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Non-linear dynamics of real wages over the business cycle

Author

Listed:
  • Carlo Altavilla

    (University of Naples Parthenope, CSEF and CESifo)

  • Concetto Paolo Vinci

    (University of Salerno)

Abstract

This paper aims at analysing the dynamic properties of real wages over the business cycle. We apply a Bayesian vector autoregressive (BVAR) model and analyse the possible asymmetric behaviour of real wages in response to different macroeconomic shocks. Finally, we use the NBER business cycle periodisation to evaluate how real wages interact with the different shocks during contractions and booms. The results indicate that real wages cyclicality substantially depends on the driving forces of business cycle fluctuations. Different time periods are dominated by different types of shocks. When the business cycle is mainly driven by supply-side shocks real wages present a pro-cyclical behaviour. On the contrary, when the business cycle is driven by aggregate demand shocks real wages move counter-cyclically.

Suggested Citation

  • Carlo Altavilla & Concetto Paolo Vinci, 2011. "Non-linear dynamics of real wages over the business cycle," Journal of Applied Economics, Universidad del CEMA, vol. 14, pages 81-99, May.
    • Handle: RePEc:cem:jaecon:v:14:y:2011:n:1:p:81-99
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    File URL: https://ucema.edu.ar/publicaciones/download/volume14/altavilla.pdf
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    Cited by:

    1. Ahmad, Manzoor, 2021. "Non-linear dynamics of innovation activities over the business cycles: Empirical evidence from OECD economies," Technology in Society, Elsevier, vol. 67(C).

    More about this item

    Keywords

    business cycle; real wage dynamics;

    JEL classification:

    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • J31 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs - - - Wage Level and Structure; Wage Differentials
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

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