productivity deadlock in the state, including its colonial history (Banerjee, 2004; Banerjee and Iyer, 2005) and the incumbent land tenure system (Bhaduri, 1973; Prasad, 1974).

While yield levels remain low in Bihar, there has been an uptick in productivity growth over the last 15 years or so. The yields of all three major crops – rice, wheat, and maize, have doubled during the 2005-2017 period. The main objective of the paper is to examine whether these impressive gains observed at the state level have been widespread and have helped in reducing disparities in land productivities across the districts. Analyzing regional inequality in yields is important for several reasons. First, the crossdistrict differences are significant. For rice and wheat, the yield in most productive district was 7–8 times that of the least productive district in 2005 (Government of Bihar, 2010). Hence, the burden of low yields is felt differently, with the situation being more concerning in some districts than others. Second, the districts lie in different agro-ecological zones with variations in agronomic endowments. As such, the principal drivers of low productivity might vary across districts, and policies that contribute to productivity growth in some may fail to address the challenges faced by others. Thus, a convergence result will support continuing the ongoing efforts as far as regional aspects of agricultural policy are concerned. On the other hand, the absence of convergence will call for fine-tuning the policy to correct the situation in laggard districts. With this motivation, I test whether a convergence in district yields accompanies the aggregate yield improvement. To test the convergence hypothesis, I collate a district-level database of both annual production and area under cultivation for the main crops using information from multiple annual surveys. A valuable feature of this study is that I examine convergence in yields for the three main crops, which usually account for more than 90 percent of the gross own area at the district level. This crop-specific approach provides further richness to policy guidance. If inequality persists for a particular crop only, policy makers can focus narrowly on the crop-specific factors which are likely to be the cause relative to the more generic ones.

The econometric test of convergence developed in Phillips and Sul (2007) admits three outcomes – convergence occurs at the yield levels (absolute), convergence occurs at the yield growth rates (conditional), or a convergence does not occur. Absence of absolute convergence indicates a rise in the cross-district disparity in yields. As mentioned previously, the convergence test relaxes the assumption of time-invariant idiosyncratic effect. This opens the possibility of transitional cross-sectional divergence. I find evidence of conditional convergence in rice yields during 2005–2017.<sup>2</sup> In contrast, the convergence hypothesis is rejected with statistical certainty for wheat and maize.

The period under analysis is long enough to make a further inquiry which helps discern whether the divergence results are transitory or not. I divide the entire sample into two periods – 2005–2011 and 2012–2017, to see whether the patterns of convergence differ across the two sub-periods. The exercise presents interesting insights when considered with the results for the entire period. The overall convergence result for rice depends crucially on the developments in the 2005–11 period. There is no evidence of convergence happening in the second sub-period. Wheat yields were also converging during 2005–11 but started diverging in the later years, which was strong enough to reject divergence for the entire period. Inequalities in maize productivity across districts grew in both sub-periods. Yet, the pace of divergence has increased over the years. Thus, the district-level behavior for the entire period masks

> 2 The choice of 2005 as the initial year of analysis is incumbent on data availability as it is the earliest year for which such information is obtainable.

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