The NSS is a weighted survey but the weights do not affect the estimated downward trends in poverty rates in Table 1. This is reassuring because the spatial panel estimators that we use in the econometric analyses below do not handle variable sampling weights. We therefore rely on the unweighted data; based on Table 1 this should provide similar patterns to the weighted data. The other pattern in Table 1 is the regional divergence in poverty rates; the coefficient of variation of rural poverty rates based on the NSS regions rises monotonically over time, for both poverty measures and whether using sampling weights or not. In other words, even though India has been escaping from mass absolute poverty it is doing so at an uneven rate over space.

The spatial pattern of rural poverty at baseline is shown in Figure 1, for the headcount index (left panel) and poverty gap index (right panel). The spatial autocorrelation in the data is apparent, with regions having high poverty likely to be neighbors with other high poverty regions and _vice versa_ for regions with low poverty rates. Roughly speaking, if one drew a straight line from Nashik in Maharashtra (which is about 100 miles northwest of Mumbai) that went at a 50 degree angle to pass west of Kanpur in Uttar Pradesh (UP), all but three regions to the southeast of that line would be in the highest three poverty classes, which had headcount poverty rates of 42 percent or more in 1993/94.<sup>9</sup> Conversely, almost all regions along the Arabian Sea coast, and in northwest India were in the lowest classes for poverty. A common test for spatial autocorrelation is Moran’s _I_ test, which has some parallels with the Durbin-Watson statistic.<sup>10</sup> This test reveals significant positive spatial autocorrelation with _I_ =0.30 for the headcount index in 1993/94 and _I_ =0.26 for the poverty gap index.

The spatial pattern of rural poverty at the endpoint of our panel, in 2011/12, is even more apparent than it was a baseline. In the left panel of Figure 2 a belt of high headcount poverty is apparent, which extends from the northeast in Assam and comes west across India at a slight downward angle to reach the Eastern Gujarat region, located on the Arabian Sea coast about midway between Mumbai and Ahmedabad. The Moran statistic for the headcount index in 2011/12 is 0.43 while for the poverty gap index it is 0.38. These increases in the Moran statistic from the baseline show that rural poverty has become increasingly spatially

> 9 The three exceptions were the Central Plains of West Bengal (WB) which surround Kolkata, Coastal Tamil Nadu (TN) and Inland Southern Andhra Pradesh (AP).

> 10 For any variable _z_ in deviation from mean form and spatial weights matrix _W_ , Moran’s _I_ is equivalent to the slope coefficient in a linear regression of _Wz_ on _z_ (Anselin, 1988). In other words, it examines the strength of the relationship between one observation and the spatially weighted average of its neighboring observations. The weights matrix used here is based on contiguity; any region sharing any boundary point with another region is considered its neighbor. These are known as Queen weights, based on where pieces move on a chessboard.

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