IDEAS home Printed from https://ideas.repec.org/p/sas/wpaper/20113.html
   My bibliography  Save this paper

The make-up of a regression coefficient: An application to gender

Author

Listed:
  • M. Grazia Pittau

    (Sapienza Universita' di Roma)

  • Shlomo Yitzhaki

    (The Hebrew University of Jerusalem and Central Bureau of Statistics)

  • Roberto Zelli

    (Sapienza Universita' di Roma)

Abstract

In this paper we illustrate the potential use of an old/new methodology which combines the use of concentration curves in order to investigate the components that make up a regression coefficient. The illustration is based on examining gender differences in the effect of age on labor market participation in Italy. Women participation rate is substantially lower than men, but their age profile is similar. The most striking difference is in terms of hours of work: while Italian men increase their work effort until the age of 35, Italian women reduce it until the age of 39. These results do not differ substantially when we split the working population into employed and self-employed. Earnings increase with age for both men and women, however the local regression coefficient is negative for Italian women in the age of 38–42. This evidence is accentuated when we focus on the employees.

Suggested Citation

  • M. Grazia Pittau & Shlomo Yitzhaki & Roberto Zelli, 2011. "The make-up of a regression coefficient: An application to gender," DSS Empirical Economics and Econometrics Working Papers Series 2011/3, Centre for Empirical Economics and Econometrics, Department of Statistics, "Sapienza" University of Rome.
  • Handle: RePEc:sas:wpaper:20113
    as

    Download full text from publisher

    File URL: http://www.dss.uniroma1.it/RePec/sas/wpaper/20113_pittau.pdf
    File Function: First version, 2011
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Edna Schechtman & Shlomo Yitzhaki & Taina Pudalov, 2011. "Gini’s multiple regressions: two approaches and their interaction," Metron - International Journal of Statistics, Dipartimento di Statistica, Probabilità e Statistiche Applicate - University of Rome, vol. 0(1), pages 67-99.
    2. Joachim R. Frick & Jan Goebel & Edna Schechtman & Gert G. Wagner & Shlomo Yitzhaki, 2006. "Using Analysis of Gini (ANOGI) for Detecting Whether Two Subsamples Represent the Same Universe," Sociological Methods & Research, , vol. 34(4), pages 427-468, May.
    3. Shlomo Yitzhaki & Edna Schechtman, 2004. "The Gini Instrumental Variable, or the “double instrumental variable” estimator," Metron - International Journal of Statistics, Dipartimento di Statistica, Probabilità e Statistiche Applicate - University of Rome, vol. 0(3), pages 287-313.
    4. Youri Davydov & Ricardas Zitikis, 2005. "An index of monotonicity and its estimation: a step beyond econometric applications of the Gini index," Metron - International Journal of Statistics, Dipartimento di Statistica, Probabilità e Statistiche Applicate - University of Rome, vol. 0(3), pages 351-372.
    5. Yitzhaki, Shlomo, 1991. "Calculating Jackknife Variance Estimators for Parameters of the Gini Method," Journal of Business & Economic Statistics, American Statistical Association, vol. 9(2), pages 235-239, April.
    6. Schechtman, Edna & Shelef, Amit & Yitzhaki, Shlomo & Zitikis, Ričardas, 2008. "Testing Hypotheses About Absolute Concentration Curves And Marginal Conditional Stochastic Dominance," Econometric Theory, Cambridge University Press, vol. 24(4), pages 1044-1062, August.
    7. Shlomo Yitzhaki, 2003. "Gini’s Mean difference: a superior measure of variability for non-normal distributions," Metron - International Journal of Statistics, Dipartimento di Statistica, Probabilità e Statistiche Applicate - University of Rome, vol. 0(2), pages 285-316.
    8. Haim Shalit & Shlomo Yitzhaki, 1994. "Marginal Conditional Stochastic Dominance," Management Science, INFORMS, vol. 40(5), pages 670-684, May.
    9. Frick, Joachim R. & Goebel, Jan & Schechtman, Edna & Wagner, Gert G. & Yitzhaki, Shlomo, 2006. "Using Analysis of Gini (ANOGI) for Detecting Whether Two Subsamples Represent the Same Universe: The German Socio-Economic Panel Study (SOEP) Experience," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 34(4), pages 427-468.
    10. Yitzhaki, Shlomo & Schechtman, Edna, 2012. "Identifying monotonic and non-monotonic relationships," Economics Letters, Elsevier, vol. 116(1), pages 23-25.
    11. Schechtman, Edna & Yitzhaki, Shlomo & Artsev, Yevgeny, 2008. "Who Does Not Respond in the Household Expenditure Survey," Journal of Business & Economic Statistics, American Statistical Association, vol. 26, pages 329-344.
    12. Yitzhaki, Shlomo, 1996. "On Using Linear Regressions in Welfare Economics," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(4), pages 478-486, October.
    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. Edna Schechtman & Shlomo Yitzhaki & Taina Pudalov, 2011. "Gini’s multiple regressions: two approaches and their interaction," Metron - International Journal of Statistics, Dipartimento di Statistica, Probabilità e Statistiche Applicate - University of Rome, vol. 0(1), pages 67-99.
    2. Schröder, Carsten & Yitzhaki, Shlomo, 2017. "Revisiting the evidence for cardinal treatment of ordinal variables," European Economic Review, Elsevier, vol. 92(C), pages 337-358.
    3. M. Grazia Pittau & Shlomo Yitzhaki & Roberto Zelli, 2015. "The “Make-up” of a Regression Coefficient: Gender Gaps in the European Labor Market," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 61(3), pages 401-421, September.
    4. Shlomo Yitzhaki, 2015. "Gini’s mean difference offers a response to Leamer’s critique," METRON, Springer;Sapienza Università di Roma, vol. 73(1), pages 31-43, April.
    5. Amit Shelef & Edna Schechtman, 2021. "A Gini-based analysis of the differences between men and women in the labor market over time," Working Papers 2102, Ben-Gurion University of the Negev, Department of Economics.
    6. Alessio Guandalini, 2022. "Things you should know about the Gini index," RIEDS - Rivista Italiana di Economia, Demografia e Statistica - The Italian Journal of Economic, Demographic and Statistical Studies, SIEDS Societa' Italiana di Economia Demografia e Statistica, vol. 76(4), pages 4-12, October-D.
    7. Shlomo Yitzhaki & Peter Lambert, 2013. "The relationship between the absolute deviation from a quantile and Gini’s mean difference," METRON, Springer;Sapienza Università di Roma, vol. 71(2), pages 97-104, September.
    8. Emanuela Raffinetti & Elena Siletti & Achille Vernizzi, 2017. "Analyzing the Effects of Negative and Non-negative Values on Income Inequality: Evidence from the Survey of Household Income and Wealth of the Bank of Italy (2012)," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 133(1), pages 185-207, August.
    9. Yitzhaki, Shlomo & Schechtman, Edna, 2012. "Identifying monotonic and non-monotonic relationships," Economics Letters, Elsevier, vol. 116(1), pages 23-25.
    10. Ndéné Ka & Stéphane Mussard, 2016. "ℓ 1 regressions: Gini estimators for fixed effects panel data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(8), pages 1436-1446, June.
    11. Barrington-Leigh, C.P., 2024. "The econometrics of happiness: Are we underestimating the returns to education and income?," Journal of Public Economics, Elsevier, vol. 230(C).
    12. Andrew E. Clark & Sarah Flèche & Claudia Senik, 2016. "Economic Growth Evens Out Happiness: Evidence from Six Surveys," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 62(3), pages 405-419, September.
    13. Thomas Masterson & Ajit Zacharias & Fernando Rios-Avila & Edward N. Wolff, 2019. "The Great Recession and Racial Inequality: Evidence from Measures of Economic Well-Being," Journal of Economic Issues, Taylor & Francis Journals, vol. 53(4), pages 1048-1069, October.
    14. S. Anger & J. R. Frick & J. Goebel & M. M. Grabka & O. Groh-Samberg & H. Haas & E. Holst & P. Krause & M. Kroh & H. Lohmann & R. Pischner & J. Schupp & I. Sieber & T. Siedler & C. Schmitt & C. K. Spie, 2008. "Zur Weiterentwicklung von SOEPsurvey und SOEPservice," Vierteljahrshefte zur Wirtschaftsforschung / Quarterly Journal of Economic Research, DIW Berlin, German Institute for Economic Research, vol. 77(3), pages 157-177.
    15. Martin Gornig & Jan Goebel, 2018. "Deindustrialisation and the polarisation of household incomes: The example of urban agglomerations in Germany," Urban Studies, Urban Studies Journal Limited, vol. 55(4), pages 790-806, March.
    16. Luis Ayala & Javier Martín‐Román & Juan Vicente, 2020. "The contribution of the spatial dimension to inequality: A counterfactual analysis for OECD countries," Papers in Regional Science, Wiley Blackwell, vol. 99(3), pages 447-477, June.
    17. Denuit, Michel M. & Huang, Rachel J. & Tzeng, Larry Y. & Wang, Christine W., 2014. "Almost marginal conditional stochastic dominance," Journal of Banking & Finance, Elsevier, vol. 41(C), pages 57-66.
    18. Mark Wooden & Ning Li, 2014. "Panel Conditioning and Subjective Well-being," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 117(1), pages 235-255, May.
    19. N. V. Gribkova & J. Su & R. Zitikis, 2022. "Empirical tail conditional allocation and its consistency under minimal assumptions," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 74(4), pages 713-735, August.
    20. Saurabh & R. V. Ramanamurthy, 2023. "Employment status and wealth inequality between scheduled caste and other caste households in India," Journal of Social and Economic Development, Springer;Institute for Social and Economic Change, vol. 25(1), pages 1-16, June.

    More about this item

    Keywords

    Gini; OLS; Concentration curves; Regression decomposition; Italian labor market.;
    All these keywords.

    JEL classification:

    • C30 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - General
    • J16 - Labor and Demographic Economics - - Demographic Economics - - - Economics of Gender; Non-labor Discrimination
    • J21 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Labor Force and Employment, Size, and Structure

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:sas:wpaper:20113. 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: Stefano Fachin (email available below). General contact details of provider: https://edirc.repec.org/data/ddrosit.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.