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Interpreting the Hours-Technology time-varying relationship

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  • Cantore, C.
  • Ferroni, F.
  • León-Ledesma, M A.

Abstract

We investigate the time varying relation between hours and technology shocks using a structural business cycle model. We propose an RBC model with a Constant Elasticity of Substitution (CES) production function that allows for capital- and labor-augmenting technology shocks. We estimate the model with Bayesian techniques. In the full sample, we find (i) evidence in favor of a less than unitary elasticity of substitution (rejecting Cobb-Douglas) and (ii) a sizable role for capital augmenting shock for business cycles fluctuations. In rolling sub-samples, we document that the transmission of technology shocks to hours worked has been varying over time. We argue that this change is due to the increase of the elasticity of factor substitution. That is, labor and capital became less complementary throughout the sample inducing a change in the sign and size of the response of hours. We conjecture that this change may have been induced by a change in the skill composition of the labor input.

Suggested Citation

  • Cantore, C. & Ferroni, F. & León-Ledesma, M A., 2011. "Interpreting the Hours-Technology time-varying relationship," Working papers 351, Banque de France.
  • Handle: RePEc:bfr:banfra:351
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    More about this item

    Keywords

    Hours Worked and Business Cycles; Bayesian Methods.;

    JEL classification:

    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • E62 - Macroeconomics and Monetary Economics - - Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook - - - Fiscal Policy; Modern Monetary Theory
    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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