We estimate net revenue by valuing all variable inputs (except land) and outputs at the market price<sup>4</sup> . Total annual farm income includes annual earnings from crop and livestock, and revenues from the sale of farm produce (milk, butter, etc). We exclude subsidies received from the government, and income received from hiring out farm equipment in the calculation of net revenues. However, we include controls for households that receive subsidies and for households that receive income from hiring out their own capital, to control for any selection bias associated with these indicators. Total costs include variable costs associated with hired labor, seeds, fertilizer and chemical sprays, tractor charges, irrigation costs, transport/packaging costs, other incidental farm expenses, rent paid to leased land, rent paid to sharecropped land, costs of maintaining livestock (including feed, veterinary fees, other costs). Net revenues per hectare are defined as the difference between revenues and costs divided by the number of hectares farmed.

We combine the household data with data on long-term climate normals. In particular, we rely on FAO data that provides weather station measurements of temperature and rainfall over the period 1960 to 1995. However, available climate data are limited in geographic coverage by the number of weather monitoring stations in Sri Lanka. As a result, we use estimates of temperature and precipitation for all the regions in which no weather station is present. Estimates are obtained with data from the eight weather stations by interpolating using an inverse-distance weighted quadratic regression approach, limiting each regression model to weather stations within a 250-kilometer radius from the centroid of each district <u>(Mendelsohn et al 1994; Dinar et al 1998).</u> Observed climate is regressed against latitude, longitude, altitude, and distance to sea, weighting each observation by the square of the distance from the district’s centroid. Interaction terms are also used. Nonlinearities are accommodated by utilizing a quadratic weighted regression for the interpolation. The estimated predicted value of temperature and precipitation at the centroid of each district is used in the Ricardian analysis.

The accuracy of the climate model we specify is tested by predicting the temperature and precipitation for a weather station using the model and then comparing the predicted climate values with the actual observation. Our tests for a sample of weather stations in different locations across the country indicate that the temperature and precipitation predictions were close to the actual measurements although the predicted precipitation estimates were less precise. In addition, the reliability of the climate estimates was also compared with separate estimates provided by the Department of Meteorology of Sri Lanka (MET). We find that both temperature and precipitation estimates were relatively close to the actual measurement, albeit precipitation estimates are less close (in absolute terms) than the temperature estimates (see Figure 1).

Because the model relies on annual weather in 2000 and not climate, we tested whether the climate in 2000 was close to the long-term average. The year 2000 is considered an average climatic year in terms of temperature and precipitation in Sri Lanka (IRI 2003). There is consequently empirical support for the assumption that the weather in 2000 reflects climate normals.

> 4  Ideally, we would have preferred to use land value or land rents but such data were not available.

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