The spatial price map of India drawn using pseudo unit values

Purpose This study aims to apply a proposed methodology for calculating spatial prices in a heterogeneous country setting such as India with limited price information. Based on the empirical evidence, the study plans to draw the spatial price map of India with different colours denoting states and districts with varying level of spatial prices. Design/methodology/approach This study shows that a procedure proposed by Lewbel (1989), based on the idea by Barten (1964) that household composition changes have “quasi-price effects”, can be used to estimate spatial prices in the absence of information on regional prices. Findings The evidence on spatial price differences in India, which is the most comprehensive to date because it goes down to district level, shows that the proposed procedure has considerable potential in future applications on other data sets with limited price information. The policy importance of the results is underlined by the sensitivity of the demand elasticities to the inclusion/omission of spatial price variation. Research limitations/implications The study uses “pseudo unit values” based on household composition and demographic effects on demand as proxy for the missing price information. While the work of Atella et al. (2004) suggests that such proxies are accurate representations of true prices, nevertheless, they are proxies and the results should be treated with caution. Practical implications The evidence on spatial prices in India that point to a high degree of price heterogeneity between regions implies that welfare applications such as income distributional and poverty studies must take account of the price heterogeneity within the country. The implications extend beyond India to cross-country exercises such as the purchasing power parity calculations undertaken by the International Comparison Project. Originality/value This is one of the first studies that provide evidence on spatial price heterogeneity within a country without requiring regional price information. Methodologically, the paper builds on the suggestion of Lewbel (RES, 1989) in showing how the demographic effects on household expenditure pattern can be used to estimate spatial prices. The value of the contribution lies in the use that the estimated spatial prices can be put to in calculating inequality and poverty rates and in standard of living comparisons between regions in the country.