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« Back to Results

Estimating Climate Change Damages

Paper Session

Sunday, Jan. 7, 2018 10:15 AM - 12:15 PM

Marriott Philadelphia Downtown, Independence Ballroom II
Hosted By: Association of Environmental and Resource Economists & American Economic Association
  • Chair: Laura Taylor, North Carolina State University

Expect Above Average Temperatures: Identifying the Economic Impacts of Climate Change

Derek Lemoine
,
University of Arizona

Abstract

One of the most glaring gaps in the economic understanding of climate change centers on the economic
costs of future climate change. A recent literature has attempted to empirically estimate these costs by
using within-unit variation in weather to identify the costs of being exposed to above-average
temperatures. These panel settings have made great progress towards identifying the implications of
weather shocks for outcomes such as GDP, farm values, crop yields, labor productivity, health, air
conditioning use, and crime. What can we learn from this literature?

Climate change is a permanent, anticipated change in the distribution of weather. I formally analyze the
effects of climate change on payoffs and control variables within a setting that captures the direct
effects of today’s altered weather, the effect of past expectations of today’s altered weather on past
long-term investments, and the effect of today’s expectations of future altered weather on today’s longterm
investments. I show that expectations matter for the costs of climate change, except in particular
special cases that may be unlikely to apply to contexts of interest. I show that identifying the costs of
climate change requires identifying the causal effect of weather shocks on dependent variables and also
the causal effect of weather forecasts on dependent variables. Further, I show that standard panel
regressions obtain systematically biased estimates of the causal effect of weather due to forecasts’
presence as unobserved covariates. I offer constructive suggestions for how to overcome this bias.
Finally, I relate my analysis to arguments from the literature. The standard appeal to the envelope
theorem to justify the informativeness of weather regressions ignores the presence of past controls in
today’s payoff functions and also ignores that many dependent variables of interest are themselves
controls, not objectives.

Same Storm, Different Disasters: Consumer Credit Access, Income Inequality, and Natural Disaster Recovery

Brigitte Roth Tran
,
Federal Reserve Board
Tamara L. Sheldon
,
University of South Carolina

Abstract

Natural disasters often produce large income shocks to households. We analyze the impact of natural disasters on household finances. Using a triple differences approach, we estimate the effect of natural disasters on credit card outcomes for individuals in varying financial positions receiving access to different types of FEMA aid. With the exception of those living in low income areas, we find few negative impacts on credit outcomes of most individuals living in areas hit by disasters that qualify for individual and household aid. Though all types of individuals affected by disasters show some signs of increasing credit utilization, the most vulnerable populations are also more likely to declare bankruptcy. While many are able to use credit cards to smooth through negative income shocks from natural disasters, current policy appears to leave the worst off even worse off.

Heterogeneous firms under regional temperature shocks: exit and reallocation, with evidence from Indonesia

Victoria Xie
,
University of California-San Diego

Abstract

Are less productive firms in developing countries disproportionately affected by climate
change both along the intensive and extensive margin? This paper provides an answer
in the context of Indonesia using gridded daily weather data and the Indonesian firm-level
survey, the Statistik Industri. In a heterogeneous firm model with capital-biased productivity,
I incorporate the thermal stress channel and illustrate how less productive firms decides
on production and re-optimize factor intensity as temperature increases. Empirically, I high-
light the presence of survival bias intrinsic to firm-level intensive margin analysis. I found
that: First, under heat shocks, the initially less productive firms are more likely to exit.
Second, on the aggregate, resources reallocate from less to more productive firms within
industries. Among surviving firms,, we observe factor substitution from unskilled to skilled
workers, and firms switching from domestic to foreign intermediate input when temperature
increases. The initially more productive firms that survived also incur output gain under
heat shocks possibly due to shifts in market structure and/or selection. These evidence high-
light the importance of incorporating the manufacturing sector in the damage functions of
traditional Integrated Assessment Models such as DICE/FUND. It also provides a potential
explanation as to why poor countries are more affected by temperature shocks from the
perspective of firm size distribution.

The Impact of Weather on Local Employment: Using Big Data on Small Places

Daniel Wilson
,
Federal Reserve Bank of San Francisco

Abstract

It is readily apparent that weather can have large short-run effects on economic activity,
and in some industries more than others. Indeed, unusual weather is routinely cited as a factor in
explaining unexpected fluctuations in macroeconomic data. Yet, the existing literature provides a
surprisingly sparse understanding of weather’s impact on overall economic activity. Prior research
has tended to focus on either the long-run economic effects of climate change or the short-run
effects of weather on the agriculture and energy sectors.

By contrast, this paper exploits the availability of vast granular geospatial data on
employment and weather to provide an in-depth understanding of weather’s local employment
effects. I combine BLS monthly administrative-record data from January 1980 to December 2015
on employment by county and industry with NOAA weather-station data. County weather
measures are constructed via spatial interpolation using data from nearby weather stations. The
resulting county-month-industry panel data set on employment and weather consists of over 10
million observations.

I use these data to estimate dynamic panel data (DPD) models of weather’s local
employment effects. I estimate the contemporaneous and cumulative effects of temperature,
precipitation, snowfall, the frequency of very hot days, the frequency of very cold days, and natural
disasters on private nonfarm employment growth. The results reveal a number of interesting
patterns. First, weather’s employment effects exhibit strong negative autocorrelation:
contemporaneous weather effects tend to be reversed over the subsequent one to three months.
Second, using spatial lag models, I find that employment growth in a given county is affected
positively by favorable weather in nearby counties but negatively by favorable weather in distant
counties, suggesting that local economies compete to some extent with distant economies.
Discussant(s)
Michael Anderson
,
University of California-Berkeley
Justin Gallagher
,
Case Western Reserve University
Kyle Meng
,
University of California-Santa Barbara
Jeffrey Shrader
,
New York University and Columbia University
JEL Classifications
  • Q5 - Environmental Economics