillas & Juan Carlos Bosch-Rodríguez, 2019. "Using Parametric Bootstrap to Introduce and Manage Uncertainty: Replicated Loaded Insurance Life Tables," North American Actuarial Journal, Taylor & Francis Journals, vol. 23(3), pages 434-446, July.
    24. Wiji Arulampalam & Alison L. Booth & Mark L. Bryan, 2007. "Is There a Glass Ceiling over Europe? Exploring the Gender Pay Gap across the Wage Distribution," ILR Review, Cornell University, ILR School, vol. 60(2), pages 163-186, January.
    25. Patrick Sevestre & Laszlo Matyas, 2008. "The Econometrics of Panel Data," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-00279977, HAL.
    26. Aguilera Moyano, Sandra & Costa Saenz de San Pedro, Àlex & Cotrina Aguirre, Dolors & Fíguls Sierra, Marc & Galletto, Vittorio & Puig Paronella, Enric & Raymond, Josep Lluís, 2020. "¿Cambia la productividad en el territorio? Una propuesta metodológica para la estimación del PIB urbano en la economía española," INVESTIGACIONES REGIONALES - Journal of REGIONAL RESEARCH, Asociación Española de Ciencia Regional, issue 47, pages 79-95.
    27. Neil Lee & Paul Sissons, 2016. "Inclusive growth? The relationship between economic growth and poverty in British cities," Environment and Planning A, , vol. 48(11), pages 2317-2339, November.
    28. David H. Autor & Lawrence F. Katz & Alan B. Krueger, 1998. "Computing Inequality: Have Computers Changed the Labor Market?," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 113(4), pages 1169-1213.
    29. Ms. Era Dabla-Norris & Ms. Kalpana Kochhar & Mrs. Nujin Suphaphiphat & Mr. Franto Ricka & Ms. Evridiki Tsounta, 2015. "Causes and Consequences of Income Inequality: A Global Perspective," IMF Staff Discussion Notes 2015/013, International Monetary Fund.
    30. Anselin, Luc, 2002. "Under the hood : Issues in the specification and interpretation of spatial regression models," Agricultural Economics, Blackwell, vol. 27(3), pages 247-267, November.
    31. Eliana Cardoso, 1993. "Cyclical variations of earnings inequality in Brazil," Brazilian Journal of Political Economy, Center of Political Economy, vol. 13(4), pages 112-124.
    32. Kevin M. Murphy & Robert H. Topel, 2016. "Human Capital Investment, Inequality, and Economic Growth," Journal of Labor Economics, University of Chicago Press, vol. 34(S2), pages 99-127.
    33. Topel, Robert H, 1994. "Regional Labor Markets and the Determinants of Wage Inequality," American Economic Review, American Economic Association, vol. 84(2), pages 17-22, May.
    34. Pickett, Kate E. & Wilkinson, Richard G., 2015. "Income inequality and health: A causal review," Social Science & Medicine, Elsevier, vol. 128(C), pages 316-326.
    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. Diana Barros & Aurora A. C. Teixeira, 2021. "Unlocking the black box: A comprehensive meta-analysis of the main determinants of within-region income inequality," Review of Regional Research: Jahrbuch für Regionalwissenschaft, Springer;Gesellschaft für Regionalforschung (GfR), vol. 41(1), pages 55-93, February.
    2. Gil-Alana, Luis A. & Škare, Marinko & Pržiklas-Družeta, Romina, 2019. "Measuring inequality persistence in OECD 1963–2008 using fractional integration and cointegration," The Quarterly Review of Economics and Finance, Elsevier, vol. 72(C), pages 65-72.
    3. Kaltenberg, Mary & Foster-McGregor, Neil, 2020. "The impact of automation on inequality across Europe," MERIT Working Papers 2020-009, United Nations University - Maastricht Economic and Social Research Institute on Innovation and Technology (MERIT).
    4. Roger Bivand & Giovanni Millo & Gianfranco Piras, 2021. "A Review of Software for Spatial Econometrics in R," Mathematics, MDPI, vol. 9(11), pages 1-40, June.
    5. Regina Pleninger & Jakob de Haan & Jan‐Egbert Sturm, 2022. "The ‘Forgotten’ middle class: An analysis of the effects of globalisation," The World Economy, Wiley Blackwell, vol. 45(1), pages 76-110, January.
    6. Millo, Giovanni, 2014. "Maximum likelihood estimation of spatially and serially correlated panels with random effects," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 914-933.
    7. Fonseca, Tiago & Lima, Francisco & Pereira, Sonia C., 2018. "Understanding productivity dynamics: A task taxonomy approach," Research Policy, Elsevier, vol. 47(1), pages 289-304.
    8. Herrera Gómez, Marcos, 2017. "Fundamentos de Econometría Espacial Aplicada [Fundamentals of Applied Spatial Econometrics]," MPRA Paper 80871, University Library of Munich, Germany.
    9. Larraz, Beatriz & Herrera, Marcos, 2016. "Factores condicionantes y dependencia espacial en el grado de concentración salarial en España/Determinants of Wage Inequality in Spain: A Spatial Approach," Estudios de Economia Aplicada, Estudios de Economia Aplicada, vol. 34, pages 597-618, Agosto.
    10. Cem Ertur & Antonio Musolesi, 2017. "Weak and Strong Cross‐Sectional Dependence: A Panel Data Analysis of International Technology Diffusion," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(3), pages 477-503, April.
    11. David Hémous & Morten Olsen, 2022. "The Rise of the Machines: Automation, Horizontal Innovation, and Income Inequality," American Economic Journal: Macroeconomics, American Economic Association, vol. 14(1), pages 179-223, January.
    12. Carlos Medina & Christian Posso, 2010. "Technical Change and Polarization of the Labor Market: Evidence for Brazil, Colombia and Mexico," Borradores de Economia 614, Banco de la Republica de Colombia.
    13. T. Gries & R. Grundmann & I. Palnau & M. Redlin, 2017. "Innovations, growth and participation in advanced economies - a review of major concepts and findings," International Economics and Economic Policy, Springer, vol. 14(2), pages 293-351, April.
    14. David J. Deming, 2017. "The Growing Importance of Social Skills in the Labor Market," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 132(4), pages 1593-1640.
    15. Ariell Reshef, 2013. "Is Technological Change Biased Towards the Unskilled in Services? An Empirical Investigation," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 16(2), pages 312-331, April.
    16. Cem Ertur & Antonio Musolesi, 2014. "Dépendance individuelle forte et faible : une analyse en données de panel de la diffusion internationale de la technologie," Working Papers halshs-01015208, HAL.
    17. Cortes, Guido Matias & Lerche, Adrian & Schönberg, Uta & Tschopp, Jeanne, 2023. "Technological Change, Firm Heterogeneity and Wage Inequality," IZA Discussion Papers 16070, Institute of Labor Economics (IZA).
    18. Gray, Rowena, 2013. "Taking technology to task: The skill content of technological change in early twentieth century United States," Explorations in Economic History, Elsevier, vol. 50(3), pages 351-367.
    19. Luigi Mastronardi & Aurora Cavallo, 2020. "The Spatial Dimension of Income Inequality: An Analysis at Municipal Level," Sustainability, MDPI, vol. 12(4), pages 1-18, February.
    20. Pi, Jiancai & Zhang, Pengqing, 2021. "Redistribution and wage inequality," International Review of Economics & Finance, Elsevier, vol. 72(C), pages 510-523.

    More about this item

    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:bla:rgscpp:v:13:y:2021:i:3:p:957-981. 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: Wiley Content Delivery (email available below). General contact details of provider: http://www.blackwellpublishing.com/journal.asp?ref=1757-7802 .

    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.