Labour Market Adjustment and Intra-Industry Trade: Empirical Results from Indian Manufacturing Sectors

Abstract

During the last two decades, India has witnessed several trade and industrial policy reforms. The objective of the study is to examine the relationship between dynamism of India’s two-way trade, measured through Marginal Intra-Industry Trade (MIIT) index, and labour market adjustments, reflected through absolute employment changes, in select manufacturing sectors over 2001–2015. India’s MIIT in select sectors generally display an upward trend over the sample period, while a mixed dynamics is observed on the employment front. The generalized method of moments (GMM) estimation results indicate that MIIT, increase in productivity, skilled workforce intensity, industrial concentration, incremental FDI inflows and trade openness positively influence absolute employment changes, whereas unskilled wage exerts a negative impact on the same. The analysis further concludes that high relative growth rate, skill-intensity, incremental FDI inflows and higher productivity in a sector, also characterized by higher MIIT, may lead the firms to employ more productive and competitive resources, resulting in higher absolute changes in employment. The obtained results do not support the Smooth Adjustment Hypothesis (SAH) predictions in the Indian context.

Revisiting Marginal Intra-Industry Trade and Portuguese Labour Market

“…The interaction between MIIT and trade openness coefficient (MIITx-Trade) has a positive impact on employment changes (|ΔEMPL|it-1), which found support in the alternative hypothesis tested by Erlat and Erlat (2006) and Aggarwal and Chakraborty (2020). The variables of apparent consumption (ΔCons) and wages (ΔWages) follow the formulated hypotheses, as well as the studies by Thorpe and Leitão (2012), Greenaway et al (2020), Faustino and Leitão (2012) and Aggarwal and Chakraborty (2020).…”

“…H6: Wages tend to decrease when employment increases. Greenaway et al (2020), Faustino and Leitão (2012) and Aggarwal and Chakraborty (2020) consider a negative sign between wages and employment changes (β 6 LogΔWages<0).…”

“…Table 11 depicts the results using the Method of Moments Quantile Regression (MMQR) suggested by Machado and Silva (2019). We use the lagged variable of employment changes to assess long-run effects, following previous empirical studies such as Thorpe and Leitão (2012), Faustino and Leitão (2012), Aggarwal and Chakraborty (2020) and Pinto et al (2019) to assess the hypothesis of SAH. According to our results, the lagged variable of employment changes presents a positive sign in the three quantiles and is statistically significant at the 1% level.…”

“…Aggarwal and Chakraborty (2020) investigated the Indian experience using a GMM-system estimator. They found a negative correlation between wages and the change of employment.…”

Industry-level determinants of India’s vertical and horizontal IIT

“…In other words, the liberalization process had thrown the Indian firms into a facet of import competition where it competed by specializing and producing only a subset of product lines within an industry while importing the different technological variations of the same product. It is worth mentioning here that very recently, Aggarwal and Chakraborty ( 2020 ) have carried out an in-depth analysis of labour market adjustment brought about by IIT in the Indian context. They found industry groups that have higher magnitude of marginal IIT witness greater adjustments in employment of labour.…”