Abstract
We investigate why some governments do not institute public policy conducive to industrialization from the viewpoint of the balance of political power between the agricultural and industrial sectors. More specifically, we examine whether a higher rural Gini coefficient—a proxy for the degree of political power of rural elites—tends to reduce the allocation of development expenditures favorable to the industrial sector at the state level in India. Our estimation results suggest that both the rural Gini coefficient and the rural population share have significant negative coefficients. These results imply that the political influence of the agricultural sector can limit the allocation of expenditures conducive to industrialization, resulting in the stagnation of regional state economies.
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Notes
- 1.
However, some scholars argue that politicians may also pay attention to the general interests of broad socioeconomic groups (Persson and Tabellini 2000). For instance, empirical research on the determinants of non-tariff trade barriers has shown that not only industries that are politically organized, but also industries that are uncompetitive, exposed to the threat of imports, or in decline, as well as those that have a high unemployment rate are also likely to be protected by such trade barriers (e.g., Finger et al. 1982; Trefler 1993; Lee and Swagel 1997). These studies indicate that incumbent political leaders may implement policies that are favorable to general interests if they believe that doing so will enhance the probability of their political survival.
- 2.
Banerjee et al. (2002) show that, in a successful case of land reforms in West Bengal in India, “the tenancy reform program called Operation Barga explains around 28% of the subsequent growth of agricultural productivity there.”
- 3.
- 4.
Jenkins (2004) stated, “India’s federal system has created 29 ‘mini-democracies’ with almost identical institutional infrastructures, at least in terms of the formal systems of representation. India’s States, moreover, operate under a set of common conditions, including New Delhi’s foreign and economic policy framework and the legal protections enshrined in the Indian Constitution. These control variables represent a major boon to students of comparative politics who seek to understand and explain the divergent patterns and outcomes that the practice of democracy can produce.”
- 5.
The Industries (Development and Regulation) Act of 1951 required both private and public entities to obtain a license to establish a new firm, expand a factory’s capacity, start selling a new product, change its location, and so forth. The licensing process often took a long time and imposed a tremendous burden on firms. Due to the discretion of bureaucrats, the approval of a license was uncertain, which also induced corruption. A portion of the licensing system was abolished in the middle of the 1980s and most of the remainder was deregulated in 1991. The time period from 1951 to 1991 is known as the “License Raj Era” in India.
- 6.
According to Bajpai and Sachs (1999), the reform-oriented states are Andhra Pradesh, Gujarat, Karnataka, Maharashtra, and Tamil Nadu; the intermediate states are Haryana, Orissa, and West Bengal; and the lagging reformers are Assam, Bihar, Kerala, Madhya Pradesh, Punjab, Rajasthan, and Uttar Pradesh.
- 7.
Interestingly, according to Mukherjee and Chakraborty (2010), the dispersion in indicators of human development in such areas as health and education has declined among Indian states.
- 8.
Ahluwalia (2000) emphasizes the importance of private investment, and says that “[p]rivate corporate investment is potentially highly mobile across states and is therefore likely to flow to states which have a skilled labor force with a good ‘work culture’, good infrastructure especially power, transport and communications, and good governance generally. The mobility of private corporate investment has increased in the post-liberalization period since decontrol has eliminated the central government’s ability to direct investment to particular areas, while competition has greatly increased the incentive for private corporate investment to locate where costs are minimized.”
- 9.
Yet at the same time other scholars (e.g., Nagaraj et al. 2000; Aiyar 2001; Trivedi 2002; World Bank 2006; Nayyar 2008) have found evidence of conditional convergence. However, since the conditions with respect to human capital, infrastructure, public policy, and so forth vary significantly across states, conditional convergence has not reduced disparities among states in the last two decades. In the words of Nayyar (2008), Indian states are “converging to very different steady states.”
- 10.
For instance, Iarossi (2009), on the basis of Investment Climate Survey data, constructed an Investment Climate Index using principal component analysis. He considered three broad business categories, namely, infrastructure, inputs, and institutions, and claimed that infrastructure and institutions are more critical bottlenecks for the business climate of Indian states. Furthermore, power outages and transportation are the most serious business constraints within infrastructure, while those within institutions are corruption and tax regulation.
- 11.
We also conducted an estimation based on the generalized method of moments (GMM). However, the data utilized herein did not pass the overidentification tests associated with that method and as such we refrain from reporting those results.
- 12.
Previous studies have shown that the composition of government expenditure may have effects on economic performance; see, for example, Marjit et al. (2013).
- 13.
Note that these studies pay attention to the effects on other dependent variables such as social welfare and infrastructure, not industrialization policy.
- 14.
Rudolph and Rudolph (1987) also state that “[o]f the many cleavages that animate Indian politics, class usually matters less than other social formations, such as caste, religious and language communities, and regional nationalisms. Other cleavages rival or surpass class on political saliency because the consciousness and commitment focused on them are usually more transparent and accessible than those focused on class.”
- 15.
The data for all of these sociological variables were acquired either from censuses (conducted every ten years in India) or from National Sample Surveys, which are undertaken roughly every 5 years. Linear interpolation was used to generate data for non-census and non-survey years. Therefore, the estimated coefficients associated with these variables should be interpreted with caution and as such we do not emphasize them in our discussion of results.
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Appendix: Data Sources and Construction of Variables
Appendix: Data Sources and Construction of Variables
(Dependent Variables)
Ratios of development (capital) expenditures for the industrial sector: Data on state government expenditures were obtained from the EPW Research Foundation database.
(Independent Variables)
Gini coefficients: Gini coefficients for both rural and urban areas are available from the Planning Commission website and the original data were collected through National Sample Surveys.
Population shares of rural and urban areas: Data on population shares were obtained from different sources, including the Planning Commission website. All such data were collected in censuses.
Political competition variables: All data on Vidhan Sabha elections were obtained from the website of the Election Commission of India. We calculate the fractionalization index of Vidhan Sabha seats based on the following equation.
where shi is the share of seats in a state assembly that party i won in the last election (see Alesina et al. 1999). The fragmentation index is one minus the Herfindahl index of political parties.
Voter turnout: Data on voter turnout rates are available from the website of the Election Commission of India.
Religious fractionalization: Data on religious distribution are available from censuses. Data on the relative number of followers of the six major religions (Hindu, Muslim, Christianity, Sikh, Buddhism, and Jain) are used to calculate the fractionalization index using the same equation as for the fractionalization index of political parties. We treat “other religions” and “religion not stated” as two separate religious groups so that the shares of all the religions add up to one. The shares of these two groups are negligible in that they do not substantively affect the calculated values of the indices. Linear interpolation was used to generate data in non-census years.
Linguistic fractionalization: We include the 22 scheduled languages and the 100 nonscheduled languages highlighted in the 2001 Census (see the Census website of the Government of India). For the 1981 and 1991 Censuses, the list of languages identified is almost the same as that in the 2001 Census. Linear interpolation was used to generate data in non-census years.
SC share and ST share: The population share of scheduled castes (SC) and scheduled tribes (ST) are available from the Planning Commission website, and the original data were collected through National Sample Surveys conducted by the National Sample Survey Organization approximately every 5 years. Linear interpolation was used to generate data in non-survey years.
Poverty rate: Data on poverty rates are available from the Planning Commission website, and the original data were collected through National Sample Surveys conducted by National Sample Survey Organization approximately every 5 years. Linear interpolation was used to generate data in non-survey years.
Literacy rate: Data on literacy rates were obtained from censuses. Linear interpolation was used to generate data in non-census years.
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Kato, A., Fukumi, A. (2020). The Political Economy of the Allocation of State Government Expenditures for the Industrial Sector. In: Aggarwal, S., Das, D., Banga, R. (eds) Accelerators of India's Growth—Industry, Trade and Employment. India Studies in Business and Economics. Springer, Singapore. https://doi.org/10.1007/978-981-32-9397-7_4
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