Convergence of energy productivity across Indian states and territories

Accepted Manuscript Convergence of energy productivity across Indian states and territories

Mita Bhattacharya, John Nkwoma Inekwe, Perry Sadorsky, Anjan Saha PII: DOI: Reference:

S0140-9883(18)30247-0 doi:10.1016/j.eneco.2018.07.002 ENEECO 4080

To appear in:

Energy Economics

Received date: Revised date: Accepted date:

30 October 2017 26 June 2018 1 July 2018

Please cite this article as: Mita Bhattacharya, John Nkwoma Inekwe, Perry Sadorsky, Anjan Saha , Convergence of energy productivity across Indian states and territories. Eneeco (2018), doi:10.1016/j.eneco.2018.07.002

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ACCEPTED MANUSCRIPT Convergence of energy productivity across Indian states and territories

Mita Bhattacharya Department of Economics, Monash University, Caulfield, Australia 3145 Email: [email protected]

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John Nkwoma Inekwe Centre for Financial Risk, Macquarie University, Sydney, Australia 2109 [email protected]

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Perry Sadorsky Schulich School of Business, York University, Toronto, Ontario, Canada M3J 1P3 Email: [email protected]

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Anjan Saha Department of Economics and Finance, La Trobe University, Melbourne, Australia 3086 Email: [email protected]

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Abstract

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The Indian government has a number of ambitious economic and energy related initiatives including increasing access to electricity (“24X7 Power for All”), greater economic activity from manufacturing (“Make in India”), and reducing carbon dioxide emissions. Energy productivity is an important factor in helping to achieve these objectives. In this paper, we test the hypothesis of energy productivity convergence in a panel of contiguous states and territories (S&Ts) in India. In measuring energy productivity at the S&T level, we use a unique firm-level dataset maintained by the Centre for Monitoring Indian Economy (CMIE) for the period 1988 to 2016. We identify convergence clubs across Indian S&Ts; i.e. we identify groups of states that converge to different equilibria. The findings from the club merging analysis show that energy productivity across the S&Ts converges into two clubs with one divergent club. Higher initial energy productivity makes it more likely for states to be in the high energy productivity club. Industry structure is also an important determinant. The club convergence of the S&Ts has implications for Indian energy policy.

JEL Classification: C33; Q43; Q48. Keywords: Indian states and territories; club convergence; energy productivity; panel data and stochastic convergence. Acknowledgements: We thank two anonymous reviewers for helpful comments.

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ACCEPTED MANUSCRIPT 1.

Introduction

Energy productivity (economic output per unit of energy input) plays a key role in enhancing economic growth and prosperity of countries.1 Countries that use their inputs of production more productively are able to achieve higher standards of living. Improvements in energy productivity are essential to addressing concerns relating to climate change and energy security. In addition to learning how energy productivity changes across time it is also of

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interest to learn if countries experience energy productivity convergence. Since the seminal paper by Barro (1991), the examination of the convergence of per capita GDP across

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countries has been a popular area of research. Convergence measures the reduction in disparities across countries or regions. In the context of energy, Markandya et al. (2006),

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Liddle (2009), Jakob et al. (2012), Mulder and de Groot (2012), Herrerias et al. (2013), Apergis and Christou (2016), and Zhang and Broadstock (2016) among others, have

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examined the convergence path of energy intensity (or electricity intensity) for a large number of developed and developing countries. Mohammadi and Ram (2012, 2017) have an

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excellent review of the literature on energy convergence across countries and regions and among the US states.

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While there is a literature analysing the convergence of energy productivity or energy intensity between countries or industries, there is no study that focuses on Indian states and

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territories. In this research, we fill this gap in the literature by analysing the convergence of energy productivity across Indian states and territories (S&Ts). Analysing energy productivity in India is important for several reasons. First, India is one of the world’s largest

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and fastest-growing economies. Since 1990, India’s economy has experienced an average annual growth rate of 6.5% making India one of the fastest growing economies in the world.

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India accounts for 18% of the world’s population and uses 6% of world primary energy. The IEA (2015) expects that one-quarter of the increase in world energy demand to 2040 will come from India. The fossil fuel share of energy demand in India, currently at 72%, is expected to rise to 81% in 2040 creating challenges for energy security and carbon dioxide emissions reduction. Second, India faces a gap in access to electricity. There are 1.3 billion people living in India and an alarming number of them, 240 million, have no access to electricity (IEA, 2015). The electricity situation in India is particularly troublesome and has been so for decades. In the late 1980s India experienced a balance of payments crisis that led to pro-market economic reforms. Electric power generation was the first area of infrastructure 1

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A related concept is energy intensity which is measured as the ratio of total energy used to GDP.

ACCEPTED MANUSCRIPT to be opened up to private investment (Ahluwalia, 2002). Low electricity tariffs and electricity theft, however, contributed to a lack of private investment in electricity generation. The expansion of electricity generation has not kept up with current needs and the electricity produced is often susceptible to frequent interruptions and fluctuations in voltage. Moreover, losses of electricity due to technical difficulties and theft are high, with some estimates averaging around 30% of the total electricity generation. Electricity generation in India will need to quadruple in size to 2040 in order to keep up with rising demand for electricity (IEA,

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2015). In response, the Indian government has initiated a program to increase electrification (“24X7 Power for All”). Third, The Indian government has a policy target to increase the

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manufacturing sector (“Make in India”). At least 10 times more energy per unit of value added is required for industry-led economic growth compared with service-led economic

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growth. Improvements in energy productivity will be central to these initiatives2. Within the 50 largest economies in the world, India ranks 34 – ahead of Australia (36), the U.S. (38) and

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China (46) – on the highest energy productivity index.3 Improving sectoral composition of energy use, fuel mix, and efficiency in the energy sector are pivotal for future economic

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growth in India. While current energy productivity in India compares favourably to some other countries these rankings do not take into account future changes in industrialization and urbanization. Fourth, India, under its Nationally Determined Contribution (NDC) program

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has set a 2030 target to lower carbon dioxide emissions intensity between 33% and 35% of

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2005 levels4. The NDC program indicates that India has ambitious plans to transition to a low carbon economy (Chakrabarty and Chakraborty, 2018). Increased energy consumption, greater access to electricity, changes in industry structure and

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a transition to a low carbon economy will require an increase in Indian energy productivity. It is therefore useful to test for energy productivity convergence between Indian S&Ts. Energy

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productivity convergence between Indian states and territories has implications for energy policy. If Indian states and territories are experiencing a similar convergence in energy productivity, then a common nationwide energy policy will be effective; however, if Indian

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Mukherjee (2008) uses DEA to analyze energy efficiency in Indian manufacturing and finds considerable variation in energy efficiency across states and that energy prices do not provide incentives for energy conservation. A higher quality labor force correlates with higher energy efficiency. Mukherjee (2010) using DEA finds that the average firm in manufacturing could increase energy efficiency and output by improving technical efficiency. 3 http://www.ecofys.com/files/files/the-2015-energy-productivity-and-economic-prosperity-index.pdf (see Table 1, page 10). The report is prepared by Ecofys, a leading think-tank in energy related research based in Utrecht, Netherlands. Energy productivity is measured in billions of euros of GDP per exojoule of energy consumed. 4 http://climateactiontracker.org/countries/india.html

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ACCEPTED MANUSCRIPT states and territories exhibit a different convergence in energy productivity, a more nuanced energy policy is required that takes these differences into account. The paper makes several important contributions to the literature. Firstly, while existing studies have analysed conditional energy convergence at the country level, there are very few studies that analyse conditional convergence of energy intensity at the dis-aggregate level (Herrerias and Liu, 2013; Zhang and Broadstock, 2016). The strength of our paper lies in its

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uniqueness of the data set. We study, for the first time, the convergence of energy productivity in Indian S&Ts using dis-aggregated data. In measuring energy productivity at

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the S&T level, we use a unique firm-level data set maintained by the Centre for Monitoring

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Indian Economy (CMIE) for the period 1988 to 2016. Secondly, in this analysis, we examine both the conditional convergence and club convergence approach to examining whether the

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findings differ using these alternative approaches. This is a departure from the existing literature, though the two methods are not comparable. In analysing club convergence, we use the Phillips and Sul (2007) regression based convergence test which endogenously selects

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S&Ts for club membership. Finally, in addition to examining evidence for club convergence we also present results on the determinants of club convergence.

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Our analysis uncovers several important results. The initial evidence revealed that four

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convergent clubs are present in Indian energy productivity, with one divergent (nonconverging) club. The Phillips and Sul (2007) approach can lead to too many clubs being chosen; in response, they suggest that tests for club merging should be conducted. After the

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club merging analysis, two convergent clubs and one divergent club were uncovered. Different clubs converge to different equilibrium and the divergent club does not converge to

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equilibrium. This result has important energy policy implications for India; for example, energy policies that are common to all states and territories will have a limited effect for the states and territories with different patterns of convergence. Indian energy policy needs to be formulated taking into account these different clubs. In analysing the determinants of club convergence we find that higher initial energy productivity makes it more likely for states to be in the high energy productivity club. Industrialization is also an important determinant of club convergence.

The rest of the paper is organised as follows. Section 2 briefly reviews the relevant literature examining the issues of energy intensity and energy productivity convergence. Section 3 4

ACCEPTED MANUSCRIPT describes the data and methodologies we use for estimation purposes. Section 4 covers the empirical findings in detail, while we draw our conclusions and policy implications in Section 5.

2.

A brief review of the literature

The concept of economic convergence is rooted in the literature on economic growth. The

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three main types of convergence are sigma (σ) convergence, beta (β) convergence, and stochastic convergence. Convergence can also be classified as conditional (relative) or

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unconditional (absolute). Sigma convergence occurs when there is a reduction in the

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dispersion of income levels across countries (Barro and Sala-i-Martin, 1992). Sigma convergence can be analysed using parametric or nonparametric techniques. Beta

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convergence occurs when poor countries grow faster than rich ones (Barro and Sala-i-Martin, 1992; Mankiw et al., 1992). Beta convergence, sometimes referred to as catching up, indicates that countries with lower initial incomes grow faster than countries with higher

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initial incomes. Another measure of convergence, stochastic convergence, tests whether shocks to a variable of interest are temporary indicating the variable is trend stationary (Quah,

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1996a). Stochastic convergence is usually analysed using panel unit root tests.

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The convergence club approach rests on the idea that rather than assuming that all countries converge to an equilibrium it is more realistic to assume that countries within a group converge to an equilibrium but different groups, or clubs, have different equilibrium (Baumol,

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1986; Durlauf and Johnson, 1995; Galor, 1996). Club convergence allows countries/regions to endogenously converge to different groups. While the concept of club convergence has

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been around for some time, formal tests for club convergence have been problematic. More recently, Phillips and Sul (2007) have introduced new tests for club convergence which overcome some of the limitations of previous studies. In the context of energy productivity or energy intensity, the majority of studies have concentrated on understanding whether there has been a decline in cross-country differences in energy productivity or energy intensity (Table 1). Mielnik and Goldemberg (2000), in one of the first papers to address this topic, use trend analysis on data from 1971 to 1992 to show energy intensity convergence between countries. Developing countries are experiencing a rising energy intensity trend while developed countries are showing a declining trend in energy intensity. Sun (2002) uses mean deviation analysis to show that the energy intensity of 5

ACCEPTED MANUSCRIPT OECD countries has decreased across time. Miketa and Mulder (2005) study energy productivity convergence in 56 countries for 10 manufacturing sectors. They find that crosscountry differences in energy productivity are persistent and convergence is local rather than global. Countries converge to different steady-state conditions and some countries are not converging. Using panel data methods, Markandya et al. (2006) find evidence of beta convergence in energy intensity for 12 European Union (EU) countries. Using nonparametric

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techniques, Ezcurra (2007) finds evidence of sigma convergence in energy intensity between countries. Liddle (2009) finds evidence of sigma convergence (narrowing of the distribution) in electricity intensity for IEA/OECD. Some evidence of gamma convergence (movement

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within the distribution) is detected.5 Le Pen and Sevi (2010) use the stochastic convergence

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test of Pesaran (2007) and find no evidence of global convergence in energy intensity but there is some evidence of regional convergence. Liddle (2010) extends the study by Ezcurra

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(2007) to include a larger data set of 134 countries covering the period 1990 to 2006. There is evidence of energy intensity convergence across groups of countries. In addition to sigma and beta-convergence, gamma convergence is also included in the analysis. Herrerias (2012)

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extended the empirical approach used by Ezcurra (2007) to include a population weighting vector to help explain distributional changes in energy intensity. Evidence is found showing

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developing (developed) countries converge at higher (lower) energy intensity ratios. Liddle (2012) finds some degree of convergence between OECD energy intensity. Convergence is

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conditional on country-specific factors. Mulder and de Groot (2012) investigate energy productivity convergence for 18 OECD countries and 50 industry sectors over the period 1970 to 2005. They find that energy intensity tends to decrease in most manufacturing sectors.

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Energy intensity decreases relatively slowly in the services sector, Herrerias et al. (2013) find that foreign and non-state investments are important contributors to energy intensity decline

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in Chinese provinces. State investments, however, do not help to reduce energy intensity. Herrerias and Liu (2013) study energy intensity for China using provincial level data. They find evidence of a dominant club and several smaller clubs. Some regions are found to diverge. Mulder et al. (2014) investigate energy intensity in the service sector for OECD countries. The service sector has contributed to lower economy-wide energy intensity, however, the service sector has not realized the same improvements in energy intensity as the manufacturing sector. Kim (2015) using the Phillips and Sul (2007) approach finds evidence of electricity intensity convergence between countries. Yu et al. (2015) use the Phillips and 5

For details on Gamma convergence see Boyle and McCarthy (1997).

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ACCEPTED MANUSCRIPT Sul (2007) approach to analyse energy intensity convergence in 109 countries. Countries form four convergent clubs and one divergent club. Ordered logit analysis show that initial energy intensity and trade openness are the main drivers responsible for the formation of clubs. Apergis and Christou (2016) use the Phillips and Sul (2007) approach to analyze energy productivity convergence for 31 countries over the period 1972 to 2012. Evidence of convergence club is found. Using convergence club analysis on Chinese provincial data,

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Zhang and Broadstock (2016) find evidence for three unique energy intensity clubs in China. The determinants of clubs appear to be club specific. Atalla and Bean (2017) use decomposition and regression analysis to study the determinants of energy productivity in 39

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countries. Sector level energy productivity gains are the main factor behind economy-wide

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gains in energy productivity. Structural economic shifts play a lesser role. Countries with similar demographics and economic conditions have similar trends in energy productivity.

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Ma and Yu (2017) study energy intensity in Chinese cities and find that small-scale enterprises have a negative impact on energy intensity. They find that a 1% increase in the output-value proportion of small-sized firms leads to a decrease in total energy intensity of

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0.067%. In contrast, the same change by medium enterprises will raise energy intensity by 0.031%. By comparison, a negative and/or non-significant coefficient is found for the most

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energy-intensive large and state-owned enterprises. Parker and Liddle (2017a) investigate energy productivity convergence in OECD manufacturing. Evidence of multiple convergence

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clubs is found. The technology structure of production and investment are associated with higher energy productivity. In a recent study by Parker and Liddle (2017b), the authors examined the transition dynamics of energy productivity among 33 countries. They identified

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four clubs of economy-wise energy productivity of which the better performing clubs were

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found in the OECD and newly industrialising countries.

Table 1: Selected studies on energy intensity/productivity convergence Author Parker and Liddle (2017a) Parker and Liddle (2017b) Ma and Yu (2017) Atalla and Bean (2017) Apergis and Christou (2016)

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Variable Manufacturing energy productivity (33 countries) Economy-wide and manufacturing energy productivity (33 countries) Energy intensity (283 Chinese cities) Energy productivity (39 countries) Energy productivity (31 countries)

Result Presence of club convergence in OECD manufacturing sector which are influenced by investment and technology structure. Four clubs for economy-wide energy productivity and six clubs for manufacturing energy productivity. The relationship between energy intensity and output depends upon the size of the firm. Sector level energy productivity gains are the main reason for economy-wide gains in energy productivity. Evidence of club convergence across 31 countries.

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Energy intensity (28 Chinese provinces)

Convergence clubs exist in Chinese provinces.

Electricity consumption/intensity (109 Countries)

Convergence of electricity intensity among 109 countries, but not for per capita electricity consumption.

Yu et al. (2015)

Energy intensity (109 countries) Energy intensity (23 service sectors in 18 OECD countries) Electricity-intensity (28 Chinese provinces) Energy intensity (28 Chinese provinces) Energy productivity (18 OECD countries, 50 sectors) OECD energy intensity

Convergence clubs exist across panel of 109 countries

Liddle (2012)

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Mulder and De Groot (2012)

There is a dominant club and other smaller clubs in Chinese provinces. Foreign and non-state investment is important contributors to energy intensity decline in Chinese provinces. Energy intensity tends to decrease in most manufacturing sectors. Lagging countries are catching up with leading ones.

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Herrerias and Liu (2013) Herrerias (2013)

Energy intensity in the service sector has decreased in OECD countries but not as much as in the manufacturing sector.

Energy intensity (83 countries)

Le Pen and Sevi (2010) Liddle (2010)

Energy intensity (97 countries) Energy intensity (134 countries) Electricity intensity (IEA/OECD countries) Energy intensity (98 countries) Energy intensity (12 EU countries) Energy productivity (56 countries and 10 manufacturing sectors) Energy intensity in OECD countries Energy intensity (41 countries)

Sun (2002)

Evidence of beta and sigma convergence across groups of countries. Evidence of sigma convergence. Evidence of sigma convergence. Evidence of beta convergence. Convergence is local in manufacturing sector. Some countries are not converging. Mixed results from sigma and beta convergence. Mean deviation analysis shows the difference in OECD energy intensity has decreased across time. Evidence of convergence from descriptive analysis showing developing (developed) countries has an increasing (decreasing) energy intensity trajectory.

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Mielnik and Goldberg (2000)

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Markandya et al. (2006) Miketa and Mulder (2005)

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Ezcurra (2007)

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Liddle (2009)

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Herrerias (2012)

OECD energy intensity shows a strong degree of convergence. Convergence is conditional on country-specific factors. Developing countries converge at higher energy intensity ratios and developed countries have at least two clubs of convergence. No evidence of stochastic convergence.

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Mulder et al. (2014)

In summary, it is relatively rare to find evidence of energy intensity/productivity convergence across countries or industries. In most cases, there is evidence of club convergence. Given the abundance of cross-country studies from developed countries, we fill the gap in the literature by considering an important emerging country, India, at the state level for our analysis.

3.

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Data and methodologies

ACCEPTED MANUSCRIPT In the following sub-section, we describe the unique database we use for measuring energy productivity and the methodology used in analysing convergence of the series across a panel of thirty-one Indian S&Ts.

3.1 Data and the measure of energy productivity We obtained data from the Prowess database over the period from 1988 to 2016. Prowess

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covers firm-level data from all sectors based on the annual balance sheet and income statements of firms compiled by the Centre for Monitoring Indian Economy (CMIE), a private think tank in India. The companies in the database account for more than 70 percent

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of industrial output, 75 percent of corporate taxes and 95 percent of excise duties collected by

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the government of India (Alfaro and Chari, 2014). The selection of sample firms proceeded in two steps. In the first step, we selected all firms (both manufacturing and non-manufacturing)

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listed within the Prowess data base, which provided a total of 37,880 firms where data on the total final cost of energy consumed is available. Given that our focus is on finding the energy productivity of all firms in each state, we identified firms in each state and summed the

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values to generate the aggregate energy consumed for each state in each year. Following this criterion, we were left with a total of 37,570 firms (observations that have no state

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identification were omitted) for the entire sample period.

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The data comprises the total income of the firms, which is the sum of all types of income (sales, income from financial services, other additional incomes) generated by a firm during the financial year. We obtained data on the quantity of total energy consumed and the value

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spent per unit of energy consumed (cost of per unit of energy consumed). We multiplied these two items to obtain the total final cost of energy consumed. Energy productivity of each

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firm was calculated as its total income scaled by the final expenses on energy consumption. We summed the values for each state to obtain the total energy productivity at the state level and used the logarithmic values for empirical purposes.

In including states and territories, we follow the Prowess listed states and territories as depicted in Table 2. 6 Here, we present the descriptive statistics for each state and three territories along with a rank in each case, considering energy productivity as the indicator. The rank is based on the total energy productivity for each state for the entire sample period 6

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We have three major cities in the panel. These territories are reported like other states in the data set.

ACCEPTED MANUSCRIPT (Appendix A shows energy productivity plots for each state). For the sample period, Tamil Nadu ranks the highest energy productivity, followed by Haryana, Madhya Pradesh and Telangana. Nagaland has the lowest rank when using energy productivity as an indicator of energy efficiency. Appendix B shows GDP per capita by Indian S&T for the year 2014. Goa, Delhi and Sikkim are the wealthiest states while Manipur, Uttar Pradesh and Bihar are the poorest. The GDP values are not directly comparable with the energy productivity values

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since the time periods are different but it is interesting to note that some of the poorer states

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have high average energy productivity.

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Table 2: Descriptive statistics of energy productivity across Indian states and territories Mean

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Rank

State

Mean

SD

Rank

Tamil Nadu

1341.881

4430.95

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Kerala

12.676

21.066

17

Haryana

1213.386

2779.169

2

Uttar Pradesh

11.868

23.75

18

Madhya Pradesh

537.845

2589.619

3

Himachal Pradesh

10.994

20.968

19

Telangana

394.026

1717.094

4

Punjab

10.967

28.748

20

Delhi

283.635

689.235

5

Daman and Diu

8.032

18.279

21

Andhra Pradesh

188.611

636.058

6

Goa

7.636

17.617

22

Maharashtra

163.554

257.351

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Assam

7.182

11.444

23

Dadra and Nagar Haveli

163.483

756.846

8

Jharkhand

5.948

13.805

24

West Bengal

102.235

372.894

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Coimbatore*

3.389

4.601

25

Rajasthan

78.824

226.65

10

Chhattisgarh

3.131

5.648

26

Gujarat

52.761

92.898

11

Uttaranchal

1.292

2.466

27

Bihar

48.596

158.376

12

Jammu and Kashmir

1.132

0.732

28

32.105

94.077

13

Meghalaya

0.356

0.356

29

15.925

76.103

14

Pondicherry

0.325

0.419

30

13.138

22.853

15

Nagaland

0.003

0.007

31

12.831

20.409

16

Orissa Karnataka

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Chandigarh

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Pune

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Note: The values are in millions. Mean and standard deviation (SD) are for the entire period and for all firms in each state. The ranks are based on the mean value of total energy productivity for each state. *-Pune and Coimbatore are large cities in the states of Maharashtra and Tamil Nadu.

3.2 Methodologies Our methodolical approach comprises both stochastic and club convergence analysis. We describe the methods used in achieving our objectives in the sections below. 3.2.1. Stochastic convergence 10

ACCEPTED MANUSCRIPT Testing for stochastic convergence has become popular with the development of panel databased stationarity tests. The idea of stochastic convergence, as described in Bernard and Durlauf (1995), is based on a time-series approach. If energy productivity contains a stochastic trend (unit root) then convergence implies that the permanent components in energy productivity are the same across countries and the difference between the levels of energy productivity to be zero in the infinite time horizon. The concept of convergence

Bernard and Durlauf, 1995; Próchniak and Witkowski, 1992).

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should be treated as complementary and tested separately, rather than substitutive (see

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In an influential paper looking into per-capita income convergence in US regions, Carlino and Mills (1993) investigated stochastic convergence and β-convergence. Stochastic

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convergence holds if shocks to per-capita incomes are temporary. This can be analysed by conducting unit root test with differences in per-capita income across states. If poor regions

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catch up to rich regions, then β-convergence holds. They found that rejecting the null hypothesis of unit roots jointly for all regional time series implied that, after a random shock,

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regional GDP tends to revert back to the group average in the long-run, reflecting convergence behaviour. The determination of stochastic conditional convergence involves the use of unit root tests to check for convergence across countries or sectors (see Meng et al.,

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2013; Payne et al., 2017). These unit root tests have several advantages over alternative

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techniques; for example, these tests allow for endogenously determined structural breaks while detecting unit roots. We achieve this purpose by employing the panel transformed unit root test with up to two breaks, considering both level and trend (Im et al., 2005; Im et al.,

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2014).

For each state or territory, we compute a measure of relative energy productivity, using the

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following formula:

𝑦𝑖𝑡 = ln(EPit / average(EPt ))

(1)

where 𝑦𝑖𝑡 is relative energy productivity, EPit is energy productivity for state i in period t and average(EPt) is the average energy productivity across S&Ts at time period t. We measure the relative energy productivity on the right-hand-side of Eq. (1) in logarithmic form.

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ACCEPTED MANUSCRIPT This method of measuring energy productivity has the advantage of removing the crosssectional shocks that affect any (or all) states in the panel; 7 therefore, the observed structural breaks will be state-specific. In examining the stochastic conditional convergence of energy productivity, as shown in Lee et al. (2012) and Meng et al. (2014), convergence is present if relative energy productivity is stationary, implying convergence for each state and territory

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towards equilibrium level. Following Im et al. (2005); Im et al. (2010), the unit root test

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statistics for the univariate LM-based unit root test with breaks in both the level and trend of

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the series are obtained from:

(2)

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yt = 'Zt + S̃t-1+ et,

where Zt contains deterministic variables. S̃t denotes the de-trended series, et is the error

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term, ' and  are coefficients.8 Under multiply breaks with additional dummy variables, Zt=[1, 𝑡, 𝐷1𝑡 , . . , 𝐷𝑅𝑡 , 𝐷𝑇1𝑡 ∗ … , 𝐷𝑇𝑅𝑡 ∗ ]) where𝐷𝑗𝑡 =1 for 𝑡 ≥ 𝑇𝐵𝑗+1, j=1,…R, and zero otherwise

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and 𝐷𝑇𝑗𝑡 ∗ = 𝑡 − 𝑇𝐵𝑗 for 𝑡 ≥ 𝑇𝐵𝑗+1 and zero otherwise. S̃t = yt−𝜓̃ − Zt𝛿̃. 𝑇𝐵 denotes the time period of the break and 𝜓̃ 𝑖s the restricted maximum likelihood of 𝜓.

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In the usual augmented type tests, the inclusion of S̃t-j, j=1,…, k, in equation (2) corrects for serial correlation.

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yt = 'Zt + S̃𝑡−1 +∑𝑘𝑗−1 dj S̃𝑡−𝑗 + et,

(3)

The LM unit root test statistics for testing =0 depends upon the parameters that identify the location of the breaks (Bi, i = 1,…,R). The variable S̃𝑡 can be replaced with a transformed version (S̃t*) as follows: (T/TB1S̃𝑡 ) for t ≤TB1, (T/(TB2 - TB1)S̃𝑡 ) for TB1 < t ≤TB2, (T/(T TBR)S̃𝑡 ) for TBR < t ≤T, 7 8

This is noted also in Meng et al. (2013) and Mishra and Smyth (2014). Equation (2) and equation (3) are time series (not panel) versions of the test.

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The panel form of equation (3) becomes (Im et al., 2014): ∗

yi,t = i'Zi,t + iS̃𝑖,𝑡−1 + ∑𝑘𝑗=1 dijS̃𝑖,𝑡−𝑗 + eit. , i=1,..,N

(4)

We can test H0: i = 0 for all i against the alternative hypothesis, H1: i ˂ 0, for some i. The

𝑡𝑁𝑇 =

1 𝑁

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standardised test statistic is given by:9 ∑𝑁 𝑖=1 𝜏𝑖

(5)

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where 𝜏𝑖 denotes the transformed test statistic (TR) for each state and territory i.10 The panel

√𝑁[𝑡𝑁,𝑇 −𝐸̃ (𝑡𝑁,𝑇 )] ̃ (𝑡𝑁,𝑇 ) √𝑉

→ 𝑁(0,1)

(6)

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ΓLΜ =

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LM unit root test statistic (Im et al., 2005; Im et al., 2010) is:

where 𝐸̃ (𝑡𝑁,𝑇 ) and 𝑉̃ ((𝑡𝑁,𝑇 ) are the expected values of the average of the mean and variance

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of the test statistic as reported in Table 2 of Im et al. (2010). The standardized LM panel unit test follows a standard normal distribution. Correlation across the innovations in the panel is

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allowed. In this method, the asymptotic distribution of the panel LM test statistics is not affected by the presence of breaks and follows a standard normal distribution and the ∗

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transformed data overcomes size distortions as 𝜏𝑖 no longer depends on the nuisance parameter that identifies the breaks. Assuming that the error term in equation (4) has a single factor structure, the regression can be tested by augmenting with the cross-section averages of

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lagged levels and first-differences of the individual series.11.

3.2.2. Club convergence Energy intensity/productivity convergence has been identified in the literature (see for example, Ezcurra, 2007; Le Pen and Sévi, 2010; Liddle, (2009, 2010, 2012); Miketa and Mulder, 2005; Parker and Liddle, 2017b; Sun, 2002). Most of the studies at the country-level 9

This application is conducted in Gauss using the codes from Junsoo Lee’s homepage (https://sites.google.com/site/junsoolee/codes). For further details see, Im et al. (2005, 2010). We omit here the detailed steps to conserve space. ∗ 10 The untransformed LM test-statistic is 𝜏𝑖 and the transformed LM test statistic is 𝜏𝑖 . 11 We employ only the cross-sectional augmented LM (CA) test statistic as shown in Table 3.

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ACCEPTED MANUSCRIPT identified conditional convergence. However, the problem faced by researchers is one of differentiation of conditional convergence from club convergence –which considers catching up among peers but not in the larger global group (Islam, 2003). Apart from this issue, the interpretation of the β within the panel data regressions is complex, due to the difficulty in accounting for the time series features of the data (Eberhardt and Teal, 2011; Lee et al., 1998). Given these limitations, we consider the Phillips and Sul (2007) approach based on panel

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estimation with a nonlinear time-varying coefficients factor. Phillips and Sul (PS hereafter, 2007) proposed a panel data model of economic convergence

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that allows heterogeneity within cross sections and different convergence paths for groups of individuals. The PS club convergence approach has a number of advantages over the

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stochastic conditional convergence approach: (1) the club convergence measures the relative convergence of cross-sectional averages instead of convergence towards the absolute level; (2)

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the PS-approach does not require unit root or cointegration tests of the variables in each panel and, therefore, performs better in the presence of a mixture of stationary and non-stationary

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series in the panel; and (3) the PS-approach considers the heterogeneity of the time series within the panel of states and territories, and identifies clubs of states and territories, each club converging toward a common club trend. The PS methodology makes use of the

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following time-varying common factor representation for our set of observable series, energy

𝐸𝑃𝑖𝑡 = 𝛿𝑖𝑡 𝜃𝑡

(7)

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element δit as follows:

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productivity, EPi,t, which can be decomposed into a common trend θt and an individual

where, EPi,t is energy productivity for state (or territory) i at time t as defined in equation (1); θt denotes a single common component and δit varies over time across all the Indian states or

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territories and measures the deviation of each state or territory i from the common trend θt. Within this framework, all N groups will converge towards steady-state if lim𝑡→∞ 𝛿𝑖𝑡 = 𝛿 for all i.

In estimating δit , following the PS-approach, Eq. (7) is modified as follows: ℎ𝑖𝑡 =

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𝐸𝑃𝑖𝑡 1 𝑁 ∑𝑖=1 𝐸𝑃𝑖𝑡 𝑁

=

𝛿𝑖𝑡 1 𝑁 ∑𝑖=1 𝛿𝑖𝑡 𝑁

(8)

ACCEPTED MANUSCRIPT The relative measure hit captures the transition path relative to the common average. To formulate an econometric test of convergence and an empirical algorithm for identifying the clubs, the PS approach considers the following semi-parametric form of δit: δit= δi + σi ξ it L(t)-1t-α

(9)

where δi is fixed across the states, σi˃0, t≥0 and 𝜉𝑖,𝑡 is an i.i.d (0,1) across i, but could be

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weakly dependent on t. L(t) is a slowly varying function, which tends to move towards infinity as t tends to infinity. 12 Under this specific form for δit, the null hypothesis of

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convergence is

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H0: 𝛿𝑖 = 𝛿 (𝑤ℎ𝑒𝑟𝑒 α≥0), against the alternative hypothesis HA: 𝛿𝑖 ≠ 𝛿 (𝑓𝑜𝑟 𝑎𝑙𝑙 𝑖 𝑎𝑛𝑑 α<0). The test of this hypothesis basically examines the sign of α where α is the speed of

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convergence.

Following the PS-approach, the following regression is estimated,

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𝐻 𝑙𝑜𝑔 ( 𝐻1 ) − 2 log 𝐿(𝑡) = 𝑎̂ + 𝑏̂𝑙𝑜𝑔(𝑡) + 𝜀𝑡 𝑡

1

(10)

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2 where 𝐻𝑡 = 𝑁 ∑𝑁 𝑖=1(ℎ𝑖𝑡 − 1) is the square cross-sectional distance relative to transition

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coefficients. Phillips and Sul (2007) show that 𝑏̂ = 2𝛼̂ and the null hypothesis can be tested as a one-sided test of 𝑏̂ ≥ 0 against 𝑏̂ < 0. This implies a one-sided test on the estimated

𝑡𝑏 =

𝑏̂ −𝑏 𝑠𝑏

⇒ 𝑁(0,1)

(11)

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coefficient for log (t) and is referred to as the log-t test.

The null hypothesis of convergence is rejected if the t-statistic has a value below -1.65 (which is the 5% level of significance). The PS-approach proposes that convergence patterns within states and territories can be tested through log-t regressions by testing for the existence of club convergence. One interesting point is that the rejection of the null of convergence does not necessarily imply divergence, since there remains the possibility to reach two separate points of equilibrium, or 12

Similar to PS-approach, we adopt L(t) = log(t) in order to guarantee convergence. This choice produces test statistics with the smallest size distortion and highest power.

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ACCEPTED MANUSCRIPT follow separate steady-state growth paths, or to converge within clusters in the same or divergent regions. In identifying the clubs in a panel, the PS-approach follows the steps below. Complete details of each step and an example are provided in Phillips and Sul (2007). Step 1 – Last Observation Ordering. The S&Ts are ordered following the last observation of each S&T’s energy productivity. This ranking assumes convergence is more apparent in the

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the last fraction of the sample can be taken to order the panel.

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most recent observations. If there are significant variations within the S&Ts, the average of

Step 2 – Core Group Formation. In identifying the core group with S&Ts, we select the first k

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highest ordered S&Ts where these S&T are the ones with the highest energy productivity within the panel to form a subgroup Gk, where k lies between 2 and N. Run the log (t)

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regression and calculate the convergence statistic tk=t(Gk) for this subgroup. In choosing the group size k*, we rank by maximising the t-statistics over k following k*=arg max[𝑡𝑘 ],

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subject to min[𝑡𝑘 ] > -1.65. If min [𝑡𝑘 ] > -1.65 does not hold, then the state or territory with the highest energy productivity in 𝐺𝑘 is removed from each sub-group and a new sub-group is created. The process is repeated as many times as required until the core group is formed.

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Step 3 – Sieve Individuals for Club Membership: Sieve the data by adding one S&T at a time

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from the remaining S&Ts to the core group and re-estimate equation (10). If tk is greater than a critical value c*, which in practice is taken as 0, add the new S&T to the convergent club.

S&Ts.

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The first convergent club satisfies tb > -1.65 and consists of the core group plus those added

Step 4 – Stopping Rule: S&Ts which do not belong to the first convergence group (club) are

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then grouped into the second club. The second club is evaluated in the same way as above using the log t test. Steps 1 through 3 are repeated until all of the convergence clubs have been found. Any S&Ts that do not belong to a convergence club are classified as divergent.

4.

Empirical findings and discussion

Results are presented for stochastic convergence, club convergence and the determinants of club convergence. 4.1. Stochastic convergence 16

ACCEPTED MANUSCRIPT In Table 3, we present the results from testing stochastic convergence with two breakpoints. We employ the cross-sectional augmented (CA) -LM test (equation (6)) for each state. For all the states examined, breaks are identified. Most of the breaks occur during 1994-2000 and 2008-2010. During these periods, major economic changes took place, particularly since the mid-1990s. Besides the structural reform programme, the Energy Conservation Act (EA, 2001, and its amendments in 2010) was enacted to facilitate and regulate energy conservation

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and to improve efficiency both at central and S&T levels.13 Another major change was to enact the Electricity Act (2003) aimed at improving the overall efficiency of the electricity sector.14 Out of thirty-one S&Ts, ten S&Ts (Andra Pradesh, Kerala, Orissa, Rajasthan, Goa,

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Chattisgarh, Jharkhand, Chandigarh, Delhi and Pondicherry) had short spans within two

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structural breaks. Most of these S&Ts had an energy efficiency action plan as part of the State’s Action Plan on Climate Change (SAPCC) and had various initiatives for improving

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energy efficiency. Only six states and territories in our panel (Assam, Bihar, Maharashtra, Meghalaya, Nagaland, and Coimbatore) have significant test statistics at the conventional level of significance indicating evidence of energy productivity stochastic convergence. The

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panel LM statistic is 0.754 indicating no evidence of energy productivity convergence for the panel as a whole. We caution, however, that with a relatively short time span and two

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possible break points, the power of the LM tests may be low. The results from testing one break point are in Appendix C. The individual LM results are mixed with 13/31 S&Ts displaying evidence

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of energy productivity stochastic convergence. The panel LM test is -4.096 and is statistically significant at the 1% level of significance indicating energy productivity stochastic convergence for Indian S&Ts.

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State Andhra Pradesh Assam Bihar Gujarat Haryana Himachal Pradesh Jammu and Kashmir Karnataka Kerala Madhya Pradesh Maharashtra Meghalaya Nagaland

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Table 3: LM test (with cross sectional dependence) for Indian S&Ts (2 break points)

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CA-LM -1.405 -4.433* -5.236** -2.683 -2.443 -2.517 -1.503 -3.367 -3.456 -1.363 -5.734*** -5.031** -4.360*

http://www.jreda.com/about/ener_conserv/06_03_17/ea_act_2001.pdf http://www.cercind.gov.in/Act-with-amendment.pdf

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𝑃̂ 4 1 3 3 3 3 3 4 0 4 1 0 0

TB1 1994 1995 1996 1994 1996 1994 1996 1997 1994 1994 1994 1994 1994

TB2 2000 2010 2009 2010 2009 2003 2009 2006 1995 2008 2010 2007 2010

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Orissa -1.873 3 2002 2006 Punjab -3.245 3 1995 2012 Rajasthan -2.076 4 2003 2009 Tamil Nadu -0.918 4 1999 2006 Uttar Pradesh -1.069 4 2003 2009 West Bengal -2.566 3 1995 2012 Goa -3.099 4 2006 2010 Uttaranchal -3.177 3 1995 2010 Chhattisgarh -0.380 4 1997 2004 Jharkhand -2.932 0 1997 2000 Chandigarh -3.264 1 2001 2005 Dadra and Nagar Haveli -3.827 2 1994 2005 Delhi -2.973 3 2002 2005 Daman and Diu -3.488 3 1995 2008 Pondicherry -0.006 4 1994 1999 Pune -2.473 4 1997 2010 Coimbatore -4.969** 1 1997 2012 Telangana -3.792 3 1995 2011 Note: Cross-sectional augmented (CA) LM tests are reported. The optimal lag length is 𝑃̂ and the break point locations are TB1 and TB2. ***, **, * denote 1%, 5% and 10% levels of significance respectively. Following Im et al. (2010), Table 1, the critical values for the transformed univariate unit root test with two break points are −5.365, −4.661, and −4.338 at the 1%, 5% and 10% level of significance respectively. The panel CA-LM transformed statistic is 0.754.

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4.2. Club convergence

Following the discussion above, we have a mixture of converging and diverging S&Ts from our empirical testing; in the next step, we apply the Phillips and Sul (2007, 2009)

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convergence clubs approach. The null hypothesis of energy productivity convergence across

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all Indian states and territories is rejected in all cases (Table 4). The results, as shown in Table 4, identify four clubs of convergence of energy productivity as the test statistics are greater than -1.65. There is, however, one divergent club.

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Our initial findings reflect that Indian S&T energy productivity falls into four broadly defined clubs with two S&Ts divergening. As shown in Figure 1 below, Club 1 is predominantly

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around the capital, south and western S&Ts in that region; Club 2 spreads across the east and the upper part of India; Club 3 appears in the upper eastern states; and Club 4 is mostly in the eastern region. While energy productivity of S&Ts within a club are converging, energy productivity between clubs is not. The difference in average energy productivity across clubs is dramatic. Club 1 has the highest average energy productivity (269.156) followed by Club 4 (19.306). The divergence state and territories together, (Club 5), has the lowest average energy productivity (1.728). Recall that b = 2α where α is the speed of convergence; Club 2 converges fastest while Club 3 converges the slowest. Since the estimated value of b is less than 2, the hypothesis of absolute level convergence is rejected. There is evidence of relative convergence. 18

ACCEPTED MANUSCRIPT Club 1 includes seventeen Indian states – Andhra Pradesh, Gujarat, Haryana, Jammu and Kashmir, Karnataka, Kerala, Madhya Pradesh, Maharashtra, Punjab, Rajasthan, Tamil Nadu, Uttar Pradesh, West Bengal, Jharkhand, Dadra and Nagar Haveli, Delhi and Telangana. Except for Dadra & Nagar Haveli, all members within this club progressed significantly and implemented some energy saving measures at the household, commercial and industrial levels over the time period studied. In most cases, energy productivity stabilised around the

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average. Club 2 includes six states – Assam, Himachal Pradesh, Orissa, Goa, Pune and Coimbatore.

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This club also includes a large city, Pune, in Maharashtra state and Coimbatore, a large city in Tamil Nadu state. Except for Goa, all members within this club have implemented

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common policies for improving energy efficiency potential. Club 1 and 2 follow a similar pattern.

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Club 3 includes three S&Ts – Meghalaya, Nagaland, and Chandigarh. Club 4 includes three

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S&Ts – Bihar, Uttaranchal and Daman and Diu.15

For Chattisgarh and Pondicherry we could not establish convergence path. The average energy productivity is the lowest for this club (1.728).16

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The Phillips and Sul (2007, 2009) approach can lead to too many clubs being chosen because

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the club classification is based on a sign criterion in the choice of c* in the algorithm. The authors recommend that additional tests for merging clubs be conducted. The recommendation is to estimate the log t test for all pairs of clubs and merge clubs that satisfy

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the convergence hypothesis. We examined convergence between two consecutive clubs to verify the initial convergence of the clubs. This checks if two consecutive clubs can be

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merged or maintained as separate clubs. The results, as reported in Table 5, reveal that clubs one and two can be merged to form a larger convergence club and, likewise, clubs three and four can be combined to form a larger convergence club. The convergence in the members of the first new larger club is faster than the convergence of the members of the second new larger club, as shown by the higher estimate of b. The club merging analysis reveals that the results can be confidently classified as belonging to two convergent clubs and one divergent club. The diverging club consists of Chhattisgarh and Pondicherry each of which has low 15

Renewable energy policies for various states can be access at: http://www.ireda.gov.in/writereaddata/CompendiumStatePolicyRE/Program.htm 16 http://niti.gov.in/writereaddata/files/new_initiatives/NEP-ID_27.06.2017.pdf

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ACCEPTED MANUSCRIPT energy productivity. Mukherjee (2008) finds that the manufacturing sector in Chhattisgarh is one of the most energy intensive and this state has the highest proportion of coal in its fuel mix. Pondicherry is a small union territory with agriculture and tourism the main industries. Pondicherry suffers from poor governance (high debt and corruption). There is no previous evidence on energy productivity convergence clubs in India so we don’t have a direct comparison between our results and the results of others. It is useful to compare

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our results with those from China, which like India is a large emerging economy. Our results are consistent with studies on China that find multiple equilibria. Herrerias and Liu (2013)

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study energy intensity convergence in China and find a mixture of convergence clubs and divergence. Zhang and Broadstock (2016) find evidence of three unique energy intensity

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clubs in China.17

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Energy intensity is inversely related to energy productivity.

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Figure 1: Convergence clubs [territories are not mapped]

Table 4: Findings for the log-t regression test and clustering algorithm for Indian S&Ts energy productivity Clubs All Club 1

Club 2 Club 3 Club 4 Club 5

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States

Andhra Pradesh, Gujarat, Haryana, Jammu and Kashmir, Karnataka, Kerala, Madhya Pradesh, Maharashtra, Punjab, Rajasthan, Tamil Nadu, Uttar Pradesh, West Bengal, Jharkhand, Dadra and Nagar Haveli, Delhi, Telangana Assam, Himachal Pradesh, Orissa, Goa, Pune, Coimbatore Meghalaya, Nagaland, Chandigarh Bihar, Uttaranchal, Daman and Diu Chhattisgarh, Pondicherry

b-coefficient

t-statistic

-0.558 0.411

-11.311 2.490

1.038 0.110 0.164 -1.611

12.834 1.405 0.246 -16.942

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Table 5: Club merging test log(t) bcoefficient t-statistic

Club1+2 -0.045

Club2+3 -0.181

Club3+4 -0.071

Club4+5 -1.395

-0.379

-2.265

-1.298

-15.300

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New Clubs States

bcoefficient -0.045

Andhra Pradesh, Gujarat, Haryana, Jammu and Kashmir, Karnataka, Kerala, Madhya Pradesh, Maharashtra, Punjab, Rajasthan, Tamil Nadu, Uttar Pradesh, West Bengal, Jharkhand, Dadra and Nagar Haveli, Delhi, Telangana, Assam, Himachal Pradesh, Orissa, Goa, Pune, Coimbatore Club B Meghalaya, Nagaland, Chandigarh, Bihar, Uttaranchal, Daman and -0.071 -1.298 Diu Club C Chhattisgarh, Pondicherry -1.611 -16.942 Notes: Testing for the one-sided null hypothesis b ≥ 0 against b < 0, the analysis makes use of the critical value of t(p = 0.05) = −1.65 across all cases. Statistical significance at the 5% level is denoted by ‘*’, rejecting the null hypothesis of convergence. The club merging analysis checks if two consecutive clubs can be merged or maintained as separate clubs. According to the results club 1 and 2 can be merged into a new club A and club 3 and 4 can be merged into a new club B. Club 5 (C) is divergent.

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Club A

tstatistic -0.379

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4.3. The determinants of convergence clubs

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In this section, we explore the determinants of the observed energy productivity club convergence. While most studies on energy convergence focus only on the convergence analysis, we make an additional contribution to the existing literature by exploring the

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determinants of energy productivity convergence between Indian states and territories. The PS-approach to club convergence uses a sequential algorithm to define and order club

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membership according to steady state energy productivity levels. Club membership is represented by an ordinal ranking. Club membership can be assumed to be related to an unobservable latent variable representing steady-state energy productivity. The logit model is useful in examining the relationship between a binary dependent variable and explanatory variables. We employed the logit model and examined the relationship between the merged clubs and their determinants. The club merging test results in Table 5 shows that there are two merged clubs (A, B). For the logit dependent variable we coded S&Ts belonging to club A as a one and S&Ts belonging to club B as a zero. In the economic growth literature, countries that have similar initial conditions exhibit convergence (Bartkowska and Riedl, 2012; Galor, 1996; Quah, 1996b). For this reason, the initial energy productivity is included 22

ACCEPTED MANUSCRIPT as a determinant of club convergence. Other variables include the average growth rate of income per capita and the average share of income from the manufacturing and service sectors. These variables are consistent with those used in the literature on economic growth convergence which we believe have influence on energy productivity in case of Indian S&Ts. 18 Atalla and Bean (2017) found that, in their study of energy productivity in 39 countries, increases in sectoral energy productivity were the main driver behind national

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improvements in energy productivity; industrial structure variables played a lesser role. Fisher-Vanden et al. (2006) find that changes in industrial structure are one of the main drivers of reduced energy intensity in China. In addition, we include the average value for

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urbanisation (population in urban cities as a percentage of the S&T population) for each state,

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and total non-renewable energy productivity. 19 Urbanization increases the efficiency and amount of economics activity and the net effect can have either a positive or negative impact

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on energy productivity (Sadorsky, 2014). The data for the explanatory variables comes from the CMIE dataset and the Reserve Bank of India (urbanization, population).20 Appendix D

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reports summary statistics for the explanatory variables.

In Table 6, we present the findings for the determinants of club convergence. In this respect,

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we run three sets of regressions adding explanatory variables in each step. We focus the

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analysis on the final clubs which we obtained through merging clubs (two convergent clubs labelled as A and B and one club with two non-converging S&Ts labelled as club C). A logit model is estimated for the two final merged clubs. 21 Based on a log likelihood statistic,

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Model 3 is preferred over Model 2. The results in Model 3 show that higher initial energy productivity makes it more likely for an S&T to be in Club A (with higher energy

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productivity). For a one unit increase in initial energy productivity we expect to see a 22% increase in the odds of being in the high energy productivity club. Service income share and manufacturing income share each have positive and statistically significant coefficients 18

We do not consider the share of income from the agricultural sector due to the unavailability of data within our sample. Agriculture is an informal sector in India and is not energy-intensive. 19 In absence of detail data on renewable energy sources, we consider only non-renewable sources. In case of India, coal is still the dominant source of energy as discussed in Bhattacharya et al. (2017). 20 This can be accessed at: https://rbi.org.in/Scripts/AnnualPublications.aspx?head=Handbook+of+Statistics+on+Indian+States 21 For the purposes of estimation, we have recoded Club A as 1 and Club B as 0. This is consistent with the usual interpretation of the logit model in which higher values of the dependent variable are associated with larger impacts. The divergent club is omitted from the logit analysis. Panel cross-section analysis is used to construct the explanatory variables.

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ACCEPTED MANUSCRIPT indicating that increases in these variables makes it more likely for S&Ts to be in the high energy productivity club. Our results are broadly consistent with those of the IEA (2007), who pointed out that improved energy efficiency and fuel switching in the service sector has helped to reduce Indian energy intensity since 1980. In addition, non-renewable energy productivity positively affects total energy productivity. A one unit increase in non-renewable energy productivity increases the odds of being in the high energy productivity club by 74%.

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From a policy perspective, state governments should consider improving energy efficiency and increase the productivity of the non-renewable energy sector in improving overall energy productivity. We did not any evidence that income per capita growth or urbanization have a

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statistical significant impact on the formation of Indian energy productivity clubs.

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The marginal effects in Table 7 were computed using the specifications (viz. Model 2 and 3) from Table 6. Marginal effects are reported for the average marginal effect (marginal effect

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calculated for each observation and then averaged (AVE)) and the marginal effect at the mean (calculated using the mean value of each covariate (MEM)). The two approaches yield

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similar results. Focusing on the average marginal effects from Model 3, the results reveal that a one unit increase in initial energy productivity increases the probability of being in the high energy productivity club (Club A) by 2%. A one unit increase in manufacturing income share

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increases the probability of being in Club A by 1%. A similar marginal effect is observed for

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service income share. An increase in the level of non-renewable productivity increases the probability of being in Club A by 5.2%. Consistent with the results from Table 6, the

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marginal effects for income per capita growth and urbanization are statistically insignificant.

Variables

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Table 6: The determinants of energy productivity club convergence across Indian S&Ts

Initial energy productivity Income per capita growth

Manufacturing income share Service income share Urbanisation Non-renewable energy productivity

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Model 1 0.463*** (0.063) -0.012 (0.324)

odds 1.59 0.99

Model 2 0.435*** (0.065) 0.065 (0.337) 0.066* (0.034) 0.077** (0.032)

Merged Club odds Model 3

1.54 1.07 1.07 1.08

0.198** (0.088) 0.086 (0.398) 0.095*** (0.037) 0.110*** (0.035) -0.327 (0.234) 0.555*** (0.062)

odds

1.22 1.09 1.10 1.12 0.72 1.74

ACCEPTED MANUSCRIPT LL -272.53 -261.52 -182.59 Chi-squared M2 vs M3 157.9*** Observations 701 686 602 The analysis is for two merged clubs. The standard errors are in parentheses, *** p<0.01, ** p<0.05, * p<0.1.

Initial energy productivity

0.051*** (0.007) 0.008 (0.039) 0.008** (0.004) 0.009** (0.003)

Income per capita growth

0.045*** (0.006) 0.007 (0.035) 0.007** (0.003) 0.008** (0.003)

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Manufacturing income share

Merged Club Model 2 Model 3 MEM AVE

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Model 2 AVE

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Variables

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Table 7: Marginal effects of the determinants of energy productivity club convergence across Indian S&Ts

Service income share

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Non-renewable productivity Urbanisation

0.019** (0.008) 0.008 (0.037) 0.009** (0.003) 0.010*** (0.003) 0.052*** (0.004) -0.031 (0.022)

Model 3 MEM

0.014** (0.006) 0.006 (0.028) 0.007** (0.003) 0.008*** (0.003) 0.039*** (0.005) -0.021 (0.017)

5.

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Observations 686 686 602 602 AVE=average marginal effect, MEM = marginal effect at the mean. The standard errors are in parentheses, *** p<0.01, ** p<0.05, * p<0.1.

Conclusion and policy implications

The existence of convergence in energy productivity or energy intensity is evident in many empirical research papers. Most studies have emphasised cross-country analysis, focussing on developed countries. Studies at the state-level are relatively few. Examples can be found in Herrerias and Liu (2013) and Zhang and Broadstock (2016). The analysis of energy productivity at the state level in India has yet to receive attention. Examining the convergence path of energy productivity at the Indian state-level is a timely and important topic to study for several reasons. According to the IEA (2015) one-quarter of the increase in world energy demand to 2040 will originate from India. The Indian government has a number of ambitious 25

ACCEPTED MANUSCRIPT initiatives including increasing access to electricity (“24X7 Power for All”), greater economic activity from manufacturing (“Make in India”), and reducing carbon dioxide emissions. Energy productivity is an important factor in helping to achieve these objectives. We fill the gap in the literature by selecting a country for which the role of energy and its use is pivotal for the coming decades. In this respect, we use dis-aggregated data at the firm-level in computing energy productivity across Indian S&Ts over the period between 1988 and

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2016. First, we focus on the notion of stochastic convergence while relying on unit root tests which consider structural break into our analysis. The panel-augmented approach is

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employed to account for cross-correlation among S&Ts and this is complemented by a cluster algorithm approach, which is relevant in the case of uneven distribution of energy resources

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across Indian states.

The findings from the stochastic convergence favour energy productivity convergence for

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some S&Ts, while many S&Ts record divergence in energy productivity. In verifying the findings from the stochastic convergence, we explore the cluster analysis. The initial results

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of the clustering algorithm reveal that convergence occurs in clubs, and that four convergent clubs and one non-converging club are present. The final results from the merged club

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analysis show that two clubs and one non-converging club are present.

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Our analysis has a number of important energy policy implications. The existence of energy productivity clubs in India implies that energy policies common to all states and territories in the country will have a limited effect as the states and territories belong to different clubs

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with different patterns of convergence. Policy advisers need to design and implement energy policy in ways that account for the fact that clubs have different patterns of convergence. India’s energy policy structure is complicated and many important decisions and

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responsibilities for energy policy are left with individual S&Ts. Our analysis suggests that S&Ts that belong to a common club should have common energy policy. Improvement across these clubs will be essential for reducing the emergence of different clubs. In the case of India, this task may not be so daunting since the results of the merged club analysis revealed two clubs and one non-converging club. In practice, it is easier to formulate energy policy when there are fewer clubs than when there are many clubs. India will need to have two aligned but different energy policies to increase energy productivity in these two clubs. Furthermore, the non-converging club (divergent club) will require a different energy policy. Future research could focus on gaining a better understanding of why this club is diverging.

26

ACCEPTED MANUSCRIPT In particular, it may be useful to conduct a deeper analysis of the economic, social, and institutional setting regarding the S&Ts in the diverging club. Our analysis offers some insight into the determinants of club convergence. Higher initial energy productivity makes it more likely for a state or territory to be in a club with higher energy productivity; this is consistent with the findings of the economic growth and convergence literature that stresses the importance of initial conditions in explaining

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convergence. A higher manufacturing sector share of income and service sector shares of income make it more likely for states to belong to the higher energy productivity club. The

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Indian government’s greater economic activity from manufacturing (“Make in India”) policy will be beneficial to higher energy productivity as newer and more modern energy

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productivity techniques are adopted. Non-renewable energy productivity is also found to be a significant determinant of Indian energy productivity convergence and this should help in

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reducing carbon dioxide emissions. Urbanization was found to have a statistically insignificant impact on India energy productivity. As discussed in Sadorsky (2014),

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urbanization leads to economic efficiency and greater economic activity making the net effect difficult to predict conceptually. Liddle and Lung (2014) provide convincing evidence that the employment and quality of life opportunities that access to electricity creates encourage

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urbanization. In the context of India’s goal of increasing access to electricity (“24X7 Power

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for All”) it is possible that greater access to electricity will encourage urbanization. The relationship between electricity consumption, urbanization and energy productivity in India is a topic for future study.

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Our results on club convergence have implications for Indian market based energy programs 22 . An example of a market-based energy efficiency programme in India is the

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Perform, Achieve and Trade (PAT) efficiency programme introduced in 2012 (Sahoo et al., 2017)23. This programme is part of The National Mission for Enhanced Energy Efficiency (NMEEE) which is one of eight missions under the Indian government’s National Action Plan for Climate Change. Under the PAT programme, targets are set for energy intensive 22

One concern is the bureaucratic structure of Indian energy policy. In India, the responsibility for energy policy is divided between five different ministries and several government commissions and agencies (IEA, 2007). Efficient and timely energy policy is often hampered by principal agent problems resulting from different objectives between and within these agencies. Changes in regulatory structure, privatisation efforts and the presence of competitive market forces will play a vital role within the energy sector in maintaining high energy productivity. 23 Sahoo et al. (2017) provide analysis of the strengths and weaknesses of the PAT programme as it applies to thermal power plants.

27

ACCEPTED MANUSCRIPT sectors to reduce energy consumption. Companies that are above their energy efficiency targets are rewarded with certificates while companies that fail to meet their targets need to purchase additional credits on the market place. Our results on club convergence suggest one common energy efficient target for all S&Ts would be sub-optimal since it does not take into account the fact there are multiple convergence club equilibria. Our analysis suggests that energy efficiency targets should be set based on club membership with different clubs having

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different targets.

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Appendix A: Energy productivity in Indian S&Ts

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Note: Vertical axis of each graph measures energy productivity (values ranging from -10 to 20); horizontal axis measures time period (1990 to 2016). States are 1-Andhra Pradesh; 2-Assam; 3-Bihar; 4-Gujarat; 5-Haryana; 6Himachal Pradesh; 7-Jammu and Kashmir; 8-Karnataka; 9-Kerala; 10-Madhya Pradesh; 11-Maharashtra; 12Meghalaya; 13-Nagaland; 14-Orissa; 15-Punjab; 16-Rajasthan; 17-Tamil Nadu; 18-Uttar Pradesh; 19-West Bengal; 20-Goa; 21-Uttaranchal; 22-Chhattisgarh; 23-Jharkhand; 24-Chandigarh; 25-Dadra and Nagar Haveli; 26-Delhi; 27-Daman and Diu; 28-Pondicherry; 29-Pune; 30-Coimbatore; 31-Telangana

31

ACCEPTED MANUSCRIPT Appendix B: Per capita net state domestic product at factor cost (2014 prices in Indian rupees per person)

89545 85468 81397 77529 76120 70059 69705 65974 61548 59279 58547 52559 51798 46131 44263 41573 36250 31199

D PT E

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AC

RI

Karnataka Arunachal Pradesh Andhra Pradesh Nagaland Mizoram West Bengal Tripura Rajasthan Meghalaya Jammu and Kashmir Chhattisgarh Odisha Madhya Pradesh Jharkhand Assam Manipur Uttar Pradesh Bihar

SC

224138 212219 176491 156951 143677 133427 117091 112664 107418 106831 103820 103716 95361 92350 92300

MA

Goa Delhi Sikkim Chandigarh Puducherry Haryana Maharashtra Tamil Nadu A & N Islands Gujarat Kerala Uttarakhand Telangana Punjab Himachal Pradesh

PT

GDP per capita

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States/Union Territories

Source: Reserve Bank of India, Handbook of statistics on Indian states.

32

ACCEPTED MANUSCRIPT Appendix C: LM test (with cross sectional dependence) for Indian S&Ts (1 break point)

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State CA-LM 𝑃̂ TB -1.888 4 1994 Andhra Pradesh 1995 -3.573 0 Assam 2009 -0.368 3 Bihar 2011 -0.437 3 Gujarat 1995 -3.080 0 Haryana 1994 -4.286** 0 Himachal Pradesh 1995 -0.394 3 Jammu and Kashmir 1994 -6.323*** 0 Karnataka 1994 -3.289 0 Kerala 2003 -5.064*** 4 Madhya Pradesh 1994 -4.113** 0 Maharashtra 1994 -3.918* 0 Meghalaya 2008 -1.465 3 Nagaland 2011 -1.404 3 Orissa 2002 -3.164 3 Punjab 2003 -6.704*** 3 Rajasthan 2000 -5.277*** 1 Tamil Nadu 1998 -5.055*** 1 Uttar Pradesh 1995 -3.097 2 West Bengal 2010 -4.64** 2 Goa 1995 -1.514 3 Uttaranchal 1998 -3.373* 1 Chhattisgarh 1994 -3.02 1 Jharkhand 2002 -5.152*** 1 Chandigarh 1994 -4.509** 0 Dadra and Nagar Haveli 1998 -2.02 3 Delhi 1998 -1.568 3 Daman and Diu 1995 -2.729 0 Pondicherry 1996 -3.208 4 Pune 1997 -4.283** 1 Coimbatore 1998 Telangana -3.002 3 Note: Cross-sectional augmented (CA) LM tests are reported. The optimal lag length is 𝑃̂ and the break point location is TB. ***, **, * denote 1%, 5% and 10% levels of significance respectively. Following Im et al. (2010), Table 1, the critical values for the transformed univariate unit root test with one break point is −4.604, −3.950, and −3.653 at the 1%, 5% and 10% level of significance respectively. The panel CA-LM transformed statistic is -4.096***.

33

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Appendix D: Summary statistics on explanatory variables No

Mean

SD

Minimum

Maximum

State Income per capita growth 28

0.054

0.224

-0.493

0.499

Assam

27

0.067

0.330

-0.465

1.208

Bihar

24

-0.016

0.432

-1.101

0.541

Gujarat

28

0.037

0.200

-0.485

0.311

Haryana

26

0.049

0.171

-0.400

0.288

Himachal Pradesh

26

0.070

0.200

-0.239

0.581

Jammu and Kashmir

11

0.041

0.186

-0.290

0.233

Karnataka

28

0.047

0.219

-0.512

0.629

Kerala

28

0.035

0.253

-0.490

0.682

Madhya Pradesh

26

0.017

0.225

-0.367

0.415

Maharashtra

29

0.035

0.183

-0.327

0.456

Meghalaya

12

0.069

0.199

-0.303

0.409

Nagaland

11

0.343

1.258

-1.998

2.142

Orissa

28

0.035

0.257

-0.494

0.610

Punjab

27

0.025

0.221

-0.350

0.807

Rajasthan

26

0.055

0.214

-0.273

0.778

Tamil Nadu

29

0.023

0.166

-0.317

0.268

28

-0.004

0.217

-0.449

0.405

28

-0.002

0.224

-0.517

0.590

26

0.108

0.326

-0.404

1.159

26

0.084

0.313

-0.452

0.767

27

0.041

0.636

-1.702

1.988

25

0.027

0.493

-1.437

1.694

West Bengal Goa

CE

Uttaranchal Chhattisgarh

Chandigarh

AC

Jharkhand

RI

SC

NU

MA D

PT E

Uttar Pradesh

PT

Andhra Pradesh

26

0.004

0.406

-0.876

0.940

Dadra and Nagar Haveli

27

0.025

0.325

-0.724

0.840

Delhi

28

-0.009

0.281

-0.735

0.766

Daman and Diu

21

0.042

0.412

-1.045

0.926

Pondicherry

25

-0.014

0.386

-0.732

0.707

27

0.025

0.208

-0.411

0.553

28

0.038

0.266

-0.485

0.778

29 -0.002 0.206 Manufacturing income share

-0.492

0.457

0.145

0.145

Pune

*

Coimbatore* Telangana Andhra Pradesh

34

28

0.145

0.000

ACCEPTED MANUSCRIPT 27

0.036

0.000

0.036

0.036

Bihar

24

0.491

0.000

0.491

0.491

Gujarat

28

0.311

0.000

0.311

0.311

Haryana

26

0.135

0.000

0.135

0.135

Himachal Pradesh

24

0.099

0.000

0.099

0.099

Jammu and Kashmir

11

0.913

0.000

0.913

0.913

Karnataka

28

0.270

0.000

0.270

0.270

Kerala

28

0.364

0.000

0.364

0.364

Madhya Pradesh

26

0.056

0.000

Maharashtra

29

0.214

0.000

Meghalaya

12

0.069

0.000

RI

Nagaland

PT

Assam

0.056

0.056

0.214

0.214

0.069

0.069

0.309

0.000

0.309

0.309

25

0.035

0.000

0.035

0.035

Punjab

27

0.090

0.000

0.090

0.090

Rajasthan

26

0.065

0.000

0.065

0.065

Tamil Nadu

29

0.306

0.000

0.306

0.306

Uttar Pradesh

28

0.140

0.000

0.140

0.140

West Bengal

28

0.207

0.000

0.207

0.207

Goa

26

0.136

0.000

0.136

0.136

Uttaranchal

24

0.054

0.000

0.054

0.054

Chhattisgarh

26

0.016

0.000

0.016

0.016

Jharkhand

21

0.029

0.000

0.029

0.029

23

0.413

0.000

0.413

0.413

23

0.018

0.000

0.018

0.018

28

0.415

0.000

0.415

0.415

21

0.036

0.000

0.036

0.036

25

0.068

0.000

0.068

0.068

27

0.192

0.000

0.192

0.192

28

0.230

0.000

0.230

0.230

0.000

0.140

0.140

Dadra and Nagar Haveli Delhi

CE

Daman and Diu Pondicherry

Telangana

AC

Pune* Coimbatore*

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MA D

PT E

Chandigarh

SC

7

Orissa

29 0.140 Service income share

Andhra Pradesh

28

0.826

0.000

0.826

0.826

Assam

27

0.870

0.000

0.870

0.870

Bihar

24

0.476

0.000

0.476

0.476

Gujarat

28

0.656

0.000

0.656

0.656

Haryana

26

0.801

0.000

0.801

0.801

Himachal Pradesh

26

0.857

0.000

0.857

0.857

Jammu and Kashmir

11

0.070

0.000

0.070

0.070

Karnataka

28

0.671

0.000

0.671

0.671

35

ACCEPTED MANUSCRIPT 28

0.594

0.000

0.594

0.594

Madhya Pradesh

26

0.826

0.000

0.826

0.826

Maharashtra

29

0.735

0.000

0.735

0.735

Meghalaya

12

0.930

0.000

0.930

0.930

Nagaland

11

0.691

0.000

0.691

0.691

Orissa

28

0.935

0.000

0.935

0.935

Punjab

27

0.894

0.000

0.894

0.894

Rajasthan

26

0.924

0.000

0.924

0.924

Tamil Nadu

29

0.673

0.000

Uttar Pradesh

28

0.772

0.000

West Bengal

28

0.706

0.000

Goa

26

0.828

Uttaranchal

26

0.878

Chhattisgarh

27

0.961

Jharkhand

25

0.960

Chandigarh

26

Dadra and Nagar Haveli Delhi Daman and Diu

21

Pondicherry

25

0.673

0.772

0.772

0.706

0.706

0.000

0.828

0.828

0.000

0.878

0.878

0.000

0.961

0.961

0.000

0.960

0.960

0.453

0.000

0.453

0.453

27

0.980

0.000

0.980

0.980

28

0.564

0.000

0.564

0.564

0.964

0.000

0.964

0.964

0.915

0.000

0.915

0.915

0.772

0.000

0.772

0.772

0.698

0.000

0.698

0.698

29 0.815 0.000 Non-renewable productivity

0.815

0.815

27 28

PT E

Telangana Andhra Pradesh

CE

Assam Bihar Gujarat Haryana

SC

NU

MA

*

Coimbatore*

RI

0.673

D

Pune

PT

Kerala

28

227.523

771.566

0.104

3226.846

26

3.500

8.361

0.098

42.161

24

50.466

158.778

0.009

747.356

28

47.950

98.149

0.007

424.868

1322.088

3452.022

0.228

15800.000

26

12.049

26.781

0.154

114.312

Jammu and Kashmir

11

1.132

0.732

0.397

2.511

Karnataka

AC

26

Himachal Pradesh

28

10.461

16.575

0.205

77.078

Kerala

28

9.354

21.085

0.006

99.961

Madhya Pradesh

26

26.745

54.697

0.289

204.370

Maharashtra

28

179.093

345.786

2.534

1724.441

Meghalaya

11

0.402

0.419

0.116

1.576

Nagaland

11

0.003

0.007

0.000

0.023

Orissa

27

14.639

30.466

0.080

115.459

Punjab

27

3.700

4.738

0.664

24.591

36

ACCEPTED MANUSCRIPT 26

84.610

277.109

0.217

1404.560

Tamil Nadu

28

1721.959

5636.967

0.037

26900.000

Uttar Pradesh

28

13.416

32.548

0.551

160.103

West Bengal

28

121.971

506.061

0.444

2696.948

Goa

26

3.106

8.038

0.059

41.879

Uttaranchal

26

0.806

0.886

0.071

3.788

Chhattisgarh

27

3.522

8.416

0.005

42.727

Jharkhand

25

6.016

13.976

0.039

70.423

Chandigarh

25

0.932

1.077

Dadra and Nagar Haveli

27

149.068

756.434

Delhi

28

335.318

884.356

2

5.990

25

0.336

27

38.657

28

3.079

0.176

3933.892

0.026

4261.344

15.838

0.051

54.435

0.427

0.015

2.184

141.681

0.080

737.045

5.149

0.334

24.175

1977.516

0.124

10700.000

0.316

0.031

0.269

0.377

0.139

0.013

0.111

0.161

0.120

0.009

0.104

0.138

0.421

0.040

0.345

0.495

0.331

0.046

0.246

0.405

0.102

0.006

0.087

0.112

29

0.282

0.025

0.229

0.329

29

0.376

0.035

0.309

0.436

29

0.362

0.108

0.260

0.592

29

0.292

0.019

0.253

0.326

29

0.461

0.032

0.387

0.515

29

0.219

0.017

0.186

0.250

29

0.244

0.048

0.172

0.346

29

0.162

0.014

0.134

0.186

Coimbatore* Telangana

29

Bihar

29

Gujarat

29

Haryana

29

Himachal Pradesh

29

PT E

29

Assam

Jammu and Kashmir Karnataka

Nagaland Orissa Punjab

AC

Maharashtra

CE

Kerala Madhya Pradesh

MA

29 383.205 urbanisation

Andhra Pradesh

Meghalaya

NU

*

D

Pune

4.406

RI

Pondicherry

0.198

SC

Daman and Diu

PT

Rajasthan

29

0.366

0.034

0.295

0.418

Rajasthan

29

0.264

0.018

0.229

0.295

Tamil Nadu

29

0.454

0.064

0.342

0.548

Uttar Pradesh

29

0.231

0.016

0.197

0.262

West Bengal

29

0.316

0.023

0.275

0.355

Goa

29

0.548

0.088

0.410

0.703

Uttaranchal

29

0.291

0.033

0.232

0.349

Chhattisgarh

29

0.225

0.028

0.174

0.277

37

ACCEPTED MANUSCRIPT Jharkhand

29

0.249

0.018

0.212

0.287

Chandigarh

29

1.046

0.092

0.897

1.224

Dadra and Nagar Haveli

29

0.347

0.200

0.006

0.681

Delhi

29

1.082

0.112

0.899

1.323

Daman and Diu

29

0.667

0.203

0.361

1.078

Pondicherry

29

0.742

0.057

0.640

0.855

Pune

*

Coimbatore*

AC

CE

PT E

D

MA

NU

SC

RI

PT

Telangana

38

ACCEPTED MANUSCRIPT Highlights Analyze energy productivity convergence in Indian S&Ts Use unique firm level data set There are two convergent clubs and one divergent club Initial energy productivity is a determinant of club convergence

AC

CE

PT E

D

MA

NU

SC

RI

PT

Industry structure affects the determinants of club convergence

39