Econometric Test on Growth-Unemployment Nexus in India

Journal Title: Journal of Quantitative Methods (JQM) - Year 2018, Vol 2, Issue 2

Abstract

Generally, the economic growth boosts employment growth rate but empirical evidences do not support these views in all cases. In this paper, the author endeavors to relate growth with unemployment rate during 1991-2016 in India using regression models, Granger Causality test, Johansen Cointegration test and Vector Error Correction model. Impulse response functions were fitted for testing stationary. Unit circle was found out to check stability of the Vector Error Correction. Output gap is measured by deducting Hodrick-Prescott Filtered trend value from the actual output. Unemployment gap is measured by deducting natural growth rate of unemployment from the actual unemployment rate. The data on Indian unemployment rate, growth rate and GDP from 1991 to 2016 have been taken from the World Bank. The paper concludes that growth-unemployment nexus is significantly negative at 10% level. Their relation is not causal but is co-integrated at 10% level. VECM is stable and nonstationary where in one error correction process the speed of adjustment is high and significant. The relation between output gap and unemployment is negative and insignificant. They are not co-integrated and have no causality. The nexus between output gap and unemployment gap is significantly negative but the relation has no causality and co-integration. VAR model is a good fit where variables are related with previous periods. The relation between growth and unemployment gap is insignificantly negative and co-integrated where VECM is stable but non-stationary and one speed of adjustment is significantly fast and other is insignificantly slow in error correction process.

Authors and Affiliations

Debesh Bhowmik

Keywords

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  • EP ID EP546514
  • DOI 10.29145/2018/jqm/020205
  • Views 272
  • Downloads 0

How To Cite

Debesh Bhowmik (2018). Econometric Test on Growth-Unemployment Nexus in India. Journal of Quantitative Methods (JQM), 2(2), 56-74. https://europub.co.uk/articles/-A-546514