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 quanti�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 Bank– 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 pro�ted from helpful comments by three anonymous referees, ´ 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. 280– 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 fledged empirical investigation of the effects of natural disasters on human capital accumulation across countries. This study aims to �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 �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 �nite lives, the potential effect of natural disaster risk on mortality would lower education investment in disaster-prone countries. Checchi and Garcı´a-Pen ˜ 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 Garcı´a-Pen ˜ 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 ´ ndez, Ley, and Steel 2001b and Sala-i-Martin, Doppelhofer, and Miller 2004 for applications to the identi�cation of robust determinants of economic growth). Model averaging ensures that the results are not speci�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 speci�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 �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 1980–2000 (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: Author’s analysis based on data described in the text. 284 THE WORLD BANK ECONOMIC REVIEW empirical analysis.3 Climate-related catastrophes include floods, cyclones, hur- ricanes, ice storms, snow storms, tornadoes, typhoons, storms, wild �re, drought, and cold waves; geologic disasters include volcanic eruptions, natural explosions, avalanches, landslides, earthquakes, and wave surges. Following Skidmore and Toya (2002), �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 ¼ log½1 þ ðNumber of disasters in country i=Population of country i in millionsފ: ð1Þ Existing data on quanti�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 influenced by reported losses or number of people affected, governments would have an incen- tive to overreport these �gures. On the other hand, a country’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 de�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 �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 dif�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 dif�- 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 ful�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 signi�cant in any of the three cases reported in �gure 1, this �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 speci�c theoretical (and thus econo- metric) speci�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 speci�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 ð2Þ ei ¼ a þ bdi þ gj xj þ 1i; ; j ¼1 286 THE WORLD BANK ECONOMIC REVIEW where ei is a proxy for educational attainment, di is the disaster risk variable, X ¼ (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 speci�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 speci�cation considered is given by equation (2) ¯ for K K. In this situation, there are 2K possible combinations of regressors, each de�ning a model Mk. The Bayesian approach implies considering model speci�cation itself as a quantity to be estimated. In this sense, it follows immediately that, by Bayes’s theorem, PðYjMk ÞPðMk Þ ð3Þ PðMk jYÞ ¼  ; P2K PðYjMm ÞPðMm Þ m¼1 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 ´ ndez, Ley, and Steel (2001a), an improper diffuse prior is set on a and s, coupled with Zellner’s (1986) g-prior on the parameter vector, which implies that 1 ð4Þ Pða; b; gj ; sjMk Þ1 Nkþ1 ð0; s2 ðgX0j Xj ÞÀ1 Þ s where Nkþ1 is a multivariate normal distribution of dimension k þ 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  ðk1 Àk0 Þ=2  ÀðNÀ1Þ=2 PðY jM1 Þ g 1 þ g À R2 1 ð5Þ B1;0 ¼ ¼ PðY jM0 Þ gþ1 1 þ g À R2 0 Where N is the sample size, kj is the dimension of model j, and R2 j is the stan- dard coef�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 ¼ 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 inflation criterion (Foster and George, 1994) sets g ¼ 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 ð6Þ EðbjY Þ ¼ EðbjY ; Mk ÞPðMk jY Þ: 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 �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 �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 speci�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 identi�ed in the literature as important determinants of differences in human capital accumulation across countries are added as poten- tial regressors in speci�cation (2). The focus is on secondary school enrollment as the variable of interest, and thus the analysis aims to explain the flow of human capital (its accumulation) rather than its stock (which is usually measured by mean years of schooling). This focus is justi�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. Ferna´ 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 ¼ 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 coef�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 �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 quanti�es a country’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 –10 to þ 10, where –10 implies a strongly autocratic regime and þ 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 De�nitions Variable Description Source e Gross secondary school enrollment, average World Bank 2006 1980– 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 — laam Dummy variable for Latin America and Caribbean — asia Dummy variable for Asia and Paci�c — safrica Dummy variable for Sub-Saharan Africa — nafrica Dummy variable for North Africa and Middle East — 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 Paci�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 1980–2000, 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 classi�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 ˆ te d’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 ´ 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: Author’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 — — — — — — clim. disasters, dc — — — 0.134 2 0.00 0.015 — — — geol. disasters dg — — — — — — 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: Author’s analysis based on data described in the text. 291 292 THE WORLD BANK ECONOMIC REVIEW ¯ that P(Mf ) ¼ 1/2K for all f, implying an average prior model size of K ¯ /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 variable’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 risk–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 speci�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 Speci�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 — 2 0.07** (0.032) Adjusted R2 0.936 0.940 Obs. 80 80 *** Signi�cance at the 1 percent level; **signi�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: Author’s analysis based on data described in the text. averaging. In this speci�cation, the effect of geologic disaster risk on enrollment is negative and signi�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 speci�cation (the lagged dependent variable is potentially part of the model), estimation using country �xed effects would lead to biased estimates. Instead, the model is estimated based on the pooled dataset using period �xed effects. X K ð7Þ eit ¼ a þ bdit þ gj x jt þ 1it; ; j¼1 ð8Þ 1it ¼ lt þ nit ; where the error term 1it, can now be decomposed into a �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 — — — — — — clim. disasters, dc — — — 0.061 0.000 0.004 — — — geol. disasters dg — — — — — — 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 — — — — — — clim. disasters, dc — — — 0.088 0.000 0.010 — — — geol. disasters dg — — — — — — 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 �xed effects. Source: Author’s analysis based on data described in the text. Crespo Cuaresma 297 The models were also estimated using country and period �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 influence 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 country’s institutions, as well as on the level of income per capita. Similarly, Crespo Cuaresma, Hlouskova, and Obersteiner (2008) �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 ð9Þ ei ¼ a þ bdi þ hdi zi þ gj xj þ 1i; ; j¼1 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 “parent variables�). This can lead to improper interpretation of the interaction effect, since the absence of the parent variables in the speci�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 ful�ll Chipman’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 speci�cations that include the interaction term without the parent variables, which means that 2K-1 þ 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 de�ned by the speci�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 inflation 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: Author’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 influential 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: † 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). † 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). † 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. † Excluding the �ve observations identi�ed as outliers through inspection of the residuals of the speci�cation that includes all potential variables. This change intensi�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. † 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 �rst, fully fledged empirical study of the effects of natural disasters on secondary school enrollment across countries. To avoid reaching conclusions that are driven by single speci�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. “Natural Disaster Situations and Growth: A Macroeconomic Model for Sudden Disaster Impacts.� World Development 21: 1417–34. ———. 1993b. Political Economy of Large Natural Disasters. Oxford, UK: Clarendon Press. Asian Development Bank and World Bank. 2005. “Pakistan 2005 Earthquake Preliminary Damage and Needs Assessment.� Asian Development Bank and World Bank, Islamabad. ´a-Pen Checchi, D., and C. Garcı ˜ alosa. 2004. “Risk and the Distribution of Human Capital.� Economics Letters 82: 53 –61. Chipman, H.A. 1996. “Bayesian Variable Selection with Related Predictors.� Canadian Journal of Statistics 24: 17 –36. Clyde, M.A., and E.I. George. 2004. “Model Uncertainty.� Statistical Science 19: 81– 94. CRED (Collaborating Centre for Research on the Epidemiology of Disasters, a World Health Organization Collaborating Centre). 2004. Emergency Events Database, EM-DAT. Universite ´ Catholique de Louvain, Brussels. Available at http://www.emdat.be/. Crespo Cuaresma, J. Forthcoming. “How Different Is Africa?�. Journal of Applied Econometrics. Crespo Cuaresma, J., G. Doppelhofer, and M. Feldkircher. 2008. “The Determinants of Economic Growth in European Regions.� Working Paper 2008-26. Faculty of Economics and Statistics, University of Innsbruck. Crespo Cuaresma, J., J. Hlouskova, and M. Obersteiner. 2008. “Natural Disasters as Creative Destruction: Evidence from Developing Countries.� Economic Inquiry 46: 214– 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. “Effects of Economic Shocks on Children’s Employment and Schooling in Brazil.� Journal of Development Economics 84: 188– 214. ´ ndez, C., E. Ley, and M.F. Steel. 2001a. “Benchmark Priors for Bayesian Model Averaging.� Ferna Journal of Econometrics 100: 381 –427. ———. 2001b. “Model Uncertainty in Cross-Country Growth Regressions.� Journal of Applied Econometrics 16: 563– 76. Flug, K., A. Spilimbergo, and E. Wachtenheim. 1998. “Investment in Education: Do Economic Volatility and Credit Constraints Matter?� Journal of Development Economics 55: 465– 81. Foster, D.P., and E.I. George. 1994. “The Risk Inflation Criterion for Multiple Regression.� Annals of Statistics 22: 1947– 75. Hanushek, E.A., J.F. Kain, and S.G. Rivkin. 2004. “Disruption versus Tiebout Improvement: The Costs and Bene�ts of Switching Schools.� Journal of Public Economics 88: 1721–46. 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: 211– 25. Guha-Sapir, D., and R. Below. 2002. “The Quality and Accuracy of Disaster Data: A Comparative Analysis of 3 Global Data Sets.� 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. “Bayes Factors.� Journal of the American Statistical Association 90, 773 –795. Kass, R.E., and L. Wasserman. 1995. “A Reference Bayesian Test for Nested Hypotheses and Its Relationship to the Schwarz Criterion.� Journal of the American Statistical Association 90: 928– 34. Kim, N. 2008. “Impact of Extreme Climate Events on Educational Attainment: Evidence from Cross Section Data and Welfare Projection.� UNDP/ODS Working Paper. United Nations Development Programme, New York. Ley, E., and M.F. Steel. 2009. “On the Effect of Prior Assumptions in Bayesian Model Averaging with Applications to Growth Regression.� Journal of Applied Econometrics 24: 651–74. Masanjala, W.H., and C. Papageorgiou. 2008. “Rough and Lonely Road to Prosperity: A Reexamination of the Sources of Growth in Africa Using Bayesian Model Averaging.� Journal of Applied Econometrics 23: 671 –82. Marshall, M.G., and K. Jaggers. 1995. Polity IV Project. Political Regime Characteristics and Transition, 1800–2004, database. Version 2005. www.systemicpeace.org/polity/polity4.htm. Noy, I. 2009. “The Macroeconomic Consequences of Disasters.� Journal of Development Economics 88 (2): 221 –31. Okuyama, Y. 2009. “Critical Review of Methodologies on Disaster Impact Estimation.� Background paper for the joint World Bank– United Nations Assessment on the Economics of Disaster Risk Reduction. The Global Facility for Disaster Reduction and Recovery, Washington, DC. Raftery, A. E. 1995. “Bayesian Model Selection for Social Research.� Sociological Methodology 25: 111 –63. Rasmussen, T. N. 2004. “Macroeconomic Implications of Natural Disasters in the Caribbean.� IMF Working Paper WP/04/224. International Monetary Fund, Washington, DC. Sacerdote, B. 2008. “When the Saints Come Marching in: Effects of Hurricanes Katrina and Rita on Student Evacuees.� Dartmouth College, Department of Economics, Hanover, New Hampshire. Sala-i-Martin, X., G. Doppelhofer, and R. Miller. 2004. “Determinants of Long-Term Growth: A Bayesian Averaging of Classical Estimates (BACE) Approach.� American Economic Review 94: 813 –35. Skidmore, M. 2001. Risk, Natural Disasters, and Household Saving in a Life Cycle Model. Japan and the World Economy 13: 15 –34. Skidmore, M., and H. Toya. 2002. Do Natural Disasters Promote Long-run Growth?, Economic Inquiry 40: 664– 87. ———. 2007. “Economic Development and the Impacts of Natural Disasters.� Economic Letters 94: 20 –25. Stijns, J-P. 2006. “Natural Resource Abundance and Human Capital Accumulation.� World Development 34: 1060–83. Tol, R., and F. Leek. 1999. “Economic Analysis of Natural Disasters.� 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. “On Assessing Prior Distributions and Bayesian Regression Analysis with g-prior Distributions.� In P.K. Goel, and A. Zellner eds., Bayesian Inference and Decision Techniques: Essays in Honour of Bruno de Finetti. Amsterdam: North Holland.