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Spatial and Temporal Aggregation in the Estimation of Labor Demand Functions

  • José Varejão
  • Pedro Portugal

The consequences of aggregation, temporal or spatial, for the estimation of demand models are theoretically well-known, but have not been documented empirically with appropriate data before. In this paper we conduct a simple, but instructive, exercise to fill in this gap, using a large quarterly dataset at the establishment-level that is increasingly aggregated up to the 2-digit SIC industry and the yearly frequency. We only obtain sensible results with the quadratic adjustment cost model at the most aggregated levels. Indeed, the results for quadratic adjustment costs confirm that aggregation along both dimensions works to produce more reasonable estimates of the parameters of interest. The fixed adjustment cost model performs remarkably well with quarterly, but also with yearly, data. We argue that is may be one more consequence of the unusually high labor adjustment costs in the Portuguese labor market.

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Paper provided by Banco de Portugal, Economics and Research Department in its series Working Papers with number w200704.

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Date of creation: 2007
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Handle: RePEc:ptu:wpaper:w200704
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  1. Daniel S. Hamermesh, 1990. "Aggregate Employment Dynamcis and Lumpy Adjustment Costs," NBER Working Papers 3229, National Bureau of Economic Research, Inc.
  2. Donald Robertson & James Symons, 1991. "Some Strange Properties of Panel Data Estimators," CEP Discussion Papers dp0044, Centre for Economic Performance, LSE.
  3. Richard Blundell & Stephen Bond, 2000. "GMM Estimation with persistent panel data: an application to production functions," Econometric Reviews, Taylor & Francis Journals, vol. 19(3), pages 321-340.
  4. Blundell, R. & Bond, S., 1995. "Initial Conditions and Moment Restrictions in Dynamic Panel Data Models," Economics Papers 104, Economics Group, Nuffield College, University of Oxford.
  5. Hamermesh, Daniel S, 1989. "Labor Demand and the Structure of Adjustment Costs," American Economic Review, American Economic Association, vol. 79(4), pages 674-89, September.
  6. M. H. Pesaran & R. G. Pierse & M. S. Kumar, 1988. "Econometric Analysis of Aggregation in the Context of Linear Prediction Models," UCLA Economics Working Papers 485, UCLA Department of Economics.
  7. M Arellano & O Bover, 1990. "Another Look at the Instrumental Variable Estimation of Error-Components Models," CEP Discussion Papers dp0007, Centre for Economic Performance, LSE.
  8. Anderson, Patricia M, 1993. "Linear Adjustment Costs and Seasonal Labor Demand: Evidence from Retail Trade Firms," The Quarterly Journal of Economics, MIT Press, vol. 108(4), pages 1015-42, November.
  9. Hansen, Lars Peter & Singleton, Kenneth J, 1982. "Generalized Instrumental Variables Estimation of Nonlinear Rational Expectations Models," Econometrica, Econometric Society, vol. 50(5), pages 1269-86, September.
  10. Robert F. Engle & Ta-Chung Liu, 1972. "Effects of Aggregation Over Time on Dynamic Characteristics of an Econometric Model," NBER Chapters, in: Econometric Models of Cyclical Behavior, Volumes 1 and 2, pages 673-737 National Bureau of Economic Research, Inc.
  11. Hansen, Lars Peter, 1982. "Large Sample Properties of Generalized Method of Moments Estimators," Econometrica, Econometric Society, vol. 50(4), pages 1029-54, July.
  12. Daniel S. Hamermesh, 1992. "Spatial and Temporal Aggregation in the Dynamics of Labor Demand," NBER Working Papers 4055, National Bureau of Economic Research, Inc.
  13. Arellano, Manuel & Bond, Stephen, 1991. "Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations," Review of Economic Studies, Wiley Blackwell, vol. 58(2), pages 277-97, April.
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