ation of annual rainfall, to influence land valuations.<sup>18</sup> Including a more detailed array of temperature and precipitation variables (e.g., monthly averages along with quadratic terms in each), as is common practice in the Ricardian literature, has the potential disadvantage of multicollinearity, leading to lack of interpretability and robustness (see Kaufman, 1998, for a critique along these lines). The results in the second column imply that a 1<sup>_◦_</sup> C increase in average annual temperature would lead to nearly a 10 percent reduction in land values in India. Neither precipitation levels nor volatility thereof appear to be strongly associated with land productivity.

Model II allows climate to influence the return to irrigation and includes other geographic controls in the second stage. Evidence reported in column three of Table 2 suggests that the return to irrigation is lower in wetter districts, which seems plausible. Temperature, however, does not interact to a significant extent with irrigation according to our results. The inclusion of controls for topography, elevation, soil texture and type,<sup>19</sup> as well as distance to nearest large city, a proxy for transport cost to markets, boosts the adjusted R<sup>2</sup> in the second stage from 0.124 to 0.508. Most of the associated coefficient estimates also make economic sense, with flatter terrain, medium soils, and closer proximity to markets all raising land values (Jacoby, 2000, reports the latter finding as well). Although a far richer model, the resulting point estimate of the temperature effect changes surprisingly little, rising in magnitude by only about a third to around _−_ 13 percent. This pattern suggests, albeit informally, that any further omitted variable bias is not likely to be too serious (see Altonji, Edler, and Taber, 2005).<sup>20</sup> In other (unreported) specifications, we include quadratic terms in temperature and rainfall and we also separate climate variables into aggregates over the two main growing seasons, kharif and rabi, none of which leads to appreciable improvements in fit. What is found, though, to be statistically significant and positive in the second stage is the interaction be-

> 18Variability could influence land values through at least three mechanisms: (1) rainfall affects contemporaneous cash flow from land in a nonlinear fashion so that expected cash flow is a function of the rainfall variance as well as the mean; (2) in making investments in agricultural assets, risk-averse farmers trade off high expected returns for safety so that productivity per acre is lower where weather variability is greater (see Rosenzweig and Binswanger, 1993, for some evidence from India); and, (3) under risk-aversion, land prices exceed the expected present discounted cash flow from land by a risk premium that is increasing in weather variability.

> 19See Table 1. Data on soils come from the Food and Agriculture Organization’s Soil Map of the World (SMW), which provides detailed data for each soil mapping unit, defined as a geographical area with similar soil classes. Dummies are created for the five dominant soil types in India (acrisols, cambisols, luvisols, nitosols, and vertisols). In addition, there are three textural classes (coarse, medium, and fine), which reflect the relative proportions of clay, silt, and sand in the soil, and three slopes classes (level to gently undulating, rolling to hilly, and steeply dissected to mountainous). 20Sanghi, Mendelsohn, and Dinar (1998), applying a cross-sectional Ricardian approach to pooled districtlevel panel data from India for the period 1966-86, find a comparable temperature effect on net farm revenues of _−_ 9 percent.

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