Modeling Trade–Employment Elasticity Nexus: Evidence from India

Abstract

The effect of trade on employment growth in India is a less-discussed issue in the international economics literature. Trade has increased the employment growth in India or not is still a debatable issue for many researchers. This study explores the impact of trade on India’s employment elasticity of growth using World development Indicator data of the World Bank and KLEMS database of India from 1982 to 2016. For this purpose, it has used the autoregressive distributed lag (ARDL) model of cointegration. The result indicates that although the share of trade in the national gross domestic product (GDP) has grown, it has failed to increase employment elasticity in the country. It may occur primarily because of the high volume of Indian imports. The share of the service sector in GDP, inflation, and foreign direct investment (FDI) are other vital factors influencing the employment intensity. Therefore, based on the empirical findings, it is suggested that policymakers should focus more on export, specifically on labor-intensive export. It will undoubtedly help to improve the employment level in the country.

Keywords

Trade employment intensity of growth GDP ARDL world development indicator India

Introduction

The role of trade is vital for every country in the present era of globalization. Its importance can be seen from the fact that it raises overall economic growth, employment, foreign earning, technological development, quality education, favorable business environment, innovation, structural transformation, efficient utilization of resources, and investment encouragement (Burange et al., 2019; Keho, 2017; Teignier 2018; Turco & Maggioni, 2013; Tyler, 1981; Winters et al., 2004; Zahonogo, 2016). Trade has also significantly contributed to the world economy over the period. The contribution of trade in the world GDP grew to 60% in 2019, from 27.30% in 1970 (World Development Indicators, 2020). However, from the employment perspective, trade has gained immense scholarly attention as a crucial employment generation factor.

Earlier studies suggest different channels through which trade may impact employment generation in a country. On the one side, trade may contribute to employment generation by raising productivity and increasing the market size (Dutt et al., 2009; Hasan et al., 2012; Kien & Heo, 2009; Turco & Maggioni, 2013). In this case, countries may experience a surplus trade balance. It is hypothesized that the higher the trade volume, the higher will be the economic growth and employment generation. On the other hand, trade may negatively impact the employment generation due to domestic production substitution with imports (Gozgor, 2014; Greenaway et al., 1999). It may lead to a trade deficit. Turco and Maggioni (2013) pointed out that trade does not always generate employment growth because of a perpetual shift for labor-saving technologies.

This study contributes to the empirical literature by estimating the trade–employment elasticity nexus for India. Since 1991, India has implemented major trade reforms (Hasan et al., 2012; Raj & Sasidharan, 2015). The government has recognized trade as an essential part of its economic policy and has therefore promised to stimulate trade share through a lesser number of restrictions on imports and exports and foreign exchange, regional integration, concession in tariff and tax rates, and devaluation of the rupee (Hye & Lau, 2015; Uchikawa, 1999). The aim was to speed up economic development and generate new employment opportunities for a growing population. These reforms have helped India in expanding the share of trade in total GDP. According to the World Bank data, India’s trade contribution has grown significantly from 9.74% during the 1961–1969 period to 20.66% during the 1990–1999 period and further to 46.96% during the 2010–2019 period. Similarly, exports and imports have grown from 4.02% and 5.72% during the 1960–1969 period to 9.85% and 10.81% during the 1990–1999 period and further to 21.61% and 25.34% during the 2010–2019 period (Table 1).

Several empirical studies have explored the role of trade in the economic growth of India. Most of the earlier studies found mixed results about trade impact on economic growth (Burange et al., 2019; Mallick & Behera, 2020; Sarkar & Bhattacharyya, 2005). However, the effect of trade on employment growth in India is less discussed in the international economics literature. Further, whether trade has increased the employment growth in India or not is still a debatable issue for many researchers (Hasan et al., 2012). Moreover, in the earlier studies, most scholars have used employment as a dependent variable, while few have used employment elasticity as a dependent variable. Theoretically, employment elasticity of growth gives more idea about the employment generation capacity of an economy. Therefore, the absence of studies and ambiguous findings motivate us to explore the impact of trade on India’s employment elasticity of growth.

With this brief background, this study aims to analyze the trade–employment elasticity nexus in India using the autoregressive distributed lag (ARDL) approach. The rest of the article is organized in the following manner: the second section deals with the literature review, focusing on the impact of trade on employment growth. The third section provides detailed data sources and methodology. The fourth section deals with empirical results. The final section summarizes the key findings with policy implications.

Table 1.

Share of India Trade in GDP between1960–1969 and 2010–2019.

Periods	Trade (% of GDP)	Imports of Goods and Services (% of GDP)	Exports of Goods and Services (% of GDP)
1960–1969	9.74	5.72	4.02
1970–1979	10.84	5.61	5.23
1980–1989	13.81	7.88	5.93
1990–1999	20.66	10.81	9.85
2000–2009	38.36	20.48	17.88
2010–2019	46.96	25.34	21.61

Source: Estimated from the World Development Indicators.

Review of Literature

Studying a panel of 167 manufacturing industries of the USA, Greenaway et al. (1999) have found reductions in the demand for derived labor to increase trade volumes (i.e., imports and exports). In estimating the impact of trade, foreign direct investment (FDI), and technology on wages and employment in the Indian manufacturing industries, Banga (2005) reported that greater export intensity improves employment growth. However, no effect has been found in the case of the wage rate. Jenkins and Sen (2006) have shown the different impact of trade on employment creation in various countries. In Bangladesh and Vietnam, they have found a significant effect of trade on employment generation. In Kenya and South Africa, they found a negative impact of trade in creating employment. Analyzing a group of 160 countries, Kapsos (2006) has found a statistically insignificant relation between trade and employment intensity of growth. However, he observed a positive sign and explained that the country might experience high employment elasticity if they have more trade share.

Studying the case of Vietnam, Kien and Heo (2009) also support the view that increasing export intensity boosts demand for derived labor. It has been observed that a high import share does not always impact employment growth in the country. Further, with the international market’s integration, the Vietnamese economy has mostly benefited from the textile, garment, and footwear industries in generating new employment opportunities. Considering India’s case, Goldar (2009) observes a favorable net marginal effect of trade on industrial employment due to the imports’ dominance. On the other hand, Sankaran et al. (2010) have found a negative impact of trade on employment due to trade’s capital-intensive nature. The Indian manufacturing sector jobs have not increased due to scale, composition effects, and foreign trade, which may not be the primary cause of job creation for India’s growing labor force, particularly for unskilled labor (Raj & Sen, 2012). A study carried out by the international Monetary Fund (IMF) shows that different economic development levels and trade openness explain cross-country differences in employment elasticities (Crivelli et al., 2012).

Assessing the case for Nigeria, Babatunde et al. (2012) have found a different trade effect on employment generation on various sectors of the economy. For the aggregate level, exports have failed to generate the desired level of employment for reducing poverty in the country. However, agricultural exports have helped in reducing poverty and inequality by generating jobs and productivity. Hasan et al. (2012) have found no sign of joblessness with India’s trade reforms. They further claim that urban unemployment declined with the trade reform in those states, which have a trade surplus and flexible labor laws.

The positive impact of foreign trade on employment growth has also been highlighted by Turco and Maggioni (2013) while studying Turkish manufacturing. They conclude that altogether, entry of the firms in the export and import market will generate more employment at the time of entry and subsequent years. Furthermore, they found a more considerable impact on the firm’s employment growth, which has higher import and export intensity. Evidence from the recent study indicates that India’s manufacturing export has positively impacted the employment elasticity of production and non-production workers. Employment elasticity has increased in industries where the wage rate is low and labor laws are more flexible (Das et al., 2014). Raj and Sasidharan (2015) have found that international trade has no significant effect on the Indian manufacturing sector’s employment generation.

Data Source and Methodology

Data Source

This study is primarily based on annual time series data on employment elasticity (percentage change in employment divided by percentage change in output), trade percentage of GDP), services, value added (percentage of GDP), and inflation (percentage) and FDI (percentage of GDP). The data for employment elasticity are obtained from the India KLEMS database. Data related to other variables, namely trade, service value added, inflation, and FDI, are sourced from the World Development Indicators of the World Bank. The period is considered from 1982 to 2016, which covers both pre-and post-reform periods. The descriptive statistics of the selected variables are presented in Table 2. It has been observed that India’s employment elasticity has grown from −0.09 to 1.96 during the past 35 years. On the other hand, the share of trade services, value added, and FDI in the national GDP have grown from 12.21%, 31.96%, and 19.93% to 55.79%, 47.91%, 28.58% during the same period. Inflation has evolved from 1.18% to 2.63% during the past 35 years (Table 2).

Table 2.

Descriptive Statistics of the Selected Variables.

Variables	Description	Data Sources	Mean	Max.	Min.	Obs.
E	% change in employment divided by % change in output	Estimated using India KLEMS data,2018	0.35	1.96	−0.09	35
LnTD	Trade (% of GDP)	World Development Indicators, World Bank; 2017	29.82	55.79	12.21	35
LnSVA	Services, value added (% of GDP)	World Development Indicators, World Bank; 2017	40.16	47.91	31.96	35
LnINF	Inflation, consumer prices (in %)	World Development Indicators, World Bank; 2017	1.97	2.63	1.18	35
LnFDI	FDI (% of GDP)	World Development Indicators, World Bank; 2017	25.75	28.58	19.93	35

Source: The authors.

Selection of Variables

Dependent Variable

In this study, employment elasticity is considered as a dependent variable and measured as the percentage change in employment divided by percentage change in output (Kapsos, 2006; Islam & Nazara, 2000; Sassi & Goaied, 2016). It indicates how much of GDP growth rate is required to achieve a certain employment level in a country (Kumar & Pattanaik, 2020). In other words, employment elasticity provides information about the country’s employment-generating capacity, state, region, industry, and firm. It also indicates how, over the period, employment and output have grown in a country. It is mostly used for studying labor market behavior, forecasting, and structural changes in the economy (Kapsos, 2006). If there is a high value of employment elasticity, GDP growth has a higher impact on employment generation. On the other hand, if there is a low value of employment elasticity, GDP growth has a lower impact on employment generation (Misra & Suresh, 2014).

Independent Variables

Trade: Trade is considered as a significant explanatory variable. Its role is widely recognized in overall economic growth (Jawaid, 2014; Keho, 2017; Mallick & Behera, 2020; Zahonogo, 2016) and employment generation (Turco & Maggioni, 2013; Vashisht, 2016). It is measured as a percentage of GDP for a given year and expressed in the logarithm. It is expected that by increasing the volume of trade, the level of employment and employment elasticity will grow at a rapid rate. It is selected based on the previous findings of Bruno et al. (2001), Kapsos (2006) and Slimane (2015). These studies have found a positive impact of trade on employment elasticity of growth.

Service value added: Service value added, according to Padalino and Vivarelli (1997) and Mourre (2006), is another important determinant of employment elasticity. Most of the earlier studies have found a positive and statistically significant relationship between service value added and employment elasticity of growth (Ali et al., 2017; Crivelli et al., 2012; Döpke, 2001; Kapsos, 2006). It is observed from the earlier studies that the higher the share of the service sector, the higher the employment elasticity will be. It is included in the model to check whether or not the service sector has a significant impact on employment elasticity growth. It is measured as a percentage of GDP and expressed in terms of the logarithm.

Inflation: Inflation represents the macroeconomic volatility, and it lowers consumption expenditure, increases investment expenditure and its relative price, and, henceforth, fosters capital accumulation in the economy (Huo, 1997). According to Chaudhry et al. (2013), inflation affects various sectors (i.e., agriculture, manufacturing, and service) differently. Similarly, it also has different effects on the short-run and long-run growth rate of output and employment (Faria, 2001). It is selected based on the findings of Kapsos (2006), Crivelli et al. (2012), and Mkhize (2019). These studies have noted a negative relationship between employment elasticity of growth and inflation rate. It is measured as nominal GDP divided by real GDP and multiplied by 100 and expressed in logarithm.

Foreign Direct Investment (FDI): The role of FDI is widely recognized in the literature regarding economic growth, export promotion, capital formation, development of better technology, and generation of employment. However, in the case of employment generation, the role of FDI is somewhat controversial. Some studies have observed a positive impact of FDI on employment generation (Abor & Harvey, 2008). In contrast, others have observed a negative effect of FDI on employment generation (Malik, 2019). The studies showing positive impact shows that FDI can generate employment in two ways: (a) establishment of the subsidiary firm and (b) backward and forward linkages (Golejewska, 2002). The studies showing the negative impact argued that FDI created competition for local firms. Similarly, its effect on employment generation varied from country to country (Coniglio et al., 2015). Therefore, different findings have motivated us to include FDI as an essential factor in the employment elasticity of growth. It has been measured as a percentage of GDP and expressed in terms of the logarithm.

Model Specification

There are several econometric methods, namely Engle and Granger (1987), Johansen cointegration (Johansen & Juselius, 1990), and the ARDL model (Pesaran & Shin, 1998) to show the long-run association between the dependent and independent variables. However, this study has used the ARDL approach to show the empirical association between the selected variables. This method is applied because it has several advantages. The ARDL is used whether the variables are integrated with order one or integrated with order zero or a combination of both (Pesaran & Shin, 1998; Shrestha & Bhatta, 2018; Sultanuzzaman et al., 2018), and are statistically more robust technique to show the cointegration in a small sample (i.e., 30–80 observations) (Narayan, 2005; Pesarn & Shin, 1998). Moreover, this method has less endogeneity because it is free from the residual correlation (Jalil et al., 2013). Shrestha and Bhatta (2018) have noted that this method takes care of the adequate number of lags in estimating the model. In addition, this method simultaneously measures both short-run and long-run relationships among the selected variables (Sankaran et al., 2019). To show the empirical relationship between the variables, we estimated the following model:

E = α + β_{1} \ln {TD}_{t} + β_{2} \ln {SVA}_{t} + B_{3} \ln {INF}_{t} + β_{4} l n F D I_{t} + μ_{t}

(1)

where E = employment elasticity; lnTD = trade; lnSVA = service value added; lnINF = inflation, lnFDI = foreign direct investment inflows; t = time period from 1982 to 2016; µ = Error term; and β₁, β₂, β₃, and β₄ are the relevant parameters.

Empirical Results

Unit Root Test

This article explores the relationship between trade and employment intensity of growth in India. As mentioned earlier, the ARDL model can be used irrespective of whether the variables are I(0) or I(1) (Pesaran & Pesaran, 1997). Therefore, to know the order of integration in the variables, we have used augmented Dickey–Fuller (ADF) and Phillips–Perron (PP) tests. A null hypothesis of unit root is tested against the alternative hypothesis of no unit root. The values of t-statistics are compared with critical values. If the computed value of t-statistics is greater than the critical value and the p-value is less than 5%, then the null hypothesis is rejected and the alternative accepted. Finally, the test concludes that the variable is stationary.

The results of the ADF and PP tests are presented in Tables 3 and 4. It is evident from the ADF and PP tests that the variables E and lnINF are stationary at level. On the other hand, variables lnTD, lnSVA, and lnFDI are not stationary at level (Table 3). But after the first difference, lnTD, lnSVA, and lnFDI become stationary at the 1% level of significance. Furthermore, it has been observed that there is no single variable that has the integration of order two or I(2). Therefore, this leads us to employ the ARDL model to examine the relationship between the selected variables. It is important to note that F-statistics gives spurious results in integrating order two (Narayan, 2005; Pesaran et al., 2001).

Table 3.

ADF Test Results.

Variables	Level		First Difference
Variables	t-Statistic	Prob.	t-Statistic	Prob.
E	−4.266	0.002	−9.858	0.000
LnTD	0.071	0.958	−4.761	0.000
LnSVA	1.904	0.999	−5.104	0.000
LnINF	−2.765	0.075	−3.540	0.015
LnFDI	−1.076	0.713	−8.674	0.000

Source: The authors.

Table 4.

PP Test Results.

Variables	Level		First Difference
Variables	t-Statistic	Prob.	Adj. t-Stat	Prob.
E	−4.224	0.002	−20.538	0.000
LnTD	0.031	0.955	−4.757	0.000
LnSVA	1.904	0.999	−5.128	0.000
LnINF	−3.241	0.026	−10.483	0.000
LnFDI	−0.801	0.805	−8.526	0.000

Source: The authors.

Autoregressive Distributed Lag Estimation

Ifa and Guetat (2018) report that the ARDL model gives the growth rate relationships among the variables based on their past and present values. While estimating the model, the number of lags is determined by the Akaike Information Criterion (AIC) through automatic selection. The number of lags in the model is (1, 1, 2, 3, 0), as presented in Table 5. Figure 1 presents the best 20 models according to the AIC. The model (1, 1, 2, 3, 0) has been selected based on the smallest value of AIC. This model shows that around 73% of variation in the employment elasticity is explained by trade, service sector, inflation, and FDI. Furthermore, the results of the autocorrelation of residuals are presented in Table 6. It shows no sign of autocorrelation in the residual, which is an essential condition for continuing the ARDL model.

Table 5.

ARDL Estimation.

ARDL (1, 1, 2, 3, 0)
Variable	Coefficient	Std. Error	t-Statistic	Prob.
ELASTICITY (−1)	−0.140	0.152	−0.917	0.370
LnTD	−1.136	0.634	−1.792	0.088
LnTD (−1)	−0.792	0.653	−1.214	0.239
LnSER	−3.022	3.352	−0.901	0.378
LnSER (−1)	−9.110	4.359	−2.090	0.050
LnSER (−2)	14.778	3.291	4.491	0.000
LnINF	0.022	0.140	0.157	0.877
LnINF (−1)	0.084	0.151	0.558	0.583
LnINF (−2)	0.213	0.147	1.452	0.162
LnINF (−3)	−0.594	0.131	−4.546	0.000
LnFDI	0.131	0.085	1.537	0.140
C	−25.405	9.051	−2.807	0.011
R-squared	0.734
Adjusted R-squared	0.588
F-statistic	5.018
Prob. (F-statistic)	0.001

Source: The authors.

Figure 1.

Source: The authors.

Table 6.

Source: The authors.

Autoregressive Distributed Lag Bound Test Approach

After checking the order of integration among the variables, the next step is to know the cointegration among the variables. This is estimated using the bound testing approach of cointegration. The result of the bound test is presented in Table 7. It shows that the estimated value of F-statistics (9.726) is more than the upper bound (1) and lowers bound (0) values in the 10%, 5%, 2.5%, and 1% levels of significance specified by Pesaran et al. (2001). This helps us to reject the null hypothesis of no long-run relationship among the selected variables. Therefore, we conclude that there exists a long-run relationship between the selected variables.

Table 7.

ARDL Bounds test. Null Hypothesis (H0): No Long-run Relationships Exist.

Test Statistic	Value	k
F-statistic	9.726968	4
Critical value bounds
Significance (%)	Bound (0)	Bound (1)
10	2.2	3.09
5	2.56	3.49
2.50	2.88	3.87
1	3.29	4.37

Source: The authors.

Long-run Estimates of the Autoregressive Distributed Lag Model

The long-run coefficients of the ARDL model are presented in Table 8 and indicate that three out of four variables, namely lnTD, lnSER, and lnINF are statistically significant at the 1% and 10% levels of significance. The coefficient of lnTD shows a negative impact on the employment intensity of growth in India. It implies that a 1% increase in lnTD leads to a 1.692 decrease in the employment intensity value in the long run in the Indian economy. It may occur primarily because of the high volume of Indian imports.

We also included some control variables such as the service sector’s share in GDP, inflation, and FDI. The coefficient of lnSER has a positive and statistically significant impact on the employment intensity of growth in India. It implies that a 1% increase in the service sector leads to a 2.32 increase in employment intensity value. This result indicates that the service sector is one of the most critical factors for determining the employment growth in the Indian economy. The service sector’s impact on employment intensity of growth in the present study supports earlier studies (Crivelli et al., 2012; Döpke, 2001; Padalino & Vivarelli, 1997). Previous studies suggested that the employment intensity of growth would be very high in countries with a higher share of the service sector in the national GDP. In the case of India, the service sector is one the major contributors to GDP. It contributes around 57.7% in GDP (Aggarwal & Goldar, 2019). Therefore, policymakers must consider the service sector as an essential yardstick for improving the level of employment.

As far as inflation is concerned, it shows a negative and statistically significant impact on India’s employment intensity. It shows that for every 1% change in inflation, an average employment elasticity would be decreased by a 0.24 point. The result of this variable is also at par with earlier empirical studies. Kapsos (2006) argues that for every 100 percentage point change in the inflation rate, there would be a 0.12 reduction in employment intensity of growth on an average.

Table 8.

Long-run Estimation.

Variables	Coefficient	Std. Error	t-Statistic	Prob.
LnTD	−1.692	0.455	−3.715	0.001
LnSER	2.322	0.674	3.443	0.003
LnINF	−0.241	0.134	−1.805	0.086
LnFDI	0.115	0.074	1.553	0.136
C	−22.292	8.299	−2.686	0.014

Source: The authors.

Short-run Estimates of the Autoregressive Distributed Lag Model

This section discusses the results of the short-run model. The results of the short-run model are reported in Table 9. It is evident from Table 9 that the short-run model results are almost similar in sign and are in line with prior expectations as compared to the long-run model. However, there is little variation in the coefficients. The short-run model coefficients are slightly lower in comparison to the long-run model. Therefore, our estimated model suggests that the selected variables significantly impact the employment intensity of growth in the long run.

Furthermore, this model shows the speed of adjustment or time taken to correct the disequilibrium in the short run and converge with the long-run equilibrium (Ilyas et al., 2010). The critical concern in this model is the estimation of the lagged error correction coefficient. It is presented in the last row of Table 9. It shows the negative and statistically insignificant coefficient of the error correlation term (ECT), indicating the relationship between the variables. ECT’s coefficient is 1.138, showing that adjustment is corrected by 1.138% per annum from short run to long run. A higher value of ECT shows that economies will achieve an equilibrium growth at the faster pace (Sultanuzzaman et al., 2018).

Table 9.

Short-run Estimation and Cointegration Form.

ARDL (1, 1, 2, 3, 0)
Variables	Coefficient	Std. Error	t-Statistic	Prob.
D (TRADE)	−1.166	0.461	−2.529	0.020
D (lnSER)	−3.128	2.151	−1.454	0.162
D (lnSER (−1))	−14.685	2.433	−6.035	0.000
D (lnINF)	0.039	0.114	0.339	0.738
D (lnINF (−1))	0.393	0.112	3.512	0.002
D (lnINF (−2))	0.584	0.106	5.493	0.000
D (lnFDI)	0.165	0.076	2.172	0.042
Coint. Eq. (−1)	−1.138	0.127	−8.951	0.000

Source: The authors.

Diagnostic Check

The estimated model has gone through a variety of diagnostic checks. It has been found that the variables used in the study are normally distributed. The model is also free from the problems of serial correlation, multicollinearity, and heteroskedasticity. The stability check shows the strength of the model. It has been checked using the cumulative sum (CUSUM) and CUSUM of SQUARE tests. The estimated results of both the tests are plotted in Figures 2 and 3, respectively. The results indicate that the model is stable, so the plots remain within the critical bounds at the 5% level of significance. Therefore, it can be used for policy implications.

Figure 2.

Source: The authors.

Figure 3.

Source: The authors.

Conclusion

In this study, we have estimated the effect of trade on employment intensity of growth in India using the ARDL approach for 1982 to 2016. Our long-run model results show the negative impact of trade on employment intensity of growth because of the high volume of Indian imports. The study also found various other explanatory variables such as service value added, inflation, and FDI as essential factors of the employment intensity of growth. The short-run model results are almost similar in sign and are in line with prior expectations as compared to the long-run model. Furthermore, the estimated model has gone through multiple diagnostic checks. It has been found that the model is stable and free from various problems such as abnormality, autocorrelation, heteroskedasticity, and multicollinearity. Therefore, based on empirical results, it is suggested that policymakers should focus more on export, specifically, labor-intensive export. It will undoubtedly help to improve the employment level in the country.

Footnotes

Declaration of Conflicting Interests

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

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