precipitation estimated from the FAO data against data from the meteorological department of Sri Lanka. We find that there is no significant difference between the estimated climate coefficients from the two different sources.

Our second test is a range of specifications using climate data from different months. We select the months that minimize the residual sum of squares. We use the natural logarithm of temperature and precipitation rather than the linear and quadratic terms. We try months that we believe to be important to each growing season. However, none of the results leads to an improved model. All the other specifications suffer from strong collinearity across the climate variables. In some instances the collinearity is between linear terms of different months, but more often it is the collinearity between the linear and the quadratic terms that makes the estimated coefficients unreliable. In the case when the climate variables are transformed using the natural logarithm, the collinearity between months was too strong to obtain reliable results.

In addition to the climate variables, we also got interesting results about farmer characteristics. We include the household head’s education level to capture effects such as ability of households to adopt new technologies, ability to better optimize on farming and marketing practices, etc. Farmer education has a concave effect on net revenues per hectare. In other words, higher levels of education generally imply higher net revenues per hectare but the rate at which the increase occur decreases with each incremental year of education. The survey does not contain information about the farmer’s experience, which is expected to have a positive impact on farm profitability. As such we use farmer’s age to proxy for experience. The age of the farmer does not have a significant impact on net revenues per hectare. This may be explained by the fact that in our sample, where the average farmer is 50 years old, more experience is countered by the hardships of manual work experienced by older workers. We also include the gender of the household head in the model. Male heads of households appear to be more profitable than equivalent households with a female head. This may be because considerable manual labor is required in farming in developing countries, or because of possible discrimination faced by women when selling the produce. Another explanation is that female-headed households generally have only one adult while a majority of male-headed households have two adults, which improves the potential labor position of the farm. Interestingly, however, the size of the household does not have a significant impact on farm profitability. In Sri Lanka, as in many other developing countries, household members substitute for paid labor on the farm, but the available data does not show this to lead to higher net revenues.

Model 2 also includes a variable to capture the household’s access to irrigation and whether or not the farmer has a legal claim to the land being cultivated. The results surprisingly indicate that rain-fed farms are more profitable than irrigated farms and that farms that relied on both rain fed and surface and ground water irrigation are more profitable than households that used one or the other source of water. This finding has to be interpreted with caution. On one hand, rain-fed agriculture is good for normal years in which an appropriate amount of rain falls at the appropriate time. In such a year, rain-fed agriculture would be superior to irrigated agriculture because there is a cost associated with the latter and not with the former. On the other hand, households with no access to irrigation in a drought year will stand to lose. At the time of the survey (1999-2000) the 18