and inputs, the number of parameters to be estimated is large (Ray, 1982). This<sup>problem is</sup> greater when many soil and water quality parameters and their interaction with input price, output level, and technology variables are also included in the function. However,<sup>this is less a</sup> problem in our case because of the large number of data points available from combining<sup>time-</sup> series and cross-sectional data and by estimating the cost function, and factor- and<sup>revenue-share</sup> equations simultaneously. One output (Divisia Index of all 33 crop outputs)<sup>and six inputs</sup> (chemicals, water, labor, land, machinery, and animal/bullock cost) were included<sup>in the function</sup> for the crop sector. In the cost function, as in any other dual function, input prices<sup>rather than</sup> physical quantities were used.

Aggregation bias can occur with group (district-level) data as used in this<sup>study. However, use of</sup> district and regional trends, infrastructure, and resource quality variables can<sup>account for</sup> differences in the cost structure across districts. Tests for heteroskedasticity<sup>and first order</sup> autocorrelation were conducted using Langrange multiplier tests (Jarque and Bera, 1980),<sup>and</sup> the presence of these effects was rejected at the 10% significance level.

The definition of the variables in the cost function are given in box 1. The effect of technological change was imperfectly proxied by two variables (i) the proportion of area sown<sup>to modem</sup> wheat varieties, and (ii) cropping intensity. Cropping intensity is a proxy for adoption<sup>of MVs in</sup> summer crops (for which varietal adoption data were not available), since one<sup>of the major</sup> impacts of MVs of cotton and rice was to shorten the growing period, and thus to resolve<sup>the</sup> conflict between harvesting of the summer crop and planting of the following wheat<sup>crop</sup> (Byerlee et al., 1987.; Sharif et al., 1992; Ali et al., 1997). The effect of resource degradation<sup>was</sup> estimated through the soil and water quality variables described above. The adult<sup>literacy rate</sup> was used to capture the effect of changes in labor quality. The inverse of the distance of a village from the nearest metal road was used to quantify the effect of improvement in<sup>physical</sup> infrastructure. Region-specific time-trend dummies were included to capture the<sup>remaining</sup> unspecified effects of technological change, resource degradation, or change<sup>in resource</sup> productivity not included in the function.

The cost function in equation (2) along with the factor- and revenue-shares equations in<sup>(4) and</sup> _(5)_ and cross equation restrictions in (3) were simultaneously estimated using the seemingly unrelated regression (SUR) procedure. In view of the adding-up requirement<sup>of the input share,</sup> one equation (for draft animal) was deleted. The interactions of one input price<sup>with all other</sup> prices and resource stock variables, as a group, were tested. Similarly, interactions of output<sup>with</sup> all input prices and resource stock variables as a group were tested. The trend<sup>variables for input</sup> and output prices were also validated. All these interactions as a group, except<sup>those of water</sup> with resource quality variables which were subsequently excluded, were significant at least<sup>at the</sup> 10% level. The results of the cost function estimates are reported in the Appendix.

The production elasticities, estimated at the mean level of prices and output and resource<sup>stock in</sup> the middle of the study period, are positive for all inputs, except for machinery.<sup>The respective</sup> elasticities for fertilizer, water, labor, land, machinery, and draft power are 0.03,<sup>0.17, 0.61, 0.58,</sup> -0.15, and 0.07. The negative elasticity for machinery can be explained by the lumpy<sup>nature of</sup> 17