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Measures of Per Capita Hours and their Implications for the Technology-Hours Debate

Listed author(s):
  • Valerie A. Ramey

    (University of California, San Diego)

  • Neville Francis

    (University of North Carolina, Chapel Hill)

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 has significant low frequency movements that are the source of the conflicting results. HP filtered hours per capita produce results consistent with those obtained when hours are assumed to have a unit root. We provide alternative measures of hours per capita that adjust for low frequency movements in government and nonprofit employment, as well as the age composition of the population. When the new measures are used to determine the effect of technology shocks on hours using long-run restrictions, both the levels and the difference specifications give the same answer: hours decline in the short-run in response to a positive technology shock.

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File URL: https://economicdynamics.org/meetpapers/2007/paper_314.pdf
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Paper provided by Society for Economic Dynamics in its series 2007 Meeting Papers with number 314.

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Date of creation: 2007
Handle: RePEc:red:sed007:314
Contact details of provider: Postal:
Society for Economic Dynamics Marina Azzimonti Department of Economics Stonybrook University 10 Nicolls Road Stonybrook NY 11790 USA

Web page: http://www.EconomicDynamics.org/
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  1. Valerie A. Ramey & Neville Francis, 2009. "A Century of Work and Leisure," American Economic Journal: Macroeconomics, American Economic Association, vol. 1(2), pages 189-224, July.
  2. Jordi Gali, 1999. "Technology, Employment, and the Business Cycle: Do Technology Shocks Explain Aggregate Fluctuations?," American Economic Review, American Economic Association, vol. 89(1), pages 249-271, March.
  3. Edward C. Prescott, 2004. "Why do Americans work so much more than Europeans?," Quarterly Review, Federal Reserve Bank of Minneapolis, issue Jul, pages 2-13.
  4. Neville Francis & Michael T. Owyang & Jennifer E. Roush & Riccardo DiCecio, 2014. "A Flexible Finite-Horizon Alternative to Long-Run Restrictions with an Application to Technology Shocks," The Review of Economics and Statistics, MIT Press, vol. 96(4), pages 638-647, October.
  5. Kent D. Wall, 1976. "Interequation Constraint and the Specification of Dynamic Structure," NBER Working Papers 0119, National Bureau of Economic Research, Inc.
  6. V. V. Chari & Patrick J. Kehoe & Ellen R. McGrattan, 2004. "A Critique of Structural VARs Using Real Business Cycle Theory," Levine's Bibliography 122247000000000518, UCLA Department of Economics.
  7. Francis, Neville & Ramey, Valerie A., 2005. "Is the technology-driven real business cycle hypothesis dead? Shocks and aggregate fluctuations revisited," Journal of Monetary Economics, Elsevier, vol. 52(8), pages 1379-1399, November.
  8. Christiano, Lawrence J & Eichenbaum, Martin, 1992. "Current Real-Business-Cycle Theories and Aggregate Labor-Market Fluctuations," American Economic Review, American Economic Association, vol. 82(3), pages 430-450, June.
  9. Eichenbaum, Martin & Fisher, Jonas D M, 2005. "Fiscal Policy in the Aftermath of 9/11," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 37(1), pages 1-22, February.
  10. Matthew Shapiro & Mark Watson, 1988. "Sources of Business Cycles Fluctuations," NBER Chapters,in: NBER Macroeconomics Annual 1988, Volume 3, pages 111-156 National Bureau of Economic Research, Inc.
  11. Christopher J. Erceg & Luca Guerrieri & Christopher Gust, 2005. "Can Long-Run Restrictions Identify Technology Shocks?," Journal of the European Economic Association, MIT Press, vol. 3(6), pages 1237-1278, December.
  12. Lawrence J. Christiano & Martin Eichenbaum & Robert Vigfusson, 2003. "What Happens After a Technology Shock?," NBER Working Papers 9819, National Bureau of Economic Research, Inc.
  13. Jordi Gali Garreta & Pau Rabanal, 2004. "Technology Shocks and Aggregate Fluctuations; How Well Does the RBC Model Fit Postwar U.S. Data?," IMF Working Papers 04/234, International Monetary Fund.
  14. King, Robert G. & Plosser, Charles I. & Stock, James H. & Watson, Mark W., 1991. "Stochastic Trends and Economic Fluctuations," American Economic Review, American Economic Association, vol. 81(4), pages 819-840, September.
  15. 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.
  16. John G. Fernald, 2005. "Trend breaks, long-run restrictions, and the contractionary effects of technology improvements," Working Paper Series 2005-21, Federal Reserve Bank of San Francisco.
  17. Burnside, Craig & Eichenbaum, Martin, 1996. "Factor-Hoarding and the Propagation of Business-Cycle Shocks," American Economic Review, American Economic Association, vol. 86(5), pages 1154-1174, December.
  18. Jordi Gali Garreta & Pau Rabanal, 2004. "Technology Shocks and Aggregate Fluctuations; How Well Does the RBC Model Fit Postwar U.S. Data?," IMF Working Papers 04/234, International Monetary Fund.
  19. Neville Francis & Valerie A. Ramey, 2002. "Is the Technology-Driven Real Business Cycle Hypothesis Dead?," NBER Working Papers 8726, National Bureau of Economic Research, Inc.
  20. John Fernald, 2004. "Trend Breaks, Long Run Restrictions, and the Contractionary Effects of Technology Shocks," 2004 Meeting Papers 477, Society for Economic Dynamics.
  21. 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.).
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