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Insights from kernel conditional-probability estimates into female labour force participation decision in the UK

Author

Listed:
  • Obbey Elamin

    (King Faisal University)

  • Len Gill

    (The University of Manchester)

  • Martyn Andrews

    (The University of Manchester)

Abstract

The female labour force participation decision in the UK is a well-researched topic in empirical economics and econometrics. In this paper, using data from the UK Labour Force Survey in 2007, we contribute to the rich body of the literature by estimating a model using the kernel mixed-data-type conditional-probability (KMDTCP) estimator. We extend the analysis by comparing the predicted probabilities of the KMDTCP estimator with the estimates of a multinomial logit (MNL) model. Additionally, kernel smoothing specification tests are applied in order to compare different functional forms for the independent variables in the MNL model against the KMDTCP estimates. Our results demonstrate that the difference between the predictions from the KMDTCP estimator and the MNL is reduced when the functional form of continuous independent variables in the MNL model is relaxed to a semi-parametric form using dummy variables and interaction terms. The properties that are captured by the KMDTCP estimator show clearly that the propensity to work decreases with the increase in the number of dependent children. Furthermore, our analyses show that females older than 40 years of age have a high propensity to work irrespective of the number of dependent children.

Suggested Citation

  • Obbey Elamin & Len Gill & Martyn Andrews, 2020. "Insights from kernel conditional-probability estimates into female labour force participation decision in the UK," Empirical Economics, Springer, vol. 58(6), pages 2981-3006, June.
    • Handle: RePEc:spr:empeco:v:58:y:2020:i:6:d:10.1007_s00181-019-01649-w
    DOI: 10.1007/s00181-019-01649-w
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    References listed on IDEAS

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    1. Bingley, Paul & Walker, Ian, 1997. "The Labour Supply, Unemployment and Participation of Lone Mothers in In-Work Transfer Programmes," Economic Journal, Royal Economic Society, vol. 107(444), pages 1375-1390, September.
    2. Zheng, John Xu, 2000. "A Consistent Test Of Conditional Parametric Distributions," Econometric Theory, Cambridge University Press, vol. 16(5), pages 667-691, October.
    3. Fan, Yanqin & Li, Qi & Min, Insik, 2006. "A Nonparametric Bootstrap Test Of Conditional Distributions," Econometric Theory, Cambridge University Press, vol. 22(4), pages 587-613, August.
    4. Paul Bingley & Yu Zhu & Ian Walker, 2005. "Education, Work and Wages in the UK," German Economic Review, Verein für Socialpolitik, vol. 6(3), pages 395-414, August.
    5. Peter Hall & Jeff Racine & Qi Li, 2004. "Cross-Validation and the Estimation of Conditional Probability Densities," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 1015-1026, December.
    6. Jianqing Fan & Tsz Ho Yim, 2004. "A crossvalidation method for estimating conditional densities," Biometrika, Biometrika Trust, vol. 91(4), pages 819-834, December.
    7. McFadden, Daniel L., 1984. "Econometric analysis of qualitative response models," Handbook of Econometrics, in: Z. Griliches† & M. D. Intriligator (ed.), Handbook of Econometrics, edition 1, volume 2, chapter 24, pages 1395-1457, Elsevier.
    8. Gerfin, Michael, 1996. "Parametric and Semi-parametric Estimation of the Binary Response Model of Labor Market Participation," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 11(3), pages 321-339, May-June.
    9. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521766555, January.
    10. Donald W. K. Andrews, 1997. "A Conditional Kolmogorov Test," Econometrica, Econometric Society, vol. 65(5), pages 1097-1128, September.
    11. Bashtannyk, David M. & Hyndman, Rob J., 2001. "Bandwidth selection for kernel conditional density estimation," Computational Statistics & Data Analysis, Elsevier, vol. 36(3), pages 279-298, May.
    12. Paul Bingley & Ian Walker, 2001. "Household Unemployment and the Labour Supply of Married Women," Economica, London School of Economics and Political Science, vol. 68(270), pages 157-186, May.
    13. Xiaodi Xie, 1997. "Children and female labour supply behaviour," Applied Economics, Taylor & Francis Journals, vol. 29(10), pages 1303-1310.
    14. Anil Kumar, 2006. "Nonparametric conditional density estimation of labour force participation," Applied Economics Letters, Taylor & Francis Journals, vol. 13(13), pages 835-841.
    15. Jan (J.) Rouwendal, 2017. "Specification Tests for The Multinomial Logit Model Revisited: The Role of Alternative-Specific Constants," Tinbergen Institute Discussion Papers 17-068/VIII, Tinbergen Institute, revised 29 Jan 2018.
    16. Li, Qi & Racine, Jeff, 2003. "Nonparametric estimation of distributions with categorical and continuous data," Journal of Multivariate Analysis, Elsevier, vol. 86(2), pages 266-292, August.
    17. Evelyn L. Lehrer, 1992. "The Impact of Children on Married Women's Labor Supply: Black-White Differentials Revisited," Journal of Human Resources, University of Wisconsin Press, vol. 27(3), pages 422-444.
    18. Philip Shaw & Marina-Selini Katsaiti & Brandon Pecoraro, 2015. "On The Determinants Of Educational Corruption: The Case Of Ukraine," Contemporary Economic Policy, Western Economic Association International, vol. 33(4), pages 698-713, October.
    19. Henderson,Daniel J. & Parmeter,Christopher F., 2015. "Applied Nonparametric Econometrics," Cambridge Books, Cambridge University Press, number 9780521279680.
    20. Zheng, Xu, 2008. "Testing for discrete choice models," Economics Letters, Elsevier, vol. 98(2), pages 176-184, February.
    21. Barry K. Goodwin & Matthew T. Holt, 2002. "Parametric and Semiparametric Modeling of the Off-Farm Labor Supply of Agrarian Households in Transition Bulgaria," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 84(1), pages 184-209.
    22. Jeff Racine & Qi Li & Xi Zhu, 2004. "Kernel Estimation of Multivariate Conditional Distributions," Annals of Economics and Finance, Society for AEF, vol. 5(2), pages 211-235, November.
    23. J. Scott Long & Jeremy Freese, 2006. "Regression Models for Categorical Dependent Variables using Stata, 2nd Edition," Stata Press books, StataCorp LP, edition 2, number long2, March.
    24. Hensher,David A. & Rose,John M. & Greene,William H., 2015. "Applied Choice Analysis," Cambridge Books, Cambridge University Press, number 9781107465923.
    25. Alice Nakamura & Masao Nakamura, 1994. "Predicting Female Labor Supply: Effects of Children and Recent Work Experience," Journal of Human Resources, University of Wisconsin Press, vol. 29(2), pages 304-327.
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    More about this item

    Keywords

    Labour force; Multinomial; Logit; Kernel; Conditional-density; Smoothing tests;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • J20 - Labor and Demographic Economics - - Demand and Supply of Labor - - - General
    • J21 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Labor Force and Employment, Size, and Structure

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