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Technology shocks and hours worked: Checking for robust conclusions

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  • Whelan, Karl T.

Abstract

This paper presents some new results on the effects of technology shocks on hours worked based on structural VAR specifications containing various measures of US productivity growth and hours. These specifications can produce different answers depending on which sector of the economy is examined, which transformation of hours worked is used, and on how many lags are chosen for the VAR. However, it is shown that the results from the stochastic trend specification used by Galí [Galí, Jordi., 1999. Technology, employment and the business cycle: do technology shocks explain aggregate fluctuations. American Economic Review, 89, 249-271] are robust across changes in data definition and lag length, while the results from the per capita hours specification of Christiano, Eichenbaum, and Vigfusson [Christiano, Lawrence., Eichenbaum, M., Vigfusson, R., 2003. What happens after a technology shock? Federal Reserve Board, International Finance Discussion Paper, 2003, p. 768] are not. These results provide support for Galí's findings that technology shocks have a negative impact effect on hours worked and that these shocks play a limited role in generating the business cycle.

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  • Whelan, Karl T., 2009. "Technology shocks and hours worked: Checking for robust conclusions," Journal of Macroeconomics, Elsevier, vol. 31(2), pages 231-239, June.
    • Handle: RePEc:eee:jmacro:v:31:y:2009:i:2:p:231-239
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    Cited by:

    1. Rebei, Nooman, 2014. "What (really) accounts for the fall in hours after a technology shock?," Journal of Economic Dynamics and Control, Elsevier, vol. 45(C), pages 330-352.
    2. Jordi Gali, 2005. "Trends in hours, balanced growth, and the role of technology in the business cycle," Review, Federal Reserve Bank of St. Louis, vol. 87(Jul), pages 459-486.
    3. Charles, Amélie & Darné, Olivier & Tripier, Fabien, 2015. "Are Unit Root Tests Useful In The Debate Over The (Non)Stationarity Of Hours Worked?," Macroeconomic Dynamics, Cambridge University Press, vol. 19(1), pages 167-188, January.
    4. Cristiano Cantore & Miguel León-Ledesma & Peter McAdam & Alpo Willman, 2014. "Shocking Stuff: Technology, Hours, And Factor Substitution," Journal of the European Economic Association, European Economic Association, vol. 12(1), pages 108-128, February.
    5. Federico S. Mandelman & Francesco Zanetti, 2008. "Technology shocks, employment, and labor market frictions," FRB Atlanta Working Paper 2008-10, Federal Reserve Bank of Atlanta.
    6. Alexiadis, Stilianos & Eleftheriou, Konstantinos & Nijkamp, Peter, 2013. "Technology adoption within a search model: Evidence from OECD countries," Economic Modelling, Elsevier, vol. 33(C), pages 137-148.
    7. Marcos Sanso-Navarro, 2012. "Broken trend stationarity of hours worked," Applied Economics, Taylor & Francis Journals, vol. 44(30), pages 3955-3964, October.
    8. Mumtaz, Haroon & Zanetti, Francesco, 2012. "Neutral technology shocks and employment dynamics: results based on an RBC identification scheme," Bank of England working papers 453, Bank of England.
    9. Klein, Mathias & Krause, Christopher, 2015. "Technology-Labor and Fiscal Spending Crowding-in Puzzles: The Role of Interpersonal Comparison," VfS Annual Conference 2015 (Muenster): Economic Development - Theory and Policy 113075, Verein für Socialpolitik / German Economic Association.
    10. Cantore, C. & Ferroni, F. & León-Ledesma, M A., 2011. "Interpreting the Hours-Technology time-varying relationship," Working papers 351, Banque de France.
    11. Federico S. Mandelman & Francesco Zanetti, 2008. "Estimating general equilibrium models: an application with labour market frictions," Technical Books, Centre for Central Banking Studies, Bank of England, edition 1, number 1, April.
    12. Don J. Webber & Michael Horswell, 2009. "Microeconomic foundations of geographical variations in labour productivity," Working Papers 0913, Department of Accounting, Economics and Finance, Bristol Business School, University of the West of England, Bristol.
    13. Cristiano Cantore & Vasco J. Gabriel & Paul Levine & Joseph Pearlman & Bo Yang, 2013. "The science and art of DSGE modelling: I – construction and Bayesian estimation," Chapters, in: Nigar Hashimzade & Michael A. Thornton (ed.), Handbook of Research Methods and Applications in Empirical Macroeconomics, chapter 18, pages 411-440, Edward Elgar Publishing.
    14. Kerstin Hotte & Melline Somers & Angelos Theodorakopoulos, 2022. "Technology and jobs: A systematic literature review," Papers 2204.01296, arXiv.org.
    15. Selgin, George & Beckworth, David & Bahadir, Berrak, 2015. "The productivity gap: Monetary policy, the subprime boom, and the post-2001 productivity surge," Journal of Policy Modeling, Elsevier, vol. 37(2), pages 189-207.

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    More about this item

    JEL classification:

    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • O41 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity - - - One, Two, and Multisector Growth Models

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