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An Econometric Model of Employment in Zimbabwe's Manufacturing Industries

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  • Heshmati, Almas

    ()
    (Dept. of Economic Statistics, Stockholm School of Economics)

  • Ncube, Mkhululi

    (Dept. of Economics, Göteborg University)

Abstract

This paper is concerned with the estimation of an employment relationship and employment efficiency under production risk using a panel of Zimbabwe's manufacturing industries. A flexible labour demand functions are used and consist of two parts: the traditional labour demand function and labour demand variance function. Labour demand is a function of wages, output, quasi-fixed inputs and time variables. The variance function is a function of the determinants of labour demand and a number of production and policy characteristic variables. It appears in a multiplicative form with the demand function and it accommodates both positive and negative marginal effects with respect to the determinants of the variance. A multi-step procedure is used to estimate the parameters of the model. Estimation of industry and time-varying employment efficiency is also considered. Employment efficiency is defined in terms of the distance from the employment frontier defined as minimum employment required to produce a given level of output. The empirical results show that the average employment efficiency is 92%.

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Bibliographic Info

Paper provided by Stockholm School of Economics in its series Working Paper Series in Economics and Finance with number 277.

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Length: 23 pages
Date of creation: 06 Nov 1998
Date of revision: 15 Aug 2003
Publication status: Published in Journal of Development Economics, 2005, pages 527-551.
Handle: RePEc:hhs:hastef:0277

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Keywords: Labour demand; variance; efficiency; manufacturing industries; Zimbabwe;

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  1. Subal Kumbhakar, 1997. "Efficiency estimation with heteroscedasticity in a panel data model," Applied Economics, Taylor & Francis Journals, vol. 29(3), pages 379-386.
  2. Pindyck, Robert S & Rotemberg, Julio J, 1983. " Dynamic Factor Demands under Rational Expectations," Scandinavian Journal of Economics, Wiley Blackwell, vol. 85(2), pages 223-38.
  3. Aigner, Dennis & Lovell, C. A. Knox & Schmidt, Peter, 1977. "Formulation and estimation of stochastic frontier production function models," Journal of Econometrics, Elsevier, vol. 6(1), pages 21-37, July.
  4. Kumbhakar, Subal C & Hjalmarsson, Lennart, 1995. "Labour-Use Efficiency in Swedish Social Insurance Offices," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 10(1), pages 33-47, Jan.-Marc.
  5. Schmidt, Peter & Sickles, Robin C, 1984. "Production Frontiers and Panel Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 2(4), pages 367-74, October.
  6. Just, Richard E. & Pope, Rulon D., 1978. "Stochastic specification of production functions and economic implications," Journal of Econometrics, Elsevier, vol. 7(1), pages 67-86, February.
  7. Layard, Richard & Nickell, Stephen, 1986. "Unemployment in Britain," Economica, London School of Economics and Political Science, vol. 53(210(S)), pages S121-69, Supplemen.
  8. Boozer, Michael A., 1997. "Econometric Analysis of Panel Data Badi H. Baltagi Wiley, 1995," Econometric Theory, Cambridge University Press, vol. 13(05), pages 747-754, October.
  9. Diewert, W E, 1974. "Functional Forms for Revenue and Factor Requirements Functions," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 15(1), pages 119-30, February.
  10. Symons, James S V, 1985. "Relative Prices and the Demand for Labour in British Manufacturing," Economica, London School of Economics and Political Science, vol. 52(205), pages 37-49, February.
  11. Heshmati, Almas, 1994. "Estimating random effects production function models with selectivity bias: an application to Swedish crop producers," Agricultural Economics, Blackwell, vol. 11(2-3), pages 171-189, December.
  12. Caudill, Steven B & Ford, Jon M & Gropper, Daniel M, 1995. "Frontier Estimation and Firm-Specific Inefficiency Measures in the Presence of Heteroscedasticity," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(1), pages 105-11, January.
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