Estimates of Spatial Prices in India and Their Sensitivity to Alternative Estimation Methods and Choice of Commodities

Abstract

This paper provides Indian evidence on sub-national PPPs that point to considerable spatial price heterogeneity within the country, based on Indian National Sample Survey (NSS) data. The prices of various commodities have been generated from the household specific unit values obtained from the information on expenditures and quantities from the NSS unit records. This paper shows that the CPD model, proposed in the cross country context, can be adapted to the household context to estimate spatial prices in the intra country context. The proposed CPD based model is shown to be formally equivalent to certain well known fixed weight price indices under certain parametric configurations. The empirical contribution includes a systematic comparison between the spatial price indices from alternative models, namely the CPD and utility based models, and the result that the utility based methods point to a much greater extent of spatial price heterogeneity than is suggested by the CPD type models. The results also record the sensitivity of the spatial price indices to the choice of commodities in the utility based approach. The pairwise comparison of estimates suggests that commodity selection may be more important than model selection in its impact on the spatial price estimates, though the latter is important as well. The study provides estimates of rural–urban differentials in spatial price indices that suggest some interesting differences between the constituent states. The results make a strong case for further research on the topic of sub-national PPPs in the context of large heterogeneous countries.

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Fig. 1

Notes

  1. 1.

    See, for example, the results of latest ICP exercise contained in World Bank (2015).

  2. 2.

    See, for example, the evidence summarised in Hill and Syed (2014).

  3. 3.

    See Clements et al. (2006) for a review of ‘stochastic index numbers’ and an extension of Diewert’s work within the context of the CPD framework.

  4. 4.

    Note that in the second stage estimation the dependent variable \( \hat{\phi }_{jrt} \) will have standard errors (se) from step 1. One possibility could have been to incorporate (1/se) as weighting factors in the second step. We have, however, not done it here.

  5. 5.

    As pointed out by Professor Ken Clements, this condition rules out inclusion of items that have unit quality elasticities (with respect to income) in the numeriare region, as \( \lambda_{jt} = 1 \) implies zero weight for such items.

  6. 6.

    We have, however, not done the calculations for other rounds.

  7. 7.

    See World Bank (2015).

  8. 8.

    See Majumder et al. (2012) for a description of the methodology for generating the prices of the various commodities from the household specific unit values obtained from the information on expenditures and quantities from the NSS unit records.

  9. 9.

    To save space, we have reported in Table 4 only the results for NSS round 66.

References

  1. Aten, B., & Menezes, T. (2002). Poverty price levels: An application to Brazilian metropolitan areas. In World Bank ICP conference, Washington, D.C., March, 2002.

  2. Banks, J., Blundell, R., & Lewbel, A. (1997). Quadratic engel curves and consumer demand. Review of Economics and Statistics, 79, 527–539.

    Article  Google Scholar 

  3. Bhattacharya, N., Chatterjee, G. S., & Pal, P. (1988). Variations in level of living across regions and social groups in India, 1963–64 and 1973–74. In T. N. Srinivasan & P. K. Bardhan (Eds.), Rural poverty in South Asia. Oxford: Oxford University Press.

    Google Scholar 

  4. Bhattacharyya, S. S., Joshi, P. D., & Roychowdhury, A. B. (1980). Regional price indices based on NSS 25th round consumer expenditure data. Sarvekshana, Journal of the NSS Organisation, 3(4), 107–121.

    Google Scholar 

  5. Biggeri, L., et al. (2010). Sub national PPPs based on integration with CPIs. Research project, draft proposal, paper presented at the 2nd ICP technical advisory group meeting, Washington DC, February 17–19.

  6. Biggeri, L., De Carli, R., & Laureti, T. (2008). The interpretation of the PPPs: A method for measuring the factors that affect the comparisons and the integration with the CPI work at regional level. Paper presented at the joint UNECE/ILO meeting on consumer price indices, May 8–9, Geneva.

  7. Clements, K., Izan, I., & Selvanathan, E. (2006). Stochastic index numbers: A review. International Statistical Review, 74, 235–270.

    Article  Google Scholar 

  8. Coondoo, D., Majumder, A., & Chattopdhyay, S. (2011). Estimating spatial consumer price indices through engel curve analysis. Review of Income and Wealth, 57(1), 138–155.

    Article  Google Scholar 

  9. Coondoo, D., Majumder, A., & Ray, R. (2004). A method of calculating regional consumer price differentials with illustrative evidence from India. Review of Income and Wealth, 50(1), 51–68.

    Article  Google Scholar 

  10. Coondoo, D., & Saha, S. (1990). Between-state differentials in rural consumer prices in India: An analysis of intertemporal variations. Sankhya, Series B, 52(3), 347–360.

    Google Scholar 

  11. De Carli, R. (2010). Intra-national price level differentials: The Italian experience. In L. Biggeri & G. Ferrari (Eds.), Price indexes in time and space: Methods and practice (pp. 115–130). Berlin: Springer.

    Google Scholar 

  12. Diewert, W. E. (2005). Weighted country product dummy variable regressions and index number formulae. Review of Income and Wealth, 51(4), 561–570.

    Article  Google Scholar 

  13. Dikhanov, Y., Palanyandy, C., & Capilit, E. (2011). Subnational purchasing power parities toward integration of international comparison program and the consumer price index: The case of Philippines. ADB economics working paper series, no. 290, Asian Development Bank.

  14. Hill, R. J., & Syed, I. (2014). Improving international comparisons of prices at basic heading level: An application to the Asia-Pacific Region. Review of Income and Wealth. doi:10.1111/roiw.12116.

    Google Scholar 

  15. Lancaster, G., & Ray, R. (1998). Comparison of alternative models of household equivalence scales: The Australian evidence on unit record data. Economic Record, 74, 1–14.

    Article  Google Scholar 

  16. Majumder, A., Ray, R., & Sinha, K. (2012). The calculation of rural–urban food price differentials from unit values in household expenditure surveys: A new procedure and comparison with existing methods. American Journal of Agricultural Economics, 94(5), 1218–1235.

    Article  Google Scholar 

  17. Majumder, A., Ray, R., & Sinha, K. (2014a). Spatial comparisons of prices and expenditure in a heterogeneous country: Methodology with application to India. Macroeconomic Dynamics. doi:10.1017/S1365100513000576.

    Google Scholar 

  18. Majumder, A., Ray, R., & Sinha, K. (2014b). Estimating purchasing power parities from household expenditure data using complete demand systems with application to living standards comparison: India and Vietnam. Review of Income and Wealth. doi:10.1111/roiw.12073.

    Google Scholar 

  19. Mishra, A., & Ray, R. (2014). Spatial variation in prices and expenditure inequalities in Australia. Economic Record, 90(289), 137–159.

    Article  Google Scholar 

  20. Prais, S. J., & Houthakker, H. S. (1955). The analysis of family budgets, 1971 (2nd ed.). Cambridge: Cambridge University Press.

    Google Scholar 

  21. Rao, D. S. P. (2005). On the equivalence of weighted country-product-dummy (CPD) method and the Rao-system for multilateral comparisons. Review of Income and Wealth, 51(4), 571–580.

    Article  Google Scholar 

  22. Summers, R. (1973). International price comparisons based upon incomplete data. Review of Income and Wealth, 19(1), 1–16.

    Article  Google Scholar 

  23. World Bank. (2015). Purchasing power parities and the real size of world economies: A comprehensive report of the 2011 international comparison program. Washington, DC: World Bank.

    Google Scholar 

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Acknowledgments

This paper draws on joint work with Manisha Chakrabarty of the Indian Institute of Management, Calcutta and Kompal Sinha of Monash University, Melbourne. The authors are grateful to Dr. Sattwik Santra for his help with the STATA programs. They also thank Professor Kenneth W. Clements for insightful remarks on the HRPD model introduced in this paper. Helpful comments from two anonymous referees are gratefully acknowledged. The disclaimer applies.

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Correspondence to Ranjan Ray.

Appendices

Appendix 1

See Tables 6 and 7.

Table 6 List of commodities used in different methods
Table 7 Abbreviation of names of states and census population

Appendix 2

See Fig. 1.

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Majumder, A., Ray, R. Estimates of Spatial Prices in India and Their Sensitivity to Alternative Estimation Methods and Choice of Commodities. Soc Indic Res 131, 145–167 (2017). https://doi.org/10.1007/s11205-015-1124-1

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Keywords

  • Household Regional Product Dummy Model
  • QAIDS
  • Spatial price index
  • Sub-national PPP

JEL Classification

  • C12
  • C18
  • D12
  • E30
  • E31