In "The Economic Impacts of Climate Change: Evidence from Agricultural Output and Random Fluctuations in Weather" which appeared in 2007 (Volume 97, Issue 1) of the
American Economic Review at
http://www.aeaweb.org/articles.php?doi=10.1257/aer.97.1.354 Olivier Deschênes and Michael Greenstone measured the economic impact of climate change on US agricultural land by estimating the effect of random year-to-year variation in temperature and precipitation on agricultural profits.
Their preferred estimates indicate that climate change would increase annual profits by $1.3 billion in 2002 dollars (2002$) or 4 percent. They claimed that this estimate was robust to numerous specification checks and relatively precise, so large negative or positive effects are unlikely. They also found the hedonic approach—which was the standard in the previous literature—to be unreliable because it produces estimates that are extremely sensitive to seemingly minor choices about control variables, sample, and weighting.
In a 2012 comment on the paper Anthony C. Fisher, W. Michael Hanemann, Michael J. Roberts, and Wolfram Schlenker
American Economic Review, Volume
102, Issue 7 pages 3749-60.
http://www.aeaweb.org/articles.php?doi=10.1257/aer.102.7.3749 utilizing a series of studies employing a variety of approaches
found that the potential impact of climate change on US agriculture is likely negative. They note that Deschanes and Greenstone (2007) report dramatically different results based on regressions of agricultural profits and yields on weather variables. The divergence is explained by (1) missing and incorrect weather and climate data in their study; (2) their use of older climate change projections rather than the more recent and less optimistic projections from the Fourth Assessment Report; and (3) difficulties in their profit measure due to the confounding effects of storage.
Deschenes and Greenstone summarize FHRS’ main critiques of the 2007 study (DG) as follows:
(i) there are errors in the weather data and climate change projections used by DG;
(ii) the climate change projections are based on the Hadley 2 model and scenarios, rather than the more recent Hadley 3 model and scenarios;
(iii) standard errors are biased due to spatial correlation;
(iv) the inclusion of state by year fixed effects does not leave enough weather variation to obtain meaningful estimates of the relationship between agriculture profits and weather;
(v) storage and inventory adjustment in response to yield shocks invalidate the use of annual profit data; and
(vi) FHRS argue that a better-specified hedonic model produces robust estimates, unlike the results reported in DG.
DG claims that four of these critiques have little basis. Nevertheless, in their reply they report estimates based on corrections and the climate model used in 2007 and a more recent one.
...
Conclusions
The New DG reanalysis of agricultural profits
with corrected data leads to three primary findings.
First, contrary to the results in DG (2007), the corrected data suggest
that an immediate shift to the projected end-of-the-century climate
would reduce agricultural profits. This impact is larger when
projections from more recent climate models are used and smaller in
econometric models that allow for local shocks to input and output
prices and productivity.
Second, the PDV over the
remainder of the century of the projected impacts from a recent climate
model is roughly $164 billion, or about 5 years of current annual
profits. This estimate is likely to overestimate the loss, because it
fails to allow for any technological advances or adaptation in response
to higher temperatures. Third, the estimated losses are more than 50
percent smaller than those from the standard approach and generally
statistically insignificant when one uses a textbook distributed lag
model and calculates the dynamic cumulative effects that account for
farmers’ dynamic inventory adjustments in response to temperature
realizations.
The resulting change in per-acre
profits is multiplied by the number of acres of farmland in the county
and then the national effect is obtained by summing across all 2,342
counties in the “REPLY” sample. The same calculation is applied to
contemporaneous and lagged weather variables. Average annual aggregate
profits in the 2,342 counties in the sample are US$(2002) 32.8 billion.
Standard errors are clustered at the county level.
