WPS6326
Policy Research Working Paper 6326
Rural Households in a Changing Climate
Javier E. Baez
Dorothy Kronick
Andrew D. Mason
The World Bank
Development Economics Vice Presidency
Partnerships, Capacity Building Unit
January 2013
Policy Research Working Paper 6326
Abstract
This paper argues that climate change poses two distinct, strategies for adapting to climate change in rural settings.
if related, sets of challenges for poor rural households: The authors find that although households possess
challenges related to the increasing frequency and severity numerous strategies for managing climate shocks and
of weather shocks and challenges related to long-term shifts, their adaptive capacity is insufficient for the task of
shifts in temperature, rainfall patterns, water availability, maintaining—let alone improving—household welfare.
and other environmental factors. Within this framework, They describe the role of public policy in fortifying the
the paper examines evidence from existing empirical ability of rural households to adapt to a changing climate.
literature to compose an initial picture of household-level
This paper is a product of the Partnerships, Capacity Building Unit, Development Economics Vice Presidency. It is part
of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy
discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org.
The authors may be contacted at jbaez@worldbank.org, dkronick@stanford.edu, and amason@worldbank.org.
The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development
issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the
names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those
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its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.
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Rural Households in a Changing Climate
Javier E. Baez Dorothy Kronick Andrew D. Mason 1
World Bank and IZA World Bank and Stanford World Bank
University
JEL Classification: Q12, Q54, O13
Keywords: climate change, rural households, adaptation, risk-coping mechanisms, long-term effects
<>Introduction
How will rural households in developing countries adapt to climate change? Rooted as it is in the
hard sciences and in high-income communities, the public dialogue on global environmental
change has only recently begun to consider the adaptive capacity of poor rural households. This
inattention is not for lack of importance; the welfare of these households concerns
millions. Rather, it may be for lack of definition: the issue is so broad and so complex that it
seems to defy focused consideration.
One way to conceptualize adaptation in rural communities is to recognize two effectively distinct
phenomena associated with climate change: first, increased weather variability and increased
frequency and severity of extreme weather events (broadly, “shocks�) and, second, gradual
changes in temperature and rainfall patterns (broadly, “shifts�). From a climate perspective, these
phenomena are not actually distinct; from the perspective of adaptation among agricultural
households, however, dividing the issue into shocks and shifts provides a useful framework.
Events that temporarily disrupt a household's modal environmental conditions can be considered
shocks, and those that permanently change the modal conditions can be considered shifts.
Of course, the increasing frequency and severity of extreme weather events is itself a shift
because it permanently changes modal environmental conditions. Likewise, gradual changes in
temperature and rainfall patterns may manifest as shocks (in the form of droughts and floods, for
example). By categorizing the ways in which rural households experience climate change, we
clarify the relevance of several established bodies of literature. This literature includes work on
ambiguity risk, agricultural technology adoption, reaction to weather-related information,
determinants of migration, income diversification, health, and past experiences with permanent
environmental changes. This paper synthesizes evidence from this literature to develop a more
inclusive and composite picture of household-level strategies for adapting to climate change in
rural settings. 2 We also briefly discuss the role of public policy in fortifying households’
capacity to adapt.
We describe rural households’ numerous strategies for dealing with climate change, and we
outline the ways in which these strategies are insufficient for the task of maintaining (let alone
improving) household welfare. The threat to welfare derives not only from the direct losses
associated with climate shocks and shifts but also from increased uncertainty, which complicates
households’ attempts to smooth consumption (with credit or insurance, for example), to smooth
income (by adjusting agricultural portfolios), and to evaluate options such as migration.
The notion that some households may lack sufficient adaptive capacity or may respond in ways
that have adverse long-term consequences suggests a possible role for public policy. To date, the
dominant policy framework, to the extent that one exists, has focused on what has been called a
no-regrets approach to climate-related adaptation policy (see Heltberg, Siegel, and Jorgensen
2009; de la Torre, Fajnzylber, and Nash 2009; and World Bank 2010). In the face of the many
uncertainties associated with the future effects of climate change, the no-regrets approach
emphasizes policies and investments today that can generate positive net benefits “under all
future scenarios of climate change and impacts� (Heltberg, Siegel, and Jorgensen 2009). This
approach has many merits, though there may also be room for more specific action. Here, we
2
draw attention to country experiences with (i) better systems of information for adaptation and
(ii) climate-adapted risk management instruments (such as indexed production insurance), and
(iii) social safety nets that are targetable and scalable on the basis of weather events.
The remainder of this paper is organized as follows. Section II defines key concepts related to
rural households’ exposure, sensitivity, and capacity to adapt to climate change. Drawing on
extensive empirical literature on household responses to weather shocks, section III analyzes
how—and how well—households may be expected to cope with the increased frequency and
severity of weather shocks associated with climate change. Drawing on several strands of
empirical literature on the determinants of household economic mobility, section IV explores
how households are likely to adapt to longer-term climatic shifts. Section V discusses directions
for public policy, and section VI concludes.
<>Exposure, Sensitivity, and Household Capacity to Adapt to Climate Change
In the literature on climate change, the concept of vulnerability comprises three elements:
exposure (experience of climate conditions), sensitivity (response of the physical environment to
exposure, such as the response of crop yield to temperature and precipitation), and adaptive
capacity (discussed below) (IPCC 2007 and many others, notably Turner 2003 and Schröter,
Polsky, and Patt 2005). This section briefly describes rural households’ exposure and sensitivity
to climate change and then defines adaptive capacity, which is the subject of the remainder of the
paper.
Substantial effort has been devoted to generating increasingly precise projections of climate
trends and the exposure of rural households to these trends. Summarizing this work is beyond the
scope of this paper; below, we outline a few points that are useful for our discussion of adaptive
capacity. As noted above, we organize our discussion according to two broad categories of
climate changes: shocks, the increase in weather variability and the frequency of extreme
weather events, and shifts, the long-term rise in global surface temperatures and related gradual
changes in sea level and rainfall patterns.
Over the past century, global mean surface temperatures rose by 0.74°C, on average (the
absolute global mean surface temperature is approximately 14°C) (IPCC 2007). The speed of
warming accelerated as the century progressed. The expected trajectory of warming over the next
hundred years is a matter of considerable debate. Existing models predict that global mean
surface temperatures will rise by an additional 1 to 5°C by the year 2050.
Global warming is one determinant of precipitation patterns and extreme weather events, such as
storms. Higher surface temperatures induce increased evaporation from soils and bodies of
water. The resulting elevated concentration of water vapor leads to more precipitation in some
places and more droughts in others. Some precipitation that used to fall as snow now falls as
rain. In the view of many researchers, extreme weather events are more frequent and more severe
as a result of global warming; there are now more extreme warm nights, heat waves, tropical
storms, and hurricanes than there were 50 years ago. Figure 1 illustrates a clear upward trend in
the number of weather-related natural disasters. The direction of the trend is clear, though the
increase may be driven in part by both better reporting practices and increasing human settlement
3
in risk areas. In regions such as Latin America and the Caribbean as well as East Asia and the
Pacific, the average annual frequency of disasters increased fivefold between the 1970s and the
2000s and increased almost twofold in the most recent two decades (during which changes in
measurement techniques were less likely to confound interpretation and inference).
<>
Agricultural production is often highly sensitive to the weather shocks and shifts associated with
climate change. Higher temperatures and changing rainfall patterns may influence crop yields; in
recent years, for example, climatic variability related to El Niño Southern Oscillation (ENSO)
affected wheat yields in Mexico, cotton and mango growing cycles in Peru, and the incidence of
plant diseases in various other parts of Latin America. Cattle and dairy productivity may also
suffer: heat waves have been shown to negatively influence milk production in Argentina. Water
supply restrictions also reduce productive potential in agriculture. Ocean acidification may
damage fish stocks, and sea-level rise may eventually submerge coastal farmlands. Extreme
weather events often destroy crops and arable land.
This list is not exhaustive, of course. The point is merely that climate change alters the natural
conditions in which agricultural households operate. Although employment in agriculture has
fallen over the past half century as developing economies have grown, hundreds of millions of
people remain in the sector. For nonagricultural households in rural areas, the environment is
sensitive to climate change: roads flood, infrastructure is damaged or destroyed, and diseases
flourish or fade.
Perhaps because the term “adaptation� seems relatively straightforward, climate change
authorities such as the Intergovernmental Panel on Climate Change and the United Nations
Framework Convention on Climate Change Secretariat have not taken it upon themselves to
sanction a common definition of the term in the context of climate change (OECD 2006).
However, some clarity of definition may be useful for understanding what adaptation means in
the context of our discussion. For the purposes of this paper, then, adaptation refers to
household-level actions taken (i) in response to observed or expected climate conditions and (ii)
with the objective of maintaining or improving household welfare.
Adaptation, in this context, involves a multistage process among household decision makers:
first, signal detection, or the identification of a weather or climate event; second, an evaluation of
the expected consequences or impacts of that event; third, a response, an observable change in
household behavior that stems from the evaluation; finally, feedback, or monitoring the
outcomes of the adaptive behavior (Kandlikar and Ribsey 2000). Planting alternative crops in
response to changing temperature, moving to a different location to avoid floods or droughts, and
insuring against weather-related asset losses could all be considered adaptive actions within this
process. Accordingly, we use the term adaptive capacity to denote the extent to which
households are able to undertake the processes and actions required to maintain or improve
welfare in the face of negative shocks and shifts associated with climate change.
Like the manifestations of climate change, the determinants of adaptive capacity among rural
households are numerous. For the purposes of this article, these determinants can be thought to
4
fall into two broad categories: (i) economic and physical factors and (ii) behavioral or
psychological factors. The former category includes factors traditionally discussed in
development literature, such as people’s levels of human capital, their access to credit, ownership
and control of assets (e.g., land, livestock), and physical mobility. The latter category involves
individuals’ perceptions of risk and of their own adaptive capacity, which have been the focus of
more recent work (e.g., Bryan, Chowdhury, and Mobarak 2011). One view of adaptation to
climate change holds that these perceptions are just as important—perhaps more important—
than physical and economic factors in determining adaptive capacity (Grothmann and Patt
2005). 3
<>Household Responses to Climate-change Shocks: Protecting Consumption,
Income, and Human Capital
Rural households have been exposed to weather shocks for millennia, and scholars have studied
households’ responses to these shocks for decades. In many ways, household adaptation to
weather shocks associated with climate change is likely to mirror past adaptation to storms,
floods, and droughts. In this sense, the empirical evidence on household responses to past
weather shocks is instructive. At the same time, the increasing frequency and severity of weather
shocks associated with climate change, if detected and evaluated, may produce qualitative
changes in household responses. Understanding these potential differences is key to
contextualizing existing evidence.
First, the increasing frequency and severity of weather shocks will almost certainly alter the
economic and physical factors that determine households’ adaptive capacity. Beyond direct
damage to physical capital, more frequent and extreme weather events may induce higher levels
of precautionary savings, which, in turn, may result in lower investment and a reduction in the
long-term income stream. Similarly, farmers may reduce overall productivity in an attempt to
mitigate risk by diversifying agricultural production portfolios in a suboptimal manner. To our
knowledge, there is little empirical evidence on the cumulative effects of repeated weather
shocks.
We might consider the damage to physical assets and the necessity of additional saving or risk-
management activities to be quantitative or magnitude-based changes in adaptive capacity. The
key concept is that climate change will simply scale up the adverse effects of weather shocks as
we currently observe them.
Perhaps more significant than these changes in scale, however, are the ways in which increasing
frequency and severity of weather shocks may imply qualitative changes in the way that
households respond to climate, principally by affecting the behavioral and psychological
determinants of adaptive capacity. In particular, trends in climate shocks may reduce households’
ability to detect and appropriately evaluate weather-related risk ex ante, thereby widening the
gap between optimal and actual adaptive behaviors. Recent theoretical and empirical work
suggests that ambiguity aversion may negatively affect investments in insurance, for example
(Bryan 2010). In Zimbabwe, farmers have been found to systematically underestimate the risk of
drought and to underestimate their own capacity to adapt to it (Patt and Gwata 2002). This
5
evidence is suggestive, although little is known empirically about the way in which changes in
the frequency or severity of climate shocks affect the appropriateness of adaptive responses.
Despite these potential differences between past and future adaptive behavior and capacity, the
existing literature on household responses to weather-related shocks can provide valuable
insights into future adaptive behaviors. The central message that emerges from previous work is
that although households employ a range of measures to manage risk and shocks, the (often
informal) adaptation mechanisms that they employ are generally insufficient for maintaining
their welfare in the face of weather shocks. Moreover, some coping mechanisms are
economically inefficient and may have adverse long-term consequences. As discussed above, we
expect that the increasing frequency and severity of climate shocks will only magnify these
insufficiencies.
<>Consumption Smoothing
Weather shocks (as compared with household-specific shocks such as illness) pose particular
challenges for households intent on smoothing consumption. Because they affect whole
communities rather than individuals, they complicate local insurance or reciprocal gift-giving
efforts, requiring risk pooling or intervention across a broader set of geographic regions.
To what extent will weather shocks associated with climate change reduce consumption among
rural households? A large body of evidence indicates that although rural households’ efforts to
smooth consumption in the face of weather shocks can significantly mitigate negative effects, the
success of these efforts varies dramatically across both communities and households. In a survey
of papers based on longitudinal data from southern rural India, for example, Morduch (2003)
finds that the effect of transfers between households (i.e., gifts given with the expectation of
reciprocity, an active consumption-smoothing strategy) ranges from a 40 percent reduction in
total income risk to a 90 percent reduction in total income risk.
Similarly, a study on Thailand finds that variation in rainfall patterns pushed rural households to
save more in their efforts to smooth consumption (Paxson 1992). However, a recent study of 162
villages in Ethiopia finds that although some households were able to protect consumption by
selling livestock or borrowing from friends and relatives, between 25 and 40 percent of
respondents “did nothing� in response to droughts, floods, and hailstorms (Deressa, Hassan, and
Ringler 2009).
Existing evidence also suggests that increased weather volatility associated with climate change
is likely to have the largest effects on the consumption of poor rural households, which have the
fewest assets, human resources, and social networks. A study from rural China finds, for
example, that a 10 percent decline in income is associated with a 4 percent decline in
consumption among poor households but only a 1 percent decline among nonhouseholds (Jalan
and Ravallion 1999). Likewise, evidence suggests that households with greater asset holdings
and members who are more educated are better able to protect themselves from the impacts of
shocks and to recover afterward. In rural Nicaragua and El Salvador, for example, recent studies
show that households with higher levels of education and greater asset holdings are both less
6
likely to fall into poverty during an aggregate shock and more successful at growing their
incomes in the aftermath (Vakis, Kruger, and Mason 2006; Beneke de SanelÃu and Shi 2004).
In sum, available evidence indicates that households’ abilities to smooth consumption in the face
of weather-related (and other) shocks are “real and significant but not complete� (Murdoch
1995). The increasing weather volatility associated with climate change is likely to further
stretch the capacities of rural agricultural households to protect their consumption and increase
the risks of their use of inefficient or costly mechanisms to respond to shocks. The evidence also
indicates that the increased frequency and severity of weather shocks is likely to have the
greatest impact among the poorest rural households.
<>Income Smoothing
In light of their inability to fully smooth consumption in the face of income shocks, rural
households dealing with decreasingly predictable returns to agriculture may turn to income-
smoothing strategies, such as altering crop portfolios or diversifying sources of household
income. In doing so, households often choose to reduce risk even at the cost of significant
foregone returns.
One strategy adopted by households in the absence of formal mechanisms for insuring against
production risk is to pursue low-risk, low-return agricultural portfolios. In a rural municipality in
southern Peru, for example, farmers plant and cultivate many small, geographically dispersed
and less profitable plots to reduce aggregate production variance (Goland 1993). Likewise,
farmers in southern India whose production is vulnerable to rainfall variability are more likely to
plant low-risk, traditional varieties of rice and castor rather than higher-risk, high-yield (more
profitable) varieties (Morduch 1990). High levels of weather variability may also discourage
farmers from adopting production-enhancing technologies.
There are significant costs associated with such forms of adaptation; conservative factor
allocations may reduce profits and, thus, farmer incomes. In the Peruvian municipality described
above, the field-scattering strategy was found to reduce farmers’ net yields by about 7 percent
(Goland 1993). In the case of southern rural India, microsimulation exercises showed that a one-
standard deviation reduction in weather risk (rainfall variability) would increase the average
profits for farmers at the bottom of the income distribution by nearly 35 percent (Binswanger and
Rosenzweig 1993).
Another way that households adapt to increasingly unpredictable agroclimatic conditions is to
diversify their sources of income. Indian households that are vulnerable to transitory shocks,
including those induced by weather risk, are more likely than similar households to have a
member employed in stable, nonagricultural sectors, even when off-farm employment offers
lower wages (Rosenzweig and Stark 1989). Studies from Guatemala, Thailand, and China,
among other places, describe similar behavior (see, e.g., World Bank 2005a; Townsend 1995;
Jalan and Ravallion 1999).
Increasing weather variability and shocks could therefore lead to movements away from
agricultural labor markets. Although diversifying out of agriculture is often associated with
income growth and poverty reduction (World Bank 2007), moving into nonagricultural
7
employment in response to weather-related shocks is not without its risks. A recent study using
panel data from Bangladesh analyzes the evolution of wages in the five years following a
massive flood in 1998 (Mueller 2010). Although agricultural labor markets there experienced
short-term negative consequences due to the flood, nonagricultural labor markets performed
worse than agricultural labor markets in the medium term.
<>Human Capital Accumulation
Human capital accumulation is also highly susceptible to unfavorable weather events. By
reducing investment in education and health and by changing the incidence of certain diseases,
weather shocks may jeopardize long-term household welfare, increasing susceptibility to future
shocks and increasing the likelihood of the intergenerational transmission of poverty.
Households in rural India, for example, pull their children out of school to cope with various
weather-related income shocks (Jacoby and Skoufias 1997). In Nicaragua, Hurricane Mitch
negatively affected the progress of students in school (Ureta 2005). There is some evidence that
such transitory coping mechanisms may solidify into permanent losses in human capital
investment. Evidence from Mexico shows that children who are withdrawn from school during
difficult times are one-third less likely to continue their studies than similar children whose
schooling is not interrupted (de Janvry, Finan, Sadoulet, and Vakis 2006). This trend is
particularly troubling in light of the fact that education—and female education, in particular—
appears to dramatically increase households’ adaptive capacity (see, for example, Blankespoor,
Dasgupta, Laplante, and Wheeler 2010).
These weather-related declines in schooling in rural areas are often associated with increases in
households’ use of child labor. In Tanzania, for example, there is evidence that transitory income
shocks created by accidental crop loss increased child labor, more so among poor than nonpoor
households (Beegle, Dehejia, and Gatti 2003). In El Salvador, the fraction of children working
outside the household following earthquakes in 2001 more than doubled, from 6.5 to 16.5
percent (Santos 2007). Child labor also increased in rural Nicaraguan households affected by the
coffee crisis and in rural Nicaraguan households directly in the path of Hurricane Mitch (Vakis,
Kruger and Mason 2006; Baez and Santos 2007).
Weather-related shocks can also have a detrimental impact on the nutritional status of poor rural
women and children. Evidence from Ethiopia suggests that households do not smooth
consumption across individuals within the household; rather, the nutritional status of women
“bears the brunt of adverse shocks� (Dercon and Krishnan 2000). Similarly, Baez and Santos
(2007) find that Hurricane Mitch increased the likelihood of early infant malnutrition by an
alarming factor of four.
As in the case of education, there is evidence that weather-induced shocks to nutrition can have
adverse long-term consequences on household welfare. In Zimbabwe, for example, a recent
study of young children who became stunted as a result of a drought finds that those affected
never fully recovered. Specifically, the study found lasting negative effects not only on affected
children’s body weights but also on their school attainment and subsequent earnings (Alderman,
Hoddinott, and Kinsey 2006).
8
Climate change may also affect health more directly by changing the incidence of diseases.
Malaria and dengue, for example, which are mosquito-borne diseases, flourish in warm, humid
conditions, depending on stagnant water (where mosquitoes breed) and wet air (in which adult
mosquitoes can best survive). Cholera and diarrheal diseases are also water-borne. Several
studies have shown that the prevalence of these diseases increased in the wake of heavy rainfall
caused by El Niño in various years in Ecuador, Bolivia, Peru, Colombia, and Venezuela (Vos,
Velasco, and Labastida 1999, WHO 1999, Bouma et al. 1997, Bouma and Dye 1997). Extremes
of heat and cold are also linked to higher morbidity and mortality, mostly among the elderly
(Rumel et al. 1993, Shumway, Azari, and Pawitan 1988, Bull and Morton 1978).
<>Household Adaptation to Long-term Shifts in Temperature and Rainfall
Patterns
Thus far, this paper has focused on how—and how well—rural households may be expected to
respond to shocks associated with climate change: tropical storms, droughts, floods, and other
extreme weather events. We now turn to gradual shifts in temperature and rainfall patterns.
Discrete weather-related shocks and gradual shifts in temperature and precipitation levels
associated with climate change are, of course, intimately related. The effective distinction that
we draw here is that shocks represent deviations from a local mean, whereas shifts represent
long-term changes in the mean.
For rural households, these two phenomena require different types of adaptive behaviors. Shocks
require efforts to smooth consumption and income, whereas shifts require longer-term—and
perhaps permanent—changes in households’ methods of income generation. Long-term climate
shifts also complicate the task of insuring against weather shocks because traditional insurance
mechanisms are designed to address variations in the mean of some variable of interest (e.g.,
agricultural productivity or income) as opposed to secular changes in the means of those
variables over time.
The existing evidence on farmers’ perceptions of climate change indicates that farmers often
recognize long-term shifts in environmental conditions and that they attempt to respond to these
shifts. In a recent survey conducted by IFPRI in Sub-Saharan Africa, more than half of the
respondents stated that they perceived long-term increases in temperature and decreases in
precipitation (Nhemachena and Hassan 2007). They also indicated that a lack of credit and a lack
of information were significant barriers to adaptation.
There are a number of ways in which households might try to adapt to gradual, long-term
changes in climactic conditions. Climate shifts may induce households to (i) adapt their use of
agricultural inputs and technologies; (ii) diversify their sources of household income, either
within agriculture or toward nonagricultural enterprises; or (iii) emigrate from affected regions,
all in an attempt to compensate for the adverse effects of climate shifts on agricultural
productivity, farm profitability, and household income.
Directly measuring how—and how effectively—households adapt to long-term climate shifts is
difficult because doing so requires long panels linking household production behavior,
productivity, and income to climate variables, data that largely do not exist at present. Most
9
panel evidence captures household economic mobility and its determinants over relatively short
periods of time, on the order of five years. 4 One recent study of a village in the Philippines uses a
unique 30-year panel of household data to examine the factors that enable household economic
mobility (Fuwa 2007). The study finds that in addition to economic growth (which is found to be
an important driver of household economic mobility), education plays a critical role in upward
economic mobility. Assets—specifically, land holdings—are found to be important in preventing
downward mobility among rural farm households. 5 The study does not examine the effect of
changing climate conditions, however.
Despite the paucity of long-term panel data, a large body of empirical literature on developing
countries that examines adaptation to long-term economic changes can inform our consideration
of how well rural households—particularly poorer households—will adapt to long-term climate
shifts.
<>Adapting Agricultural Input Use and Adopting New Technologies
Adopting new technologies is one way in which rural agricultural households can adapt to long-
term climate shifts. A substantial body of empirical farm studies, some dating back several
decades, examines determinants of farm productivity and profitability in developing countries
across Asia, Africa, and Latin America. The evidence indicates that not all households are
equally capable of adapting their use of productive inputs or adopting new technologies in the
face of changes in the economic environment. Studies consistently demonstrate the importance
of farmers’ education levels in facilitating higher farm productivity through several channels: (i)
direct improvements in worker productivity; (ii) increased ability of workers and enterprise
managers to make appropriate decisions about the most efficient mix of inputs and technologies
in production; (iii) increased ability of decision makers to adapt to changing technologies,
policies, and the external economic environment; and (iv) at sufficiently high levels of education,
an ability to generate productivity-enhancing innovations. 6
<>Diversifying Household Incomes
Diversifying household income sources, both within agriculture and toward nonagricultural
enterprises, is another form of adaptation that can potentially help households manage longer-
term climate shifts. The empirical literature suggests that income diversification strategies are
often at the heart of rural households’ ability to escape poverty (World Bank 2007). However, as
the earlier discussion on income smoothing suggests, not all households are equally capable of
diversifying their income sources in an efficient, welfare-enhancing way.
Recent evidence using panel data from Nicaragua shows that family education levels, wealth,
control of physical and financial assets (specifically, land and credit), more diversified income
portfolios in the initial period, and greater access and proximity to markets (and urban centers of
nonagricultural activity) are all positively associated with households’ economic mobility,
income diversification, and income growth (Vakis, Kruger, and Mason 2006). The findings from
Nicaragua are consistent with those from other recent panel studies. In El Salvador, for example,
households with higher levels of education, greater asset holdings, and greater proximity to
10
markets experienced faster real consumption growth over the period analyzed (Beneke de
SanelÃu and Shi 2004).
<>Migration
A third form of adaptation that can potentially help households manage longer-term climate
shifts is migration away from affected regions, either to other rural areas or to urban areas. Two
studies on Brazil find that weather-related factors currently induce emigration from affected
areas (Assuncao and Chein Feres 2008, Mueller and Osgood 2009).
Consistent with the evidence on input use, technology adoption, and income diversification,
migration appears to be positively associated with people’s education levels and income. In
addition, the propensity to migrate appears to be positively associated with initial income levels.
A recent study from Bangladesh suggests that the poorest households may be least likely to
migrate because returns on migration are uncertain and because, relative to wealthier households,
the consequences of a negative outcome are severe. Paying for transport to the city and then
failing to obtain a job could mean starvation (Bryan, Chowdhury, and Mobarak 2011).
Similarly, analyses of Brazilian and Mexican census data indicate that illiterate people are least
likely to migrate, whereas those with higher education have a higher probability of migrating
than do other rural inhabitants (World Bank 2007). Higher schooling levels are also associated
with higher probabilities of migration from rural areas in China (Du, Park, and Wang 2005).
<>Directions for Public Policy
The fact that some rural households, particularly poorer ones, lack sufficient capacity to adapt to
climate change suggests an important role for public policy. To date, climate change studies that
focus on household adaptation have tended to take one of two approaches to strengthening
household adaptation policies. The first approach, common in agency reports, is to establish a
comprehensive agenda for action across sectors, often without attention to prioritization across
sectors or activities (see, e.g., IPCC 2007). The second, more recent discussion has focused
largely on what has been termed a no-regrets approach to fostering household adaptation (see
Heltberg, Siegel, and Jorgensen 2009; de la Torre, Fajnzylber and Nash 2009; and World Bank
2009).
The no-regrets approach emphasizes policies and investments that are part of a sound
development and poverty-reduction strategy for developing countries, actions that can generate
positive net benefits “under all future scenarios of climate change and impacts� (Heltberg,
Siegel, and Jorgensen 2009). Such an approach might include, for example, efforts to increase
educational attainment among members of rural households, improving poor households’ control
of economic assets (such as land and livestock), deepening of rural financial markets, and
improving rural households’ access to markets through further investments in infrastructure.
The evidence presented here suggests that even in the face of considerable uncertainty regarding
climate change—or perhaps precisely because of the uncertainties—there may be occasion for
more specific attempts to strengthen rural households’ adaptation capacity. We highlight three
11
such areas here: (i) systems of information for adaptation, (ii) climate-adapted risk-management
instruments (such as indexed production insurance), and (iii) social safety nets that are targetable
and scalable on the basis of weather events.
<>Information for Adaptation
Because signal detection and the evaluation of the expected consequences are critical to
households’ ability to respond to climate change, developing systems of information for
adaptation will may improve households’ ability to adapt to both weather shocks and long-term
climate shifts.
Indeed, better information on expected rainfall and seasonal temperatures, coupled with
appropriate agricultural extension advice, can help farm households to make more appropriate
decisions about crop mix, irrigation, the timing of planting, and other ex ante production options.
Since the major ENSO event of 1982/1983, various governments have attempted to affect the
behavior of agricultural households by producing and disseminating weather forecast
information (Magrin et al. 2007). In the northeastern Brazilian state of Ceará, for example, a
government agency engaged poor rural households in the Planting Time Program. Households
would receive high-yield seeds in exchange for a portion of their crop; the agency would
distribute the seeds at a time deemed most suitable for planting, given seasonal weather
forecasts. The agency also encouraged households to plant drought-resistant crops before the
low-rainfall season of 1991. Various assessments considered this intervention highly successful
(Orlove and Tosteson 1999). Likewise, in Mexico, ENSO forecasts have been used to adjust crop
mix (Magrin et al. 2007).
The development of effective information systems for adaptation will face important
implementation challenges. As Orlove and Tosteson (1999) note, the institutions that produce
climate forecasts do not traditionally interact with the institutional end users of those forecasts
(such as food security planning agencies), much less with households or individuals. Establishing
relationships between producers and users of climate forecasts can be complex. The Brazilian
agency mentioned above, for example, struggled with sharp public criticism after issuing
incorrect forecasts in the mid-1990s.
In this context, Patt and Gwata (2002) identify six factors that are considered critical if climate
forecasts are to be effective in strengthening rural households’ ability to adapt to climate change.
To induce the desired effect on farmer behavior, climate information needs to be (1) considered
credible, (2) communicated by agents who are considered legitimate (that is, not beholden to
special interests), (3) calculated at appropriately detailed (that is, local) scales, (4) expressed in
terms that the audience can understand, (5) appropriately timed, and (6) relevant to the decisions
that farmers actively make. In this sense, the process of developing effective interventions to
strengthen household adaptive capacity requires the active participation of the households
themselves (Schröter, Polsky, and Patt 2005).
Although it is difficult to quantify the expected gains associated with developing information
systems for adaptation, several studies have attempted to estimate how past information
dissemination programs have increased net returns to agricultural production. Estimates range
12
from 5 percent for Argentine soybeans (Magrin and Travasso 2001) to 20 percent for Mexican
maize (Jones 2001). Attention to effective communication strategies and the effective use of new
communications technologies may serve to increase net returns associated with the provision of
information for adaptation. Efforts to disseminate weather and other information relevant to
production via cell phone text messages, a method that is increasingly viable given the high and
rising cell phone penetration rates in poor rural areas, could help to expand outreach in some
countries while lowering the costs of disseminating information. Such approaches are already
being used to deliver market price information to farmers in developing countries. 7
<>Climate-adapted Production Insurance
As discussed above, changes in average temperature and rainfall patterns over time will require
adaptation of the traditional insurance model to address the fact that mean climate (and
agronomic) conditions change in a systematic way over time. 8 More frequent and more intense
weather shocks may also warrant a closer link between climate variables and production
insurance, perhaps building on a small but a growing body of experience with weather-indexed
insurance.
Indeed, weather-indexed insurance may hold some potential for mitigating the negative effects of
climate change. In an attempt to address the moral hazard and adverse selection issues that
plague traditional crop insurance, some insurers now offer products in which payouts are linked
to the occurrence of a weather event, such as low rainfall, which can be observed without a
costly loss assessment procedure. Larson, Anderson, and Varangis (2004) provide a useful
review of weather-indexed insurance and related risk management tools. One difficulty, they
note, is that these products require reliable climate-related information and therefore significant
weather station infrastructure. Basis risk (that is, the risk that index-based payouts will
correspond only poorly with actual losses) may also constitute a significant barrier to the
purchase of index insurance products (e.g., Clarke 2011), although informal risk sharing that
protects farmers against individual-level idiosyncratic shocks may mitigate the problem of basis
risk (Mobarak and Rosenzweig 2012).
Experience with weather-indexed insurance remains relatively limited, but several recent
applications are being monitored with interest to determine whether they may have broader
applicability in developing countries. For example, weather indices are currently being used for
agricultural insurance in Ontario and Alberta, Canada. In Argentina, in light of the positive
correlation between rainfall and milk yields, a rainfall insurance contract is being used by a milk-
producing cooperative. In Mexico, the agricultural reinsurance company AGROSEMEX recently
used weather derivatives to protect part of its crop reinsurance portfolio exposed to weather
risks.
Index insurers cannot begin with a few clients and then expand (as in microfinance). Because of
the covariate nature of weather shocks, index insurers must begin their operations with many
clients in numerous areas (Morduch 2006). In this context, the potential of weather-index
insurance to strengthen adaptive capacity may be constrained by smallholder farmers’ demand
for such instruments, although recent work from India suggests that the availability of informal
insurance may increase the demand for index insurance under certain circumstances (Mobarak
13
and Rosenzweig 2012). Several pilot projects, in which information was provided and
smallholder farmers’ demand for insurance was “aggregated� by local producer organizations
and cooperatives, such as those in El Salvador and Nicaragua, may provide lessons on how best
to link such risk mechanisms to those who need them (Varangis et al. 2003).
<>Social Safety Nets
Safety nets represent another important component of a risk management strategy for climate
change and are well suited to fortifying households’ ability to adapt to both shocks and shifts. In
the face of recent shocks (related both to weather and economic shocks), it has become common
to call for flexible safety net programs that can respond to affected households’ needs without
compromising governments’ long-term fiscal positions. Indeed, a number of developing
countries around the world have adapted their safety net programs to help poor and crisis-
affected households deal with the recent food, fuel, and global financial crises.
One example of successful flexibility along these lines is the Honduras Social Investment Fund
(FHIS). Credited with limiting the rise of poverty in the wake of Hurricane Mitch (which killed
thousands of Hondurans, left one million homeless and inflicted damage equivalent to two-thirds
of GDP), the FHIS was a public program created in 1990 to finance small-scale investments in
poor communities. Originally conceived as a way to address the social impacts of structural
adjustment policies, the FHIS nimbly transformed into an emergency response program after
Mitch devastated the country in 1998. Within 100 days of the hurricane, the program approved
$40 million for 2,100 community projects; by the end of 1999, the FHIS had financed 3,400
projects, four times the number financed in a comparable prehurricane period (Warren 2003). 9
There have been more recent experiences with safety net programs helping to protect the poor
from shocks, including weather shocks. In 2000, Nicaragua established a conditional cash
transfer program called Red de Protección Social (RPS). Although this program was developed
to address structural poverty and to foster human capital development among the poor, the RPS
was also effective in reducing the vulnerability of poor rural households to income shocks.
During a sharp downturn in world coffee prices from 2000 to 2003, consumption among coffee-
growing households that were RPS beneficiaries declined by only 2 percent, whereas
consumption among coffee-growing households who were not beneficiaries declined by 30
percent (Vakis, Kruger, and Mason 2006). Building on the success of the RPS, the government
of Nicaragua introduced a cash-transfer pilot program in a drought-prone region. After a
mudslide in eastern Nicaragua, the program quickly responded by transferring resources to all
affected households. 10 An evaluation of the pilot program found that the income and
consumption of beneficiaries was significantly more resilient to droughts, price shocks, and
health shocks than that of nonbeneficiary families.
One key feature of the Nicaragua pilot was targeting the program, ex ante, to geographic regions
of the country that faced elevated levels of weather-related risk (specifically drought risk, in this
case). 11 Indeed, as with climate-adapted insurance mechanisms, safety net programs designed to
protect households from increasingly frequent and severe weather shocks must address the
increased information-related challenges associated with targeting on the basis of weather risk.
14
Recent advances in mapping technology, combined with increasingly precise weather-risk
forecasts, can help policy makers to address these issues.
<>Conclusion
Under the best of circumstances, agricultural livelihoods are difficult to sustain and improve.
Even in the friendliest of agronomic environments, small-scale farming can be incredibly
challenging and risky. For those rural households in developing countries that depend on
agriculture, climate change adds yet another dimension of difficulty to the problem of
maintaining and improving welfare. The same is true, albeit via different mechanisms, for rural
households whose income does not come primarily from agricultural activity.
This paper has argued that climate change poses two distinct, if interrelated, sets of challenges
for poor rural households: challenges related to the increasing frequency and severity of weather
shocks and those related to long-term shifts in temperature, rainfall patterns, water availability,
and other environmental factors. Although much is unknown about how poor rural households
will adapt to these shocks and shifts, a substantial body of empirical evidence from developing
countries provides a reasonably robust sense of the strengths and limitations of available
adaptation mechanisms.
An important and encouraging finding from the literature is that rural households—even poor
ones—possess a number of strategies for mitigating the effects of shocks associated with climate
change. They may attempt to smooth consumption in the face of income shocks (using credit,
insurance, savings, and other methods), and they may attempt to smooth income (by adjusting
agricultural portfolios or diversifying sources of income, for example). The evidence indicates,
however, that households’ abilities to smooth consumption and income are less than perfect.
Moreover, some strategies, such as employing child labor, come with clear risks.
Longer-term climate shifts create other challenges to rural households’ livelihoods and call for
other types of adaptation responses. Rural agricultural households confronted with significant
environmental and agronomic changes due to climate change will need to adjust their livelihood
strategies if they are to maintain or increase their incomes. Adapting to climate shifts may
require farming households to adjust their cropping choices and alter their use of productive
inputs and technologies. Farmer households may also seek to diversify their income sources or to
migrate to areas less affected by long-term changes in temperatures and precipitation.
Understanding the factors that affect rural households’ capacity to adapt to shocks and shifts will
be important in designing effective interventions to strengthen their adaptation capacity. Here,
the evidence provides some consistent messages: policies and programs that help to increase
households’ human capital and improve their access to physical assets, financial capital, and
markets can play important roles in strengthening their capacity to adapt to climate change. In
this sense, many interventions that can fortify rural households’ adaptive capacity are also part of
an effective long-term development and poverty-reduction strategy for developing countries.
Hence, there is an appeal to a no-regrets approach that focuses on interventions with positive net
benefits regardless of how the shocks and shifts associated with climate change unfold.
15
Yet, perhaps precisely because of the inherent uncertainties of climate change, an intense focus
on the adaptive capacity of rural households warrants consideration. We have discussed three
potential focus areas. In addition, investing in improved household data collection and analysis,
including the development of longer-term panels of data that can facilitate direct observation and
analysis of households’ and policy makers’ responses to climate-related shocks and shifts, will
ultimately improve our understanding of adaptation to climate change in rural areas.
16
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21
Figure 1: Climate-related Disasters by Region (1970–2009)
Total Number of Climate-related Disasters
120
LAC
SSA
100
ECA
SAS
80
EAP
MENA
60 OECD
40
20
0
70 72 74 76 78 80 82 84 86 88 90 92 94 96 98 00 02 04 06 08
Source: World Bank staff calculations based on EM-DAT: The OFDA/CRED International Disaster Database, Catholic
University of Louvain
Note: The count includes events that meet at least one of the following criteria: (1) 10 or more people reported as dead,
(2) 100 people reported as affected, (3) a declaration of a state of emergency, or (4) a call for international assistance.
LAC (Latin America and the Caribbean), SSA (Sub-Saharan Africa), ECA (East and Central Europe), SAS (South
Asia), EAP (East Asia and Pacific), MENA (Middle East and North Africa).
1
Andrew D. Mason is a Lead Poverty Economist with the Poverty Reduction and Economic Management Department, East Asia
and Pacific Region, The World Bank, Washington, DC 20433. Email: amason@worldbank.org. Javier Baez is an Economist with
the Poverty Reduction and Economic Management Department, Latin America and Caribbean Region, The World Bank,
Washington DC 20433. Email: jbaez@worldbank.org. Dorothy Kronick is a doctoral candidate at Stanford University. Email
dkronick@stanford.edu. An earlier version of this article was written as a background paper to the World Bank study for Latin
America and the Caribbean, Low Carbon, High Growth: Latin American Responses to Climate Change. The authors gratefully
acknowledge support and feedback from Augusto de la Torre, Pablo Fajnzylber, and John Nash, the lead authors of that report, as
well as Helena Ribe from the Bank’s Human Development Department for Latin America and the Caribbean. We also thank
three anonymous referees for their comments. Corresponding author: Andrew D. Mason. The findings, interpretations, and
conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the
International Bank for Reconstruction and Development/World Bank and its affiliated organizations, the Executive Directors of
the World Bank, or the governments that they represent.
2
Although climate change creates benefits and opportunities for rural households in regions where expected shifts may actually
improve local agronomic conditions, we focus here on household adaptation to expected adverse shocks and shifts.
3
In the context of climate change, behavioral factors will likely be linked to economic factors through human capital in the sense
that people’s perceptions of risk and, even, of their own adaptive capacity are a function of their ability to detect and evaluate the
actual (or likely) impacts of a weather/climatic event. Although observing and evaluating the impact of shocks may be relatively
straightforward, assessing the risks associated with shifts can be much more complex.
22
4
Overall, panel studies support the notion that education, assets, access to finance, and access to markets (to different degrees in
different places) are important for household income growth and the movement of the poor out of poverty (see Dercon and
Shapiro 2007 for a review of evidence). Dercon and Shapiro (2007) also note that risk and shocks serve to inhibit household
income growth and mobility. In that sense, households’ vulnerability to shocks can affect their ability to adapt to long-term
climate shifts.
5
Interestingly, among smallholder farmers, larger landholdings are found to inhibit movement toward nonagricultural
employment, perhaps by strengthening these farmers’ ties or commitment to farming activities.
6
Among the studies presenting empirical findings on the relationship between farmer education and agricultural productivity,
including the effect of education on input use and technology adoption, see Lockheed et al. 1980; Jamison and Lau 1982; Moock
and Addou 1994; Hussain and Byerlee 1995; and World Bank 2007.
7
For example, seminomadic herders in rural Mongolia can now subscribe to a service through which they can obtain market
price data on key commodities in the nearest district towns through their cell phones.
8
It should be noted that this, by itself, argues for better, more accessible, information on local shifts in temperature and rainfall
levels and may generate incentives for the production and dissemination of such information, independent of the perceived
demand among rural households.
9
Projects prioritized clearing debris and repairing or rebuilding water lines, sanitation systems, roads, bridges, health centers, and
schools, thus hastening national recovery and generating approximately 100,000 person-months of employment in the three
months following the crisis. The decentralized structure and institutional flexibility of the FHIS facilitated its brisk and influential
response. Building on strong preexisting partnerships with municipalities and communities, FHIS directors established 11
temporary regional offices and quickly delegated resources and responsibilities. Directors reduced the number of steps in the
subproject cycle from 50 to 8, established safeguards to ensure accountability and transparency, and effectively accessed
International Development Association financing.
10
The transfers took place within three months of the event; by the sixth month after the event, RPS had enrolled all affected
households as beneficiaries.
11
Beneficiaries were selected from among RPS enrollees using a proxy-means test.
23