Structural Change and Economic Growth across Major States of India

Abstract

The structural change in an economy is an important feature of the economic development process. Structural change becomes a potential source of growth in an economy as it induces reallocation of labour from low-productivity to high-productivity sectors, thus leading to fuller and better utilization of overall resources. This article studies the relationship between structural change and growth in 15 major states of India over the 30-year period from 1983–1984 to 2014–2015. The study aims at discovering whether structural changes have contributed to economic growth of these states or otherwise. This is achieved by decomposing the overall labour productivity growth of states into contribution by structural change and within sector change. The results show that in all the states under study structural changes have contributed positively to growth; however, contribution of within sector changes is found to be much more than structural change in all states except Maharashtra.

Keywords

Structural change labour productivity sources of growth economic development states India

I. Introduction

The structural change in an economy is one of the important features of economic development. Kuznets (1966) identified high rate of structural transformation as one of the six important features of modern economic growth. For Kuznets, ‘some structural changes, not only in economic but also in social institutions and beliefs, are required, without which modern economic growth would be impossible’ (Kuznets, 1971, p. 348, emphasis in the original). Traditional literature viewed economic development as associated with shifts of labour away from agricultural to non-agricultural pursuits. Early growth literature based on dual economy models also saw movement of labour from traditional to modern sectors as growth enhancing. Clark (1957), Chenery (1960), Kuznets (1966) and Syrquin (1988) have all highlighted the interrelationship between structural change and growth. Chenery (1960) while analysing the demand and supply side causes behind rising industrial output with growth, acknowledge the fact that the increase in per capita income of a country is normally accompanied by a rise in the share of industrial output. The special importance of structural change for developing economies stems from the fact that changing the structure of economy from low-productivity towards high-productivity sectors is growth enhancing.

The classical development thinking viewed the growth process in developing countries as associated with a shift in structure of production towards activities with high productivity levels and industrialization played a vital role in this process (I. Singh & L. Singh, 2018). In the absence of a continuous equalization of factor returns across sectors, the reallocation of resources to sectors of higher productivity contributes to growth and in such disequilibrium situations, structural change becomes a potential source of growth if it leads to a fuller or better utilization of resources and the potential gains are likely to be more important for developing countries than for developed ones (Syrquin, 1988). The countries that manage to pull out of poverty and get richer are those that are able to diversify away from agriculture and other traditional products, as labour and other resources move from agriculture into modern economic activities, overall productivity rises and incomes expand (McMillan & Rodrik, 2011). Developing economies are characterized by large productivity gaps in different parts of the economy. In context of India consisting of a large number of state economies, we may expect similar productivity gaps to exist across sectors in relatively less developed states compared to the more developed ones. An effort has been made to analyse whether the states have utilized the productivity gap differentials to their advantage by setting off on a path of growth enhancing structural change or otherwise. It would be interesting to examine the structural changes in the states economies over the years with a special focus on whether they have been growth enhancing or not. Apart from introductory section, the article is organized into six sections. In the second section, a brief review of literature on structural change with an emphasis on studies that have dealt with the growth affecting aspect of structural change is presented. The subsequent section details the data and methodology used in the article for the analysis. The fourth section provides a descriptive analysis of the structural change and growth patterns as observed in the selected states. The next section carries out a state-wise decomposition of the aggregate labour productivity growth into within sector and across sectors productivity growth in order to assess the contribution of structural change to the overall labour productivity growth of a state. Conclusions are presented in the final section.

II. Review of Literature on Structural Changes and Growth

Structural change has been widely analysed in the literature, on the one hand as an important feature of the economic development and on the other for its impact on the economic growth process. The special interest in our present analysis is to focus on how structural change impinges on economic growth. The classical development thinking viewed the growth process in developing countries as associated with a shift in structure of production towards activities with high productivity levels and industrialization played a vital role in this process. In the absence of a continuous equalization of factor returns across sectors, the reallocation of resources to sectors of higher productivity contributes to growth and in such disequilibrium situations, structural change becomes a potential source of growth if it leads to a fuller or better utilization of resources and the potential gains are likely to be more important for developing countries than for developed ones (Syrquin, 1988).

McMillan and Rodrik (2011) have made a significant contribution to the literature on the relationship between structural change and growth in their article ‘Globalization, structural change and productivity growth’. The countries that manage to pull out of poverty and get richer are those that are able to diversify away from agriculture and other traditional products, as labour and other resources move from agriculture into modern economic activities, overall productivity rises and incomes expand. Their study documents the gaps between high- and low-productivity sectors and highlight that labour flows from low-productivity activities to high-productivity activities are a key driver of development. The results of the empirical exercise show that since 1990 structural change has been growth reducing in both Africa and Latin America, with the most outstanding changes taking place in Latin America. They find that a huge chunk of difference in the productivity performance of countries in Africa and Latin America and that of Asia is accounted for by differences in the pattern of structural change—with labour moving from low- to high-productivity sectors in Asia, but in the opposite direction in Latin America and Africa. They identify three factors that help determine whether structural change contributes to overall productivity growth. The study finds structural change to be growth reducing in countries with a relatively large share of natural resources in exports as the process of structural transformation is stunted in such countries.

Hasan, Lamba and Gupta (2013) have examined the issue of growth, structural change and poverty reduction. As a part of their larger study, using the decomposition methodology devised by McMillan and Rodrik, they analyse whether structural change is growth enhancing or growth reducing over the period 1987–2009. Utilizing state-level data on poverty and productivity across 11 broad sectors of production from 1987 to 2009 they examine the impact of aggregate labour productivity growth and its components—within-sector productivity growth and productivity growth due to reallocation of labour—on poverty reduction. They find that the movement of workers from lower to higher productivity sector is an important channel through which increases in aggregate productivity translate into poverty reduction. They also find state-level variations in the contribution of structural change to the aggregate labour productivity growth. Specifically, their study finds that both within-sector productivity growth and structural change have contributed positively to aggregate labour productivity growth in all states, although the extent of the contribution of the two components have varied significantly across the states. The contribution of structural change to aggregate labour productivity is found to be the highest in Karnataka, Maharashtra and Haryana, and lowest in Punjab, Bihar and West Bengal.

Mitra and Ahsan (2017) have undertaken a similar decomposition analysis of the aggregate labour productivity across 15 major states of India to ascertain the contribution of structural change to the aggregate productivity growth. They find that all the 15 major states (except Assam) show a positive growth in labour productivity between 1987 and 2004. The average ‘within-sector’ growth was positive for all states except Assam, while the contribution of structural change was positive everywhere except for Gujarat, where it was slightly negative. Maharashtra to be the best performer in terms of structural change with the contribution of structural change to total labour productivity growth being highest for Maharashtra.

In the context of India, Cortuk and Singh (2011) have also examined the link between structural change and growth in India by constructing the norm of absolute value (NAV) and modified linen index (MLI) and carrying out a Vector Auto Regression (VAR) analysis using the two indices. The analysis is done on data for the period 1951–2007. They find that 1988 marks a break in the time series of growth and structural change. There is one-way causality from structural change to growth in the period 1988–2007, whereas there is no evidence for this linkage before 1988.

Cortuk and Singh (2015) undertake a similar analysis of the linkage between structural change and growth for the 16 states of India for the period 2000–2006. The authors found out that there is one-way positive impact from structural change to growth for the period 2000–2006.

The foregoing review of recent studies has underlined the importance to examine the relationship between structural change and economic growth. However, in the case of India, the studies conducted pertain to different period and in some cases too short period of time was covered. The present study covers the period 1980–1981 to 2014–2015 and not only covering longer time analysis but also covers the crucial period of shift of public policy in India. This is a comprehensive attempt to examine the relationship between structural change and economic growth across Indian states, while covering the shift of public policy from import substitution to liberalization, privatization and globalization period and is a modest attempt to fill the gap in economic literature.

III. Data and Methodology

The study focuses upon the structural changes in selected major states of India namely Andhra Pradesh, Bihar, Assam, Haryana, Tamil Nadu, Kerala, Karnataka, Uttar Pradesh, Madhya Pradesh, Punjab, Orissa, West Bengal, Gujarat, Maharashtra and Rajasthan. The data used in the analysis have been derived from two main sources namely the Central Statistical Organization (CSO) and the National Sample Survey Organization (NSSO). The labour productivity is more accurately measured by the value added per worker; however, CSO does not provide a time series of the value added for all the states. Therefore, the Gross State Domestic Product (GSDP) data as provided by CSO for 15 major states have been used. The data were available for different base years, namely, 1980–1981, 1993–1994, 1999–2000 and 2004–2005, respectively. The GSDP series for the 15 major states has been converted to a common base of 2004–2005 using the simple splicing method.

Another important issue with regard to the state GSDP series is the bifurcation of three states such as Uttar Pradesh, Madhya Pradesh and Bihar in the year 2000. The separate GSDP data for the newly formed states Uttarakhand, Jharkhand and Chhattisgarh are available only from the year 1993–1994 onwards, therefore to enable an analysis from 1980 to 1981 onwards, the data for new states have been combined with that of old states to arrive at a uniform GSDP series for the undivided states such as Uttar Pradesh, Madhya Pradesh and Bihar. Thus, the study does not deal with the newly formed states separately.

The employment data have been sourced from the quinquennial surveys on employment and unemployment as conducted by the NSSO. The data have been used from the 38th round (1983–1984) to the latest 68th round (2011–2012). To arrive at the workers estimates for the year 2014–2015, the state-wise population has been projected for 2014–2015 from the 2011 population levels and the worker participation rates provided by the 68th round have been applied to the projected population to arrive at the workers estimates for the year 2014–2015.

The study utilize two methodologies, one to measure structural change by constructing a structural change index (SCI) as described in Dietrich (2009) as follows:

N A V_{s, t} = 0.5 \cdot \sum_{i = 1}^{n} | x_{[i t]} - x_{[i s]} |

(1)

For its computation, first the difference of the sector shares xi between two points in time s and t are calculated. Then the absolute amounts of these differences are summed up. Because all changes are counted twice by using this technique standardization is usually done by a division by two. This leads to a range of the NAV between zero and unity and therefore also the interpretation of the NAV is very easy. The amount of structural change equals exactly the share of the movements of the sectors as a percentage of the whole economy. If the structure remains unchanged the indicator is equal to zero and if all sectors change at its most, which means whole economy has a total change then the index is equal to unity (Dietrich, 2009). The SCI has been constructed for the states under study and they are ranked based on the index values.

Another methodology as proposed by McMillan and Rodrik (2011) has been devised to assess whether the structural change across the states has been growth enhancing or not. In order to do so, the total labour productivity growth is decomposed into growth due to increased productivity within sectors due to capital deepening, technological change and reduced misallocation across plants. The other is productivity increase due to movement of labour across sectors mainly from low-productivity sectors to high-productivity sectors, increasing overall labour productivity in the economy. This is expressed by the following decomposition equation:

Δ Y_{t} = \sum_{i = n} θ_{i, t - k} Δ y_{i, t} + \sum_{i = n} y_{i, t} Δ θ_{i, t}

(2)

In the equation, ΔY_t and Δy_i,t refer to economy-wide and sectoral labour productivity levels, respectively, and θ_i,t-k is the share of employment in sector i. The Δ operator denotes the change in productivity or employment shares between t–k and t. The first term in the decomposition is the weighted sum of productivity growth within individual sectors, where the weights are the employment share of each sector at the beginning of the time period. We will call this the ‘within’ component of productivity growth. The second term captures the productivity effect of labour reallocations across different sectors. It is essentially the inner product of productivity levels (at the end of the time period) with the change in employment shares across sectors. We will call this second term the ‘structural change’ term. When changes in employment shares are positively correlated with productivity levels this term will be positive, and structural change will increase economy-wide productivity growth.

IV. Structure Change across State Economies

Table 1 summarizes for the 15 major states, the share of the three broad sectors of the economy in the state GSDP. It shows that for all the states the service sector contributes more than half of GSDP. Notably, during 2014–2015, in Kerala, Tamil Nadu and Maharashtra services contributed 74, 65 and 64 per cent to the GSDP, respectively. In the same period, the contribution of Karnataka and Haryana economies to the services sector to the GSDP was around 60 per cent. However, Gujarat and Madhya Pradesh are the only states where services contribute less than 50 per cent of GSDP. Likewise, the services contribution is just close to 50 per cent in Rajasthan and Orissa.

There exists a high degree of sectoral diversity across Indian states (Dore & Narayanan, 2017). Table 2 presents the shares of these sectors (averaged over 1980–1981 to 2014–2015) in the total GSDP of states. The analysis of the changes in the shares of three broad sectors (agriculture and allied, industry and services) in the overall GSDP of a state for the last 35 years period is presented in Figure 1. As expected, the share of agriculture in GSDP has declined for all the states. The decline in agricultural share has been the highest in Orissa (–35%) followed by Kerala and Haryana (–30%). The decline in share of agriculture GSDP has been the lowest in West Bengal (6%) followed by Tamil Nadu (14%) and Maharashtra (16%). The industry share in GSDP recorded highest increase in Madhya Pradesh (12%) followed by Punjab, Rajasthan 10 per cent each and Orissa (9%).

Table 1.
State-wise Sector-wise Share of Output for Different Years (2004–2005 Prices; in %)

1980–1981 1992–1993 2002–2003 2014–2015

Andhra Pradesh

Agriculture & allied 45 38 30 24

Manufacturing 11 11 12 10

Industry 17 19 21 20

Services 38 43 49 56

Assam

Agriculture & allied 40 35 28 20

Manufacturing 9 8 10 7

Industry 20 21 26 23

Services 40 44 46 57

Bihar

Agriculture & allied 41 29 28 18

Manufacturing 16 18 17 8

Industry 25 30 28 25

Services 34 41 44 57

Gujarat

Agriculture & allied 38 27 16 20

Manufacturing 17 25 27 24

Industry 31 38 40 36

Services 31 36 45 44

Haryana

Agriculture & allied 44 38 25 14

Manufacturing 14 19 22 17

Industry 27.48 29 32 26.96

Services 28 33 43 59

Karnataka

Agriculture & allied 43 34 20 15

Manufacturing 13 17 18 15

Industry 23 26 31 26

Services 34 40 50 59

Kerala

Agriculture & allied 39 29 20 9

Manufacturing 8 10 9 7

Industry 18 21 21 20

Services 43 50 59 71

Maharashtra

Agriculture & allied 24 18 13 8

Manufacturing 20 21 20 18

Industry 32 33 29 28

Services 44 49 58 64

Madhya Pradesh

Agriculture & allied 52 36 29 25

Manufacturing 8 12 13 12

Industry 19 25 29 31

Services 29 39 42 44

Orissa

Agriculture & allied 50 34 24 15

Manufacturing 7 9 9 13

Industry 24 32 32 33

Services 26 34 44 51

Punjab

Agriculture & allied 41 41 34 20

Manufacturing 9 14 15 18

Industry 17 21 23 27

Services 43 38 43 53

Rajasthan

Agriculture & allied 39 35 21 19

Manufacturing 11 10 13 15

Industry 21 23 32 31

Services 41 41 47 50

Tamil Nadu

Agriculture & allied 22 20 11 7

Manufacturing 26 22 20 18

Industry 34 32 31 28

Services 45 48 57 65

Uttar Pradesh

Agriculture & allied 46 35 31 20

Manufacturing 8 13 13 14

Industry 15 19 21 24

Services 40 46 47 56

West Bengal

Agriculture & allied 33 32 34 27

Manufacturing 12 11 11 11

Industry 21 20 19 20

Services 46 48 47 54

	1980–1981	1992–1993	2002–2003	2014–2015
Andhra Pradesh
Agriculture & allied	45	38	30	24
Manufacturing	11	11	12	10
Industry	17	19	21	20
Services	38	43	49	56
Assam
Agriculture & allied	40	35	28	20
Manufacturing	9	8	10	7
Industry	20	21	26	23
Services	40	44	46	57
Bihar
Agriculture & allied	41	29	28	18
Manufacturing	16	18	17	8
Industry	25	30	28	25
Services	34	41	44	57
Gujarat
Agriculture & allied	38	27	16	20
Manufacturing	17	25	27	24
Industry	31	38	40	36
Services	31	36	45	44
Haryana
Agriculture & allied	44	38	25	14
Manufacturing	14	19	22	17
Industry	27.48	29	32	26.96
Services	28	33	43	59
Karnataka
Agriculture & allied	43	34	20	15
Manufacturing	13	17	18	15
Industry	23	26	31	26
Services	34	40	50	59
Kerala
Agriculture & allied	39	29	20	9
Manufacturing	8	10	9	7
Industry	18	21	21	20
Services	43	50	59	71
Maharashtra
Agriculture & allied	24	18	13	8
Manufacturing	20	21	20	18
Industry	32	33	29	28
Services	44	49	58	64
Madhya Pradesh
Agriculture & allied	52	36	29	25
Manufacturing	8	12	13	12
Industry	19	25	29	31
Services	29	39	42	44
Orissa
Agriculture & allied	50	34	24	15
Manufacturing	7	9	9	13
Industry	24	32	32	33
Services	26	34	44	51
Punjab
Agriculture & allied	41	41	34	20
Manufacturing	9	14	15	18
Industry	17	21	23	27
Services	43	38	43	53
Rajasthan
Agriculture & allied	39	35	21	19
Manufacturing	11	10	13	15
Industry	21	23	32	31
Services	41	41	47	50
Tamil Nadu
Agriculture & allied	22	20	11	7
Manufacturing	26	22	20	18
Industry	34	32	31	28
Services	45	48	57	65
Uttar Pradesh
Agriculture & allied	46	35	31	20
Manufacturing	8	13	13	14
Industry	15	19	21	24
Services	40	46	47	56
West Bengal
Agriculture & allied	33	32	34	27
Manufacturing	12	11	11	11
Industry	21	20	19	20
Services	46	48	47	54

Source: Authors’ own calculations based on GSDP data available from CSO, MOSPI.

Manufacturing is the most important constituent of industry and accounts for a majority share in the industrial GSDP. In order to track the changes exclusively in manufacturing sector, Figure 1 depicts the percentage point changes in the manufacturing GSDP separately. Out of 15 states, seven have recorded a decline in manufacturing in the last over three decades, with Bihar recording the highest decline. These states are Andhra Pradesh (–1), Assam (–2), Bihar (–8), Kerala (–1), Maharashtra (–2), Tamil Nadu (–8) and West Bengal (–1).

Figure 1.

Source: Authors’ own calculations based on GSDP data available from CSO, MOSPI.

The percentage point increase in the share of manufacturing in GSDP has been the highest for Punjab (9) followed by Gujarat (7), Orissa and Uttar Pradesh (6) each, Madhya Pradesh and Rajasthan (4) each, Haryana (3) and Karnataka (2).

To gain a better understanding of the changes in sector shares of GSDP in the pre-reform and the post-reform periods, Table 2 presents the changes in shares of these sectors for three time periods separately. The pre-reform era (1980–1981 to 1992–1993) is period I. The post-reform period is divided into two sub-periods, the period II immediately after the reforms (1993–1994 to 2002–2003), and period III (2003–2004 to 2014–2015). For all the states, the share of agriculture in GSDP has declined in all the three periods. However Punjab, Haryana and Rajasthan, notably recorded a rapid decline in the agriculture shares during the post-reform periods as compared to the pre-reform era.

Table 2.

State-wise Changes in Sectoral Shares of GSDP: Pre- and Post-reform Periods (Constant 2004–2005 Prices; Figures in %)

State	Agriculture & Allied			Manufacturing			Industry			Services
State	1980–1981 to 1992–1993	1993–1994 to 2002–2003	2003–2004 to 2014–2015	1980–1981 to 1992–1993	1993–1994 to 2002–2003	2003–2004 to 2014–2015	1980–1981 to 1992–1993	1993–1994 to 2002–2003	2003–2004 to 2014–2015	1980–1981 to 1992–1993	1993–1994 to 2002–2003	2003–2004 to 2014–2015
Andhra Pradesh	–7	–9	–7	1	0	–1	2	2	–1	4	7	8
Assam	–5	–7	–6	–1	2	–3	1	5	–4	4	1	10
Bihar	–12	–1	–7	2	–1	–9	5	–2	–5	7	4	11
Gujarat	–12	–6	1	8	2	–3	6	1	–3	5	5	1
Haryana	–6	–13	–10	5	2	–5	1	3	–5	5	10	16
Karnataka	–9	–14	–2	4	2	–4	3	5	–5	6	9	7
Kerala	–10	–8	–10	2	–1	–2	2	–2	–2	7	9	11
Maharashtra	–5	–4	–5	1	–2	–2	0	–4	–1	5	8	7
Madhya Pradesh	–17	–8	1	4	0	–1	6	4	–1	10	4	–1
Orissa	–17	–12	–10	2	1	3	8	1	2	8	11	8
Punjab	1	–7	–13	5	0	3	4	1	3	–5	6	10
Rajasthan	–3	–10	–9	–1	3	4	2	7	2	1	3	7
Tamil Nadu	–2	–9	–3	–4	–3	–2	–2	0	–3	4	9	7
Uttar Pradesh	–11	–4	–11	5	0	1	4	2	3	6	2	8
West Bengal	–1	1	–7	–2	1	–1	–1	0	0	2	–1	6

Source: Authors’ estimates based on CSO data.

During the pre-reform period I (1980–1981), the share of manufacturing in GSDP increased for all except four states: Assam (–1), Tamil Nadu (–4), West Bengal (–2) and Rajasthan (–1). In the post-reform period also the share of manufacturing shows decrease across majority of the states in period III (2003–2004 to 2014–2015). It is during the later period III that the decline in manufacturing GSDP gathered pace in almost all the states. As expected, the trend in industrial GSDP is similar to that of manufacturing for all the three time periods. The share of industry in GSDP declines for almost all the states during the post-reform sub-period III 2003–2004 to 2014–2015. Exceptionally, the three states such as Orissa, Punjab and Uttar Pradesh have not recorded a reduction in the share of manufacturing and industry in either the pre-reform or the two post-reform sub-periods under study.

The process of tertiarization of the state economies gained strength from the post-reform sub-period III (2003–2004 to 2014–2015) onwards. During 2003–2004 to 2014–2015, in all the states, the decline in agricultural GSDP was offset by a rise in the share of services in GSDP, thus signifying a move towards tertiarization of these economies. Apart from Gujarat and Madhya Pradesh the share of services in GSDP increased in the range of 7–11 percentage points for all other states, with Haryana recording the highest 16 percentage point increase in share of services in GSDP during 2003–2004 to 2014–2015.

However, in case of Orissa, Punjab and Rajasthan, during the same period a declining agriculture share saw a corresponding rise in both the shares of industry and services although change in the share of services was more as compared to industry.

Interestingly, Madhya Pradesh seems to be following a different trend, with the change in share of services in GSDP showing a consistent decline in all the three periods. During the pre-reform period, in Madhya Pradesh, the share of services in GSDP increased by 10 percentage point. However, the same increased by just 4 percentage point in post-reform sub-period II (1993–1994 to 2002–2003) and went down by 1 percentage point in the post-reform sub-period III (2003–2004 to 2014–2015). Thus, the pace of tertiarization has slowed down in the state despite the reforms gaining momentum in the later years. A similar low change though positive, in service sector GSDP is evident in case of Gujarat. In Gujarat, while the share of services in GSDP increased by 5 percentage point each in the pre-reform and the immediate post-reform periods, it increased by just 1 percentage point in the sub-period 2003–2004 to 2014–2015.

The states Tamil Nadu, Karnataka and Maharashtra also witnessed a reduction in the rate of change of share of services in GSDP during the period 2003–2004 to 2014–2015.

In order to measure and give an idea of the extent to which structural changes have taken place across states, a NAV index has been constructed for the 15 states and their rank were based on the index numbers. The results are presented in Table 3. It shows that the structural change in the economies for the entire period 1980–1981 to 2014–2015, the top five states economies whose structure has undergone high levels of change were Orissa, Haryana, Kerala, Karnataka and Madhya Pradesh. Whereas if we look at only the post-reform period (1993–1994 to 2014–2015), then the rankings change with Haryana, Kerala, Punjab, Orissa and Tamil Nadu being the top five states having experienced high levels of structural change. Gujarat and West Bengal on the other hand have undergone very low levels of structural changes in their respective economies for both the time periods.

Table 3.

NAV-based SCI Rankings of State

1980–1981 to 2014–2015			1993–1994 to 2014–2015
State	SCI	Rank	State	SCI	Rank
Orissa	0.345	1	Haryana	0.260	1
Haryana	0.310	2	Kerala	0.220	2
Kerala	0.300	3	Punjab	0.210	3
Karnataka	0.280	4	Orissa	0.205	4
Madhya Pradesh	0.270	5	Tamil Nadu	0.205	4
Uttar Pradesh	0.255	6	Karnataka	0.190	6
Bihar	0.230	7	Bihar	0.165	7
Andhra Pradesh	0.210	8	Uttar Pradesh	0.155	8
Punjab	0.205	9	Andhra Pradesh	0.150	9
Tamil Nadu	0.205	9	Maharashtra	0.140	10
Assam	0.200	11	Assam	0.140	10
Maharashtra	0.200	11	Madhya Pradesh	0.120	12
Rajasthan	0.195	13	Rajasthan	0.120	12
Gujarat	0.180	14	West Bengal	0.060	14
West Bengal	0.075	15	Gujarat	0.045	15

Source: Authors’ estimates.

V. Structural Changes in Sector-wise Employment in 15 Major States

In order to get a complete picture of the structural changes it is important to analyse the change in employment shares across different sectors. How labour is reallocated across sectors has crucial implications for overall productivity growth and economic growth in general. Analysing the sector-wise output data together with the corresponding employment data allows us to track the changes in labour productivity that have taken place over the years. Table 4 depicts the state-wise sector-wise share in employment for the years 1984 and 2014. All the states have experienced a decline in share of agricultural employment in total employment. Kerala and Punjab have recorded the sharpest decline of 32 percentage point in agriculture employment between 1983 and 2014. Maharashtra and Rajasthan are the other two states which have seen a decline of more than 25 percentage point in agricultural employment. Madhya Pradesh is the only state where dependence on agriculture continues to be really high as the share of agriculture employment has declined by just 4 percentage point over the last three decades from 67 to 63 per cent.

Table 4.
State-wise Sector-wise Share of Employment in Total Employment (Per cent)

State Year Agriculture & Allied Mining & Quarrying Manufacturing Electricity, Gas & Water Supply Construction Trade, Hotels & Restaurants Transport, Storage & Communication FIREBS PAOS Total

Andhra Pradesh 1983 69 1 10 0 2 7 3 0 8 100

2014 53 1 10 0 8 10 6 2 9 100

Assam 1983 72 0 5 0 1 8 3 0 10 100

2014 56 1 6 0 7 15 4 1 11 100

Bihar 1983 77 1 7 0 1 6 2 0 6 100

2014 59 1 6 0 12 11 3 1 6 100

Gujarat 1983 66 0 13 0 2 6 3 1 9 100

2014 49 1 20 1 5 12 4 2 7 100

Haryana 1983 67 1 8 0 3 6 3 1 12 100

2014 43 0 13 1 13 11 6 3 10 100

Karnataka 1983 69 1 11 0 3 6 2 1 7 100

2014 50 0 12 0 6 12 7 3 9 100

Kerala 1983 57 1 15 0 3 8 4 1 10 100

2014 26 1 13 0 17 16 9 4 13 100

Maharashtra 1983 78 2 7 0 2 4 1 0 6 100

2014 49 0 12 0 6 11 6 4 11 100

Madhya Pradesh 1983 67 0 11 0 3 6 3 1 8 100

2014 63 1 6 0 11 8 3 1 7 100

Orrissa 1983 73 1 10 0 2 5 1 1 8 100

2014 56 1 10 0 12 10 4 1 7 100

Punjab 1983 68 0 10 1 2 6 3 1 8 100

2014 36 0 17 2 13 11 5 3 13 100

Rajasthan 1983 76 0 7 1 4 4 2 0 5 100

2014 50 1 9 1 19 8 3 2 7 100

Tamil Nadu 1983 58 1 17 0 2 8 3 1 9 100

2014 35 1 20 1 13 13 6 3 9 100

Uttar Pradesh 1983 72 0 10 0 2 6 2 0 8 100

2014 52 0 13 0 13 10 3 1 7 100

1983 58 1 16 0 2 8 4 1 11 100

2014 39 0 23 0 8 11 5 2 10 100

State	Year	Agriculture & Allied	Mining & Quarrying	Manufacturing	Electricity, Gas & Water Supply	Construction	Trade, Hotels & Restaurants	Transport, Storage & Communication	FIREBS	PAOS	Total
Andhra Pradesh	1983	69	1	10	0	2	7	3	0	8	100
2014	53	1	10	0	8	10	6	2	9	100
Assam	1983	72	0	5	0	1	8	3	0	10	100
	2014	56	1	6	0	7	15	4	1	11	100
Bihar	1983	77	1	7	0	1	6	2	0	6	100
	2014	59	1	6	0	12	11	3	1	6	100
Gujarat	1983	66	0	13	0	2	6	3	1	9	100
	2014	49	1	20	1	5	12	4	2	7	100
Haryana	1983	67	1	8	0	3	6	3	1	12	100
	2014	43	0	13	1	13	11	6	3	10	100
Karnataka	1983	69	1	11	0	3	6	2	1	7	100
	2014	50	0	12	0	6	12	7	3	9	100
Kerala	1983	57	1	15	0	3	8	4	1	10	100
	2014	26	1	13	0	17	16	9	4	13	100
Maharashtra	1983	78	2	7	0	2	4	1	0	6	100
	2014	49	0	12	0	6	11	6	4	11	100
Madhya Pradesh	1983	67	0	11	0	3	6	3	1	8	100
2014	63	1	6	0	11	8	3	1	7	100
Orrissa	1983	73	1	10	0	2	5	1	1	8	100
	2014	56	1	10	0	12	10	4	1	7	100
Punjab	1983	68	0	10	1	2	6	3	1	8	100
	2014	36	0	17	2	13	11	5	3	13	100
Rajasthan	1983	76	0	7	1	4	4	2	0	5	100
	2014	50	1	9	1	19	8	3	2	7	100
Tamil Nadu	1983	58	1	17	0	2	8	3	1	9	100
	2014	35	1	20	1	13	13	6	3	9	100
Uttar Pradesh	1983	72	0	10	0	2	6	2	0	8	100
2014	52	0	13	0	13	10	3	1	7	100
	1983	58	1	16	0	2	8	4	1	11	100
	2014	39	0	23	0	8	11	5	2	10	100

Source: Own calculations based on NSSO 38th Round (1983–1984) and for 2014, projected figures based on NSSO 68th round results.

Note: FIREBS, finance, insurance, real estate and business services; PAOS, public administration and other services.

Between the period 1983 and 2014, the employment share of manufacturing has remained the same for Andhra Pradesh and Orissa, whereas it has declined in the states Bihar, Kerala and Madhya Pradesh. The largest rise in the employment share of manufacturing is noticed in the states Punjab (7 percentage point) and West Bengal (7 percentage point). Haryana has also seen an increase of 5 percentage point.

Almost all states have experienced a strong rise in construction employment. The shift of labour towards construction is the largest in Rajasthan (15 percentage point) followed by Kerala (14 percentage point). Agriculture-oriented states Haryana and Punjab have also seen a huge increase in share of construction employment from 3 and 2 per cent in 1983 to 13 per cent each in 2014.

Among services the labour has moved more towards the sub-sectors trade, hotels and restaurants, public admin and other services. Table 5 shows the state-wise sector-wise labour productivity for the 15 major states during 1983 to 2014. Traditionally agriculture has remained a low-productivity sector whereas all the services sectors exhibit high levels of productivity.

Table 6 depicts the state-wise and sector-wise compound average growth rate (CAGR) of labour productivity from 1983 to 2014. It may be seen that Haryana has recorded the highest CAGR (5.97%) of total labour productivity growth followed by Tamil Nadu (5.62%), Karnataka (4.70%), Gujarat (4.67%) and further followed by Andhra Pradesh (4.51%), Rajasthan (4.49%) and Kerala (4.45%). Bihar and Orissa have experienced relatively low growth in labour productivity as compared to other states.

Sector-wise results show that in the 15 major states, almost all the sectors witnessed a positive growth in labour productivity. Similarly, no wide variations were evident in sector-wise labour productivity growth across states. However, in the construction sector, the seven states such as Assam, Bihar, Kerala, Maharashtra, Orissa, Punjab and Uttar Pradesh recorded a negative productivity growth during the study period. Haryana recorded the highest 3.74 per cent productivity growth in agriculture and Orissa the lowest 0.13 per cent. In manufacturing the highest productivity growth took place in Madhya Pradesh (6.04%) and the lowest 1.21 per cent in Bihar.

The next step involves the decomposition of the total labour productivity growth into within and structural components using the methodology described above in the article and the results are depicted in Table 7.

The decomposition results show that both within change and structural change have contributed positively to overall labour productivity growth in all the 15 major states. Hasan, Lamba and Gupta (2013) also find both within-sector productivity growth and structural change contributing positively to aggregate labour productivity growth in the states under study between the period 1987 and 2009. Although the extent of the contribution of the two components was found to differ significantly across the states. Similarly, Mitra and Ahsan (2017) find a positive growth in labour productivity between 1987 and 2004 for all the 15 major states (except Assam). They also find average ‘within-sector’ growth to be positive for all states except Assam, while the contribution of structural change was found to be positive everywhere except for Gujarat, where it was slightly negative. Though significant structural changes have taken place in the state economies as was shown above, the contribution of this movement of labour has been low as seen by the low structural change component of total labour productivity growth. The growth in overall productivity seems to have been driven by the within-sector component more.

Table 5.

State-wise Sector-wise Labour Productivity, 1983 and 2014, at Constant 2004–2005 Prices (In Rupees)

State	Year	Agriculture & Allied	Mining & Quarrying	Manufacturing	Electricity, Gas & Water Supply	Construction	Trade, Hotels & Restaurants	Transport, Storage & Communication	FIREBS	PAOS	Total
Andhra Pradesh	1983	17,072	74,420	34,452	395,149	71,823	47,030	53,378	723,508	49,464	29,692
Andhra Pradesh	2014	41,683	262,134	142,047	498,000	85,028	170,831	262,062	985,412	150,668	116,394
Assam	1983	12,309	937,479	48,943	65,231	133,158	48,963	62,120	314,039	38,161	25,673
	2014	29,573	660,112	104,043	1,392,222	121,738	70,961	228,334	586,586	191,707	82,172
Bihar	1983	12,519	96,268	66,280	275,621	82,219	49,577	89,487	608,579	53,957	25,637
	2014	21,819	466,067	96,228	444,520	63,592	130,863	213,738	620,772	179,693	71,417
Gujarat	1983	22,816	1,807,187	65,468	333,547	71,858	80,808	58,687	602,396	15,555	40,486
	2014 *	45,228	532,875	208,322	441,960	285,494	285,094	370,350	1036,038	166,815	166,424
Haryana	1983	24,142	11,785	69,867	155,535	142,545	62,838	58,967	497,444	28,201	38,192
	2014	75,247	67,528	292,832	739,399	145,337	474,313	374,631	1,571,474	212,277	230,580
Karnataka	1983	18,127	42,993	39,640	230,940	62,451	41,299	33,374	394,407	43,031	30,378
	2014	37,209	149,644	156,572	547,229	181,323	52,480	157,621	1,102,798	179,920	117,601
Kerala	1983	24,445	8,356	29,008	89,365	121,788	124,546	38,873	384,417	55,923	44,218
	2014 *	57,988	92,976	90,018	397,968	112,581	181,667	371,194	782,231	191,211	170,740
Maharashtra	1983	15,080	27,237	145,557	256,677	300,665	162,212	206,633	2,551,780	88,626	52,495
	2014 *	26,995	580,755	264,132	790,461	213,359	223,589	316,977	1,350,054	156,244	175,826
Madhya Pradesh	1983	18,799	532,430	23,014	66,986	37,823	41,740	26,021	236,679	30,327	25,908
Madhya Pradesh	2014	35,562	557,286	141,726	761,103	81,583	111,671	227,545	888,915	160,716	83,268
Orrissa	1983	21,780	50,732	19,452	1,167,621	242,193	38,557	67,476	483,614	30,633	31,135
	2014	22,654	941,366	105,750	573,630	79,886	110,061	239,673	1,055,209	156,890	81,988
Punjab	1983	31,453	18,002	55,302	159,646	129,784	114,058	30,782	592,238	101,186	53,018
	2014	89,165	0	174,368	272,788	75,374	172,243	209,520	1,065,285	212,041	160,851
Rajasthan	1983	13,381	72,181	40,617	56,274	32,422	59,194	39,187	1,169,517	46,671	23,784
	2014	35,758	404,055	152,850	431,571	42,668	166,049	191,840	772,118	196,912	92,759
Tamil Nadu	1983	10,811	40,178	44,035	125,509	55,473	42,195	55,481	327,934	41,539	28,068
	2014	31,001	129,242	139,983	177,731	102,895	201,339	249,096	1,223,518	208,359	152,974
Uttar Pradesh	1983	17,409	326,360	30,659	115,925	75,149	71,157	60,480	690,800	35,195	28,996
Uttar Pradesh	2014	32,411	127,388	93,011	336,047	53,190	117,004	261,159	1,099,205	173,062	84,660
West Bengal	1983	18,439	142,954	23,492	179,497	84,042	53,391	51,173	321,132	55,124	32,289
	2014	42,644	214,677	45,164	547,809	87,816	143,229	210,875	875,453	200,170	106,216

Source: Own calculations based on GSDP data (at constant 2004–2005 prices) from CSO and employment data from NSSO 38th and 68th rounds.

Notes: *Figures for GSDP of Gujarat, Maharashtra and Kerala are for 2013–2014. FIREBS, finance, insurance, real estate and business services; PAOS, public administration and other services.

Table 6.

State-wise Sector-wise Compound Annual Growth Rate (CAGR) of Labour Productivity (2004–2005 Prices; in %)

State	Agriculture & Allied	Mining & Quarrying	Manufacturing	Electricity, Gas & Water Supply	Construction	Trade, Hotels & Restaurants	Transport, Storage & Communication	FIREBS	CSPSGS	Total
Andhra Pradesh	2.92	4.15	4.68	0.75	0.55	4.25	5.27	1.00	3.66	4.51
Assam	2.87	–1.13	2.46	10.38	–0.29	1.20	4.29	2.04	5.34	3.82
Bihar	1.81	5.22	1.21	1.55	–0.83	3.18	2.85	0.06	3.96	3.36
Gujarat	2.23	–3.86	3.80	0.91	4.55	4.15	6.12	1.76	7.95	4.67
Haryana	3.74	5.79	4.73	5.16	0.06	6.74	6.15	3.78	6.73	5.97
Karnataka	2.35	4.11	4.53	2.82	3.50	0.78	5.14	3.37	4.72	4.46
Kerala	2.83	8.08	3.72	4.94	–0.25	1.23	7.55	2.32	4.05	4.45
Maharashtra	1.90	10.37	1.94	3.70	–1.10	1.04	1.39	–2.03	1.85	3.98
Madhya Pradesh	2.08	0.15	6.04	8.16	2.51	3.23	7.25	4.36	5.53	3.84
Orrissa	0.13	9.88	5.61	–2.27	–3.51	3.44	4.17	2.55	5.41	3.17
Punjab	3.42	–100.00	3.77	1.74	–1.74	1.34	6.38	1.91	2.42	3.64
Rajasthan	3.22	5.71	4.37	6.79	0.89	3.38	5.26	–1.33	4.75	4.49
Tamil Nadu	3.46	3.84	3.80	1.13	2.01	5.17	4.96	4.34	5.34	5.62
Uttar Pradesh	2.03	–2.99	3.64	3.49	–1.11	1.62	4.83	1.51	5.27	3.52
West Bengal	2.74	1.32	2.13	3.66	0.14	3.23	4.67	3.29	4.25	3.92

Source: Own calculations based on CSO GSDP data and NSSO employment data.

Note: FIREBS, finance, insurance, real estate and business services; CSPSGS, Community, Social, Personal and Government Services.

Table 7.

Contribution of Within and Structural Changes to Total Labour Productivity Growth (In %)

State	Within	Structural
Andhra Pradesh	2.73	1.77
Assam	2.76	1.06
Bihar	2.07	1.29
Gujarat	2.77	1.90
Haryana	3.57	2.41
Karnataka	2.70	2.0
Kerala	2.33	2.12
Maharashtra	0.96	3.02
Madhya Pradesh	3.75	0.08
Orissa	2.04	1.14
Punjab	2.51	1.14
Rajasthan	2.80	1.69
Tamil Nadu	3.30	2.32
Uttar Pradesh	2.32	1.19
West Bengal	2.79	1.13

Source: Own calculations based on CSO GSDP data and NSSO employment data.

However, Maharashtra is an exception where the movement of labour across sectors has contributed more strongly to overall productivity growth than the within sector component. Mitra and Ahsan (2017) too find Maharashtra to be the best performer in terms of structural change with the contribution of structural change to total labour productivity growth being highest for Maharashtra. Likewise, Hasan, Lamba and Gupta (2013) find Maharashtra to be the second-best performer after Karnataka in terms of contribution of structural change in total productivity growth.

VI. Conclusions

This article provided insights on the output, employment and labour productivity trends in India over the three decades between 1983–1984 and 2014–2015. The article looked at the structural changes across 15 major states in India and in order to assess the magnitude of structural change in output of states an NAV-based SCI was constructed for each of the 15 states. The findings reveal that for the entire period 1980–1981 to 2014–2015, the top five states economies whose structure has undergone high levels of change are Orissa, Haryana, Kerala, Karnataka and Madhya Pradesh. For the post-reform period (1993–1994 to 2014–2015), states of Haryana, Kerala, Punjab, Orissa and Tamil Nadu emerge as the top five states having experienced high levels of structural change. Gujarat and West Bengal on the other hand have undergone very low levels of structural changes in their respective economies for both the time periods.

Almost all states witnessed strong labour productivity growth which has more than doubled for all of them. The labour productivity growth of these states has been decomposed to find out the relative contribution of structural change and within sector. The study also documents the large labour productivity gaps that have existed in the traditional and modern sectors of the economies of the 15 major states of India. We expect that movement of labour from low- to high-productivity sectors should lead to overall productivity increase.

It is found that for all the 15 major states under the study, the structural change has contributed positively to overall labour productivity growth and is therefore growth enhancing. However, though the labour has moved from the low-productivity sectors like agriculture towards high-productivity sectors like services as reflected in rising share of employment in services sector, this movement has not contributed as much to overall labour productivity growth as has the within component. Even in states that have undergone high level of structural change like Orissa, Haryana, Kerala, Karnataka and Tamil Nadu, the contribution of structural change to overall productivity growth is lower than the within component. The only exception is Maharashtra where contribution of structural change to overall productivity growth is higher than the within component.

Footnotes

Declaration of Conflicting Interests

The author declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Funding

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

Acknowledgements

The authors are grateful to the anonymous referees of the journal for their constructive suggestions on the earlier draft of the paper. However, the usual disclaimer applies.

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