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Macroeconomic Regimes, Technological Shocks and Employment Dynamics

  • Tommaso Ferraresi EMAIL logo , Andrea Roventini and Willi Semmler
Published/Copyright: May 29, 2019
Jahrbücher für Nationalökonomie und Statistik
From the journal Jahrbücher für Nationalökonomie und Statistik Volume 239 Issue 4

Abstract

The debate about the impact of technology on employment has always had a central role in economic theory. At the same time, the nexus of technological progress and employment might depend on macroeconomic regimes. In this work we investigate the interrelations among technology, output and employment in the U.S. economy in growth recessions vs. growth expansions. More precisely, using U.S. data we estimate different threshold vector autoregressions (TVARs) with TFP, hours, and GDP, employing the latter as threshold variable, and assess the generalized impulse responses of GDP and hours as to TFP shocks. For our entire period of observation, 1957Q1–2011Q4, positive technology shocks, while spurring GDP growth, by and large, display a negative effect on hours worked in growth recessions, but they are not significantly different from zero in good times. Yet, since the mid eighties (1984Q1–2011Q4) productivity shocks increase hours worked in low growth periods. The results are mainly driven by the response of labor along the extensive margin (number of employees), and remain persistent so in the face of a battery of robustness checks.

Keywords: technology shocks; employment; threshold vector autoregression; generalized impulse response functions
JEL Classification: E32; O33; C32; E63; E20

Acknowledgements

Thanks, with all usual disclaimers, to Fredj Jawadi, Katarina Juselius, Barbara Rossi, Marica Virgilito, Peter Winker and two anonymous referees, as well to the participants to the 19th FMM Conference, Berlin, October 2015; the 9th CFE Conference, London, December 2015; the Large-scale Crises: 1929 vs 2008 International Conference, Ancona, December 2015; and the participants to the seminar at the University of Pisa. Andrea Roventini gratefully acknowledges the support by the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 649186 – ISIGrowth

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Appendices

A Data

Data on adjusted TFP and aggregate hours worked are recovered from Fernald (2012)’s database (http://www.frbsf.org/economic-research/economists/john-fernald/#). Both series are in annualized rates of growth but transformed in quarterly rates of change for the analysis.

Further data are from the FRED database (http://research.stlouisfed.org/ fred2/) provided by the Federal Reserve Bank of St. Louis and transformed in order to get real values through the most appropriate deflator. The series employed in the empirical analysis are:

  1. Gross Domestic Product (GDP);

  2. GDP Implicit Deflator (GDPDEF);

  3. Nonfarm Business Sector: Hours of All Persons (HOANBS);

  4. Nonfarm Business Sector: Employment (PRS85006013);

  5. Nonfarm Business Sector: Average Weekly Hours (PRS85006023);

  6. Employment Level: Nonagricultural Industries (LNS12035019);

  7. Employment Level: Part-Time for Economic Reasons, Nonagricultural Industries (LNS12032197);

  8. Employment Level: Part-Time for Noneconomic Reasons, Nonagricultural Industries (LNS12032200);

  9. Civilian Noninstitutional Population (CNP16OV).

B Generalized impulse response functions

The algorithm to get the generalized impulse response function (GIRF) specific to each regime with R observations works as follows (see Baum and Koester, 2011):

  1. pick a history Ωt−1r;

  2. pick a sequence of shocks by bootstrapping the residuals of the TVAR taking into account the different variance-covariance matrix characterizing each regime;

  3. given the history Ωt−1r, the estimated TVAR coefficients and bootstrapped residuals, simulate the evolution of the model over the period of interest;

  4. repeat the previous exercise by adding a new shock at time 0;

  5. repeat B times the steps from 2 to 4;

  6. compute the average difference between the shocked path on the non-shocked one;

  7. repeat steps from 1 to 6 over all the possible starting points;

  8. compute the average GIRF associated with a particular regime with R observations as:

    yt+m(ε0)=1R∑r=1Ryt+m(Ωt−1r|ε0,εt+m∗)−yt+m(Ωt−1r|εt+m∗)B

Once GIRFs are obtained, we apply the algorithm in Schmidt (2013) to compute the related confidence bands:

  1. artificial data are generated recursively using the estimated coefficients and errors from the TVAR structure;

  2. using the recursive dataset, the TVAR regression coefficients and the error terms are calculated assuming that the threshold corresponds to the estimated value;

  3. employing the original dataset and the newly computed coefficients and errors, GIRFs are computed following the steps described above;

  4. steps 1-3 are repeated S = 500 times to generate a sample distribution of the GIRFs from which confidence bands are drawn at the respective significance level.

C Series

Figure 10: Series (first difference of natural logarithm). NBER recessions in parenthesis.
Figure 10:

Series (first difference of natural logarithm). NBER recessions in parenthesis.

Received: 2018-01-07
Revised: 2018-07-15
Accepted: 2018-09-04
Published Online: 2019-05-29
Published in Print: 2019-07-26

© 2019 Oldenbourg Wissenschaftsverlag GmbH, Published by De Gruyter Oldenbourg, Berlin/Boston

Articles in the same Issue

  1. Frontmatter
  2. Original Articles
  3. Macroeconomic Regimes, Technological Shocks and Employment Dynamics
  4. Regulating Speculative Housing Markets via Public Housing Construction Programs: Insights from a Heterogeneous Agent Model
  5. Unfilled Training Positions in Germany – Regional and Establishment-Specific Determinants
  6. Under Debate
  7. Twenty Steps Towards an Adequate Inferential Interpretation of p-Values in Econometrics
  8. Data Observer
  9. The German Management and Organizational Practices (GMOP) Survey
  10. Population Projection for Germany 2015–2050 on Grid Level (RWI-GEO-GRID-POP-Forecast)
  11. PASS-ADIAB – Linked Survey and Administrative Data for Research on Unemployment and Poverty
  12. Book Reviews
  13. Hommes, Cars LeBaron, Blake: Handbook of Computational Economics, Volume 4, Heterogeneous Agent Modeling
  14. Luis C. Corchón and Marco A. Marini (eds.), Handbook of Game Theory and Industrial Organization, Volume I Theory.
https://doi.org/10.1515/jbnst-2018-0003
Keywords for this article
technology shocks; employment; threshold vector autoregression; generalized impulse response functions

Articles in the same Issue

  1. Frontmatter
  2. Original Articles
  3. Macroeconomic Regimes, Technological Shocks and Employment Dynamics
  4. Regulating Speculative Housing Markets via Public Housing Construction Programs: Insights from a Heterogeneous Agent Model
  5. Unfilled Training Positions in Germany – Regional and Establishment-Specific Determinants
  6. Under Debate
  7. Twenty Steps Towards an Adequate Inferential Interpretation of p-Values in Econometrics
  8. Data Observer
  9. The German Management and Organizational Practices (GMOP) Survey
  10. Population Projection for Germany 2015–2050 on Grid Level (RWI-GEO-GRID-POP-Forecast)
  11. PASS-ADIAB – Linked Survey and Administrative Data for Research on Unemployment and Poverty
  12. Book Reviews
  13. Hommes, Cars LeBaron, Blake: Handbook of Computational Economics, Volume 4, Heterogeneous Agent Modeling
  14. Luis C. Corchón and Marco A. Marini (eds.), Handbook of Game Theory and Industrial Organization, Volume I Theory.
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