« Back to Results

Climate Change: Connecting Theory with Empirics

Paper Session

Sunday, Jan. 7, 2018 8:00 AM - 10:00 AM

Marriott Philadelphia Downtown, Independence Ballroom II
Hosted By: Association of Environmental and Resource Economists
  • Chair: Stephie Fried, Arizona State University

Firm and Household Responses to Climate Change Risks

Marc Conte
Fordham University
David Kelly
University of Miami


Using county-level data on insurance claims and premiums in Florida, we explore the extent to which insurance firms and households are able to successfully update their beliefs about the potential impacts of future catastrophic tropical cyclones. Previous work on policies through the National Flood Insurance Program suggests that homeowners increase insurance purchase after major floods, with coverage decreasing in the time since the last event, described as evidence of a failure to appropriately update beliefs of flood impacts (Gallagher 2014). Kousky and Cooke (2012) use modeling and simulation results to suggest that fat-tailed disaster damages lead insurers to pass on the cost of reserve requirements to consumers through increased premiums. Because the cost of reserves increases premiums above actuarially fair levels, foregoing coverage may be optimal behavior. We propose and test a third hypothesis: households and firms may be optimally updating beliefs using a fat-tailed distribution. With a fat tailed cyclone damage distribution, damages from previous catastrophic cyclones tend to poorly predict future catastrophic damages. Hence, rational (Bayesian) learning tends to produce large changes in beliefs following a catastrophic cyclone. Therefore, rapidly changing beliefs following increases in storm intensity associated with climate change cause substantial adjustment costs. The difficulty of learning parameters of fat tailed distributions from catastrophic cyclones implies households and firms make errant insurance and adaptation decisions for a substantial period of time. We turn to the data from Florida for evidence of optimal updating on the part of both insurers and households in their responses to the fat-tailed damages from tropical cyclones.

Calibrating Informational Dynamics: Learning About the Sensitivity of the Climate to Emissions

Ivan Rudik
Iowa State University
Derek Lemoine
University of Arizona
Maxwell Rosenthal
University of Arizona


Carbon dioxide (CO2) concentrations are projected to increase for the foreseeable future. We know
emitting more CO2 will warm the earth, but by how much is uncertain despite large amounts of research
devoted to this area. How sensitive the climate is to CO2, the climate sensitivity, is a major determinant
of optimal policy stringency. Thus, uncertainty about the climate sensitivity and anticipated refinements
of our climate sensitivity beliefs will also be critical for contemporaneous climate policy.

Uncertainty and learning about the climate sensitivity have been studied by economists through
dynamic stochastic climate-economy models. Although the dynamics of our beliefs about the climate
sensitivity are surely important, economists have largely abstained from trying to accurately calibrate
this component of the climate-economy system due to computational burdens. This has resulted in stark
inconsistencies between climate-economy models suggesting relatively fast rates of learning, and the
reality that we have not honed down our beliefs for decades.

Here we calibrate the informational dynamics of a Bayesian climate policymaker. Using frontier
computational methods, we nest a general Bayesian learning framework into a dynamic stochastic
climate-economy model in a way that closely matches how climate scientists actually estimate the
climate sensitivity distribution using historical data. We generalize previous approaches by not making
distributional assumptions about climate sensitivity beliefs and the climate data generating process that
have resulted in artificially fast learning in climate-economy models. We show that our approach can
correctly recover a climate sensitivity distribution consistent with climate scientists’ current best
estimates, and it can accurately recover the dynamics of real world climate sensitivity beliefs.
Simulations of optimal policy and beliefs suggest that future learning will be several times slower than
suggested by previous work, and that future climate policy may not be as flexible to new observations of
CO2 and temperature as once believed.

ACE - Analytic Climate Economy (with Temperature and Uncertainty)

Christian Traeger
University of Oslo


Integrated assessment of climate change analyzes the interactions of long-term economic
growth, greenhouse gas emissions, and global warming. The present analytic climate economy
(ACE) competes quantitatively with numeric models used to derive the US federal social cost of
carbon. The analytic solution permits new insights into the evaluation of climate change, and it
overcomes numeric obstacles in incorporating uncertainties (Bellman’s curse of dimensionality).
The paper relates the optimal carbon tax directly to the characteristics of the carbon cycle and
the climate system.

Today’s policy advising remains in the hands of essentially deterministic models that explore
and average large samples of deterministic worlds. Yet, recent findings suggest that uncertainty
surrounding climate change could be the major driver of mitigation policy and welfare loss. In
highly stylized models, Pindyck (2013) and Weitzman (2009) argue that uncertainty outweighs
all other evaluation components and even makes the discount rate irrelevant. In contrast, ACE
shows that the integrated assessment’s sensitivity to discounting is even higher under
uncertainty than under certainty. ACE also show that nature’s stochasticity and epistemological
uncertainty imply opposing sensitivies, and that the Bayesian learning framework is the most
sensitive because updates change the long-run picture of the future.

Guided by the long run risk literature in asset pricing, ACE disentangles risk aversion from
consumption smoothing to calibrate the risk-free discount rate and risk premia separately.
Models lacking this feature are forced to either discount the future too highly, or to disrespect
the risk premia. I show that the relevant risk aversion for climate change evaluation is not Arrow
Pratt’s measure of risk aversion, but by how much Arrow Pratt risk aversion exceeds the desire
to smooth consumption over time (intrinsic aversion to risk). Higher moments of the uncertainty
distribution are evaluated with higher powers of such risk aversion.

How Do Households Discount Over Centuries? Evidence From Singapore’s Private Housing Market

Eric Fesselmeyer
National University of Singapore
Haoming Liu
National University of Singapore
Alberto Salvo
National University of Singapore


We examine Singapore's fairly homogeneous private-housing market and
show that new apartments on historical multi-century leases trade at a non-zero
discount relative to property owned in perpetuity. Descriptive regressions indicate that
new apartments with 825 to 986 years of tenure remaining are priced 4 to 6% below
new apartments under perpetual ownership contracts that are otherwise comparable.
We consider an empirical model in which asset value is decomposed into the utility of
housing services and a second factor that shifts with asset tenure and the discount rate
schedule. Exploiting the supply of new property with tenure ranging from multiple
decades to multiple centuries, we estimate the discount rate schedule, restricting it to
vary smoothly over time through alternative parametric forms. Across different
specifications and subsamples, we estimate discount rates that decline over time and,
to accommodate the observed price differences, fall to 0.5% p.a. by year 400-500. The
finding that households making sizable transactions do not entirely discount benefits
accruing many centuries from today is new to the empirical literature on discounting
and, with the appropriate risk adjustment, of relevance to evaluating climate-change
Laura Bakkensen
University of Arizona
Lint Barrage
Brown University
Stephie Fried
Arizona State University
Christian Gollier
Toulouse School of Economics
JEL Classifications
  • Q5 - Environmental Economics