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Measures of per Capita Hours and Their Implications for the Technology-Hours Debate

  • NEVILLE FRANCIS
  • VALERIE A. RAMEY

Structural vector autoregressions give conflicting results on the effects of technology shocks on hours. The results depend crucially on the assumed data generating process for hours per capita. We show that the standard measure of hours per capita and productivity have significant low-frequency movements that are the source of the conflicting results. Hodrick-Prescott (HP)-filtered hours per capita produce results consistent with those obtained when hours are assumed to have a unit root. We show that important sources of the low-frequency movements in the standard measure are sectoral shifts in hours and the changing age composition of the working-age population. When we control for these low-frequency components to determine the effect of technology shocks on hours using long-run restrictions we get one consistent answer: hours decline in the short run in response to a positive technology shock. We further extend the analysis by examining the effects of demographic controls on the impulse responses to investment-specific technology shocks. Our results are less conclusive. Copyright (c) 2009 The Ohio State University.

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Article provided by Blackwell Publishing in its journal Journal of Money, Credit and Banking.

Volume (Year): 41 (2009)
Issue (Month): 6 (09)
Pages: 1071-1097

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Handle: RePEc:mcb:jmoncb:v:41:y:2009:i:6:p:1071-1097
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  2. Baxter, Marianne & King, Robert G, 1993. "Fiscal Policy in General Equilibrium," American Economic Review, American Economic Association, vol. 83(3), pages 315-34, June.
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  9. Lawrence J. Christiano & Martin Eichenbaum & Robert Vigfusson, 2003. "What Happens After a Technology Shock?," NBER Working Papers 9819, National Bureau of Economic Research, Inc.
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  11. Gali, J., 1996. "Technology, Employment, and the Business Cycle: Do Technology Shocks Explain Aggregate Fluctuations?," Working Papers 96-28, C.V. Starr Center for Applied Economics, New York University.
  12. Neville Francis & Michael T. Owyang & Jennifer E. Roush, 2005. "A flexible finite-horizon identification of technology shocks," International Finance Discussion Papers 832, Board of Governors of the Federal Reserve System (U.S.).
  13. Cooley, Thomas F. & Dwyer, Mark, 1998. "Business cycle analysis without much theory A look at structural VARs," Journal of Econometrics, Elsevier, vol. 83(1-2), pages 57-88.
  14. Neville Francis & Michael T. Owyang & Jennifer E. Roush & Riccardo DiCecio, 2010. "A flexible finite-horizon alternative to long-run restrictions with an application to technology shock," Working Papers 2005-024, Federal Reserve Bank of St. Louis.
  15. Kent D. Wall, 1976. "Interequation Constraint and the Specification of Dynamic Structure," NBER Working Papers 0119, National Bureau of Economic Research, Inc.
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  17. John Fernald, 2004. "Trend Breaks, Long Run Restrictions, and the Contractionary Effects of Technology Shocks," 2004 Meeting Papers 477, Society for Economic Dynamics.
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