# 1. Introduction

India’s growth performance over the past three decades has been nothing short of spectacular. Between 1985 and 2017, gross domestic product (GDP) per capita grew on average 4.5 percent a year (World Bank 2018). As a result, India is catching up with more advanced economies, defying the claim that the world suffers from “divergence, big time” (Pritchett 1997). Growth patterns at the subnational level are less encouraging, however. Income disparities within India are wide, to the point of justifying a distinction between high‐ and low‐income states. And a key question is thus whether poorer locations are catching up with the richer ones as the country experiences rapid growth.

The consensus among economists is that within itself India is characterized by “divergence, big time.” Datt and Ravallion (2002) found that the states that were richer in the 1980s grew faster in the 1990s. Kumar and Subramanian (2012) documented not only a continuation of state‐level divergence in the 2000s, but also an increase in its magnitude. In the most positive assessment, Ghate and Wright (2013) concluded that there was no clear evidence in favor of absolute convergence or absolute divergence across states from the 1980s to the 2000s. A recent Economic Survey by the government of India featured this debate, showing the sharp contrast between growth divergence in India and growth convergence within China and other large federations (Ministry of Finance 2017).

However, the same Economic Survey also pointed to new evidence that nonmonetary measures of well‐ being are improving across the board and especially in low‐income states. The Survey also reported an increase in cross‐state transactions and cross‐state migration, two trends that could support convergence (Ministry of Finance 2017). Unlike previous research, a recent study using the intensity of nighttime light to proxy for income found evidence of convergence across Indian states, districts, and cities (Tewari and Godfrey 2016).

In this paper, we take a fresh look into the Indian convergence debate building on both the growth and the urban economics literatures. But we depart from previous studies in three important ways:

- We assess growth patterns across districts and across places below the district level. Some Indian states are bigger than all but a few countries in the world. Taking the state as the spatial unit of analysis can hide substantial heterogeneity in local growth patterns.

- We rely on household consumption expenditure per capita to measure living standards. This indicator, estimated on the basis of household expenditure surveys, provides greater spatial granularity than GDP per capita, while still being highly correlated with it.

- We use a Bayesian model averaging approach to identify the best predictors of local economic growth. Compared to the classical econometric approach, this allows us to better address the uncertainty on the true model of the economy resulting from the many potential predictors.

Our results reveal that there is growth divergence at the state level, but strong convergence when districts or places below district level are considered. This finding runs against the growth divergence consensus but is in line with more indirect evidence provided by nonmonetary indicators of well‐being and by nighttime light intensity. Growth is fastest in the midrange between purely rural places and major urban centers. But the best‐performing locations do equally well in high‐ and low‐income states. It is just that there are not many top performers among the latter.