clubs. Though the evidence is weaker for the latter two given the negative estimate of , the null of convergence is not rejected at 10 percent level of significance. γγ Figure 4(c) tracks the mean yields of the clubs over the years. The yield growth rate varies over a tight range initially but diverges in the later period. The growth in the leading club outpaces growth elsewhere, effectively creating a massive yield gap. The next two clubs do not see any marked difference in growth rates over the two periods. However, a higher growth rate for club 2 leads to a rise in the gap between the two clubs. The lagging club 4 fails to maintain even the modest growth of the first period and records a falling yield. Looking at the ACZs, it is no surprise that the northeastern zone has performed better given the evidence in Figure 2(c). The leading club exclusively consists of districts from the region with the remaining regional districts falling in club 2. However, club 2 does not entirely consist of northeastern districts. Of the 13 districts in club 2, seven and four districts lie in the southern and northwestern zones respectively. Like in the case of rice yields, the formation of non-leading clubs does not follow ACZ classification more generally. The other two regions have a balanced representation in clubs 3 and 4. The figure also shows the vast difference in the fortunes of the clubs. The mean log yield in the leading club increased by more than 75 percent during 2005–17. Yet, the mean log yields in clubs 3 and 4 in the most recent year are lower than that of the leading club in 2005.

The key takeaway is that Sub-period II is characterized by increased divergence in maize yields. Still, club convergence materialized at significantly difference levels. While the northeastern region pulled ahead, districts in other regions formed clusters. The worst performing cluster consisting of five districts lying in both the northwestern as well as the southern region experienced not much expansion during 2012-17.

# **4.3   Agricultural credit over the two sub-periods**

The previous two sub-sections show that there is vast heterogeneity in yield progression across districts. Some have seen significantly higher growth while some have also witnessed contraction of late. There exists evidence of club convergence in which districts are converging to yield levels with considerable variation. Both relative and absolute gaps in yields between the leading and the lagging clubs appear to be increasing. More importantly, the results show that the club formation does not align well with the ACZ classification, especially so in the non-leading clubs. Therefore, it is likely that the divergence in yields indicates inefficiency in which districts in the lagging clubs are not able to reap productivity gains.

To address the concern of low gains in lagging districts, it is essential to identify areas that policy could target or where the current policy is lacking. While I am not attempting to address this issue in detail, I check whether the differences in the supply of agricultural credit can explain the divergence patterns observed more recently. Though not a control for the universe of agricultural inputs, credit holds a special status as it is closely tied to the usage of more direct factors such as seeds, fertilizers, machinery, etc.

The government also believes the supply of credit to be a critical policy instrument when it comes to boosting agricultural productivity. Credit allows farmers to make many decisions that are directly related to productivity – adoption of capital-intensive technology, managing risks associated with investments, using yield-enhancing inputs like better seeds and fertilizers etc. Moreover, many observers argue that cooperative credit crowds out borrowing from local moneylenders as they carry a much lower rate of interest. The state economic surveys contain consistent long time-series data on the supply of 12