Macroeconomic Determinants of Employment Intensity of Growth in India

Abstract

Despite India’s resurgent growth over the past years, the country seems to have failed miserably on the employment front. The employment content of economic growth—the employment intensity of growth—is on the decline. The objective of the present study is to identify the macroeconomic determinants which influence the employment intensity of growth in India. The study covers data for the period 1993–94 to 2009–10 across 15 major Indian states and applies a panel data model to find out these determinants. The results tend to suggest that labour supply, economic structure, price instability and human capital are major determining factors. Pro-employment growth in India may require measures like diversification of economic activities towards labour-intensive sectors, price stability, skill-based education and adoption of labour-intensive technology.

JEL Classification: J21, J23

Keywords

Employment intensity of growth Economic structure Human capital Labour supply Inflation

1. INTRODUCTION

Growth and employment are twin goals considered central to the economic policy agenda in both developed and developing countries. Growth of output may bring changes in the growth of employment. Periods of buoyant gross domestic product (GDP) expansion are often associated with rising employment opportunities, and conversely, slow-downs bring about growing unemployment (Boltho and Glyn, 1995). Linking output growth to employment has wider ramifications. With the expansion of employment, the benefits of growth are likely to be shared. Enhanced employment opportunities provide better and new sources of income (Heintz, 2006). Greater employment content in economic growth also leads to a reduction in poverty (Islam, 2004). To this extent, the employment intensity of growth, which measures the responsiveness of employment growth to output growth, assumes significance.

The employment intensity of growth also provides useful insights into the labour market paradigm, including the overall macroeconomic performance of an economy. It serves as a valuable instrument to examine how the growth in output and employment evolve together, creating, in turn, varying impacts across time and space, and possibly causing structural economic changes (Kapsos, 2005).

In recent decades, many economies have witnessed far-reaching changes on several key macroeconomic indicators, thanks to the adoption of globalisation and liberalisation measures. Along with opportunities, however, the latter have often brought enormous challenges, especially with regard to employment. Globalisation has been associated with extensive changes in the structure of employment, including pressure for increasing flexibility, scenarios of ‘jobless growth’, unprecedented rise in informalisation and casualisation, and declining opportunities for the less-skilled (Heintz, 2006). There is growing concern over the weakening of the linkage between output and employment growth. While the possibility of a strong output–employment linkage remains an empirical regularity in some advanced countries, there is evidence of such links weakening in developing ones (Bhattacharya and Sakhtivel, 2004a; Jha, 2003).

While India has experienced rapid income growth in recent years, this appears to have failed to improve the employment situation in the country, even though the growth rate of its labour force has been quite low (Unni and Raveendran, 2007). The employment performance of post-reform economic growth has been extremely disappointing, given the ‘jobless growth’ of the 1990s and the ‘near-zero employment growth’ accompanying the highest-ever GDP growth during 2004–05 to 2009–10. Employment growth in India has slowed from 2 per cent per annum during 1983–84 to 1993–94 to a meagre 0.22 per cent per annum during 2004–05 to 2009–10 (Papola, 2012). Despite falling real wages and wage shares, the demand for labour is on the decline (Ghosh and Chandrasekhar, 2007). Given the magnitude of requirements, employment opportunities in India must grow at over 3 per cent per annum during the 12th plan to provide work to all by the end of the plan period. Increasing informalisation, casualisation and contractualisation have also raised the questions about the quality of most of whatever new jobs being created (Papola, 2012).

Ironically, the structural changes in the economy’s employment have not been as large as in its GDP. Against an increase in the share of services from about 36 per cent in 1972–73 to 59 per cent in 2009–10, the corresponding increase in employment share has been much slower—from 15 per cent in 1972–73 to 27 per cent in 2009–10. Contrarily, the share of agriculture to GDP has come down from a high of 41 per cent in 1972–73 to a meagre 15 per cent in 2009–10. However, in 2009–10, about 51 per cent of the workers were still engaged in agriculture, while the remaining 22 per cent were engaged in the secondary sector with its contribution to GDP standing at 26 per cent. Not only are there sectoral distortions, but also challenges in the labour market composition, such as a high share of self-employed (51 per cent in 2009–10), rise in the share of casual workers (33 per cent in 2009–10), rise in underemployment and the size of the working poor, and a constant share of regular employees (Papola, 2012; Sundaram, 2009; Sundaram and Tendulkar, 2005). The continuation of this pattern of structural change has serious implications for equity as well as for the sustenance of high growth rates (Papola, 2012).

Persistent regional disparities remain yet another serious challenge for the country. States differ greatly in both GDP growth and employment generation (Ahsan and Pages, 2008; Bhattacharya and Sakthivel, 2004b). In recent years, barring Gujarat, no other fast-growing state has been successful in generating job-intensive growth. Some of the poorer states have fared far worse than their richer counterparts.

Against this backdrop, it may be pertinent to identify and understand the fundamental macroeconomic factors that influence the employment intensity of growth in India, which is what the present study does, along with examining the implications. The paper is divided into five sections. A brief review of literature is presented in Section 2. Section 3 outlines concepts and methodology and the database of the study. The empirical results of the study are presented in Section 4, while Section 5 offers the implications of the findings and concludes the study.

2. REVIEW OF LITERATURE

The literature on the existence and stability of a relationship between growth and employment (unemployment) is often confronted with problems of direction of causality. So, is it the GDP per capita growth that increases employment growth or the employment growth that increases output growth? Or are employment growth and output growth determined by other factors and, hence, no simple and direct relationship exists between growth and output (Perugini and Signorelli, 2007)?

The logical starting point of the debate on growth and (un)employment is ‘Okun’s law’, which establishes a formal linkage between the output growth rate and unemployment rate (Okun, 1962). The law suggests that each percentage point decline in the unemployment rate is associated with an increase in real gross national product (GNP) by 3 per cent. In this significant work, employment is taken as exogenous and real GNP as the dependent variable (Perugini and Signorelli, 2007). There are, however, empirical evidences to assume causality in the opposite direction as well (Perman and Tavera, 2005).

Despite Okun’s law having received the status of an empirical regularity, the approach remains implicitly ‘supply-side’ oriented (Prachowny, 1993). Indeed, one important area of departure is by Okun himself (1970), as he states that this relationship hides changes in other factors like increases in the size of the labour force, longer working hours and productivity that accompany employment growth and foster output growth. In this context, there are attempts to incorporate capital and labour to augment estimates of Okun’s law from the perspective of a production function (Sogner and Stiassny, 2002).

Although the direct relationship between growth and employment is not as popular as Okun’s law is, the employment effect of economic growth—the employment intensity of growth—has gained momentum in recent years, thanks to the policy centrality of the issue (Padalino and Vivarelli, 1997; Perugini, 2009). Its simplest formulation relies upon a familiar concept of elasticity—a responsiveness index—that describes the percentage change in employment to a one percentage change in output. The concept of elasticity implies a casual direction (Islam and Nazara, 2000). The common approach considers the labour-output relationship in a production function context, that is, labour is one input and the productive circumstances determine the output elasticity to the factors employed. Arguably, the estimation of elasticity may have certain advantages over Okun’s coefficient measurement. First, it allows us to avoid measurement problems pertaining to the unemployment rate. Second, it can be estimated and studied under various sub-categories like age, sex, regions and sectors, thereby offering wider implications (Islam, 2004).

The relationship between output and employment is, however, not straightforward as it is affected inter alia by various macroeconomic factors, such as economic structure (Kapsos, 2005), productivity (Mourre, 2006), prices (Flaig and Rottman, 2001), institutional factors (R. Freeman, 2005), exchange rate volatility (Stirböck and Buscher, 2000), quality of human capital (Webber, 2002) and technology (Saget, 2000). Empirical evidences of cross-country comparisons of these conjectures are provided in the works of Padalino and Vivarelli (1997) for the G-7 countries, Freeman (2001) for a set of industrialised countries and Lee (2000) for selected OECD countries. The studies by Perman and Tavera (2005) focus on European countries, while Izyumov and Vahaly (2002) refer to transition economies. There are also studies evaluating employment elasticity in different sectors of various developing countries (Islam, 2004).

As mentioned above, in India, a growth-led employment strategy seems to have failed since the 1990s (Papola, 2012; Unni and Raveendran, 2007). There is disproportionate growth across sectors in terms of the output-employment share (Joshi, 2004; Kannan and Raveendran, 2009), indicating an unfavourable structural shift. Arguably, the slowdown in the rate of employment growth and mismatch between the sectoral composition of employment and output put forth serious questions about the growth pattern of the last two decades (Papola, 2008).

3. HYPOTHESES FORMULATION, MODEL SPECIFICATION AND DATABASE

Following the available literature, the present study identifies four broad macroeconomic factors, namely labour supply, economic structure, macroeconomic volatility and human capital, which could influence India’s employment intensity. The rationale behind the selection of these factors and their possible relations with employment intensity of growth are discussed as follows.

3.1 Hypotheses Formulation

Increasing labour supply tends to raise employment (Beaudry and Collard, 2002). Following the classical scheme of things, it may be argued that a higher labour supply lowers the average wage, which may, in turn, lead to increased labour demand. The relative abundance of low-wage labour and wage flexibility may enable an economy to experience higher employment intensity with a rise in the labour supply (ILO, 2006). An increasing labour supply affords India a comparative advantage in terms of availability of cheap labour and, in turn, has led to a rise in foreign direct investment in the past (Lall and Mohammed, 1983). Hence, the present study hypothesises a positive relationship between labour supply and employment intensity. Following the measurement considered by Kapsos (2005), the labour force participation rate is considered a proxy for labour supply.

Increases in labour productivity tend to decrease employment. If increases in labour productivity lead to increased wages and such increases bring about substitution of capital for labour, the effect on employment becomes negative (Krugman, 1994). Further, given the negative relationship between productivity and employment, the differences in productivity growth can explain differences in the employment intensity of growth (Appelbaum and Schettkat, 1995). Hence, it is proposed that the higher the labour productivity, the lower the employment intensity of growth. Labour productivity is measured by dividing output by the total number of persons employed (Mourre, 2006).

Sectoral composition is considered as yet another important factor. Structural change in favour of fast-growing and job-intensive sectors may lead to an improvement in the employment intensity of growth (Mourre, 2006). While most earlier studies have incorporated the share of services into the model to find out the role of compositional effects, in the context of the Indian economy, the role of manufacturing as an employment-generating sector remains critical along with the fast-growing service sector (Kapsos, 2005; Padalino and Vivarelli, 1997). Hence, the present study introduces both these sectors and hypothesises that the corresponding employment shares of industry and services positively influence the employment intensity of growth in India.

Macroeconomic volatility may lead to uncertainties in the labour market. Inflation is one indicator of volatility, but there are conflicting views on the role of inflation in employment intensity. There are two types of effects inflation can create, namely the ‘grease effect’ (Tobin, 1972) and the ‘sand effect’ (Friedman, 1977). While the grease effect suggests that inflation can speed up the adjustment to long-run equilibrium, the sand effect posits the possibility of resource misallocation leading to a decline in employment. In the empirical literature on developed countries, the grease effect plays a predominant role, whereas in developing countries, the sand effect becomes crucial (Loboguerrero and Panizza, 2003). Further, inflation-causing employment elasticity to reduce significantly is evident only at very high inflation rates (Kapsos, 2005). With these inconclusive findings, the impact of inflation on employment intensity of growth remains an empirical question. Following Loboguerrero and Panizza (2003), inflation is operationalised by considering the annual rate of inflation based on the GDP deflator.

High employment content in economic growth presupposes quality human capital, which may be gauged by the quality of health services (Mayer, 2001) and the level of education (Knowles and Owen, 1997). Better health and education may enhance labour productivity, growth and economic development (Webber, 2002). Hence, it is hypothesised that high human capital may lead to high employment intensity of growth. In the present study, health and education, as proxies for human capital, are captured by life expectancy at birth and literacy rate, respectively, following Knowles and Owen (1997). Table 1, accordingly, presents all the variables, dependent and independent, and indicates the methods of their measurement.

3.2 Measuring Employment in India

In India, researchers primarily rely on quinquennial surveys released by the National Sample Survey Organisation (NSSO) to measure the level of employment. The NSSO defines employment according to the status of economic activities undertaken (NSSO, 2006). Economic activity is measured in terms of the ‘Usual Status Approach’ and the ‘Current Status Approach’. While the former considers the number of persons in the workforce, the latter denotes the number of man-days.

Table 1
Method of Measurement of the Variables

Variables Method of Measurement

Employment Intensity Employment elasticity with respect to GDP

Labour Supply Size of labour force

Economic Structure Share of total employment in secondary sector

Share of total employment in tertiary sector

Labour productivity

Macroeconomic Volatility Rate of inflation

Human Capital Literacy rate

Life expectancy at birth

Variables	Method of Measurement
Employment Intensity	Employment elasticity with respect to GDP
Labour Supply	Size of labour force
Economic Structure	Share of total employment in secondary sector
	Share of total employment in tertiary sector
	Labour productivity
Macroeconomic Volatility	Rate of inflation
Human Capital	Literacy rate
	Life expectancy at birth

Source: Authors’ calculation.

A person is considered employed under the ‘Usual Status Approach’, if s/he had pursued gainful economic activity for a relatively longer period of time in the one year immediately preceding the date of the NSS survey. This is known as ‘Usual Principal Activity Status’. On the other hand, if a person had spent a relatively shorter time span in the one year immediately preceding the date of survey, s/he is accounted under the ‘Usual Subsidiary Activity Status’. Principal activity status and subsidiary activity status together constitute ‘Usual Activity Status (UPSS)’.

The ‘Current Status Approach’, however, assigns a unique activity status to a person engaged in gainful economic activities for a period preceding one week or the previous day of the survey. If a person has pursued any one or more gainful economic activities for at least one hour during the preceding week, s/he is considered employed under the ‘Current Weekly Status’ (CWS). If a person has spent four hours or more during the previous day of the survey, s/he is considered employed according to the ‘Current Daily Status’ (CDS).

The present study uses employment data based on the UPSS. This approach deals with chronic unemployment over a long period, while the CWS and CDS primarily deal with seasonal unemployment and underemployment. Since the current study focuses on chronic unemployment, UPSS may be considered an ideal approach.

3.3 Measuring Employment Intensity of Growth

The employment intensity of growth is defined as the elasticity of employment with respect to output growth. Quantitative estimates of employment elasticity are based on the assumption that employment is primarily a function of output. Accordingly, employment elasticity during a given period can be measured either arithmetically through the ratio of the proportionate change in employment to the proportionate change in output or by applying regression analysis postulating a functional relationship between employment and output (Islam and Nazara, 2000). The present study measures employment intensity of growth for each state by estimating an economy-wide total. The relevant formula is

E = \frac{Δ L / L}{Δ Y / Y}

(1)

where L stands for employment at the state level and Y represents gross state domestic product (GSDP). Elasticity is, thus, interpreted as the percentage change in employment for a state for a one percentage change in its GSDP (ILO, 2006; Islam, 2004; Islam and Nazara, 2000). Accordingly, annual estimates of employment elasticity have been obtained for all states in the study. The estimates so obtained measure the arc elasticity that computes the elasticity between two different points in time for each state.

3.4 Model Specification

The present study, in order to find out the impact of macroeconomic factors on employment intensity of growth, considers 15 time periods and 15 major states. Time-series analysis for such a short period is inappropriate. A study based on simple cross-sectional data at the state level also becomes ineffective due to the limited number of observations (Baltagi, 2005). Since employment is a dynamic process, panel data may become more appropriate for a systematic and efficient analysis of determinants (Dunning, 1993).

Accordingly, the following panel data model is specified.

Y_{i t} = α_{i t} + \sum_{k =1}^{K} β_{k i t} \ln X_{k i t} + u_{i t}

(2)

where, i = 1, 2 … N refer to cross-section units

t = 1, 2 … T refer to time periods

k = 1, 2 … K refer to number of explanatory variables

where Y_it is employment elasticity for the i^th individual (state) at time t. X_kit represents the determinants, namely, labour force participation rate, share of total employment in the secondary and tertiary sectors, labour productivity, rate of inflation, literacy rate and life expectancy in the i^th^{state at time t. The independent variables are expressed in percentages and then transformed into log-linear functional forms. b_k in the above lin-log model directly measures elasticity coefficients with respect to the explanatory variables (Table 2).}

Table 2

Summary of the Econometric Models Applied in the Study

Objective	Model Applied	Functional Form	Justification
To find the impact of macroeconomic factors on employment intensity of growth	Panel Data Model	Pooled Linear Regression Model: $Y_{i t} = α_{i} + \sum_{k =1}^{K} β_{k} X_{k i t} + u_{i t}$ Fixed Effect Model: $Y_{i t} =(α + μ_{i})+ X^{'}_{i t} β + v_{i t}$ Random Effect Model: $Y_{i t} = α + X^{'}_{i t} β + μ_{i} + v_{i t}$	It increases the efficiency of econometric estimates and captures the dynamics of both cross-sectional and time-series data set.
	Specification Tests:Incremental F test: OLS Model vs Fixed Effect ModelBreusch–Pagan LM test: OLS Model vs Random Effect ModelHausman test: Fixed Effect Model vs Random Effect Model

Source: Authors’ calculation.

3.4.1 Fixed Effect (FE) Model

Generalisation of the constant intercept and slope for the panel data involves introduction of a dummy variable to allow for the effects of those omitted variables that are specific to the state cross-sectional units, but stay constant over time and the effects that are specific to each time-period but are the same for all cross-sectional units. In the present study, no time-specific effects are considered and the focus is only on individual-specific effects.

Thus, the dependent variable Y_it, depends on the K^th exogenous variables (X_1it, X_2it………, X_kit) that differ across individuals at time t and also shows variation through time, but the variables specific to the i^th units stay constant over time.

In the FE model, since a_iare assumed to be fixed, we can write the model as:

Y_{i t} = e_{i} α_{i} + \underset{(1*k)}{β^{'}} \underset{(k*1)}{X_{i t}} + u_{i t}

(3)

where b' (1*K) is a constant vector and the a_i (1*1) scalar is constant representing the effects of those variables peculiar to the i^th state cross-sectional units that stay constant over time. e_i is the dummy variable for the i^th state, and u_it represents the effects of the omitted variables peculiar to both individuals and time. We assume u_it to be uncorrelated with X_it and independently and identically distributed random variables with mean zero and variance $σ_{u}^{2}$ .

3.4.2 Random Effect (RE) Model

In the RE model, the individual-specific effects are treated as random. In this case, a_i is distributed independently and identically with mean zero and constant variance.

Hence, the model is:

Y_{it} = μ + \underset{1* k}{β^{'}} \underset{k *1}{X_{i t}} + v_{i t}

(4)

v_it = a_i + u_it(5)

where a_i is the individual-specific time invariant variable and u_it represents the effects of the omitted variables that vary with both individuals and time. The properties of a_i are as follows: $E (α_{i})= E (u_{i t})=0$ $E (α_{i} α_{j})={σ_{α}^{2} if i = j$ $E (α_{i} α_{j})={0 if i \neq j$ $E (u_{i t} u_{j s})={σ_{u}^{2} if i = j, t = s o r = 0 (o t h e r w i s e)}$ $a n d E (α_{i} x_{i t})= E (u_{i t} x_{i t})=0$

It exhibits serial correlation over time between disturbances of the same individual because o_itand o_is both contain a_i, and so the residuals are correlated. To get an efficient estimate, we have to use the generalised least squares (GLS) estimator. The BLUE in the latter case is the GLS estimator.

3.5 Sources of Data

The study is based on secondary data. The span of the study for the panel data regression is 15 years from 1993–94 to 2009–10. The study considers data of both thick and thin rounds of the NSS surveys. Due to non-availability of NSSO data on employment for 2006–07 and 2008–09, those two years are not considered for the study. In order to estimate employment elasticity for the initial year 1993–94, the employment and GSDP data for 1992–93 are used. State-level data on labour force participation rates, share of employment in service and industry, and employment were collected from the NSSO, while GDP data were collected from the Central Statistical Office. Data on inflation were collected from publications of the Reserve Bank of India. Data on life expectancy at birth and literacy rate were procured from the publications of the Ministry of Health and Census of India, respectively.

4. RESULTS AND DISCUSSION

For the present data set, the F test and LM test results tend to suggest the superiority of a panel model over a pooled model. The Hausman test proves that the FE model is relatively more efficient than the RE model. The economic interpretation of the results is, thus, based on the FE model only. However, for the sake of comparison, the results of the FE, RE and pooled data models are presented in Table 3.

The results, by and large, follow the expected lines. The findings support the notion of a positive relationship between the economy’s shares of employment in the tertiary and secondary sectors and the employment elasticities. These two factors represent the structure of the economy and seem to be powerful explanatory variables for an improvement in employment elasticity.

Table 3
Coefficients of Employment Intensity of Growth on Determining Variables (State-wise Panel Data Results)

Variable Pooled Fixed Effects Random Effects

Work ForceSecondary 0.356(26.128) 0.169(10.160) 0.025(17.606)

Work ForceTertiary 0.603(32.432) 0.038(11.760) 0.058(26.396)

Labour Force 0.422(1.882) 0.230(2.000) 0.207(0.186)

Inflation −0.837(−1.541) −0.011(−3.621) −0.746(−2.422)

Literacy Rate −0.963***(−3.131) 0.752(1.728) −0.161(−0.430)

Life Expectancy −0.003 (−5.312) 0.044(2.521) −0.222(−0.163)

Labour Productivity −0.057(−30.456) −0.064(−23.751) −0.065(−26.781)

Constant 0.456(55.193) ----------- 0.485***(41.950)

R² 0.98 Within = 0.80 Within = 0.74

Between = 0.94 Between = 0.98

Overall = 0.94 Overall = 0.98

F(7,217) = 2738.26(P > F = 0.001) F(21,203) = 4784.85(P > F = 0.001) Waldχ²(21,203)^{= 1396.47(P > χ²= 0.001)}

Nobs, Nvar 225,7 225,7 225,7

F-Test F(14, 210) =1405.62 (P > F = 0.001)

LM Test χ²(1) = 579.01 (P >χ²= 0.001)

Hausman Test χ²(7) = 100.69 (P > χ²= 0.001)

Variable	Pooled	Fixed Effects	Random Effects
Work ForceSecondary	0.356***(26.128)	0.169***(10.160)	0.025***(17.606)
Work ForceTertiary	0.603***(32.432)	0.038***(11.760)	0.058***(26.396)
Labour Force	0.422**(1.882)	0.230***(2.000)	0.207(0.186)
Inflation	−0.837(−1.541)	−0.011***(−3.621)	−0.746**(−2.422)
Literacy Rate	−0.963***(−3.131)	0.752*(1.728)	−0.161(−0.430)
Life Expectancy	−0.003 ***(−5.312)	0.044**(2.521)	−0.222(−0.163)
Labour Productivity	−0.057***(−30.456)	−0.064***(−23.751)	−0.065***(−26.781)
Constant	0.456***(55.193)	-----------	0.485***(41.950)
R²	0.98	Within = 0.80	Within = 0.74
		Between = 0.94	Between = 0.98
		Overall = 0.94	Overall = 0.98
	F(7,217) = 2738.26(P > F = 0.001)	F(21,203) = 4784.85(P > F = 0.001)	Waldχ²(21,203)^{= 1396.47(P > χ²= 0.001)}
Nobs, Nvar	225,7	225,7	225,7
F-Test	F(14, 210) =1405.62 (P > F = 0.001)
LM Test	χ²(1) = 579.01 (P >χ²= 0.001)
Hausman Test	χ²(7) = 100.69 (P > χ²= 0.001)

Source: Authors’ calculation.

Notes: Against each variable, the first row represents the coefficient followed by t-statistics in parentheses. Nobs: number of observations; Nvar: number of variables. *Significant at the 10 per cent level; **significant at the 5 per cent level; ***significant at the 1 per cent level.

The study considers the labour force participation rate as a proxy for labour supply. The empirical analysis indicates a systematic positive relationship between the labour force participation rate and the employment intensity of growth, corroborating past findings that growth in labour supply deters productivity growth, leading to improvements in employment (Kapsos, 2005).

There exists an inverse relationship between the inflation rate and employment elasticity, supporting thereby the sand effect (Friedman, 1977). It is further found that a rise in the life expectancy and literacy rates, considered proxies for human capital, ensures high employment intensity of growth. Further, the lower the labour productivity, the higher is the employment intensity of growth, thereby supporting Keynesian fundamentals (Hussain and Nadol, 1997).

5. IMPLICATIONS AND CONCLUSION

Given the preceding discussions on the determinants of employment intensity of growth in India, the following possible implications can be offered. The empirical analysis indicates a positive relationship between labour supply and the employment intensity of growth. This corroborates previous findings that growth in labour supply tends to lower productivity growth leading to an improvement in employment (Kapsos, 2005). The Indian economy may have the scope to build on the comparative advantage of its abundant labour. It is, however, important to ensure that education and skill levels match with the requirements.

The notion of a positive relationship between the shares of the secondary and tertiary sectors and the employment elasticities may indicate a favourable scenario for the Indian economy. The Indian economy has been experiencing structural economic changes, with its service sector growing at the fastest rate followed by industry. Consequently, the country is expected to transfer its labour force from agriculture to the industry and service sectors, and in turn, reduce pressure on the primary sector.

There is a need to maintain a fair degree of price stability. It is also pertinent to create a healthy and educated labour force to ensure a high employment intensity of growth in India. The possibility of a mismatch between the requirements of skilled manpower in industry and services and its availability needs to be addressed.

Further, as the results indicate that the lower the labour productivity the greater would be the employment intensity of growth, it suggests for an inter-sectoral transfer of labour and convergence of sectoral labour productivities, which may bring about improvement in employment conditions in India. In India, not only is the pace of transfer slow but the economy has a divergence in labour productivity between the agricultural and non-agricultural sectors. India does not seem to have followed the conventional path of agriculture–industry–service shifts. The growth of industrial output has been quite slow, while the service sector has grown at a remarkable pace with, however, no commensurate rise in its share in employment (Papola, 2012). Hence, it may be necessary to put a thrust on developing the industrial sector, which is relatively more labour-intensive. The skills required for the latter would be relatively lower and, thus, the transfer of the labour force from agriculture to industry could be easily achievable. This would open the door for a further shift to services in the later stages inter alia through an upgradation of skills.

Following Keynesian principles, one may argue that the level of employment in an economy is dependent on aggregate demand which, in turn, is dependent on both private and public spending. Private investment in a market-driven economy is likely to be highly capital intensive. Government spending should, therefore, be given a boost, especially on basic infrastructure and social services. This, via the multiplier effect, is likely to enhance employment (Floyd, 1979). An expansionary fiscal policy seems to be an important instrument, which may enhance not only growth but also employment and it may also become instrumental in eradicating poverty. Although the present study did not incorporate public expenditure as a variable in the model, following the findings of Sahoo (2009) that public organised sector employment is highly responsive to total public expenditure, it may be stated that an increase in public developmental expenditure especially on the capital account may be a necessity.

There is also strong debate on labour market reforms in India today. It is argued that but for restrictive labour laws that have introduced inflexibility in the labour market, the Indian economy would have experienced a higher growth of employment. Indian labour laws are so numerous, complex and even ambiguous that they promote litigation rather than the resolution of problems related to industrial relations (Sharma, 2006). The problem with the debate on labour market reforms is that an integral view of labour market regulation is missing. Chapter VB of the Industrial Disputes Act, 1947 and the Contract Labour (Regulation and Abolition) Act, 1976 appear to restrict both employers and labour to take a holistic view of labour market regulations.

Further, Indian labour laws are highly protective of labour, and inflexible labour markets are applicable to the organised sector only. As a consequence of unfriendly labour laws, labour mobility is restricted, leading to a rise in labour costs. This, in turn, hinders investment and growth, and promotes capital-intensive methods of production in the organised sector which adversely affects the sector’s long-run demand for labour (Government of India, 2006). A past study on the pattern of manufacturing growth during 1958–92 established that states which amended the Industrial Disputes Act in a pro-worker direction, experienced lowered output, employment and investment in registered formal manufacturing (Besley and Burgess, 2004). In 1980s, employment in the manufacturing sector was adversely affected (Sundaram and Tendulkar, 2002) primarily by labour market rigidities, leading to high labour adjustment costs (Besley and Burgess, 2004) and a failure to link wages to productivity.

A balanced approach towards labour market flexibility should be adopted for stimulating investment and employment, guaranteeing equity (Standing, 2002). An active labour market policy of skill development and re-deployment should be pursued in which trade unions, employers and government should closely collaborate. Furthermore, increasing employment in the informal sector, along with its low productivity, low wages, fragile employment and income insecurity, necessitates regulations for this sector in such a way as to create organised sector-like conditions of improved productivity with better employment and wages (Sharma, 2006).

To conclude, it may be stated that the present study is an attempt to analyse the determinants of employment intensity of growth across major Indian states by examining the econometric properties of the data set within a panel framework. The results provide an empirical overview of the relationship between employment intensity of growth and macroeconomic factors. India is expected to reap the benefits of its surplus labour as labour supply acts as a favourable factor. The findings also support the notion of a positive role of the economy’s share in services and industries. As agriculture is over-manned, the pressure from agriculture is likely to reduce with the increase in the share of manufacturing and services. In order to maintain and sustain high employment elasticity, it is also pertinent to maintain price stability. Besides, the creation of a healthy and educated workforce is yet another important measure, which cannot be wished away. There is perhaps a need for inter-sectoral transfer of labour and the convergence of sectoral labour productivities to bring about an improvement in employment conditions in India.

In a labour surplus economy like India, the attainment of higher growth without a commensurate rise in employment would accentuate poverty and inequality. Unless a well-thought-through attempt is made to address the employment issue, the latter may act as a grave threat to India’s thrust towards achieving prosperity. The ‘one-size fits all’ approach to macroeconomic policies may fail to provide solutions (ILO, 2012). There is a need to take a multi-pronged approach with measures to foster pro-employment growth. In order for growth to become truly inclusive, it must ensure high employment content and the direct and underlying macroeconomic factors affecting the same need adequate attention.

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