The preceding sections have discussed the advantages and disadvantages of cross-sectional as well as panel models. While a cross-sectional analysis of farmland values seems appealing as it can capture crop switching, it might also suffer from omitted variable bias, which is less of a concern in a panel data set. While some argue strongly for cross-sectional analysis to measure adaptation (Mendelsohn et al. 1994), others will argue strongly for a panel model (Deschênes and Greenstone 2007) to avoid omitted variable bias. Since each model has its unique advantage, which model should be preferable?
The most fruitful exercise is to estimate various models and examine whether they agree or disagree. For example, in the case of US agriculture, the two models do agree if they are correctly specified and incorporate important agronomic principles like degree days. Schlenker et al. (2005) argue that highly subsidized irrigation water, which is correlated with climate and capitalizes into farmland values, is an important omitted variable in a cross-sectional analysis of farmland values in the entire United States and biases the climate coefficients. Schlenker et al. (2006) therefore estimate a model for the eastern United States only. While they include highly irrigated areas, e.g., 79% of the corn area in Arkansas was irrigated in 2007, they exclude farms with access to highly subsidized irrigation water in the Western United States. The highly subsidized public works programs in the West should not be counted as societal benefits but rather as a transfer from taxpayers to farmers. Moreover, the later analysis uses degree days instead of average temperatures. The resulting hedonic regression is highly stable between various Census years and gives robust estimates that very warm temperatures are the key drivers of farmland values.
Finally, a panel of crop yields reveals that the sensitivity of corn, soybeans, and cotton is comparable to the results of the hedonic regression in the sense that is predominantly extreme temperatures that determine yield outcomes (Schlenker and Roberts 2009). Moreover, the cross-sectional estimates for each crop are identical to panel and time series estimates, suggesting that there is limited potential for adaptation to extreme temperatures. In this sense, both cross-sectional analysis and panel models seem to give conforming answers.
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Renewable energy is energy that is generated from sunlight, rain, tides, geothermal heat and wind. These sources are naturally and constantly replenished, which is why they are deemed as renewable. The usage of renewable energy sources is very important when considering the sustainability of the existing energy usage of the world. While there is currently an abundance of non-renewable energy sources, such as nuclear fuels, these energy sources are depleting. In addition to being a non-renewable supply, the non-renewable energy sources release emissions into the air, which has an adverse effect on the environment.