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Modeling how macroeconomic shocks a ect regional employment: analyzing the Brazilian formal labor market using the global VAR approach

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  • Barbosa, Bruno Tebaldi de Queiroz
  • Marçal, Emerson Fernandes

Abstract

Assessing linkages across different regions and how macroeconomic shocks spread out across regions is not an easy task. In this study we address this problem using a global vector autoregressive methodology that deals with the curse of dimensionality in an ingenious form. Focusing on the Brazilian labor market, identified and quantified how a shock in an aggregate economic activity spreads out regionally and throughout time. Another novelty of our work is the use of information collected by the Brazilian Bureau of Geography and Statistics to measure how regions are linked by analyzing infrastructure linkages of Brazilian municipalities in terms of airports, roads, ports, education, health, and tourism activities. Interdependence among regions is measured not only by closeness but also by considering economic linkages. In terms of regional sensitivity to macroeconomic shocks, we provide evidence that these shocks tend to cause stronger effects on the South, Southeast, and Midwest regions than the Northeast and North regions. This conclusion is in line with the idea that the formal labor market is better developed in the former regions than the latter. The South, Southeast, and Midwest regions in Brazil have better economic and social indicators.

Suggested Citation

  • Barbosa, Bruno Tebaldi de Queiroz & Marçal, Emerson Fernandes, 2018. "Modeling how macroeconomic shocks a ect regional employment: analyzing the Brazilian formal labor market using the global VAR approach," Textos para discussão 468, FGV EESP - Escola de Economia de São Paulo, Fundação Getulio Vargas (Brazil).
    • Handle: RePEc:fgv:eesptd:468
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    References listed on IDEAS

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    4. Pesaran M.H. & Schuermann T. & Weiner S.M., 2004. "Modeling Regional Interdependencies Using a Global Error-Correcting Macroeconometric Model," Journal of Business & Economic Statistics, American Statistical Association, vol. 22, pages 129-162, April.
    5. Schanne, Norbert, 2015. "A Global Vector Autoregression (GVAR) model for regional labour markets and its forecasting performance with leading indicators in Germany," IAB-Discussion Paper 201513, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
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