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Empirical Climate Change Economics

Paper Session

Friday, Jan. 3, 2020 8:00 AM - 10:00 AM (PDT)

Manchester Grand Hyatt, Gaslamp D
Hosted By: Association of Environmental and Resource Economists
  • Chair: Stefano Carattini, Georgia State University

Estimate the Impact of Climate Change: An Exploration of the Bin Regression Model

Richard Carson
,
University of California-San Diego
Dalia Ghanem
,
University of California-Davis
Chu (Alex) Yu
,
University of California-San Diego

Abstract

In recent years, a large number amount of empirical work has been undertaken to study the
influence of climate variables on a wide range of different social and economic outcomes. As noted
by Hsiang (2016), the measurement and representation of these climate variables is a critical step
in identifying the impacts of climate change. The “bin” regression model, which is a flexible semiparametric
method for representing one or more of these climate variables, has emerged as the
workhorse approach for empirical work (e.g., Deschênes and Greenstone, 2011).
This paper is the first to formally explore the econometric properties of the bin regression approach
in climate economics literature. The bin regression approach takes the desired explanatory variable
and discretizes in a manner like a histogram with each bin now being represented by a count (e.g.,
number of days during the growing seasons where mean temperature falls in a specified interval).
Formally, the bin approach is a M-piecewise constant function, where the intervals defining the M
bins are chosen by the researcher.
We show that, although the bin regression approach often produces reasonable results, it produces
consistent parameter estimates only under very stringent and unlikely assumptions on true data
generating procedure, which we characterize in detail. Furthermore, because the researcher choses
the bin definitions, the approach is not truly semiparametric.
The bin approach has two other major problems. First, because information within a bin is lost and
the bins themselves represent points of discontinuity, it is possible for an overall shift in the
distribution of the variable of interest such as an average increase in temperature either have little
or an extreme effect.
We propose alternatives to bin model for estimating the impact of climate change that assume the
same underlying DGP. The main line of attack we take comes from recognizing that the bin
regression approach conflates two problems: (a) how to summarize the distribution of an
exogenous stimulus variable and (b) how determine

Be Caution with the Precautionary Principle: Evidence from Fukushima Daiichi Nuclear Accident

Matthew Neidell
,
Columbia University
Shinsuke Uchida
,
Nagoya City University
Marcella Veronesi
,
University of Verona and ETH Zurich

Abstract

This paper provides a large scale, empirical evaluation of unintended effects from invoking the precautionary principle after the Fukushima Daiichi nuclear accident. After the accident, all nuclear power stations ceased operation and nuclear power was replaced by fossil fuels, causing an exogenous increase in electricity prices. This led to a reduction in energy consumption during very cold temperatures which caused an increase in mortality. We estimate that the increase in mortality from higher electricity prices outnumbers the mortality from the accident itself, suggesting the decision to cease nuclear production has contributed to more deaths than the accident itself.

Aggregate effects of temperature in dynamic spatial general equlibrium

Ivan Rudik
,
Cornell University
Ariel Ortiz-Bobea
,
Cornell University
Gary Lyn
,
Iowa State University

Abstract

We here demonstrate a new structural approach for estimating the aggregate
effects of climate change. We do so by constructing a dynamic spatial general equilibrium
trade model with forward-looking households, and an arbitrary number of
industries and regions. In the model, weather shocks arrive exogenously as a function
of the current climate and directly affect factor productivity. We then compute
the dynamic equilibrium conditions of the model which guide us in how to estimate
aggregate damage functions with simple fixed effects regressions.
The model allows us to produce several novel results for estimating damages.
First, we use one dynamic equilibrium condition to show that well-identified estimates
of aggregate damage functions in the presence of spatial general equilibrium
effects can be recovered with a fixed effects regression. The estimating equation
relates normalized trade expenditures between two countries to their differences
in damages and differences in effective productivity. The only additional data requirement
over common approaches that relate GDP or income to damages is information
on wages at the country-industry-year level to control for how weather
shocks affect how the spatially linked labor markets clear.
Second, we use another dynamic equilibrium condition to recover an estimating
equation nearly identical to ones commonly used in the literature that relate
GDP or income to weather shocks. Unlike these approaches which typically only
include weather and a set of fixed effects as explanatory variables, our equation also
includes an extra term. This term captures general equilibrium trade effects and is
non-linear in both the data and the damage parameters we wish to estimate, and
thus does not facilitate a simple fixed effect regression. Despite this limitation, this
equation is very useful for showing why spatial general equilibrium effects matter
for damage estimation. We show that the effect of weather shocks on a region’s GDP
works through three channels: (1) the direct reduction in productivity, (2) the associated
within-region price response which dampens the effect of the first channel,
and (3) positively correlated shocks in other

Extreme Weather and the Politics of Climate Change: a Study of Campaign Finance and Elections

Yanjun Liao
,
University of Pennsylvania
Pablo Ruiz Junco
,
University of California-San Diego

Abstract

In this paper, we study how extreme weather and natural disasters affect political outcomes such as campaign contributions and elections. Weather events associated with climate change may influence these outcomes by leading voters to re-evaluate the incumbent politician's environmental position. In a short-run analysis, we find that the number of online contributions to the Democratic Party increases in response to higher weekly temperature and that the effect is stronger in counties with more anti-environment incumbent politicians. In a medium-run analysis, we find evidence that when a natural disaster strikes, the election becomes more competitive if the incumbent has a more anti-environment stance: total campaign contributions increase for both candidates, though the increase is skewed towards the challenger; the race is more likely to be contested, and; the incumbent is less likely to be re-elected. Finally, we address alternative mechanisms and explanations for our results.
Discussant(s)
Jeffrey Wooldridge
,
Michigan State University
Katrina Jessoe
,
University of California-Davis
Edward Balistreri
,
Iowa State University
Stefano Carattini
,
Georgia State University
JEL Classifications
  • Q5 - Environmental Economics