We are grateful to Shawn Sprague at the Bureau of Labor Statistics for providing the raw series used in the BLS hours index. We thank Mark Gertler, Clive Granger, James Hamilton, Mark Watson, seminar participants at the New York Federal Reserve, and two anonymous referees for their helpful comments. Chris Nekarda provided outstanding research assistance. Valerie Ramey gratefully acknowledges financial support from National Science Foundation grant SES-0617219 through the NBER.
Measures of per Capita Hours and Their Implications for the Technology-Hours Debate
Article first published online: 10 AUG 2009
© 2009 The Ohio State University
Journal of Money, Credit and Banking
Volume 41, Issue 6, pages 1071–1097, September 2009
How to Cite
FRANCIS, N. and RAMEY, V. A. (2009), Measures of per Capita Hours and Their Implications for the Technology-Hours Debate. Journal of Money, Credit and Banking, 41: 1071–1097. doi: 10.1111/j.1538-4616.2009.00247.x
- Issue published online: 10 AUG 2009
- Article first published online: 10 AUG 2009
- Received May 22, 2007; and accepted in revised form March 2, 2009.
- business cycles;
- technology shocks;
- demographic shifts
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.