ï»¿ 77642
Natural Disasters and Human Capital
Accumulation
Jesus Crespo Cuaresma
The empirical literature on the relationship between natural disaster risk and invest-
ment in education is inconclusive. Model averaging methods in a framework of cross-
country and panel regressions show an extremely robust negative partial correlation
between secondary school enrollment and natural disaster risk. This result is driven
exclusively by geologic disasters. Exposure to natural disaster risk is a robust determi-
nant of differences in secondary school enrollment between countries but not necess-
arily within countries Natural disasters, human capital, education, school enrollment,
Bayesian model averaging. JEL codes: Q54, I20, E24, C11.
This article quantiï¬?es the effect of natural disaster risk on investments in edu-
cation by exploiting both cross-country and time differences in school enroll-
ment. Because of the large number of theories explaining differences in the rate
of human capital accumulation across countries, model averaging techniques
are used to explicitly take into account model uncertainty in extracting the
effect of catastrophic risk on school enrollment.
The empirical literature on the economic effects of natural disasters has
traditionally concentrated on the short-run effects of catastrophic events (for
example, Dacy and Kunreuther 1969; Albala-Bertrand 1993a, b; Tol and
Leek 1999; Rasmussen 2004; and Noy 2009). In contrast, Skidmore and
Toya (2002) and Crespo Cuaresma, Hlouskova, and Obersteiner (2008)
Jesus Crespo Cuaresma ( jcrespo@wu.ac.at) is a professor in the Department of Economics, Vienna
University of Economics and Business; a research scholar at the World Population Program,
International Institute of Applied Systems Analysis; and a consultant at the Austrian Institute for
Economic Research and the World Bank. This study was prepared as a background paper for the joint
World Bankâ€“ United Nations Assessment on the Economics of Disaster Risk Reduction. The work was
supported by the Global Facility for Disaster Reduction and Recovery. The author would like to thank
Apurva Sanghi, the coordinator of the assessment, for many intellectually challenging discussions that
helped shape the article. The article also proï¬?ted from helpful comments by three anonymous referees,
Â´ Miguel Albala-Bertrand, Jed Friedman, Samir KC, Reinhard Mechler, Norman Loayza,
Jose
Paul Raschky, Gallina Vincelette, and participants at the Brown Bag Lunch Seminar at the World Bank.
THE WORLD BANK ECONOMIC REVIEW, VOL. 24, NO. 2, pp. 280â€“ 302 doi:10.1093/wber/lhq008
Advance Access Publication July 9, 2010
# The Author 2010. Published by Oxford University Press on behalf of the International Bank
for Reconstruction and Development / THE WORLD BANK. All rights reserved. For permissions,
please e-mail: journals.permissions@oxfordjournals.org
280
Crespo Cuaresma 281
concentrate on long-run effects of disaster risk on the macroeconomy.1 With
the exception of some results in Skidmore and Toya, there has been no fully
ï¬‚edged empirical investigation of the effects of natural disasters on human
capital accumulation across countries. This study aims to ï¬?ll that gap. Case
studies of individual economies have, however, examined the effect of
natural disasters on educational attainment. Recently, Kim (2008) used data
from the Demographic and Health Surveys and the Living Standard
Measurement Study to examine empirically the effects of climate shocks on
educational attainment in Burkina Faso, Cameroon, and Mongolia. Kim
tends to ï¬?nd negative effects of disaster risk on secondary school
completion.
From a theoretical perspective, the effect of natural disaster risk on edu-
cational investments is ambiguous. Skidmore and Toya (2002) argue that to
the extent that natural catastrophes reduce the expected return to physical
capital, rational individuals would shift their investment toward human
capital.2 But this is just one of the possible effects of natural disasters on
human capital. One could also argue that, in a framework of models of agents
with ï¬?nite lives, the potential effect of natural disaster risk on mortality would
lower education investment in disaster-prone countries. Checchi and
GarcÄ±Â´a-Pen Ëœ alosa (2004) present a simple theoretical model assessing the effect
of production risk on education in which aggregate production risk determines
the average level of education and its distribution. Checchi and
GarcÄ±Â´a-Pen Ëœ alosa show both theoretically and empirically that higher output
volatility leads to lower educational attainment. If natural disaster risk is inter-
preted as a component of aggregate production risk in the economy, countries
that are more affected by disasters should also exhibit lower levels of human
capital accumulation, ceteris paribus.
These types of arguments stem from theoretical models and aim at unveil-
ing the role of natural disaster risk as a determinant of cross-country differ-
ences. In this sense, these theoretical explanations refer to the long-run
effects of natural disasters on education investments. Short-run effects on
human capital accumulation associated with the actual occurrence of the dis-
aster could be extremely important as well. Consider the 2005 earthquake in
Pakistan. The Asian Development Bank and the World Bank (2005), esti-
mated that 853 teachers and 18,095 students lost their lives in the disaster.
More than 7,500 schools were affected by the earthquake, and the estimated
reconstruction costs for education were the second highest by sector, after
private housing. To the extent that reconstruction efforts are unable to
restore capacity and education infrastructure after a disaster, long-run effects
1. See Okuyama (2009) for a review of the literature on assessing and measuring the economic
effects of natural disasters.
2. Skidmore (2001) studies investment decisions under catastrophic risk, but the empirical results
are based on a very reduced dataset.
282 THE WORLD BANK ECONOMIC REVIEW
may also emanate directly from the losses caused by the disaster. Natural
disasters may also affect educational attainment through the effect of evacua-
tions and school switching on the dropout rate and academic performance,
as Sacerdote (2008) recently investigated using data from evacuations
following hurricanes Katrina and Rita in New Orleans (see also Hanushek,
Kain, and Rivkin 2004). The literature has also highlighted the effects on
human capital investment related to loss of parents and to child labor
decisions.
Ultimately, the question of how natural disaster risk affects human capital
accumulation is an empirical one. Because a single theoretical framework
cannot be relied on for explaining the link, an explicit assessment of model
uncertainty is called for when quantifying the effect of natural disasters on edu-
cation investments. This article uses Bayesian model averaging (BMA) to
obtain robust estimates of the effect of disaster risk on secondary school enroll-
ment rates (see Raftery 1995 and Clyde and George 2004, for general discus-
sions of BMA and Ferna Â´ ndez, Ley, and Steel 2001b and Sala-i-Martin,
Doppelhofer, and Miller 2004 for applications to the identiï¬?cation of robust
determinants of economic growth). Model averaging ensures that the results
are not speciï¬?c to the choice of model and take into the account not only the
uncertainty of the estimates for a given model, but also the uncertainty in the
choice of speciï¬?cation.
The results indicate that geologic disaster risk is a robust variable for
explaining differences in secondary school enrollment rates across countries.
The effect is sizable and well estimated. The school enrollment effect corre-
sponding to the mean geologic disaster risk is around 1.65 percentage
points in secondary school enrollment compared with a country with zero
disaster risk. The maximum disaster risk-driven effect in the dataset implies
approximately a 20 percentage point decrease in secondary school
enrollment.
The article is structured as follows. Section I describes the empirical relation-
ship between disaster risk and educational attainment. Section II describes
BMA exercises to assess the robust effect of natural disaster risk as a determi-
nant of differences in school enrollment rates in both a cross-section and a
panel of countries. It also explicitly assesses subsample heterogeneity in the
response of human capital accumulation to disaster risk. Section III summarizes
the key ï¬?ndings.
I . A F I R S T LO O K AT E D U CAT I O N AND DISASTERS
This section explores the relationship between natural disasters and human
capital accumulation. Figure 1 presents scatterplots of average secondary
school enrollment in 1980â€“2000 (after controlling for income per capita and
geographic dummy variables based on world regions) against geologic and
climate disasters and for all disasters combined for the 80 countries in the
Crespo Cuaresma 283
F I G U R E 1. Natural Disaster Risk and Secondary School Enrollment
(unexplained part)
Source: Authorâ€™s analysis based on data described in the text.
284 THE WORLD BANK ECONOMIC REVIEW
empirical analysis.3 Climate-related catastrophes include ï¬‚oods, cyclones, hur-
ricanes, ice storms, snow storms, tornadoes, typhoons, storms, wild ï¬?re,
drought, and cold waves; geologic disasters include volcanic eruptions, natural
explosions, avalanches, landslides, earthquakes, and wave surges. Following
Skidmore and Toya (2002), ï¬?gure 1 concentrates on a simple measure of
natural disaster risk based on average disaster occurrence, here normalized by
1 million people. Disaster risk is thus measured as4
di Â¼ logÂ½1
Ã¾ Ã°Number of disasters in country i=Population of country i in millionsÃžÂŠ:
Ã°1Ãž
Existing data on quantiï¬?ed losses and received aid are not used, since such
measures are known to be plagued by endogeneity and other measurement pro-
blems. On the one hand, to the extent that disaster aid decisions are inï¬‚uenced by
reported losses or number of people affected, governments would have an incen-
tive to overreport these ï¬?gures. On the other hand, a countryâ€™s income level
(which is highly correlated with human capital accumulation) is a basic determi-
nant of the effectiveness of natural disaster risk management. Since successful risk
management mechanisms will reduce the negative macroeconomic effects of disas-
ters, using estimated losses could lead to a spurious negative correlation between
disaster risk and education when the real correlation is between education and the
reduction in natural disaster loss. Skidmore and Toya (2007), for instance, show
that higher levels of education reduce the losses from natural disasters. The pro-
blems related to the use of reported losses from natural disasters have been noted
in the recent comparative literature. Guha-Sapir and Below (2002) highlight some
of these problems and conclude that existing datasets on the socioeconomic
impact of disasters are unsatisfactorily deï¬?ned and incomplete.
Vulnerability to natural disasters can be thought of as comprising risk
exposure as well as the ability to cope with disaster shocks. The disaster variable
used in this analysis proxies exclusively the ï¬?rst vulnerability component and
thus is free of information on the ability to resist and recover from a natural dis-
aster. Variables such as total estimated loss as a share of GDP or number of
people injured or killed combine aspects of both vulnerability components. In
this context, it would be difï¬?cult to argue that human capital does not affect the
second component, the ability to cope with disaster shock. This would raise
3. The choice of countries is determined exclusively by data availability. The 80 countries in the
scatterplot are those for which data on all variables used in the Bayesian model averaging analysis are
available.
4. The source of disaster data is the Emergency Events Data Base (EM-DAT), which reports on
catastrophic events that meet at least one of the following criteria: 10 or more people reported killed,
100 people reported affected, a call for international assistance was issued, or a state of emergency was
declared (CRED 2004).
Crespo Cuaresma 285
serious doubts about the empirical study unless good instruments were found to
identify the exogenous component of disaster risk, a task that is extremely difï¬?-
cult in practice. Instead, the analysis concentrates on measures based on the fre-
quency of disaster occurrence that do not contain information on the magnitude
of the disaster, thus fulï¬?lling the necessary condition of exogeneity.
Figure 1 shows a weak positive relationship between disaster risk and the
education variable for total disasters, which disappears when the data are dis-
aggregated into subgroups of climate and geologic disasters. Although the
relationship is not statistically signiï¬?cant in any of the three cases reported in
ï¬?gure 1, this ï¬?rst glimpse at the relationship of interest seems to support the
conclusions in Skidmore and Toya (2002) for the aggregated data.
To extract the pure effect of disaster risk on education investment, however,
other variables that independently affect differences in educational attainment
across countries must be controlled for. Learning about the pure impact of
natural disasters on education implies formulating a potentially large model
that hypothesizes that a human capital accumulation measure depends on a set
of determinants and natural disaster risk. Obviously, the choice of extra con-
trols for a model linking disaster risk to human capital accumulation depends
on the theoretical setting. The literature presents many competing theories and
effects to explain cross-country differences in educational attainment when
assessing empirically the determinants of human capital accumulation. So that
the empirical results do not depend on a speciï¬?c theoretical (and thus econo-
metric) speciï¬?cation or a particular choice of controls, BMA methods are used
to investigate the robustness of disaster risk as a determinant of educational
attainment in the framework of model uncertainty. Model averaging methods
present a consistent framework to quantitatively assess model uncertainty
when studying problems too ambiguous or theoretical complex to be convin-
cingly represented by a single speciï¬?cation.
II . A N E M PI R I CA L A N A LY S I S O F TH E EF FE C T O F DI S A S T E R R I S K ON
H U M A N CA P I TA L AC C U M U L AT I O N
This section assesses natural disaster risk as a determinant of differences in
school enrollment in both a cross-section and a panel of countries using BMA.
It also assesses subsample heterogeneity in the response of human capital
accumulation to disaster risk.
Model Uncertainty
The effect of catastrophic risk on human capital accumulation is estimated
using linear econometric models of the type:
X
K
Ã°2Ãž ei Â¼ a Ã¾ bdi Ã¾ gj xj Ã¾ 1i; ;
j Â¼1
286 THE WORLD BANK ECONOMIC REVIEW
where ei is a proxy for educational attainment, di is the disaster risk variable,
X Â¼ (x1 . . . xK) are other explanatory variables, and 1 is a zero-mean error term
with variance equal to s2. In Skidmore and Toya (2002), for instance, the
initial level of the educational variable and income per capita are the only vari-
ables in the X set. Because numerous variables affect educational attainment,
the aim is to obtain a measure that summarizes the effect of natural disaster
risk on human capital accumulation after taking into account the degree of
uncertainty embodied in speciï¬?cation (2) when the size of the model and the
nature of the variables in X that belong to the model are unknown.
BMA presents a consistent framework for assessing the dimension of model
uncertainty highlighted above.5 Consider a set of K variables, X of which are
potential determinants of educational attainment in a cross-country regression
framework, so that the stylized speciï¬?cation considered is given by equation (2)
Â¯
for K K. In this situation, there are 2K possible combinations of regressors,
each deï¬?ning a model Mk. The Bayesian approach implies considering model
speciï¬?cation itself as a quantity to be estimated. In this sense, it follows
immediately that, by Bayesâ€™s theorem,
PÃ°YjMk ÃžPÃ°Mk Ãž
Ã°3Ãž PÃ°Mk jYÃž Â¼
;
P2K
PÃ°YjMm ÃžPÃ°Mm Ãž
mÂ¼1
which indicates that the posterior probability of model Mk (the probability that
the model is the true one given data Y) is related to its marginal likelihood,
P(Y j Mk), and its prior probability, P(Mk), as compared with the other models
in the model space. Following Ferna Â´ ndez, Ley, and Steel (2001a), an improper
diffuse prior is set on a and s, coupled with Zellnerâ€™s (1986) g-prior on the
parameter vector, which implies that
1
Ã°4Ãž PÃ°a; b; gj ; sjMk Ãž1 NkÃ¾1 Ã°0; s2 Ã°gX0j Xj ÃžÃ€1 Ãž
s
where NkÃ¾1 is a multivariate normal distribution of dimension k Ã¾ 1, and Xj is
a matrix whose columns are given by the independent regressors in model Mk.
This setting implies that the Bayes factor (ratio of marginal likelihoods) for
two competing models, M0 and M1, is given by
Ã°k1 Ã€k0 Ãž=2 Ã€Ã°NÃ€1Ãž=2
PÃ°Y jM1 Ãž g 1 Ã¾ g Ã€ R2
1
Ã°5Ãž B1;0 Â¼ Â¼
PÃ°Y jM0 Ãž gÃ¾1 1 Ã¾ g Ã€ R2
0
Where N is the sample size, kj is the dimension of model j, and R2
j is the stan-
dard coefï¬?cient of determination for model j. Some particular values of g, the
5. Raftery (1995) and Clyde and George (2004) present general discussions of the use of BMA in
linear regressions.
Crespo Cuaresma 287
hyperparameter governing the prior over the slopes, have been systematically
used in the literature. For g Â¼ 1/N (the unit information prior), the Bayesian
information criterion should be used in forming Bayes factors (see, for
example, Kass and Wasserman 1995 and Kass and Raftery 1995), and thus
BMA weights, while the risk inï¬‚ation criterion (Foster and George, 1994) sets
g Â¼ 1/K 2.6
P(MkjY) can be used to build an estimate of the quantity of interest as, say,
a weighted average of all estimates of b, where the weights are given by the
posterior probability of each model from which the estimate was obtained,
X
Ã°6Ãž EÃ°bjY Ãž Â¼ EÃ°bjY ; Mk ÃžPÃ°Mk jY Ãž:
k
Similarly, model averaged estimates of the posterior variance of b can be com-
puted from the model averaged variance of the estimate, which in this setting
summarizes information about the precision not only for a given model, but
also across models.
While the method has been put forward in the setting of a cross-sectional
dataset, it can be generalized to panel data in a straightforward fashion using
the Frisch-Waugh-Lovell theorem. In particular, in models with cross-sectional
ï¬?xed effects, the method can be applied to deviations of the mean for each
cross-section (the within transformation) or to the mean of the cross-sections
for each period when ï¬?xed period effects are assumed. The method is used
here to estimate the effects of natural disaster risk on secondary school enroll-
ment, which are robust to model uncertainty. In addition to the distribution of
the estimated parameter, also of interest here is whether the data support the
inclusion of natural disaster risk in speciï¬?cations explaining differences in sec-
ondary school enrollment. This characteristic can be estimated by summing the
posterior probability of the models containing the natural disaster variable, a
statistic referred to as the posterior inclusion probability of the variable.
The Empirical Setting
A group of variables identiï¬?ed in the literature as important determinants of
differences in human capital accumulation across countries are added as poten-
tial regressors in speciï¬?cation (2). The focus is on secondary school enrollment
as the variable of interest, and thus the analysis aims to explain the ï¬‚ow of
human capital (its accumulation) rather than its stock (which is usually
measured by mean years of schooling). This focus is justiï¬?ed because primary
schooling is compulsory in most countries in the sample and because the most
important results for the issue under study were obtained using gross secondary
school enrollment as the human capital variable (Skidmore and Toya 2002).
6. FernaÂ´ ndez, Ley, and Steel (2001a) recommend using a benchmark prior based on the size of the
group of potential regressors compared with sample size, so that g Â¼ 1/max(K2,N).
288 THE WORLD BANK ECONOMIC REVIEW
Table 1 presents the regressors used in the BMA exercise. As potential expla-
natory variables, proxies for initial income ( y0) and initial school enrollment
(e0) account for wealth-induced human capital accumulation effects and for
the observed persistence of human capital accumulation variables across and
within countries and for their potential convergence across countries. National
Gini coefï¬?cients capture differences in income distribution across economies,
and the standard deviation of annual GDP growth rates is used as a proxy in
analyzing the potential effect of macroeconomic instability. Life expectancy at
birth in the initial period controls for differences in health. Credit constraints
are included in the model using domestic credit to the private sector as a per-
centage of GDP as a proxy for ï¬?nancial depth.
The quality of political institutions is controlled for with the help of the
Polity IV database, which offers a score variable ( polity2) that quantiï¬?es a
countryâ€™s political system based on competitiveness and openness of executive
recruitment, constraints on the chief executive, regulation, and competitiveness
of participation. The polity2 measure ranges from â€“10 to Ã¾ 10, where â€“10
implies a strongly autocratic regime and Ã¾ 10 a strongly democratic regime.
The models also control for war in a given country during the period under
study.
T A B L E 1 . Variables and Deï¬?nitions
Variable Description Source
e Gross secondary school enrollment, average World Bank 2006
1980â€“ 2000
e0 Initial gross secondary school enrollment, 1980 World Bank 2006
y0 Initial level of GDP per capita, 1980 World Bank 2006
gini Gini index for income World Bank 2006
life0 Life expectancy, 1980 World Bank 2006
vol Volatility of GDP per capita growth World Bank 2006
polity Polity 2 indicator Marshall and Jaggers 1995
pavroad Percentage of paved roads World Bank 2006
cred Credit to private sector (percent of GDP) World Bank 2006
area Land area World Bank 2006
popdens Population density, 1980 World Bank 2006
inv Investment in physical capital, 1980 Heston, Summers, and Aten
2006
war Dummy variable for occurrence of war â€”
laam Dummy variable for Latin America and Caribbean â€”
asia Dummy variable for Asia and Paciï¬?c â€”
safrica Dummy variable for Sub-Saharan Africa â€”
nafrica Dummy variable for North Africa and Middle East â€”
Disaster risk, based on total disasters per million CRED 2004
inhabitants
Disaster risk, based on climate disasters per million CRED 2004
inhabitants
Disaster risk, based on geologic disasters per million CRED 2004
inhabitants
Crespo Cuaresma 289
To control for the effect of country characteristics other than disaster risk
on human capital investment, variables measuring total area and population
density are included. Physical investment as a percentage of GDP is also con-
sidered as a potential determinant of human capital accumulation, to capture
the complementarity or substitutability effects of physical and human capital.
Regional dummy variables (for Asia and Paciï¬?c, Latin America and the
Caribbean, North Africa and the Middle East, and Sub-Saharan Africa are
added to the set of potential determinants of enrollment rates. The cross-
country dataset contains data on all 80 countries for which all variables in
table 1 are available.7 Table 2 presents descriptive statistics for all variables
over 1980â€“2000, as well as for the dataset divided into 10- and 5-year
periods.
Several empirical studies of the determinants of schooling have used these
variables in econometric models. Flug, Spilimbergo, Wachtenheim (1998), for
instance, assess the effect of macroeconomic volatility on investment in edu-
cation and present models that control for income inequality, credit market
development, initial per capita income, and initial education levels. Some
studies have noted the importance of social and political institutions as factors
affecting human capital accumulation (Stijns 2006).
The results of the BMA exercise, obtained by averaging over the full model
space, are presented in table 3.8 Before the analysis, the variables were standar-
dized by subtracting the mean and dividing by the standard deviation, so the
resulting parameter estimates should be interpreted as the effect of increasing
the variable by one standard deviation. The table reports the posterior
inclusion probability of each variable computed as the sum of the posterior
probability of the models including that variable plus the mean of the posterior
distribution of the parameter attached to the variable and its standard devi-
ation. The posterior inclusion probability can be interpreted as the probability
that a given variable belongs to the true model. Explanatory variables are
classiï¬?ed as robust if the probability that the variable belongs to the model
increases is higher than the prior inclusion probability of the variable. For the
BMA results in table 3, a diffuse prior was imposed over the model space, so
7. The countries in the sample are Algeria, Australia, Austria, Bangladesh, Belgium, Bolivia,
Botswana, Brazil, Burkina Faso, Burundi, Cameroon, Canada, Central African Republic, China,
Colombia, Costa Rica, Co Ë† te dâ€™Ivore, Denmark, Dominican Republic, Ecuador, Egypt, El Salvador,
Finland, France, Gambia, Ghana, Greece, Guatemala, Honduras, India, Indonesia, Iran, Ireland, Israel,
Italy, Jamaica, Japan, Jordan, Kenya, Republic of Korea, Lesotho, Malawi, Malaysia, Mali, Mauritania,
Mexico, Morocco, Nepal, Netherlands, New Zealand, Nicaragua, Niger, Nigeria, Norway, Pakistan,
Panama, Papua New Guinea, Paraguay, Peru, Philippines, Portugal, Rwanda, Senegal, Sierra Leone,
Singapore, Spain, Sri Lanka, Sweden, Switzerland, Thailand, Trinidad and Tobago, Tunisia, Turkey,
Uganda, United Kingdom, United States, Uruguay, Repu Â´ blica Bolivariana de Venezuela, Zambia, and
Zimbabwe.
8. In many other applications, the size of the model space renders the computation of all models
intractable, and Markov Chain Monte Carlo methods tend to be used to reduce the number of models
to be estimated.
290
T A B L E 2 . Descriptive Statistics
Cross-country data Ten-year panel Five-year panel
Standard Standard Standard
Variable Mean Median Maximum Minimum deviation Mean Median Maximum Minimum deviation Mean Median Maximum Minimum deviation
e 58.721 54.217 125.871 6.226 35.124 61.660 59.396 160.763 5.618 35.913 59.146 55.828 160.763 3.572 35.882
e0 44.919 38.777 104.812 2.697 30.285 49.592 43.370 119.509 2.697 31.483 52.942 47.527 142.488 2.697 33.520
y0 8.333 8.334 10.088 6.579 1.065 8.406 8.447 10.223 6.526 1.091 8.421 8.466 10.284 6.522 1.102
life0 61.118 61.920 76.092 35.403 11.138 62.378 65.614 78.837 0.000 12.532 63.481 66.417 79.531 35.196 11.401
vol 3.771 3.657 15.327 1.160 2.086 2.990 2.492 8.706 0.581 1.744 3.110 2.389 27.554 0.258 2.490
THE WORLD BANK ECONOMIC REVIEW
polity 1.088 1.500 10.000 2 10.000 7.736 2.145 6.000 10.000 2 10.000 7.588 2.671 6.000 10.000 2 9.000 7.343
pavroad 45.039 39.567 100.000 4.657 27.264 45.568 45.808 100.000 4.300 26.471 45.504 45.808 100.000 4.300 26.095
gini 42.205 40.555 63.010 24.700 10.296 41.957 40.150 63.010 24.700 9.910 41.958 40.270 63.010 24.700 9.939
cred 35.935 29.135 122.146 0.965 25.990 41.100 30.873 175.731 0.000 33.361 41.872 30.683 180.509 0.965 34.805
area 0.979 0.296 9.327 0.001 2.100 0.930 0.294 9.327 0.001 1.997 0.972 0.296 9.327 0.001 2.063
popdens 0.132 0.045 3.603 0.002 0.408 0.131 0.054 4.084 0.002 0.363 0.139 0.052 4.548 0.002 0.423
inv 23.113 23.290 56.490 1.470 10.898 20.546 19.705 56.100 3.030 10.697 21.307 21.320 56.490 1.470 10.840
dt 1.004 0.912 2.394 0.000 0.577 0.081 0.048 0.395 0.000 0.080 0.084 0.054 0.477 0.000 0.087
dc 0.875 0.739 2.334 0.000 0.526 0.066 0.041 0.377 0.000 0.067 0.068 0.044 0.445 0.000 0.075
dg 0.279 0.067 1.781 0.000 0.410 0.017 0.002 0.195 0.000 0.034 0.018 0.000 0.322 0.000 0.039
Source: Authorâ€™s analysis based on data described in the text.
T A B L E 3 . Bayesian Model Averaging Results for Cross-section of Countries
Total disasters Climate disasters Geologic disasters
Variable PIP PM PSD PIP PM PSD PIP PM PSD
eo 0.999 0.609 0.074 0.999 0.610 0.076 0.999 0.615 0.077
y0 0.958 0.259 0.096 0.859 0.191 0.106 0.880 0.202 0.106
life0 0.716 0.15 0.12 0.934 0.237 0.102 0.920 0.228 0.104
vol 0.195 2 0.00 0.022 0.184 2 0.00 0.022 0.201 2 0.00 0.024
polity 0.158 0.005 0.021 0.108 0.000 0.013 0.108 0.000 0.013
pavroad 0.150 2 0.00 0.014 0.145 2 0.00 0.014 0.140 2 0.00 0.014
gini 0.127 0.002 0.018 0.117 0.001 0.016 0.122 0.002 0.017
cred 0.891 2 0.10 0.053 0.815 2 0.09 0.057 0.840 2 0.09 0.057
war 0.149 0.004 0.016 0.122 0.002 0.012 0.119 0.001 0.012
area 0.13 0.002 0.012 0.153 0.004 0.015 0.148 0.003 0.014
popdens 0.35 2 0.01 0.029 0.24 2 0.01 0.023 0.274 2 0.01 0.026
inv 0.138 0.003 0.014 0.136 0.003 0.014 0.136 0.003 0.014
safr 0.567 2 0.07 0.080 0.265 2 0.02 0.061 0.264 2 0.02 0.060
nafr 0.174 0.004 0.020 0.170 0.004 0.019 0.164 0.003 0.018
asia 0.224 2 0.01 0.037 0.250 2 0.01 0.042 0.234 2 0.01 0.041
laam 0.945 2 0.12 0.051 0.996 2 0.15 0.043 0.987 2 0.14 0.047
total disasters, dt 0.318 2 0.01 0.033 â€” â€” â€” â€” â€” â€”
clim. disasters, dc â€” â€” â€” 0.134 2 0.00 0.015 â€” â€” â€”
geol. disasters dg â€” â€” â€” â€” â€” â€” 0.868 2 0.08 0.049
g-prior BIC BIC BIC
Prior model size 8.5 8.5 8.5
Number of observations 80 80 80
Number of models 131,072 131,072 131,072
PIP is posterior inclusion probability, PM is posterior mean, PSD is posterior standard deviation, and BIC is Bayesian information criterion.
Crespo Cuaresma
Note: Values in italics have a PIP higher than 0.5.
Source: Authorâ€™s analysis based on data described in the text.
291
292 THE WORLD BANK ECONOMIC REVIEW
Â¯
that P(Mf ) Â¼ 1/2K for all f, implying an average prior model size of K Â¯ /2 and a
prior inclusion probability of 0.5 for each regressor.
The results in table 3 include a group of regressors with a different natural
disaster risk proxy in each set of covariates (all disasters, climate disasters, and
geologic disasters). The initial educational attainment variable and the initial
per capita income are highly robust in explaining secondary education enroll-
ment. The parameter attached to initial educational attainment is estimated
very precisely, and its posterior distribution has a mean below unity, implying
(conditional) convergence in secondary school enrollment levels across
countries. Initial income level and life expectancy also appear as robust deter-
minants of school enrollment, with positive effects that are estimated with
good precision. The regional dummy variables for Latin American countries is
robust and negatively related to school enrollment, while that for Sub-Saharan
Africa is marginally robust in one of the two settings. The results for the credit
variable, which are robust but negatively related to school enrollment, are sur-
prising and counterintuitive; they seem to be caused by the credit variableâ€™s
high correlation with the regional dummy variables. In other settings that
excluded the regional dummy variables and single variables, the credit variable
was no longer robust, while all other results were unchanged. The effects of
the other nondisaster variables were neither robust in posterior inclusion prob-
ability nor estimated with precision.
The results for the natural disaster risk variables shed light on the channels
between human capital accumulation and catastrophic risk. When data on all
disasters or climate disasters are used, the implied risk levels do not appear to
be robustly linked to school enrollment. For geologic disasters, however, the
risk variable is robust and negatively linked to educational attainment, and the
effect is well estimated, with a ratio of posterior mean to posterior standard
deviation of around 1.7.9 The results imply that the decline in secondary
school enrollment for the mean country associated with geologic disaster risk is
around 2.13 percentage points higher than that for a country with zero disaster
risk. The maximum disaster riskâ€“driven effect implies an approximately 13.6
percentage point decline in secondary school enrollment.
Table 4 shows the results for the estimated models with the highest posterior
probability. The setting with geologic disasters as the natural disaster risk vari-
able belongs to the best model for posterior probability, which would have
been the chosen speciï¬?cation had model selection been used instead of model
9. Although the ratio of the posterior mean to the posterior standard deviation is often used as a
measure of precision in estimating the effect of an independent variable on the dependent variable, the
usefulness of this statistic depends on the shape of the posterior distribution of the corresponding
parameter. This is more the case if posterior distributions based on the full model space (and thus with
a mass point at zero) are used instead of those computed using only models that include a given
variable. Results that concentrate only on models including a given variable are not qualitatively
different from those presented here (available from the author on request).
Crespo Cuaresma 293
T A B L E 4 . Single Speciï¬?cations with Highest Posterior Probability
Variable Best model 1a Best model 2b
Intercept 0.000 (0.028) 0.000 (0.027)
e0 0.598*** (0.068) 0.594*** (0.066)
y0 0.227*** (0.069) 0.261*** (0.068)
life 0.257*** (0.074) 0.240*** (0.072)
cred 2 0.11*** (0.040) 2 0.12*** (0.039)
laam 2 0.15*** (0.031) 2 0.11*** (0.034)
Geolog. disasters â€” 2 0.07** (0.032)
Adjusted R2 0.936 0.940
Obs. 80 80
*** Signiï¬?cance at the 1 percent level; **signiï¬?cant at the 5 percent level.
Note: Numbers in parentheses are standard errors.
a. Model with the highest posterior probability in the Bayesian model averaging (BMA) setting
corresponding to columns 1 and 2 in table 2.
b. Model with the highest posterior probability in the BMA setting corresponding to column 3
in table 2.
Source: Authorâ€™s analysis based on data described in the text.
averaging. In this speciï¬?cation, the effect of geologic disaster risk on enrollment
is negative and signiï¬?cant.
Climate and geologic disasters have several differential characteristics that
can be helpful in understanding and interpreting the results of the BMA analy-
sis. Climate disasters, which tend to occur at regular intervals, are more pre-
dictable than geologic disasters, and their damage tends to be linked to
physical capital, whereas geologic disasters affect primarily human lives.10
While economists have traditionally discussed the economic impact of natural
disaster risk in terms of behavioral effects (related to the discounting of future
utility or income) in the framework of theoretical models, several other chan-
nels link natural disaster risk to educational attainment on both the supply and
demand sides. Damage to schools and other infrastructure, and teacher casual-
ties, are obvious factors affecting the supply of education in the aftermath of a
natural disaster. On the demand side, in addition to the potential indirect chan-
nels linking natural disaster risk with educational attainment through income,
several studies show that children who lose a parent tend to have lower invest-
ment in human capital, after controlling for other differences (see Gertler,
Levine, and Ames 2004). In this sense, the results can be interpreted as sup-
porting the belief that the effects of natural disasters on human capital accumu-
lation work through increased mortality risk. Apart from the fact that human
losses affect educational attainment at the aggregate level through the increased
mortality of educated individuals in disaster-prone countries, human losses also
10. Skidmore and Toya (2002) interpret the climate disaster group as proxying risks related to
physical capital and the geologic disaster groups as proxying risks related to human life.
294 THE WORLD BANK ECONOMIC REVIEW
have an effect on child labor decisions, in particular since empirical results
show that child labor is used to counteract short-run income shocks to the
household (see Duryea, Lamb, and Levison 2007 for evidence from Brazil).
Education and Disasters: Panel Setting
The results indicate that natural disaster risk is a robust variable for explaining
differences in secondary school enrollment across countries. The question natu-
rally arises whether these effects are also observable within countries. Does the
occurrence of a natural disaster reduce schooling rates immediately, so that the
effect captured in the econometric analysis is a direct consequence of the disas-
ter? Variation in disaster risk within countries could provide information on
the direct effect of disasters instead of the effect of ex ante disaster risk. Thus,
a clearer picture of the differential effect of disaster risk and disaster incidence
might be obtained by complementing the cross-country results with time vari-
ation in disaster incidence.
To assess this possibility, the analysis was conducted again, this time using
two panels based on 5- and 10-year subperiods. Because of the dynamic nature
of the speciï¬?cation (the lagged dependent variable is potentially part of the
model), estimation using country ï¬?xed effects would lead to biased estimates.
Instead, the model is estimated based on the pooled dataset using period ï¬?xed
effects.
X
K
Ã°7Ãž eit Â¼ a Ã¾ bdit Ã¾ gj x jt Ã¾ 1it; ;
jÂ¼1
Ã°8Ãž 1it Â¼ lt Ã¾ nit ;
where the error term 1it, can now be decomposed into a ï¬?xed time effect
common to all countries (lt), which summarizes common shocks to the edu-
cation variable, and the usual error term with constant variance (vit,).
The results reveal that the robust negative effect of natural disaster risk on
human capital accumulation found in the cross-country regressions disappears
when the focus is exclusively on shorter run variation in school enrollment
(table 5). Although the sign of the parameter for geologic disasters remains
negative, it is estimated with low precision and has an inclusion probability
below 0.5. The inclusion probability of the disaster variables, particularly the
geologic disaster variable, increases as the horizon under consideration moves
toward long-run comparisons. These results provide an interesting insight into
the determinants of human capital accumulation in the short and medium
runs. The posterior inclusion probabilities of the variables for the 5-year panel
show that, apart from the natural persistence of human capital accumulation
variables, only income is an important determinant of secondary school enroll-
ment rate differences. For the 10-year panel, life expectancy appears as an
additional robust variable in explaining schooling differences.
T A B L E 5 . Bayesian Model Averaging Results for Panel Setting
Variable PIP PM PSD PIP PM PSD PIP PM PSD
Five-year panel
eo 0.999 0.844 0.039 0.999 0.848 0.038 0.999 0.848 0.038
y0 0.899 0.114 0.052 0.897 0.112 0.051 0.896 0.112 0.052
life0 0.177 0.012 0.033 0.173 0.011 0.032 0.174 0.012 0.032
vol 0.062 2 0.00 0.004 0.062 2 0.00 0.004 0.062 2 0.00 0.004
polity 0.262 0.011 0.022 0.218 0.008 0.019 0.221 0.008 0.019
pavroad 0.068 2 0.00 0.004 0.068 2 0.00 0.004 0.068 2 0.00 0.004
gini 0.067 2 0.00 0.005 0.069 2 0.00 0.005 0.068 2 0.00 0.005
cred 0.078 2 0.00 0.007 0.076 2 0.00 0.007 0.076 2 0.00 0.007
war 0.072 2 0.00 0.005 0.074 2 0.00 0.005 0.074 2 0.00 0.005
area 0.290 0.008 0.015 0.311 0.009 0.016 0.309 0.009 0.016
popdens 0.085 2 0.00 0.007 0.082 2 0.00 0.006 0.082 2 0.00 0.006
inv 0.115 0.002 0.010 0.117 0.002 0.010 0.117 0.002 0.010
safr 0.114 2 0.00 0.013 0.099 2 0.00 0.012 0.099 2 0.00 0.012
asia 0.077 0.000 0.006 0.075 0.000 0.006 0.075 0.000 0.006
laam 0.182 2 0.00 0.013 0.206 2 0.00 0.014 0.202 2 0.00 0.014
nafr 0.182 0.004 0.012 0.170 0.004 0.012 0.169 0.004 0.011
total disasters, dt 0.300 2 0.00 0.016 â€” â€” â€” â€” â€” â€”
clim. disasters, dc â€” â€” â€” 0.061 0.000 0.004 â€” â€” â€”
geol. disasters dg â€” â€” â€” â€” â€” â€” 0.081 2 0.00 0.005
g-prior BIC BIC BIC
Prior model size 8.5 8.5 8.5
Number of observations 292 292 292
Number of models 131,072 131,072 131,072
Ten-year panel
eo 0.999 0.610 0.082 0.999 0.609 0.082 0.999 0.610 0.082
y0 0.900 0.232 0.111 0.902 0.233 0.111 0.900 0.232 0.111
Crespo Cuaresma
life0 0.554 0.077 0.084 0.550 0.076 0.084 0.554 0.077 0.084
(Continued)
295
296
TABLE 5. Continued
Variable PIP PM PSD PIP PM PSD PIP PM PSD
vol 0.104 2 0.00 0.018 0.104 2 0.00 0.018 0.104 2 0.00 0.018
polity 0.273 0.022 0.045 0.263 0.021 0.044 0.273 0.022 0.045
pavroad 0.124 2 0.00 0.014 0.124 2 0.00 0.015 0.124 2 0.00 0.014
gini 0.097 0.000 0.015 0.098 0.000 0.015 0.097 0.000 0.015
cred 0.098 2 0.00 0.016 0.098 2 0.00 0.016 0.098 2 0.00 0.016
war 0.107 2 0.00 0.013 0.106 2 0.00 0.013 0.107 2 0.00 0.013
area 0.339 0.021 0.035 0.345 0.021 0.035 0.339 0.021 0.035
popdens 0.095 2 0.00 0.016 0.094 2 0.00 0.016 0.095 2 0.00 0.016
inv 0.094 0.001 0.014 0.094 0.001 0.014 0.094 0.001 0.014
safr 0.191 2 0.01 0.043 0.192 2 0.01 0.043 0.191 2 0.01 0.043
THE WORLD BANK ECONOMIC REVIEW
asia 0.130 0.001 0.021 0.131 0.001 0.021 0.130 0.001 0.021
laam 0.324 2 0.02 0.041 0.340 2 0.02 0.042 0.324 2 0.02 0.041
nafr 0.241 0.013 0.031 0.241 0.013 0.031 0.241 0.013 0.031
total disasters, dt 0.156 2 0.00 0.019 â€” â€” â€” â€” â€” â€”
clim. disasters, dc â€” â€” â€” 0.088 0.000 0.010 â€” â€” â€”
geol. disasters dg â€” â€” â€” â€” â€” â€” 0.156 2 0.00 0.019
g-prior BIC BIC BIC
Prior model size 8.5 8.5 8.5
Number of observations 292 292 292
Number of models 131,072 131,072 131,072
PIP is posterior inclusion probability, PM is posterior mean, PSD is posterior standard deviation, and BIC is Bayesian information criterion.
Note: Values in italics have a PIP higher than 0.5. All models include period ï¬?xed effects.
Source: Authorâ€™s analysis based on data described in the text.
Crespo Cuaresma 297
The models were also estimated using country and period ï¬?xed effects, but
excluding the initial level of schooling from the pool of potential explanatory
variables. The results are unchanged for natural disaster risk but differ for
other explanatory variables. In particular, the BMA estimate of the effect of
credit to the private sector is very robust and positively related to schooling,
implying that credit constraints have a strong inï¬‚uence on medium-run human
capital accumulation dynamics. A comparison of this result to the previous
estimates implies that credit constraints are a robust determinant of schooling
within countries but not necessarily across countries. These results complement
those of Flug, Spilimbergo, and Wachtenheim (1998).11
Parameter Heterogeneity and Interaction Effects
An important question is whether the effect of natural disaster risk on human
capital accumulation depends on other country characteristics. Studies have
found that the effects of natural disaster risk on several macroeconomic vari-
ables are modulated by institutional and economic factors. Noy (2009) shows
that the GDP costs depend on the strength of a countryâ€™s institutions, as well
as on the level of income per capita. Similarly, Crespo Cuaresma, Hlouskova,
and Obersteiner (2008) ï¬?nd that the potential positive effects of disasters on
technology imports exist only for more developed countries, not for poor econ-
omies. The usual approach to assessing heterogeneity in elasticities is to include
interaction terms. In this case, the class of models considered for the cross-
country case is given by
X
K
Ã°9Ãž ei Â¼ a Ã¾ bdi Ã¾ hdi zi Ã¾ gj xj Ã¾ 1i; ;
jÂ¼1
where variable z (in this case, z [ X, although that need not be so in all cases)
is responsible for explaining differences in the elasticity of school enrollment to
disaster risk.
There is some debate in the literature on how to treat interaction terms in
the framework of variable selection and BMA. While some analysts include the
interaction as an extra linear covariate in the model, without setting any par-
ticular prior structure on models including the product of variables (see
Masanjala and Papageorgiou 2008), others provide special treatment to models
with interaction terms (see Chipman 1996 for a general discussion and Crespo
Cuaresma, Doppelhofer, and Feldkircher 2008 and Crespo Cuaresma forth-
coming for applications).
The main problem in interpreting BMA results when the interaction term is
considered a standard variable and the model averages over all possible combi-
nations of variables is that some estimates will be based on models that include
the interaction terms but do not specify the main effect of the interacted
11. The detailed results are available from the author.
298 THE WORLD BANK ECONOMIC REVIEW
variables (the â€œparent variablesâ€?). This can lead to improper interpretation of
the interaction effect, since the absence of the parent variables in the speciï¬?ca-
tion implies that the interaction term may actually be capturing the direct
effect of one or both of the parent variables. In this sense, if the aim is to fulï¬?ll
Chipmanâ€™s (1996) strong heredity principle, only models that include both the
interaction term and the parent terms should be considered. For instance, in a
more general setting, with standard variables and an interaction term (consist-
ing of variables from the former group), standard BMA would imply averaging
over all possible combinations of these variables. But the strong heredity prin-
ciple requires excluding model speciï¬?cations that include the interaction term
without the parent variables, which means that 2K-1 Ã¾ 2K-3 models would be
evaluated.
Both approaches are applied to the dataset to evaluate the existence of sub-
sample heterogeneity in the effects of natural disaster risk on human capital.
Different model spaces are evaluated, each containing potential interactions of
the disaster variable with the initial level of school enrollment, the level of
income per capita, the political regime, and the degree of credit constraint.
Thus, BMA estimates are alternatively obtained for model spaces deï¬?ned by
the speciï¬?cation in equation (9) with the interaction variable z given by each
one of these covariates. Table 6 presents the posterior inclusion probability,
posterior mean, and posterior standard deviation for the interaction terms for
model spaces comprising all combinations of all possible variables plus the
interaction term and for model spaces respecting the strong heredity prin-
ciple.12 Several interesting results emerge. There is little evidence for robust het-
erogeneous effects of natural disasters on education. In the results obtained by
imposing the strong heredity principle, the only interaction with a posterior
inclusion probability higher than 0.5 is for the combined effect of geologic dis-
asters and political regime ( polity) in the cross-section setting. The BMA esti-
mate indicates that, ceteris paribus, school enrollment is more sensitive to
natural disasters in democratic countries. A similar negative effect is found in
the 10-year panel using the standard BMA prior across models instead of the
strong heredity prior.
Other Robustness Checks
Other robustness checks were also performed to ensure that the results are not
driven by the prior structure imposed on the BMA procedure. The results are
robust to changing the parameter prior from the unit information prior to the
risk inï¬‚ation criterion as well as to the use of a hyperprior on model size as
proposed by Ley and Steel (2009). For the cross-country setting, BMA was con-
ducted on an alternative set of covariates, enlarging the group of explanatory
12. Complete results for all other variables are available from the author. The results presented in
previous sections are not qualitatively affected by the inclusion of the interaction terms as extra
variables.
Crespo Cuaresma 299
T A B L E 6 . Bayesian Model Averaging Results for Interaction Terms
Standard Bayesian model
averaging Strong heredity priora
Variable PIP PM PSD PIP PM PSD
Cross-section of countries
Total disasters* eo 0.314 2 0.02 0.048 0.048 2 0.00 0.023
Clim. disasters * eo 0.185 2 0.01 0.031 0.025 2 0.00 0.018
Geol. disasters * eo 0.266 2 0.01 0.039 0.095 0.000 0.020
Total disasters* y0 0.371 2 0.05 0.154 0.122 2 0.05 0.180
Clim. disasters * y0 0.155 2 0.01 0.069 0.031 2 0.00 0.072
Geol. disasters * y0 0.636 2 0.18 0.337 0.336 2 0.25 0.443
Total disasters* polity 0.475 2 0.05 0.080 0.140 2 0.01 0.044
Clim. disasters * polity 0.180 2 0.00 0.028 0.026 2 0.00 0.010
Geol. disasters * polity 0.951 2 0.11 0.045 0.736 2 0.07 0.059
Total disasters* cred 0.878 2 0.11 0.057 0.186 2 0.01 0.042
Clim. disasters * cred 0.624 2 0.06 0.062 0.467 2 0.06 0.076
Geol. disasters * cred 0.624 2 0.06 0.060 0.268 2 0.03 0.064
Five-year panel
Total disasters* eo 0.069 2 0.00 0.005 0.005 0.000 0.002
Clim. disasters * eo 0.061 0.000 0.005 0.003 2 0.00 0.002
Geol. disasters * eo 0.179 2 0.00 0.012 0.018 2 0.00 0.004
Total disasters* y0 0.081 0.000 0.005 0.004 2 0.00 0.002
Clim. disasters * y0 0.061 2 0.00 0.009 0.003 2 0.00 0.008
Geol. disasters * y0 0.269 2 0.01 0.053 0.028 2 0.00 0.058
Total disasters* polity 0.102 2 0.00 0.009 0.003 2 0.00 0.002
Clim. disasters * polity 0.064 0.000 0.004 0.000 2 0.00 0.000
Geol. disasters * polity 0.483 2 0.02 0.026 0.044 2 0.00 0.017
Total disasters* cred 0.164 2 0.00 0.012 0.000 2 0.00 0.001
Clim. disasters * cred 0.107 2 0.00 0.008 0.000 2 0.00 0.001
Geol. disasters * cred 0.168 2 0.00 0.011 0.001 2 0.00 0.000
Ten-year panel
Total disasters* eo 0.142 2 0.00 0.022 0.014 2 0.00 0.010
Clim. disasters * eo 0.093 2 0.00 0.015 0.009 2 0.00 0.009
Geol. disasters * eo 0.399 2 0.03 0.045 0.068 2 0.00 0.020
Total disasters* y0 0.153 2 0.00 0.015 0.014 2 0.00 0.009
Clim. disasters * y0 0.089 2 0.00 0.030 0.007 2 0.00 0.027
Geol. disasters * y0 0.527 2 0.14 0.338 0.182 2 0.16 0.433
Total disasters* polity 0.143 2 0.00 0.025 0.007 2 0.00 0.007
Clim. disasters * polity 0.090 0.000 0.013 0.002 2 0.00 0.002
Geol. disasters * polity 0.592 2 0.06 0.061 0.089 2 0.00 0.036
Total disasters* cred 0.223 2 0.01 0.028 0.002 2 0.00 0.004
Clim. disasters * cred 0.154 2 0.00 0.022 0.001 2 0.00 0.004
Geol. disasters * cred 0.205 2 0.01 0.027 0.006 2 0.00 0.003
PIP is posterior inclusion probability, PM is posterior mean, and PSD is posterior standard
deviation.
Note: Values in italics have a PIP higher than 0.5.
a. Bayesian model averaging using only models that include the parent variables of the inter-
action terms.
Source: Authorâ€™s analysis based on data described in the text.
300 THE WORLD BANK ECONOMIC REVIEW
variables in table 1 by an extra variable that measures the percentage of moun-
tainous terrain in the countries. This variable controls for geographic and topo-
graphic effects that may be correlated with the disaster risk variables but that
exert an independent effect on human capital investment (for instance, by
affecting the return of infrastructure in terms of providing access to schools
and thus affecting school enrollment). The BMA results for the importance and
size of the effect of geologic disaster risk were essentially unchanged, while the
mountainous terrain variable achieved a low posterior inclusion probability.
To assess the impact of inï¬‚uential observations, BMA parameters and
inclusion probabilities were estimated based on subsamples. The results for the
long-run effects of geologic disaster risk on secondary school enrollment rates
are robust to the following changes in the dataset:
â€ Excluding the observations for disasters with the highest ratio of affected
individuals per square kilometer (so as not to reduce the estimation
sample dramatically, the cut-point was set at percentiles of the distri-
bution of affected people by area ranging from the 80th to the 95th).
â€ Excluding the observations for the poorest countries in the sample
(thresholds based on observed income levels ranging up to the 30th per-
centile were tried).
â€ Excluding the observations for zero disasters, so that the results are not
driven exclusively by the differences between observations with zero dis-
aster risk and those with a positive disaster risk.
â€ Excluding the ï¬?ve observations identiï¬?ed as outliers through inspection
of the residuals of the speciï¬?cation that includes all potential variables.
This change intensiï¬?es the effect of disasters on schooling, with the geo-
logic disaster variable achieving even higher posterior inclusion prob-
ability and a higher estimated effect in absolute value.
â€ Allowing for differential effects in developed and developing countries.
In this case, there is strong evidence of homogeneity of the effect across
subsamples.
III. CONCLUSIONS
The effects of natural disaster risk on human capital accumulation have
received little attention in the academic literature. This article offers a ï¬?rst,
fully ï¬‚edged empirical study of the effects of natural disasters on secondary
school enrollment across countries. To avoid reaching conclusions that are
driven by single speciï¬?cations, Bayesian model averaging techniques were used
to assess the robustness and size of the effects of natural disaster risk on
human capital accumulation.
The results offer strong evidence of the negative effects of geologic natural
disaster risk on secondary school enrollment rates and complement the case
Crespo Cuaresma 301
study literature. The effects tend to be homogeneous across countries and do
not depend on income or the degree of human capital accumulation within a
country. The empirical results presented here are robust to numerous variations
in setting.
The evidence presented in this article unveils a negative effect of natural dis-
aster risk that had hitherto been largely ignored in the academic literature.
Further research on the issue should concentrate on isolating empirically the
channels leading to the aggregate effect of disasters on educational attainment
found in this analysis.
REFERENCES
Albala-Bertrand, J. 1993a. â€œNatural Disaster Situations and Growth: A Macroeconomic Model for
Sudden Disaster Impacts.â€? World Development 21: 1417â€“34.
â€”â€”â€”. 1993b. Political Economy of Large Natural Disasters. Oxford, UK: Clarendon Press.
Asian Development Bank and World Bank. 2005. â€œPakistan 2005 Earthquake Preliminary Damage and
Needs Assessment.â€? Asian Development Bank and World Bank, Islamabad.
Â´a-Pen
Checchi, D., and C. GarcÄ± Ëœ alosa. 2004. â€œRisk and the Distribution of Human Capital.â€? Economics
Letters 82: 53 â€“61.
Chipman, H.A. 1996. â€œBayesian Variable Selection with Related Predictors.â€? Canadian Journal of
Statistics 24: 17 â€“36.
Clyde, M.A., and E.I. George. 2004. â€œModel Uncertainty.â€? Statistical Science 19: 81â€“ 94.
CRED (Collaborating Centre for Research on the Epidemiology of Disasters, a World Health
Organization Collaborating Centre). 2004. Emergency Events Database, EM-DAT. Universite
Â´
Catholique de Louvain, Brussels. Available at http://www.emdat.be/.
Crespo Cuaresma, J. Forthcoming. â€œHow Different Is Africa?â€?. Journal of Applied Econometrics.
Crespo Cuaresma, J., G. Doppelhofer, and M. Feldkircher. 2008. â€œThe Determinants of Economic
Growth in European Regions.â€? Working Paper 2008-26. Faculty of Economics and Statistics,
University of Innsbruck.
Crespo Cuaresma, J., J. Hlouskova, and M. Obersteiner. 2008. â€œNatural Disasters as Creative
Destruction: Evidence from Developing Countries.â€? Economic Inquiry 46: 214â€“ 26.
Dacy, D.C., and H.C. Kunreuther. 1969. The Economics of Natural Disasters. New York: Free Press.
Duryea, S., D. Lamb, and D. Levison. 2007. â€œEffects of Economic Shocks on Childrenâ€™s Employment
and Schooling in Brazil.â€? Journal of Development Economics 84: 188â€“ 214.
Â´ ndez, C., E. Ley, and M.F. Steel. 2001a. â€œBenchmark Priors for Bayesian Model Averaging.â€?
Ferna
Journal of Econometrics 100: 381 â€“427.
â€”â€”â€”. 2001b. â€œModel Uncertainty in Cross-Country Growth Regressions.â€? Journal of Applied
Econometrics 16: 563â€“ 76.
Flug, K., A. Spilimbergo, and E. Wachtenheim. 1998. â€œInvestment in Education: Do Economic
Volatility and Credit Constraints Matter?â€? Journal of Development Economics 55: 465â€“ 81.
Foster, D.P., and E.I. George. 1994. â€œThe Risk Inï¬‚ation Criterion for Multiple Regression.â€? Annals of
Statistics 22: 1947â€“ 75.
Hanushek, E.A., J.F. Kain, and S.G. Rivkin. 2004. â€œDisruption versus Tiebout Improvement: The Costs
and Beneï¬?ts of Switching Schools.â€? Journal of Public Economics 88: 1721â€“46.
Heston, A., R. Summers, and B. Aten. 2006. Penn World Table Version 6.2. University of Pennsylvania,
Center for International Comparisons of Production, Income, and Prices, Philadelphia, Pa. Available
at http://pwt.econ.upenn.edu.
302 THE WORLD BANK ECONOMIC REVIEW
Gertler, P., D.I. Levine, and M. Ames. 2004. Schooling and Parental Death. Review of Economics and
Statistics 86: 211â€“ 25.
Guha-Sapir, D., and R. Below. 2002. â€œThe Quality and Accuracy of Disaster Data: A Comparative
Analysis of 3 Global Data Sets.â€? Working paper prepared for the Disaster Management Facility.
World Bank and Collaborating Centre for Research on the Epidemiology of Disasters, Brussels.
Kass, R.E., and A.E. Raftery. 1995. â€œBayes Factors.â€? Journal of the American Statistical Association 90,
773 â€“795.
Kass, R.E., and L. Wasserman. 1995. â€œA Reference Bayesian Test for Nested Hypotheses and Its
Relationship to the Schwarz Criterion.â€? Journal of the American Statistical Association 90: 928â€“ 34.
Kim, N. 2008. â€œImpact of Extreme Climate Events on Educational Attainment: Evidence from Cross
Section Data and Welfare Projection.â€? UNDP/ODS Working Paper. United Nations Development
Programme, New York.
Ley, E., and M.F. Steel. 2009. â€œOn the Effect of Prior Assumptions in Bayesian Model Averaging with
Applications to Growth Regression.â€? Journal of Applied Econometrics 24: 651â€“74.
Masanjala, W.H., and C. Papageorgiou. 2008. â€œRough and Lonely Road to Prosperity: A
Reexamination of the Sources of Growth in Africa Using Bayesian Model Averaging.â€? Journal of
Applied Econometrics 23: 671 â€“82.
Marshall, M.G., and K. Jaggers. 1995. Polity IV Project. Political Regime Characteristics and
Transition, 1800â€“2004, database. Version 2005. www.systemicpeace.org/polity/polity4.htm.
Noy, I. 2009. â€œThe Macroeconomic Consequences of Disasters.â€? Journal of Development Economics
88 (2): 221 â€“31.
Okuyama, Y. 2009. â€œCritical Review of Methodologies on Disaster Impact Estimation.â€? Background
paper for the joint World Bankâ€“ United Nations Assessment on the Economics of Disaster Risk
Reduction. The Global Facility for Disaster Reduction and Recovery, Washington, DC.
Raftery, A. E. 1995. â€œBayesian Model Selection for Social Research.â€? Sociological Methodology 25:
111 â€“63.
Rasmussen, T. N. 2004. â€œMacroeconomic Implications of Natural Disasters in the Caribbean.â€? IMF
Working Paper WP/04/224. International Monetary Fund, Washington, DC.
Sacerdote, B. 2008. â€œWhen the Saints Come Marching in: Effects of Hurricanes Katrina and Rita on
Student Evacuees.â€? Dartmouth College, Department of Economics, Hanover, New Hampshire.
Sala-i-Martin, X., G. Doppelhofer, and R. Miller. 2004. â€œDeterminants of Long-Term Growth: A
Bayesian Averaging of Classical Estimates (BACE) Approach.â€? American Economic Review 94:
813 â€“35.
Skidmore, M. 2001. Risk, Natural Disasters, and Household Saving in a Life Cycle Model. Japan and
the World Economy 13: 15 â€“34.
Skidmore, M., and H. Toya. 2002. Do Natural Disasters Promote Long-run Growth?, Economic
Inquiry 40: 664â€“ 87.
â€”â€”â€”. 2007. â€œEconomic Development and the Impacts of Natural Disasters.â€? Economic Letters 94:
20 â€“25.
Stijns, J-P. 2006. â€œNatural Resource Abundance and Human Capital Accumulation.â€? World
Development 34: 1060â€“83.
Tol, R., and F. Leek. 1999. â€œEconomic Analysis of Natural Disasters.â€? in T.E. Downing, A.J.
OlsthoornR.S.T. Tol eds., Climate, Change and Risk. London: Routledge.
Bank, World. 2006. World Development Indicators 2006. Washington, DC: World Bank.
Zellner, A. 1986. â€œOn Assessing Prior Distributions and Bayesian Regression Analysis with g-prior
Distributions.â€? In P.K. Goel, and A. Zellner eds., Bayesian Inference and Decision Techniques:
Essays in Honour of Bruno de Finetti. Amsterdam: North Holland.