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Nonlinearity in dynamic adjustment: Semiparametric estimation of panel labor supply

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Author Info

  • Qi Li

    (Department of Economics, Texas A&M University, College Station, TX 77843, U.S.A.)

  • Thomas J. Kniesner

    (Center for Policy Research, 426 Eggers Hall, Syracuse University, Syracuse, NY 13244-1020, U.S.A.)

Abstract

We estimate a semiparametric dynamic panel data model by the local linear kernel method and we interpret the slope of the nonparametric component function as a varying slope coefficient. Thus, the slope coefficient is a smooth, but otherwise unknown, function of some of the regressors. A Monte Carlo experiment is reported to examine the finite sample performance of the local linear estimator. We apply the estimation method to a labor supply equation for men from the triannual Survey of Income and Program Participation (SIPP). Specification tests based on the estimated labor supply elasticities, partial adjustment coefficients, and residuals demonstrate the improvements from a semiparametric partially linear model. Our empirical results point to a need by economists to revisit the issue of the speed of labor market adjustment to policy induced shifts in labor demand and to take more formal econometric account of heterogeneity in wage effects when studying the distributional consequences of tax reforms for labor supply earnings.

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Bibliographic Info

Article provided by Springer in its journal Empirical Economics.

Volume (Year): 27 (2002)
Issue (Month): 1 ()
Pages: 131-148

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Handle: RePEc:spr:empeco:v:27:y:2002:i:1:p:131-148

Note: received: July 2000/Final version received: January 2001
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Related research

Keywords: Nonlinearity · semiparametric estimation · labor supply · panel data · SIPP;

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Cited by:
  1. Henderson, Daniel J. & Olbrecht, Alexandre & Polachek, Solomon, 2005. "Do Former College Athletes Earn More at Work? A Nonparametric Assessment," IZA Discussion Papers 1882, Institute for the Study of Labor (IZA).
  2. repec:wyi:wpaper:001966 is not listed on IDEAS
  3. Tran, Kien C. & Tsionas, Efthymios G., 2009. "Estimation of nonparametric inefficiency effects stochastic frontier models with an application to British manufacturing," Economic Modelling, Elsevier, vol. 26(5), pages 904-909, September.
  4. Henderson, Daniel J. & Ullah, Aman, 2005. "A nonparametric random effects estimator," Economics Letters, Elsevier, vol. 88(3), pages 403-407, September.
  5. Francisca Antman & David McKenzie, 2007. "Poverty traps and nonlinear income dynamics with measurement error and individual heterogeneity," Journal of Development Studies, Taylor & Francis Journals, vol. 43(6), pages 1057-1083.
  6. Roy, Nilanjana & Cornelis van Kooten, G., 2004. "Another look at the income elasticity of non-point source air pollutants: a semiparametric approach," Economics Letters, Elsevier, vol. 85(1), pages 17-22, October.
  7. Anil Kumar, 2005. "Nonparametric estimation of the impact of taxes on female labor supply," Working Papers 0505, Federal Reserve Bank of Dallas.
  8. Qi Li & Jeffrey Scott Racine, 2006. "Nonparametric Econometrics: Theory and Practice," Economics Books, Princeton University Press, edition 1, volume 1, number 8355.
  9. Ozkan Eren & Daniel J. Henderson, 2008. "The impact of homework on student achievement," Econometrics Journal, Royal Economic Society, vol. 11(2), pages 326-348, 07.
  10. Li, Kui-Wai & Zhou, Xianbo, 2010. "Openness, domestic performance and growth," Economics Letters, Elsevier, vol. 107(1), pages 13-16, April.
  11. Kumar, Anil, 2008. "Labor supply, deadweight loss and tax reform act of 1986: A nonparametric evaluation using panel data," Journal of Public Economics, Elsevier, vol. 92(1-2), pages 236-253, February.
  12. Kusum Mundra, 2005. "Nonparametric Slope Estimators for Fixed-Effect Panel Data," Econometrics 0502008, EconWPA.
  13. Cai, Zongwu & Das, Mitali & Xiong, Huaiyu & Wu, Xizhi, 2006. "Functional coefficient instrumental variables models," Journal of Econometrics, Elsevier, vol. 133(1), pages 207-241, July.

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