Inter-state Growth Spillovers in Indian Major States: An Empirical Analysis

Abstract

This article verifies the inter-state linkages of economic growth across Indian states in a panel framework. Data have been used for the 15 major states for 1980–1981 to 2013–2014. For studying the long-run association between aggregate output of a state and that of the rest of the states, panel cointegration techniques have been used. Fully modified ordinary least squares estimation technique is used to find long-run coefficients. In addition to it, inter-state growth spillovers of output for three principal sectors of economy have been verified separately. The findings confirm the existence of inter-state association in the long run for the aggregate output as well as the sectoral output. Unidirectional causality runs from rest of the state’s output to a state’s output. There has also been a substantial increase in the extent of linkages after the reforms for the aggregate output. Though coefficient is positive, agricultural output has witnessed a decrease in the extent of linkages after the reforms. But, the linkages among states for industrial and service sector output have been improved as a result of the reforms. With the increasing flow of goods and services, growth spillovers are evident and economic spillovers of states are complementary. Removing barriers on inter-state trade, flow of investment, knowledge, and services will supplement to growth spillover among Indian states.

Keywords

Agriculture sector industrial sector service sector inter-state growth spill overs panel cointegration India

Introduction

Regional economists have found great interest in studying the concept of spillovers. The existing literature suggests different types of spillovers. First, knowledge-based spillovers where knowledge once generated does not stay with a particular firm alone. Second, industrial spillovers that occur due to the existence of input–output linkages. Third, growth spillovers that occur due to specific factors of a region that has an impact on another local region. Such spillovers occur due to the movement of goods and services and the relationship across markets (Capello, 2009).

The reasons for spillovers across Indian states are mainly due to some factors like the reforms of the 1990s. The rationale behind the reforms was the abolition of license raj, privatization, liberalization of trade policies, and a number of financial sector reforms to increase production and efficiency. The reforms have also led to the concentration of industries in areas that have infrastructure facilities, access to ports, and other geographical factors. Some states have benefited from it. Therefore, those states that are economically well-off could influence growth because of factors such as mobility of labor, capital, and the country’s fiscal policy. Second, states in India have different characteristics pertaining to spatial disorganization, agrarian structure, poor infrastructure base, social indicators, natural factors and calamities. Such differences have also added to regional differences and need for interdependencies in growth and employment.

Third, India’s inter-state trade in terms of goods and services also aids in growth dependencies. For a country like India to support its economic growth strategies that strengthen economic linkages among states should be adopted if it mutually benefits them. Bhide, Chadha, and Kalirajan (2006), Chakravarthy (2006), Kar and Sakthivel (2006) observe that states within India are fully open, and so growth in any region can affect the flow of goods and services across the states. On the other hand, the liberal policies of the working inter-state can help in the growth of goods and services. In this way, the Goods and Service Tax (GST) is another major reform and appears as a boom in creating a wide national market. It also becomes important to study the nature and magnitude of the externality being generated. Hence, to support India’s’ economic growth, one has to adopt the strategies to improvise the economic inter-state connectivity to benefit the broader economy. Therefore, in forming development policies, the assessment of economic development in states as well as inter-state dependence across states is very important.

This article analyzes the spillover of growth that is taking place in the agricultural sector, industrial sector, service sector, and in overall economic output among Indian states. Therefore, the main aim of the article is to analyze the existence of inter-state spillover across different sectoral outputs as well as for the states’ aggregated output. Thus, the study helps to understand the growth spillovers between states for promoting overall growth.

The second section of the article reviews selected literature associated with the study. The third section describes data and methodology. The fourth section discusses the empirical results. The fifth section concludes the study.

Literature Review

The neoclassical growth model assumes that those states that have initial lower per capita income will have a higher growth rate than those states that have an initial higher per capita income. In the long run, all economies converge to their steady state due to technological improvements (Solow, 1956). In the Indian context, Cashin and Sahay (1995), Marjit and Mitra (1996) have found that the states that have initial lower per capita income do not have a higher growth rate over the years. Bhide et al. (2006) observed that lack of convergence does not mean lack of inter-state linkages. States still follow a leader–follower relationship due to the mobility of factors of production.

The reviews associated with growth linkages have been classified into inter-sectoral and interstate growth linkages. Dhawan and Saxena (1992), Hansda (2006), Sastry, Singh, Bhattacharya, and Unnikrishnan (2003), Kaur, Bordoloi, and Rajesh (2009), Goldar and Mitra (2010), and Varkey and Panda (2018) have analyzed growth linkages across sectors. Dhawan and Saxena (1992) have found the existence of different degrees of forward, and backward linkages for specific products and services. Sastry et al. (2003) find that agriculture is important for the development of other two sectors. Hansda (2006) has observed that trade and transport services, construction, and other crops have contributed highest in backward and forward linkages. Kaur et al. (2009) have found that the demand for industrial goods is highly associated with the services than agriculture from the production side, whereas the agricultural products have a greater degree of association with the industry from the demand side. Behera (2012) have used cointegration test to study the long-run association across sectors for Odisha and observed some sector specific relationships. Varkey and Panda (2018) have observed that industry is a major determinant contributing to agricultural growth. Services, on the other hand, contribute negatively to the agricultural output. The impact of the industries and services of other states was not found to be significant.

Sparse studies have analyzed the concept of growth linkages in an inter-state perspective. Bhide et al. (2006) have examined interstate spillover of net state domestic product and found that growth of certain states is influenced by other states. Chakravarthy (2006) has observed that industry is important for development of services. The rest of the state’s agriculture and industry in the pre-reforms period have influence for Haryana, Gujarat, Himachal Pradesh, Tamil Nadu, Uttar Pradesh, Bihar, and Kerala, whereas, in the reform period, they have influence for Rajasthan, Maharashtra, Kerala, and Punjab.

Sodsriwiboon and Kalra (2010) have characterized a dynamic relationship for the high-, medium-, and low-income states of India through a system of equation. Each period of growth has been regressed with its own lag as well as the lag of the other two periods, taking into consideration of fixed effects of each group. The spillovers from within groups are larger than the spillovers from other groups. The spillover effects are experienced more from the higher-income groups to medium-income groups than high- to low- or medium- to low-income groups. Debnath and Roy (2012) have studied the inter-state linkage for the north eastern states using grange causality. Tripura is the most influential state. Following the order of influential states, the states are Arunachal Pradesh, Nagaland, and Assam. The study finds mixed results of both polarization and spillover effects.

From the analysis of literature it is observed that the studies mainly examine the spillovers across sectors. However, sparse studies have addressed the issue of inter-state linkages in economic growth. In addition, the present study also looks at the concept of inter-state linkages across aggregated as well as sectoral gross state domestic product (GSDP). Our analysis uses econometric techniques like cointegration and granger causality to analyze inter-state spillovers.

Data and Methodology

Data

The study is based on secondary data taken from the Central Statistical Organization for the 15 general category states¹ for 1980–1981 to 2013–2014. The special category sates are not as they heavily depend on central transfers for funding their development activities. The variables considered for analysis are: GSDP, agricultural gross state domestic product (ASDP), industrial gross state domestic product (ISDP), and service sector gross state domestic product (SSDP) for each states and rest of the states. The aggregate as well as the sector-wise GSDP data have been spliced to make harmonized with 2004–2005 base year. Logarithmic transformation has been performed.

Methodology

The time series property of the data set has been verified using the Panel unit root test. The results have been reported in Table1.

The variables are found to be stationary at first difference as reported in Table 1. The long-run association across variables has been studied using Pedroni cointegration (Pedroni, 2004). Panel causality test developed by Dumitrescu and Hurlin (2012) has been used to study direction of causality. The fully modified ordinary least square is used to find long run coefficients.

The model in its general form has been given below. The GSDP of a state has been regressed on the rest of the states’ GSDP. Agricultural sector output of a state has been modelled with the rest of the states’ agricultural output. In a similar manner, regression equations have been specified for sectors of industry and services.

G S D P_{i t} = β_{0} + β_{1} R O S G S D P_{i t} + u_{i t}

(1)

A G S D P_{i t} = β_{0} + β_{1} R O S A G S D P_{i t} + u_{i t}

(2)

I G S D P_{i t} = β_{0} + β_{1} R O S I G S D P_{i t} + u_{i t}

(3)

S G S D P_{i t} = β_{0} + β_{1} R O S S G S D P_{i t} + u_{i t}

(4)

The state-specific coefficients have been estimated using fixed effect within estimation techniques for the pre- and post-reform period for the national aggregate.

Table 1.

Im Pesaran and Shin

Variable	Test Statistic (at level)	Test Statistic (at first difference)
AGSDP	0.02181	−16.3106*
IGSDP	6.41930	−6.03912*
SGSDP	18.7164	−2.77254*
ROSAGSDP	−0.48785	−11.4780*
ROSIGSDP	0.73586	−6.67929*
ROSSGSDP	0.73586	−6.67929*

Source: Authors calculation using EViews.

Note: *Indicates statistical significance at 1% level.

Empirical Results and Discussion

Inter-state Linkages in a Panel Framework

The results pertaining to inter-state linkages of one state’s GSDP with rest of the states’ GSDP is shown in Table 2 The results show that there is long-run equilibrium relationship between aggregated output of a state and output of the rest of states as shown in Table 2. The long-run association between agriculture and rest of the state’s agriculture has been given in Table 3. Table 4 shows the long-run association across the variables industry and the rest of the state’s industry, whereas Table 5 shows the relationship across the variable’s services and the rest of the state’s services in the long run. For all the three tables, the results show the existence of a long-run association. Thus, there exists inter-state association for the aggregated output as well as for the three sectoral outputs in the long run. For verifying the robustness of the results, the cointegration test developed by Kao as well as the Fischer’s cointegration test have been performed. They provide similar results.

Unidirectional causality has been observed that runs from the rest of the state’s aggregate output to the aggregate output of a state as shown in Table 6. On the other hand, sectors like agriculture and industry also witnessed the existence of unidirectional causality. The rest of the state’s agriculture causes agricultural output of a state. A unidirectional causality runs from rest of the state’s industry to industry. Similarly, rest of the state’s service SDP has unidirectional causality on states’ service sector output.

The long-run association has been estimated using Fully modified ordinary Least Square (FMOLS). The estimates using FMOLS provide a better estimate as it corrects for the problem of serial correlation. The rest of the states’ GSDP has a positive and significant relationship with the aggregate output of a state as shown in Table 7. When analyzing the sectoral relationship across states, the rest of the state’s agriculture has a positive significant relationship on agricultural production of a state as shown in Table 8. These linkages may also take place due to increasing linkages of food supply between states through exports and imports. A decline in food prices may be addressed to the economic growth in other regions, which leads to decline in both employment and GDP for a small open economy (Anderson, 1987). On the other hand, industry output of a state has also had a positive growth stimulus from the rest of the state’s industry as evident from results in Table 9. The rest of the state’s service output has been positively significant for the services of a state as seen in Table 10. But the size of the coefficient is not encouraging.

Table 2.

Pedroni Panel Cointegration (GSDP, ROSGSDP)

	Statistic	Prob	Weighted Statistic	Prob
Panel v-statistic	2.150039	0.0158	−1.201227	0.8852
Panel rho-statistic	−6.665805	0.0000	−7.136362	0.0000
Panel PP-statistic	−8.315578	0.0000	−8.580331	0.0000
Panel ADF-statistic	−2.511602	0.0060	−3.827445	0.0001
	Statistic	Prob
Group rho-statistic	−3.945873	0.0000
Group ADF-statistic	−7.362568	0.0000
Group PP-statistic	−2.374382	0.0088

Source: Authors calculations using EViews 8.

Table 3.

Pedroni Panel Cointegration (AGSDP, ROSAGSDP)

	Statistic	Prob	Weighted Statistic	Prob
Panel v-statistic	2.483704	0.0065	2.010817	0.0222
Panel rho-statistic	−7.171316	0.0000	−9.253365	0.0000
Panel PP-statistic	−6.588148	0.0000	−8.458259	0.0000
Panel ADF-statistic	−2.663474	0.0039	−3.360937	0.0004
	Statistic	Prob
Group rho-statistic	−5.670315	0.0000
Group ADF-statistic	−7.082241	0.0000
Group PP-statistic	−2.658819	0.0039

Source: Authors calculations using EViews 8.

Table 4.

Pedroni Panel Cointegration (IGSDP and ROSIGSDP)

	Statistic	Prob	Weighted Statistic	Prob
Panel v-statistic	3.676144	0.0001	2.631850	0.0042
Panel rho-statistic	−1.226247	0.1101	−4.858165	0.0000
Panel PP-statistic	−0.350311	0.3631	−4.573389	0.0000
Panel ADF-statistic	0.827033	0.7959	−2.416479	0.0078
	Statistic	Prob
Group rho-statistic	−2.348923	0.0094
Group ADF-statistic	−3.476939	0.0003
Group PP-statistic	−1.417729	0.0781

Source: Authors calculations using EViews 8.

Table 5.

Pedroni Panel Cointegration (SGSDP, ROSSGSDP)

	Statistic	Prob	Weighted Statistic	Prob
Panel v-statistic	−0.826703	0.7958	−2.853593	0.9978
Panel rho-statistic	−4.866024	0.0000	−4.818435	0.0000
Panel PP-statistic	−3.748257	0.0001	−3.717362	0.0001
Panel ADF-statistic	−3.692835	0.0001	−3.663018	0.0001
	Statistic	Prob
Group rho-statistic	−1.626514	0.0519
Group ADF-statistic	−3.501460	0.0002
Group PP-statistic	−3.423997	0.0003

Source: Authors calculations using EViews 8.

Table 6.

Panel Causality of Aggregated and Sector-Specific GSDP of State with Those of ROSGSDP

Null Hypothesis	W-stat	Z-bar-stat	Prob
ROSGSDP does not homogeneously cause GSDP	6.34365	6.92356	0.0000
GSDP does not homogeneously cause ROSGSDP	2.44883	0.47799	0.6327
ROSAGSDP does not homogeneously cause AGSDP	5.50268	5.53183	0.0000
AGSDP does not homogeneously cause ROSAGSDP	2.66704	0.83910	0.4014
ROSIGSDP does not homogeneously cause IGSDP	4.06489	3.15242	0.0016
IGSDP does not homogeneously cause ROSIGSDP	2.71954	0.92600	0.3544
ROSSGSDP does not homogeneously cause SGSDP	3.02338	2.92881	0.00531
SGSDP does not homogeneously cause ROSSGSDP	−0.81546	−0.57018	0.5686

Source: Authors calculations using EViews 8.

Table 7.

Fully Modified Ordinary Least Squares (GSDP as the dependent variable)

	GSDP	GSDP (pre-reform)	GSDP (reform)
ROSGSDP	1.002940*	0.994236*	1.005794*
R-squared	0.989993	0.996840	0.992418

Source: Authors calculation using EViews.

Note: * and ** Indicate statistical significance at 1% and 5% level of significance, respectively.

In order to understand the impact of the reforms on the inter-state linkages, we have divided the analysis into pre- and post-reform period. The aggregate GSDP shows an improvement in the slope coefficient from 0.99 to 1 as shown in Table 7. The reforms and abolition of licensing have allowed the private companies to enter into all new areas. Second the devaluation of the Indian rupee increased the country’s export competitiveness. Third, tariff liberalization plays an important role. Fourth, the multinational company’s investment in India was non-debt creating. Lastly, the entrepreneurial activity of the private business associates made use of the deregulation in India (Mukherji, 2009). On the other hand, the size coefficient for linkages across states for the agriculture sector in the reform period is lower than that of the pre-reform period. It has been reduced from 0.95 to 0.92 as shown in Table 8. The decline in the extent of linkages may be associated with the decrease in the agricultural growth rate during the period. The 1970s was characterized by a decline in the agricultural growth rate across states and the positive growth impulse shed by the green revolution was washed out. The factors associated were; first, the increased water pollution due to the excessive use of fertilizers and pesticides, second, highly intensive cultivation has led to the depletion of fertility of the soil, third, the diminishing marginal returns to capital is associated to the increased use of inputs (Singh, 2016). The continuous decline in agricultural growth rate during the period would have facilitated a decline in the extent of linkages.

The industrial relationship across states has increased due the implementation of the reforms. The coefficient has increased from 0.97 in pre-reform period to a level of 1.05, indicating higher linkage during the reform period. The results of the industrial linkages across states has been reported in Table 9. The rest of the services’ contribution has been positively significant in the pre-reform period, and coefficient has been marginally improved in post-reform period as shown in Table 10.

Table 8.

Fully Modified Ordinary Least Squares (AGSDP as the dependent variable)

	AGSDP	AGSDP (pre-reform)	AGSDP (reform)
ROSAGSDP	0.941947*	0.954069*	0.924815*
R-squared	0.980949	0.986522	0.986545

Source: Authors calculation using EViews.

Note: * and **Significance at 1% and 5% level, respectively.

Table 9.

Fully Modified Ordinary Least Squares (IGSDP as the dependent variable)

	IGSDP	IGSDP (pre-reform)	IGSDP (reform)
ROSIGSDP	1.051240*	0.971237*	1.056920*
R-squared	0.970441	0.994864	0.971157

Source: Authors calculation using EViews.

Note: * and ** Significance at 1% and 5% level, respectively.

Table 10.

Fully Modified Ordinary Least Squares (SGSDP as the dependent variable)

	SGSDP	SGSDP (pre-reforms)	SGSDP (reforms)
ROSSGSDP	0.076474*	1.002600*	1.01060*
R-squared	0.637265	0.997867	0.78890

Source: Authors calculation using EViews.

Note: * and ** Indicate significance at 1% and 5% level of significance, respectively.

State-specific Analysis

In the previous sections, results were inferred on the basis of findings from estimations on panel data. In this section, separate regressions have been run for each state^’s output against the output of the rest of the states, considering time series data. This will help infer which states’ output are associated by the output of rest of the states.

Table 11 shows the interaction across states in the pre-reform period while Table 12 reports the same for reform period. Only four states have shown an improvement after the reforms, which include Gujarat, Goa, Bihar, and Odisha. Gujarat, on the other hand, is a state that has witnessed a significant decrease in poverty and a rapidly increasing growth rate over the years. It is also one among the most industrialized states in India. It is one among the largest importing and exporting states facilitating inter-state trade. Goa has witnessed an increase in the inter-state linkages due to the advent of the new economic reforms. Goa has a coastline that is 100 km long with beaches and beauty spots making it an idle tourist destination. Beach tourism has also helped in improving the infrastructure facilities in the coastline. Services which accounted to 46 per cent in the 1970s increased to 50.2 per cent in the 1990–1999. The service sub-sectors like trade, hotel and restaurants, and Banking and insurance were the fastest growing services for Goa. Therefore, the service sector growth in the 1990s has further improved due to the tourism services. Such factors have also facilitated the states’ inter-state connectivity. Bihar and Orissa have witnessed an increase in their inter-state connectivity after the reforms. Bihar witnessed a zero-growth rate in the beginning of the 1990s. It also showed a declining trend in agriculture due to the droughts and flood (Sundberg & Kaul, 2005). It becomes necessary for low-income states like Odisha and Bihar to have a positive economic inter-linkages across states so that these linkages can contribute for the improvement of states’ social as well as economic structure.

Table 11.

Time Series Analysis (GSDP as the dependent variable) Ordinary Least Squares

States	ROSGSDP	C	R-square	DW
1 Goa	1.096699*	−7.217671*	0.947271	1.747241
2 Gujarat	0.899411*	−0.714174	0.882760	2.233792
3 Haryana	1.231959*	−7.499975*	0.979628	2.345287
4 Maharashtra	1.187125*	−5.106683*	0.981564	1.970049
5 Punjab	0.985843*	−2.701297**	0.971056	1.556926
6 Andhra Pradesh	1.231340 *	−7.077758***	0.970483	2.582793
7 Karnataka	1.028261 *	−3.163040 *	0.974675	2.295931
8 Kerala	0.807333 *	0.548816 *	0.962607	2.392767
9 Tamil Nadu	1.108797 *	−4.827518 **	0.971011	2.139625
10 West Bengal	0.209257	39.55984	0.998967	2.256602
11 Bihar	0.732740 *	1.850510 *	0.933219	2.345411
12 Madhya Pradesh	1.187125*	−5.106683*	0.981564	1.970049
13 Orissa	0.796507*	0.543912	0.854711	2.325888
14 Rajasthan	1.253771 *	−7.524271 *	0.944988	1.894402
15 Uttar Pradesh	0.929449*	−0.508641	0.992272	2.332525

Source: Authors calculation using EViews.

Note: *, ** and *** Indicate significance at 1%, 5% and 10% level of significance, respectively.

Table 12.

Time Series Analysis (GSDP as the dependent variable reforms) Ordinary Least Squares (reform period)

States	ROSGSDP	C	R-square	DW
1 Goa	1.210372*	−9.266786*	0.992205	1.814257
2 Gujarat	1.192252*	−6.044202*	0.993638	1.526640
3 Haryana	1.181426*	−6.689335*	0.997790	1.854409
4 Maharashtra	1.111288*	−3.627651*	0.996944	1.746039
5 Punjab	0.821056*	0.312089*	0.999000	1.798832
6 Andhra Pradesh	0.980579**	−2.466589 *	0.994709	1.795420
7 Karnataka	0.985088*	−2.287143**	0.995992	1.877818
8 Kerala	0.489498*	15.18813*	0.998519	1.716776
9 Tamil Nadu	1.084502 *	−3.840898*	0.996287	1.707192
10 West Bengal	0.209257 *	39.55984*	0.998967	2.256602
11 Bihar	0.992323 *	−3.201982*	0.972032	2.423967
12 Madhya Pradesh	0.868533*	−0.406506	0.989752	2.224237
13 Orissa	0.960020 *	−2.640504 *	0.990805	2.161309
14 Rajasthan	0.978171*	−2.419263*	0.985662	2.093344
15 Uttar Pradesh	0.773810 *	2.276066*	0.998098	1.906808

Source: Authors calculation using EViews.

Note: *, ** and ***Indicate significance at 1%, 5% and 10% level of significance, respectively.

Conclusion and Policy Suggestions

The main aim of this article was to analyze the existence of inter-state spillovers of growth in Indian states. The findings confirm the existence of a long-run equilibrium relationship across states for aggregate GSDP and sectoral GSDP. The gross domestic output of rest of the states positively contributes to the output of a state. The long-run association between state and rest of the states also holds for the sector-specific output, that is, agriculture, industry, and services. Therefore, there are spillovers of growth taking place between states in India.

The long-run coefficient verifying inter-state linkages during the reform period for the aggregated output shows improvement over the same in pre-reform period. For agriculture, though, the linkage is positive, the size of the coefficient is declining in reform period. The coefficient for industries and service sector output in the reform period marginally improved, indicating higher inter-state linkages. The state-level coefficients have shown an improvement in the inter-state linkages in GSDP in the reform period only for the states of Goa, Punjab, Bihar, and Odisha.

Removing barriers on inter-state trade, flow of investment, knowledge, and services will supplement to growth spillover among Indian states. The introduction of Goods and Service Tax will further help in the seamless movement of goods and services across states and inter-state linkages of growth in service sector and other sectors. Therefore, the importance of the rest of the state’s production on states’ economy will be more realized with the increase in flow of goods and services, and that will supplement to the overall growth of the economy.

Footnotes

Declaration of Conflicting Interests

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

Funding

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

Note

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