A Comparative Analysis of Employment Intensity of Growth in South Asian Countries.

AuthorKumar, Sanjeev


Currently, the creation of productive employment occupies a vital place in developed and developing countries. It is the employment that gives sufficient returns and improves the living standard of the labors (ILO, 2012). Its importance is also highlighted in the 'Sustainable Development Goals' and recently published studies (Islam, 2019; Kumar & Pattanaik, 2020). In this perspective, one cannot ignore the economic growth role. It is the channel that helps to improve the standard of living of the people by increasing their earnings (Khan, 2007). However, it is primarily based on the sources and pattern of growth and how it benefits the society's deprived sections (Islam, 2004).

Usually, it is expected that economic growth will generate more employment opportunities with a better income source and reduce poverty and inequality during the expansion phases (Heintz, 2006). In this context, Adams (2004) observed that a rise (10 per cent) in the country's income would decrease the poverty level (20 to 30 per cent). Some researchers linked economic growth with shifting workers from low productive sectors to high productive sectors (Kuznets, 1973) and a consistent decline in informal jobs (Islam, 2019). It is too helpful in increasing the tax revenue and makes it viable for the government to devote more funds toward social sectors like health and education (Department for International Development, 2008), thereby increasing its employment level. On the other hand, economic growth brings down employment and earnings during the contraction phases. As a result, it reduces the economy's spending due to less money available with people. Therefore, to know the dynamics of economic growth and employment growth, a proper insight into the employment intensity of growth is necessary. Besides, knowledge of the factors impacting employment growth is also essential. It helps the government and the researchers to design appropriate policy for improving the employment growth rate. Notably, for South Asian countries such as India, Pakistan, and Bangladesh, where the population is growing rapidly, it is vital to develop a policy design.

'UN Population Division' projection shows that by 2050 these will become the most populated countries in the world. These are also the countries where more than 60 % of population is in the age group of 15-64, and it has been proliferating since the last few decades (Fig. 1). In addition, the total unemployment rate is too high in some South Asian countries such as Afghanistan, India, Sri Lanka, and the Maldives (Fig. 2). Kapsos (2005) notes that between 1991 and 2003, South Asian countries have achieved better living standards, reduced poverty rates and rapid overall development due to strong economic growth. However, the countries remain poorest compared to the other regions in this world. Beyer, Chocce and Rama (2019) highlight that employment in most South Asian countries is decreasing rapidly, and females suffer the most.

Moreover, the concern is also increasing on the deteriorating association between economic growth and employment growth in many South Asian countries (Islam, 2019). During the last few years, most South Asian economies have experienced a reasonable growth rate in GDP. However, employment has grown at a meagre rate. It has raised questions about the employment generation capacity of these countries. Hence, it is relevant to analyze the GDP growth, employment growth, and corresponding employment intensity of growth in South Asian countries. It is also significant to identify the determinants of employment growth.

Review of Literature

Theoretically, discussion on the employment intensity of growth had commenced during the sixties, when Okun examined the link between growth and unemployment rate and found that a high economic growth (3 per cent or more) is required to reduce the county's unemployment rate. Since then, several researchers have explored the strength of Okun law for several nations and found heterogeneity in the coefficients (Moosa, 1997; Lee, 2000; Harris and Silverstone, 2001; Christopoulos, 2004; Villaverde and Maza, 2009; Elshamy 2012; Huang & Yeh, 2013; Sadiku, Ibraim & Sadiku, 2015). Previous studies believed that the Okun law is a valuable instrument for policymakers in enhancing aggregate output by lowering the unemployment rate. It is also helpful in projecting and policy-making purposes (Harris & Silverstone, 2001).

Furthermore, several researchers have estimated the employment elasticity of growth for different countries based on this law. For instance, Padalino and Vivarelli (1997) have calculated employment intensity of growth for the G-7 nations; Kapsos (2005) for a panel of 139 countries; Saget (2000) for transition economies; Das, Baten, Rana, and Kheleque (2008) for the manufacturing sector of Bangladesh; Papola and Sahu (2012), Mishra, and Suresh (2014), Kumar and Pattanaik (2020), and Padhi and Panda (2020) for India; Mouelhi and Ghazali (2018), Goaied and Sassi (2015) for Tunisia; Slimane (2015) a panel of 90 developing countries; Ali, Ghazi, and Msadfa (2017) for the manufacturing sector of developing and emerging economies, and Ben-Salha and Zmami (2021) for the GCC countries.

Aforesaid studies assumed that employment intensity of growth has several advantages over Okun law. On the one hand, employment elasticity avoids the measurement problem of the unemployment rate. On the other hand, it estimates employment elasticity separately for sectors, genders, age, education, and regions (Islam, 2004; Pattanaik & Nayak, 2014). Earlier studies also show that labor supply, education and health, inflation rate, wage rate, trade openness, FDI inflows, urbanization, economic structure, and tax policy and labor regulation are the significant factors of employment elasticity of growth (Kapsos, 2005; Crivelli, Furceri & Bernate, 2012; Pattanaik & Nayak, 2014; Mouelhi & Ghazali, 2018; Kamar, Bakardzhieva & Goaied, 201 9; Ben-Salha & Zmami, 2021).

Data Source & Methodology

This study is primarily based on secondary data obtained from 'ILOSTAT' and 'World Development Indicators of the World Bank. Employment data are collected from 'ILOSTAT', while GDP data are gathered from the 'World Development Indicators' of the World Bank. Both sources provide country level data. The study period is 2000-2019, which is further split into two sub-periods: pre-financial crisis (2000-2008) and post-financial crisis period (2009-2019).

Employment elasticity is estimated using the "point elasticity" method. This method is like the one applied in previous studies by Pattanaik and Nayak (2013) and Goldar and Aggarwal (2019). This indicator shows the employment creation capacity of the country (Kapsos, 2005). Further, it explains how employment growth and GDP growth inrease simultaneously over the period (Goaied & Sassi, 2016). The value of employment elasticity shows how much GDP growth rate is needed to attain a definite employment level (Kumar & Pattanaik, 2018). It also explains the contribution of employment and labor productivity to GDP growth. A high value of employment elasticity indicates the high impact of GDP growth on employment creation. On the other hand, a low value of employment elasticity is a sign of GDP growth's low influence on employment creation. It is quantitatively estimated as a function of the output growth rate. Therefore, based on this assumption, this study estimated employment elasticity by applying a simple formula given in equation (1).

[euro] = [DELTA][PHI]/[PHI]/[DELTA][lambda]/[lambda] (1)

Where [euro] stand for employment elasticity, [DELTA][PHI] indicate a change in employment, and [DELTA][lambda] denotes a change in GDP. The estimated employment elasticity suggests the change in employment with a change in GDP in percentage terms.

Further, this study employed a simple panel regression model under the pooled OLS, fixed effects, and random effects framework to find out the determinants of employment. It has been explained by gross domestic product (GDP), labor supply represented by working-age population, inflation...

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