Empirical studies of vulnerability to
weather catastrophes
How vulnerable are poor households to weather catastrophes? Why are
some households and communities more vulnerable than others? How can
government policies help poor households prepare for and cope with
weather shocks? While much has been written about vulnerability, few
empirical studies show how households, particularly poor households in
developing societies, respond to and recover from external stresses. I
will use longitudinal household surveys to study household responses to
the 1997 El Niño event in Peru and Hurricane Mitch in Nicaragua.
In both cases household surveys were conducted before and after the
weather shock. Preliminary analysis of both datasets shows that they
contain information relevant to the research questions posed below. In
the case of Peru, I have already augmented the household survey data
with remote sensing and agricultural census data. The Nicaragua data
are also geographically explicit, and I am currently working to obtain
additional variables.
In first phase of this work I will formulate a conceptually valid
empirical measure of household vulnerability that can be estimated from
household survey data. I anticipate that this measure will draw on
recent work by Pamela Matson’s research group at Stanford, which has
been developing a quantitative measure of ecosystem vulnerability using
remotely sensed data from Mexico. The essence of their approach is to
model vulnerability as a function of sensitivity to perturbations and
proximity to some damage threshold.
A second phase will explore underlying causal relationships between
vulnerability and household and community characteristics, and will
explore what kind public policy can mitigate the social impacts of
weather shocks. Characteristics of interest include education, income,
access to credit, and agricultural practices. In addition to improving
the analytical foundation for policies related to El Niño and
other sporadic weather shocks, this work will contribute to ongoing
efforts to predict the impacts of climate change in developing
countries.
Learning and the diffusion of clean
household energy technology
What factors affect the diffusion of technologies that promote both
environmental quality and human well being? In particular, how do
households learn about the benefits and costs of new technologies, and
how does this learning process affect diffusion dynamics? I ask these
questions in the context of household energy technology. According to
recent World Health Organization analysis, indoor air pollution from
cooking with solid fuels in poorly ventilated indoor spaces kills
approximately 1.5 million people each year and adds hugely to the
burden of disease. Yet households with alternatives continue to cook
with traditional fuels.
My dissertation research analyzes the spread of clean household fuels
in Peru. I use longitudinal household survey data to study the extent
to which poor households learn from their neighbors about the health
benefits of clean household energy technologies. I show that
household’s beliefs regarding the health benefits of switching to
clean-burning fuels reflect neighbors’ experiences in a manner
consistent with a Bayesian learning model. This establishes that
information about health effects plays a role in fuel choice.
As an EI Postdoctoral Fellow I hope to explore how much “expert”
information affects behavior. I plan to study this question by carrying
out experiments in which communities in the central Sierra of Peru are
exposed to different kinds of information about the risks of indoor air
pollution. I am currently working with The Mountain Institute, a
non-governmental organization that works in Peru, and the Peruvian
Ministry of Social Affairs to lay the groundwork and obtain funding for
these experiments.
Ecosystems services for the poor
Efforts to quantify the services rendered by ecosystems have largely
focused on developing countries, despite the greater dependence of
non-urban poor on ecosystems. Household survey data contains
potentially useful information about the nature and magnitude of the
benefits that poor households derive from ecosystems (water, food,
fuel, waste disposal, etc.). This research investigates how standard
methods for ecosystem valuation using household production function
models can be adapted to household survey data from developing
countries. To test these new methods I will study variation in societal
reliance on ecosystem services in several Latin American countries.
A central challenge here is to frame valuation questions in terms of
changes in levels of ecosystem services resulting from human activity
(e.g. the loss attributable to a mining project that disrupts a village
water supply). Another challenge is that approaches to ecosystem
valuation that rely on willingness to pay for ecosystem services are
extremely sensitive to income. In wealthy countries where these tools
evolved analysts have handled these distributional concerns on an ad
hoc basis. Large variations in household income typical in developing
countries underscore the need for a more systematic way of comparing
the value of ecosystem services across populations with disparate
levels of income.