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Development goals, population demography and state expenditure on human priority sectors: a study of Indian major states

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Abstract

The paper examines the spending pattern of Indian states on human priority sectors (HPS) during 2001–2019 particularly stressing on its responsiveness towards the Development Goals and Population Demography. The observed expenditure pattern reveals that among all the major Indian States, Bihar is the most vulnerable state having the lowest spending and lowest growth rate of expenditures. We also find Water Sanitation and Family Welfare to be the most neglected subsectors of HPS as these sectors received only 3% of HPS expenditure on an average. Besides, the pattern also exposes the huge inter– state disparities in HPS expenditure, which is detected to be highest as regard Nutrition and has a rising trend for the Family Welfare component. Moreover, in response to Millennium Development Goals we find there was no significant change in spending pattern, however, we find mixed bearing of population demography on the HPS. For instance, states with a large base of rural population are observed to spend more on the priority sector, but contrary to the expectation, the size of the poor population has no bearing on the states’ allocation of resources. The findings of the paper are particularly significant for policy prescriptions to attain the Sustainable Development Goals as it offers sufficient evidence of negligence of timely spending on the HPS in response to the development goals and population composition. So, we find there is a need for introspection on HPS spending and its concurrent evaluation in terms of its allocation criterion and timing of allocation.

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Notes

  1. Ramakumar (2008) defined the priority sectors following United Nations Development Programme (UNDP) definition.

  2. All information regarding the India’s progress towards MDGs is collected from official website of UNDP

    https://www.in.undp.org/content/india/en/home/post– 2015/mdgoverview.html.

  3. It is defined by the ratio of state expenditure to targeted population. Here, targeted population means the age group of population who can directly benefit from the particular expenditure, for instance expenditure in the education sector has direct benefit to the education age group, i.e., 6–21 years.

  4. For instance, the spending on rural development is aimed at bettering the condition of the rural residents.

  5. For average targeted per capita expenditure, first we calculated year– wise targeted per capita expenditure = Total expenditure in a particular sector/ Targeted Population. Then, we calculated average targeted per capita expenditure = Total targeted per capita expenditure during study period/Number of years.

  6. For measuring trend growth rate, targeted per capita expenditure is transformed to log values and then regressed on t (time periods).

  7. To measure the significance of each priority sector we calculate the expenditure share (in %) of each sector in total HPS expenditure, i.e., for education say Education expenditure/Total HPS Expenditure.

  8. To measure the relative position of the states in the expenditure ladder with respect to the highest spending state we divide each state priority expenditure/highest spending state. For example, relative education expenditure = State’s education expenditure/Highest spending state in education.

  9. Coefficient of variation = (Standard Deviation/Mean) *100.

  10. Increment of these variables is calculated by Final Year Value/Base Year Value.

  11. Non– working age is referred to age group 0–14 years and above 65, whereas working age refers to age group 15–64.

  12. Since expenditure is already normalized by population the impact of population size will depend on scale economies.

  13. Fiscal deficit = total expenditure – total revenue.

  14. Empirical model specification can be states as PCEit = α + βPit + γXit + θi + ηt + εit; i = 1…N, and t = 1,..,T where PCEit is the measure of per capita expenditure for each priority sector, Pit is the vector of policy and population demography, Xit is a set of control variables that are expected to affect the state expenditure in HPS, whereas, θi represents unobservable state– specific effects, ηt will capture common time– specific effects for all states, and εit is the error term.

  15. The model for Nutrition sector was dropped since data was not available for all states for all years in case of nutrition expenditure.

  16. Given the presence of first– ordered autocorrelation in our regression equation (confirmed by Wooldridge test), it is unlikely that the standard ordinary least squares (OLS) assumption of independent and identically distributed errors will be satisfied, thus, the calculated coefficient parameters will be supposedly biased so we used PCSE model which is developed by Beck and Katz (1995). They used Monte Carlo analysis to establish this estimation method to be better than other available methods.

  17. Figure 1 in ‘Appendix’ diagrammatically represent the overall sectoral share of spending in total HPS expenditure.

  18. It consists of Bihar, Madhya Pradesh, Rajasthan, and Uttar Pradesh.

  19. Diagrammatic representation of CoV over the years is given in ‘Appendix’ Fig. 2.

  20. Year of MDG announcement and commitment.

  21. The structural break for a particular state in particular expenditure over the years can be due to various reasons and may not particularly for MDGs.

  22. Diagrammatic representation of Population versus Expenditure Growth for Water Sanitation and Family Welfare is presented in Figs. 3 and 4, respectively.

  23. To measure growth or increment of each targeted population size, we divide the population in 2011 by 2001. Growth of expenditure of each sector is also calculated in the similar fashion.

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Acknowledgements

We are grateful to Prof. Manmohan L. Agarwal for his valuable suggestions on the earlier draft. An earlier version of the paper was presented at a seminar organized by Centre for Development Studies on 23rd October 2019. We are thankful to Dr Srikanta Kundu for his insightful comments on an earlier draft and also to the participants at the seminar. We would also like to particularly thank the anonymous referee who provided useful and detailed comments on the earlier version of the manuscript. However, any error still in the paper remains with us.

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  1. Centre for Development Studies, Trivandrum, Kerala, 695011, India

    Rimon Saha & Udaya S. Mishra

  2. Triveni Devi Bhalotia College, Raniganj, West Bengal, 713347, India

    Rimon Saha

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  1. Rimon Saha
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Appendix

Appendix

See Tables 11, 12 and Figs. 1, 2, 3, 4.

Table 11 Coding of political ideology
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Table 12 Variable construction, data sources and expected impacts of expenditure
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Fig. 1
figure 1

Expenditure share (in %) of each sector in total Human Priority Sector expenditure. Source: Author’s calculation using RBI data

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Fig. 2
figure 2

Coefficient of variation of targeted per capita expenditure of each sector (2001–19). Source: Author’s calculation using RBI data

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Fig. 3
figure 3

Contrasting the growth of targeted per capita expenditure versus population growth in Indian States for Water Sanitation Sector. Source: Author’s calculation using RBI and Census data. Note: States labelled as (State, U) represent the states whose growth in targeted per– capita fall short of the growth in targeted population

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Fig. 4
figure 4

Contrasting the growth of targeted per capita expenditure versus population growth in Indian States for Family Welfare Sector. Source: Author’s calculation using RBI and Census data. Note: States labelled as (State, U) represent the states whose growth in targeted per– capita fall short of the growth in targeted population

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Saha, R., Mishra, U.S. Development goals, population demography and state expenditure on human priority sectors: a study of Indian major states. Int Rev Econ 69, 21–47 (2022). https://doi.org/10.1007/s12232-021-00382-0

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