present discounted cash flow from its optimal use. Since land values are not influenced by realized weather or prices at the time of the survey, they should more accurately reflect long-term climate and other agronomic characteristics than measures of current land productivity such as yields or net revenues. While plot values are self-assessed rather than based on records of actual market transactions, farmers are generally knowledgeable about the value of this their primary asset. Indeed, evidence from elsewhere in south Asia (Jacoby, 2000) indicates that self-assessed plot values accurately capture rental income from land. Idiosyncratic biases, moreover, would tend to wash out when averaging over a vast number of observations on individual plot values collected from tens of thousands of farmers.

Data on the value of more than a hundred thousand individual parcels of cropland nationwide are available in the NSS 59th round, along with area and irrigation status (see Table 1 for descriptive statistics at the district level).<sup>16</sup> Figure 2 shows how median land values per hectare (adjusted for regional cost-of-living differences) vary over the Indian subcontinent. The highest valued cropland lies across the Indo-Gangetic plain, the most intensively farmed zone in the country, whereas land values are lowest in the far-western desert region.<sup>17</sup> Comparison of the two maps in Figure 2 shows a distinct positive relationship between land values and irrigation, one strongly confirmed in the micro-data to which we now turn.

In Table 2, we report alternative specifications of equation (6) using different sets of explanatory variables. Since our data do not identify the main source of irrigation for a plot (i.e., canal or groundwater), we can only estimate an average return to irrigation. Model I, at any rate, assumes that this return is constant across districts. The resulting estimates (first column) indicates that, on average, an irrigated plot is 37% more productive than a rainfed plot within the same district. For the second stage, we regress the 542 district fixed effects on district-specific determinants of land productivity, weighting by the number of plot observations per district. Standard errors are calculated using Conley’s (1999) spatial GMM estimator with bandwidth set at 8 degrees lat./long. ( _≈_ 800 km).

The second stage of Model I does not control for any geographic variables apart from climate, specifically average annual temperature and precipitation (see Table 1 for descriptive statistics). In addition, we allow weather variability, in the form of the coefficient of vari-

> 16Aside from irrigated and unirrigated land devoted to seasonal crops, the only other category of agricultural land specified in the survey is ”orchards and plantations (including forests),” which constitutes 4 percent of agricultural land area. Due to its heterogeneous nature, we exclude this category from our analysis. Privately held grassland dedicated exclusively to pasture also appears to be rare in India, as livestock are typically fed fodder or grazed in fallow fields or village common areas.

> 17Districts with high land values correspond fairly closely to those with high median net revenues per hectare in the 2002-03 crop year based on farm-level data from the 59th round of the NSS (The district-level correlation coefficient is 0.48). As noted, however, net revenues are affected by weather realizations and in 2002-03 India experienced an extremely poor monsoon, though rainfall in the northeast was close to normal.

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