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Does Productivity Affect Unemployment? A Time-Frequency Analysis for the US

In: Wavelet Applications in Economics and Finance

Author

Listed:
  • Marco Gallegati

    (Polytechnic University of Marche)

  • Mauro Gallegati

    (Polytechnic University of Marche)

  • James B. Ramsey

    (New York University)

  • Willi Semmler

    (New School for Social Research)

Abstract

The effect of increased productivity on unemployment has long been disputed both theoretically and empirically. Although economists mostly agree on the long run positive effects of labor productivity, there is still much disagreement over the issue as to whether productivity growth is good or bad for employment in the short run. Does productivity growth increase or reduce unemployment? This paper try to answer this question by using the property of wavelet analysis to decompose economic time series into their time scale components, each associated to a specific frequency range. We decompose the relevant US time series data in different time scale components and consider co-movements of productivity and unemployment over different time horizons. In a nutshell, we conclude that, according to US post-war data, productivity creates unemployment in the short and medium terms, but employment in the long run.

Suggested Citation

  • Marco Gallegati & Mauro Gallegati & James B. Ramsey & Willi Semmler, 2014. "Does Productivity Affect Unemployment? A Time-Frequency Analysis for the US," Dynamic Modeling and Econometrics in Economics and Finance, in: Marco Gallegati & Willi Semmler (ed.), Wavelet Applications in Economics and Finance, edition 127, pages 23-46, Springer.
    • Handle: RePEc:spr:dymchp:978-3-319-07061-2_2
    DOI: 10.1007/978-3-319-07061-2_2
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    Citations

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    Cited by:

    1. Mutascu, Mihai, 2021. "Artificial intelligence and unemployment: New insights," Economic Analysis and Policy, Elsevier, vol. 69(C), pages 653-667.
    2. Samuel Ajayi-Obe, 2020. "Key Determinants of Job Creation: A Comparative analysis between OECD Countries and Emerging Economies," Economic Alternatives, University of National and World Economy, Sofia, Bulgaria, issue 4, pages 619-647, December.
    3. María del Carmen Valls Martínez & Pedro Antonio Martín Cervantes, 2021. "Testing the Resilience of CSR Stocks during the COVID-19 Crisis: A Transcontinental Analysis," Mathematics, MDPI, vol. 9(5), pages 1-24, March.
    4. Disli, Mustafa & Nagayev, Ruslan & Salim, Kinan & Rizkiah, Siti K. & Aysan, Ahmet F., 2021. "In search of safe haven assets during COVID-19 pandemic: An empirical analysis of different investor types," Research in International Business and Finance, Elsevier, vol. 58(C).
    5. Valentina Diana Rusu & Adina Dornean, 2019. "The Quality of Entrepreneurial Activity and Economic Competitiveness in European Union Countries: A Panel Data Approach," Administrative Sciences, MDPI, vol. 9(2), pages 1-21, April.
    6. Hegelund, Erik & Taalbi, Josef, 2023. "What determines unemployment in the long run? Band spectrum regression on ten countries 1913–2016," Structural Change and Economic Dynamics, Elsevier, vol. 64(C), pages 144-167.
    7. Mark Partridge & Alexandra Tsvetkova & Michael Betz, 2021. "Are the most productive regions necessarily the most successful? Local effects of productivity growth on employment and earnings," Journal of Regional Science, Wiley Blackwell, vol. 61(1), pages 30-61, January.
    8. Antonis A. Michis, 2021. "Wavelet Multidimensional Scaling Analysis of European Economic Sentiment Indicators," Journal of Classification, Springer;The Classification Society, vol. 38(3), pages 443-480, October.
    9. Md Hakim Ali & Md Akther Uddin & Md. Atiqur Rahman Khan & Blake Goud, 2021. "Faith‐based versus value‐based finance: Is there any portfolio diversification benefit between responsible and Islamic finance?," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(4), pages 5570-5583, October.
    10. Patrick M. Crowley & David Hudgins, 2021. "Okun’s law revisited in the time–frequency domain: introducing unemployment into a wavelet-based control model," Empirical Economics, Springer, vol. 61(5), pages 2635-2662, November.
    11. Bera, Anil Kumar & Uyar, Umut & Kangalli Uyar, Sinem Guler, 2020. "Analysis of the five-factor asset pricing model with wavelet multiscaling approach," The Quarterly Review of Economics and Finance, Elsevier, vol. 76(C), pages 414-423.
    12. Abid, Fathi & Kaffel, Bilel, 2018. "Time–frequency wavelet analysis of the interrelationship between the global macro assets and the fear indexes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 1028-1045.
    13. Fousekis, Panos & Grigoriadis, Vasilis, 2016. "Spatial price dependence by time scale: Empirical evidence from the international butter markets," Economic Modelling, Elsevier, vol. 54(C), pages 195-204.
    14. Fernando Vadillo, 2016. "On the Historical Exchange Rates Euro/US Dollar," Computational Economics, Springer;Society for Computational Economics, vol. 48(3), pages 463-472, October.

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