Most convergence studies for India followed the tradition of the growth literature and used the state as the unit of observation. Indeed, there was ample consensus that imbalances among states are wide. For example, Goa has a GDP per capita comparable to that of Mexico, whereas Bihar’s GDP per capita is closer to that of Benin. Large subnational gaps in living standards, including both monetary and nonmonetary dimensions of well‐being, had also been documented in multiple studies.

The literature on this topic up to 2007 has been summarized by Kalra and Sodsriwiboon (2010). With the addition of more recent publications, there have been 20 studies of subnational convergence in India (table 1). These studies used different methodologies, including cross‐sectional regression, panel regression and distributional analysis. Bajpai and Sachs (1996), Rao, Shand and Kalirajan (1999), and Kumar and Subramanian (2012) split the sample into various time periods, which led to multiple results. Kochhar et al. (2006) also reported multiple results because they used different estimation methods.

Of the 26 results from these 20 studies, 17 support absolute divergence, 2 are inconclusive, and 7 favor absolute convergence. The evidence in favor of absolute divergence is strongest at the state level, and especially in the 1980s and 1990s. Thus, Datt and Ravallion (2002) reported absolute divergence across states in the 1990s, except for the richest two. Rodrik and Subramanian (2004) found that states were  diverging at an annual rate of about 1.2 percent a year during that same period a result they saw as a striking case of “divergence, big time.” Findings were more mixed for the 2000s, but on balance the evidence suggested that absolute divergence remained the norm. Kumar and Subramanian (2012) even reported an increase in the magnitude of divergence from the 1990s to the 2000s. The Ministry of Finance (2017) expanded the analysis to 2004 for income and to 2011 for expenditure, confirming that there was continuing divergence across states. Using income as the performance indicator, Das (2012) also reported strong divergence across states in 1980–2005. However, Ghate and Wright (2013) found no clear evidence to support either absolute convergence or absolute divergence across major states in 1987–2004. In addition to assessing the correlation between initial income per capita and growth directly, they conducted millions of convergence regressions controlling for other factors potentially affecting growth and found that the coefficient on initial income per capita was symmetrically distributed around zero.

Because few studies considered higher levels of spatial disaggregation, the results at the district or below‐district level appear to be inconclusive. Das, Ghate and Robertson (2015) found that richer districts in 2001 grew slightly faster during the period 2001–08. When using expenditure as the performance indicator, Das (2012) also found evidence of divergence across the rural areas of states but reported convergence across their urban areas. This finding is consistent with the evidence presented by Tewari and Godfrey (2016) using nighttime light intensity as the performance indicator, which showed absolute convergence across states, districts and cities.

Studies also differ in the list of potential drivers of local performance they consider when assessing conditional convergence. At the global level, the selection of these potential drivers was initially influenced by the growth literature, focusing on indicators such as trade openness or infrastructure. However, over time some indicators explored by the urban economics literature – such as population density– have been incorporated as well. Also, while the growth literature focuses on key drivers at the country or state level, the urban economics literature considers drivers defined at a much more granular level.

3