NBER Working Papers by Alan Barreca
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| January 2013 | Adapting to Climate Change: The Remarkable Decline in the U.S. Temperature-Mortality Relationship over the 20th Century
with Karen Clay, Olivier Deschenes, Michael Greenstone, Joseph S. Shapiro: w18692
Adaptation is the only strategy that is guaranteed to be part of the world's climate strategy. Using the most comprehensive set of data files ever compiled on mortality and its determinants over the course of the 20th century, this paper makes two primary discoveries. First, we find that the mortality effect of an extremely hot day declined by about 80% between 1900-1959 and 1960-2004. As a consequence, days with temperatures exceeding 90°F were responsible for about 600 premature fatalities annually in the 1960-2004 period, compared to the approximately 3,600 premature fatalities that would have occurred if the temperature-mortality relationship from before 1960 still prevailed. Second, the adoption of residential air conditioning (AC) explains essentially the entire decline in the temper... |
| October 2011 | Agricultural Policy, Migration, and Malaria in the 1930s United States
with Price V. Fishback, Shawn Kantor: w17526
The Agricultural Adjustment Act (AAA) caused a population shift in the United States in the 1930s. Evaluating the effects of the AAA on the incidence of malaria can therefore offer important lessons regarding the broader consequences of demographic changes. Using a quasi-first difference model and a robust set of controls, we find a negative association between AAA expenditures and malaria death rates at the county level. Further, we find the AAA caused relatively low-income groups to migrate from counties with high-risk malaria ecologies. These results suggest that the AAA-induced migration played an important role in the reduction of malaria. |
| September 2011 | Heaping-Induced Bias in Regression-Discontinuity Designs
with Jason M. Lindo, Glen R. Waddell: w17408
This study uses Monte Carlo simulations to demonstrate that regression-discontinuity designs arrive at biased estimates when attributes related to outcomes predict heaping in the running variable. After showing that our usual diagnostics are poorly suited to identifying this type of problem, we provide alternatives. We also demonstrate how the magnitude and direction of the bias varies with bandwidth choice and the location of the data heaps relative to the treatment threshold. Finally, we discuss approaches to correcting for this type of problem before considering these issues in several non-simulated environments. |
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