recent shifts in trends in each of the three principal crops. Neither of three crop yields exhibit a move towards convergence going forward.

Another advantage of adopting the convergence test proposed in Phillips and Sul (2007) is that it allows to ascertain whether overall non-convergence happens in the presence of club convergence (Baumol, 1986). Specifically, the rejection of the null does not preclude the possibility of club convergence. Certain subsets of districts may form a club, with the possibility of multiple clubs existing. Districts within a club can then converge either on an absolute or conditional basis.

In the context of Bihar, it means testing whether districts are forming clubs and converging at different levels of yields. Moreover, the identification of which districts constitute clubs is contingent on statistical properties of the data rather than an ad-hoc and ex-post taxonomy (De Long, 1988). The statistical identification has important implications for policy design, and I discuss this in detail later when presenting results. Given the lack of evidence of any convergence happening yields during 2012-2017, I examine if there is evidence of club convergence. For rice, I find that the southern districts form a fastgrowing club that is increasingly gaining ground over the north. Wheat and maize yields paint a different picture. Districts from all three zones (two from the northeast, three from the northwest, five from the south) make up the leading wheat club, which towers over others. The northeastern zone outpaces districts elsewhere in maize yields that align themselves in various lagging clusters.

A critical question that the analysis poses is what factors drive the divergence in rice and maize yields in the second sub-period. While a rigorous investigation of this query is outside the scope of this paper, I take a first step towards ruling out an important factor – supply of agricultural credit. Though not a control for the universe of inputs, credit holds a special status as it is closely tied to the usage of more direct inputs like seeds, fertilizers, machinery, etc. Moreover, multiple studies have found that agricultural credit has enabled the use of purchased inputs and has affected the input mix in India (Bhalla and Singh, 2010; Narayanan, 2016).<sup>3</sup> Thus, productivity growth might be lagging in some districts due to relatively lower credit allocation.

Using the credit data, I ask whether the supply of credit in the districts forming the lagging clubs has declined during 2012–17 relative to the districts in the leading clubs using a difference-in-difference approach. I do not find any evidence of the credit supply shrinking in the former relative to the latter. Credit expansion – both in absolute value and share, is higher in lagging rice clubs relative to the most productive club. The test also does not lend support to the credit hypothesis for the other two crops, and its presence can be reasonably rejected. The finding does not imply that productivity is unresponsive to credit, especially given the evidence that it encourages the use of inputs. Instead, in line with Das _et al._ (2009) – who find a positive but variable impact across states, the analysis here suggests that a similar phenomenon holds at the district level in Bihar. Credit might be more efficient in some districts and future research should focus on explaining the heterogenous impact as it has crucial policy implications.

This paper is most closely related to papers that study agricultural productivity convergence in the context of developing economies. For instance, McErlean and Wu (2003) and Wu (1995) test for regional convergence in agricultural productivity in China. Martin and Mitra (2001) consider cross-country

> 3 Yet, the evidence on the credit-growth relationship is varied. For instance, Binswanger and Khandkher (1995) and Subbarao (2012) estimate a positive effect, while Burgess and Pande (2005) and Narayanan (2016) find a negligible to weak impact.

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