Estimated Ricardian models have been criticized because they rely on mean temperatures and do not explicitly include extreme temperatures. This paper uses a cross sectional approach to compare a standard quadratic Ricardian model of mean temperature with a fully flexible daily temperature bin...

Farmland values have traditionally been valued using seasonal temperature and precipitation. A new strand of the literature uses degree days over the growing season to predict farmland value. We find that degree days and daily temperature are interchangeable over the growing season. However, the...

Farmland values have traditionally been valued using seasonal temperature and precipitation. A new strand of the literature uses degree days over the growing season to predict farmland value. We find that degree days and daily temperature are interchangeable over the growing season. However, the...

This study relies on the Ricardian method to estimate the damages of climate change to US agriculture. The study uses repeated cross sectional analyses of US Census data collected at the county level from 1978-2002. Regressions of farmland value on climate and other control variables reveal that...

Many nonmarket valuation models, such as the Ricardian model, have been estimated using cross sectional methods with a single year of data. Although multiple years of data should increase the robustness of such methods, repeated cross sections suggest the results are not stable. We argue that...

Although the Ricardian model is a cross sectional method, there are advantages to estimating the model with additional years of data. For instance, with a panel, one can more easily separate events in a single year (e.g. weather and price shocks) from longer term phenomenon such as climate. Many...

Many nonmarket valuation models, such as the Ricardian model, have been estimated using cross sectional methods with a single year of data. Although multiple years of data should increase the robustness of such methods, repeated cross sections suggest the results are not stable. We argue that...