THREE ESSAYS EXPLORING THE IMPACT OF NATURAL DISASTERS ON

EDUCATION AND POVERTY IN EL SALVADOR AND INDONESIA

A DISSERTATION SUBMITTED TO THE GRADUATE DIVISION OF THE UNIVERSITY OF HAWAI I  IN PARTIAL FULFILLMENT OF

THE REQUIREMENTS FOR THE DEGREE OF

DOCTOR OF PHILOSOPHY

IN

ECONOMICS

AUGUST 2014

By

John V. Rush IV

Dissertation Committee:

Timothy Halliday, Chairperson Ilan Noy

Sumner La Croix Inessa Love

Makena Coffman

Keywords: Natural disasters, poverty, enrollment, Indonesia, El Salvador

 

ii    

Copyright

THREE ESSAYS EXPLORING THE IMPACT OF NATURAL DISASTERS ON EDUCATION AND POVERTY IN EL SALVADOR AND INDONESIA  Copyright © 2014 John V. Rush IV All rights reserved. No part of this document may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or by any information storage and retrieval system, without permission in writing from the author.    

iii    

Acknowledgments

 I could never have accomplished this on my own. I would like to thank Timothy Halliday

and Ilan Noy for their consistent availability, always useful advice, and encouragement. I

am also extremely grateful to Sumner La Croix, Inessa Love, and Makena Coffman for

their flexibility and their willingness to read multiple drafts of work. In addition to my

committee, numerous members of the department faculty have provided crucial

assistance in a variety of ways. I would especially like to recognize the excellent teaching

and refreshing humor of the late Gerard Russo. I was blessed to have the opportunity to

know and learn from him. The East-West Center sponsored me for the majority of my

time as a graduate student in Hawai i, and the programs and students they brought into

my life have deeply enriched my education.

Numerous other people helped me in various ways. I received assistance in

translation from Karla Borja, Carolina Beck, and Riana Agnesia. I owe a debt to Sean

Doyle, Richard Dadzie, Michael Yoder, Joel Elies, and my classmates who have shown

me how important a little help and a lot of encouragement can be at the right time.

I would never have found my calling to pursue economics without the mentorship

of Richard Schatz and Karla Borja who spent so much time teaching me economics and

preparing me for graduate school. My parents, John and Shari, have always been there for

me, and I can never repay the sacrifices they have made in support of my education.

Finally, my wife, Alexandra, has never failed to believe in me, encourage me, and take on

any burden necessary to make completing this possible. I look forward to many years

spent expressing my appreciation.

 

iv    

Abstract

The first essay investigates the relationship between natural disasters and poverty at the

district level in Indonesia. System generalized method of moments (GMM) and regional

fixed effects models are employed, and the results suggest that damage to manufacturing

facilities, hospitals, education centers, and religious buildings are important sources of

increased poverty. The results also suggest that disasters associated with real losses can

reduce inequality among the poor by primarily harming the relatively less poor. Disasters

are also associated with a lower poverty line in the case of real losses, suggesting the

estimates obtained using that measure are biased downward.

In the second essay, data on enrollment rates in primary and lower secondary

school are used to explore the ways natural disasters influence enrollment in education in

Indonesia. The estimated coefficients are obtained using regional fixed effects

regressions and suggest that disasters are generally (but not always) associated with lower

enrollment. Damage to the employment sector is more important for primary school

enrollment, while damage to agriculture and educational institutions is more important

for lower secondary school enrollment. Damage to crops is associated with higher

enrollment in lower secondary school. Additional regressions indicate that higher poverty

exacerbates the negative impact of disasters on enrollment.

In the third essay, household survey data is used to examine the impact of

earthquakes on investment in education in El Salvador. Investment in education is

measured using enrollment in and expenditures on education. Applying a difference-in-

differences approach, it is estimated that being directly affected by the earthquakes leads

to larger expenditures on education but that being located in a treated region is not

v    

associated with expenditures. A direct impact of the earthquakes is not associated with

enrollment, but being located in a treated region leads to lower enrollment in the year of

the earthquake. The negative impact of the earthquakes on enrollment dissipates quickly

as there is no association between treatment and enrollment in the year following the

earthquake.

vi    

Table of Contents

Acknowledgments ............................................................................................................ iii

Abstract ............................................................................................................................. iv

L ist of Tables ................................................................................................................... vii

L ist of F igures ................................................................................................................. viii

Chapter 1. The Impact of Natural Disasters on Poverty in Indonesia ..........................1 I. Introduction .............................................................................................................................. 1 II. Data ......................................................................................................................................... 4 III. Methodology and Results ...................................................................................................... 8 IV. Discussion ............................................................................................................................ 13 V. Conclusion ............................................................................................................................ 18

Chapter 2. The Impact of Natural Disasters on Education in Indonesia ...................36 I. Introduction ............................................................................................................................ 36 II. Data ....................................................................................................................................... 39 III. Methodology and Results .................................................................................................... 41 IV. Discussion ............................................................................................................................ 46 V. Conclusion ............................................................................................................................ 51

Chapter 3. Rural Households, Education, and The Earthquakes......................................................................................................................68

I. Introduction ............................................................................................................................ 68 II. Data and Methodology .......................................................................................................... 70 III. Results .................................................................................................................................. 76 IV. Interpretation........................................................................................................................ 77 V. Conclusion ............................................................................................................................ 80

Appendix A : Provinces Contained in Regions ..............................................................93

Appendix B : B ASIS Survey ............................................................................................94

References .......................................................................................................................102  

 

vii    

List of Tables

1.1. Summary Statistics for District Poverty Measures ............................................................. 20

1.2. Percentage Poor, P1, and P2 by Region and Year .............................................................. 20

1.3. Means and Standard Deviations of Reported District Level Disaster Damage .................. 21

1.4. Estimated Disaster Damage Coefficients from Fixed Effects Regressions ........................ 26

1.5. Estimated Disaster Damage Coefficients from System GMM Regressions ....................... 32

2.1. Means and Standard Deviations of Reported District Level Disaster Damage .................. 55

2.2. Summary Statistics for District Enrollment Rates in Secondary School ............................ 56

2.3. Est. Disaster Damage Coefficients from Fixed Effects Regressions .................................. 57

2.4. Est. Disaster Damage Coefficients from Fixed Effects Regressions, Literacy ................... 60

2.5. Est. Coefficients for Interaction between Disaster Damage and Literacy .......................... 62

2.6. Est. Disaster Damage Coefficients from Fixed Effects Regressions, Poverty .................... 64

2.7. Est. Coefficients for Interaction Between Disaster Damage and Poverty .......................... 66

3.1. National Impact of 2001 Earthquakes ................................................................................. 82

3.2. Summary of Losses (Colón) Experienced by Sample Households ..................................... 82

3.3. Education Enrollment and Real Expenditures (2001 Colón) ............................................. 82

3.4. Expenditures by Type for Different Household Groups (2001 Colón) ............................... 83

3.5. OLS DID Treatment by Household: Dependent Variable is Natural Log of Expenditure . 85

3.6. OLS DID Treatment by Region: Dependent Variable is Natural Log of Expenditure ....... 86

3.7. OLS DID Treatment by Household .................................................................................... 87

3.8. OLS DID Treatment by Region .......................................................................................... 88

3.9. OLS DID Treatment by Household .................................................................................... 89

3.10. OLS DID Treatment by Region ........................................................................................ 90

3.11. Estimated Impact of Treatment ......................................................................................... 91

3.12. Reasons for Non-Enrollment ............................................................................................ 92

viii    

List of Figures

1.1. Poverty over Time by Region ............................................................................................. 22

1.2. Disaster Outcome by Region (2003-2010) ......................................................................... 23

1.3. Damage Incidence by Year (2003 2010) (DesInventar 2013) ........................................... 24

1.4. Maps of Disaster Severity by District (DesInventar 2013) ................................................. 25

1.5. Product of Estimated Coefficient and One Standard Deviation in Impact on PL ............... 28

1.6. Product of Estimated Coefficient and One Standard Deviation in Impact on P1 ............... 29

1.7. Product of Estimated Coefficient and One Standard Deviation in Impact on P2 ............... 30

1.8. Estimated Impact by Damage Category on PL ................................................................... 31

1.9. Product of Estimated Coefficient and One Standard Deviation in Impact on P2 ............... 34

1.10. Average Estimated Impact by Damage Category on P2 ................................................... 35

2.1. Enrollment in Primary and Lower Secondary School by Region ....................................... 53

2.2. Maps of Disaster Severity by District (DesInventar 2013) ................................................. 54

2.3. Normalized Estimated Impact of Disaster Damage on Primary School Enrollment .......... 58

2.4. Normalized Estimated Impact of Disaster Damage on Lower Secondary Enrollment ....... 59

3.1. Enrollment Rates by Group (2000-2002) ............................................................................ 84

 

 

     

1  

Chapter 1. The Impact of Natural Disasters on Poverty in Indonesia

I . Introduction

Natural disasters are of growing interest to development economists and the general

public. Modern media coverage increasingly brings to the attention of citizens in more

developed countries the devastation disasters cause in the developing world. This

awareness and corresponding increase in potential sources of assistance make rigorous

Overall, exploration

of the link between natural disasters and poverty suggests that natural disasters are a force

that can aggravate poverty in developing countries (Freeman, Keen, and Mani 2003;

Jakobsen 2012). Developing countries like Indonesia are especially vulnerable to natural

disasters both because of geography and a lack of measures to prevent disaster damage

(Freeman et al. 2003). As a result, damages as a percentage of GDP and deaths resulting

from disasters are much higher in developing countries (Freeman et al. 2003, Henderson

2004).

This essay seeks to explore how disasters have contributed to poverty in Indonesia

between 2003 and 2010. Indonesia is an ideal subject for this area of research as its

geographic location makes it subject to a large number of diverse natural disasters and

because data on poverty and natural disasters are available at the district level. This

allows exploration of a neglected middle ground between the household level impact of

disasters and the national impact of disasters. Much of the literature exploring the

connection between natural disasters and poverty has focused on specific events and their

household level effects. The traditional datasets used to study disasters are almost always

made up of national level or household level data. In Indonesia, districts are an important

policy-making unit so, by using a dataset that reports disaster impacts at the district level,

this essay explores the question of poverty at a level that more closely corresponds to

policy goals and evaluation. Additionally, this approach has the advantage of a larger

number of units of observation (397) than a cross-country study, and many

cultural/historical factors that can make cross-country comparisons more difficult are less

of a problem when studying different regions in the same country.

2    

A second important contribution is the exploration of possible heterogeneity

within disaster impacts. Natural disasters can affect individuals directly through deaths,

injuries, and losses, which are the measures most commonly found in the most cited

datasets. The dataset used for this research includes those measures but also includes

damages to housing, infrastructure, education/health institutions, and private sources of

employment/production, among others. This diversity allows for an examination of which

consequences of disasters are the most potent in exacerbating poverty. For example, the

results suggest that an additional 3,784 houses damaged in the average district results in

the percentage of people in poverty rising by 0.81 percent or 757 additional people in

poverty. Disruption of an additional 88 religious buildings in the average district results

in an increase of 2.28 percent in the monthly poverty gap, and damage to an additional 40

office buildings raises the inequality-weighted poverty gap by 2.27 percent. There is also

evidence that while religious institutions are important for preventing poverty, their

support may not be directed at the neediest members of the community. The results from

a regression of the poverty line on disaster damage indicate that higher disaster damage is

sometimes associated with a lower poverty line, suggesting a downward bias in the

estimated impact of the associated damage. Perhaps most strikingly, the results suggest

that by primarily harming households with assets, some types of disasters may reduce

inequality among those in poverty.

In a study closely resembling this one (Rodriguez-Orregia, de la Fuente, de la

Torre, and Moreno 2013), the authors use difference-in-differences (DID) estimation to

investigate the relationship between experiencing a disaster between 2000 and 2005 and a

variety of social outcomes in Mexico by area. Disasters have a statistically significant and

negative association with the local Human Development Index value, and a positive

association with food, capacities, and asset poverty.

Natural disasters can contribute to poverty in a number of ways. One of the most

obvious is the loss of immediate income from labor due to deaths or injuries. If the

deceased played an important role in providing productive labor within the household or

earning labor income, this can exert immediate pressure on the household s ability to

maintain consumption or accumulate assets. Thanh, et al. (2006), engage in a longitudinal

study of Vietnamese households and find that being injured is a significant contributor to

3    

poverty. An injury increases the probability of falling into poverty and decreases the

likelihood of escaping poverty. Infrastructure is another important mechanism linking

natural disasters to poverty. Freeman (2000) describes how infrastructure destruction can

be an important creator of poverty. The poor are often extremely dependent on

infrastructure for access to labor and goods markets (Freeman et al. 2003). Damage to

health or education infrastructure could have long-term impacts on the ability of the poor

to invest in human capital, making poverty more persistent.

Another effect that has been discussed is the influence disasters have on poverty

and consumption through the destruction of assets (Berloffa and Modena 2013, Dercon

2004, Jakobsen 2012, Mechler 2009, Morris et al. 2002, Narayan 2003). Disasters have

been found to destroy assets and negatively affect asset investment (Carter, Little,

Mogues, and Negatu 2007). A number of studies find that for the poorest households,

disasters have a large impact on essential consumption. These impacts often have a

disproportionate impact on the poor (Carter et al. 2007) and are more significant for those

who have restricted access to labor markets, insurance, credit markets, or have existing

loans (Berloffa and Modena 2013, Carter et al. 2007, Dercon 2004, Jakobsen 2012,

Morris et al. 2002, Sawada and Shimizutani 2008, Shoji 2010).

Traditional consumption-smoothing theory suggests that households will use

assets to support consumption following a negative shock, but an asset poverty trap can

reverse this behavior for those near the poverty trap threshold (Berloffa and Modena

2013, Carter et al. 2007, Dercon 2004, Jakobsen 2012, Morris et al. 2002, Shoji 2010).

The lack of credit access means using assets to support consumption would result in the

household being trapped in poverty. As a result, households that already have lower

consumption from the disaster reduce consumption further to avoid liquidating assets. If

disasters do trigger sales of income-producing assets, these assets will likely be

purchased by the relatively wealthy, and the sales revenue will be quickly consumed.

Reflection on these observations has led many to suggest that disasters have a tendency to

increase inequality. As many measures of poverty, including those used in this research,

are consumption based, this provides a powerful lever by which natural disasters can

raise measured poverty in Indonesia (Rodriguez-Orregia et al. 2013).

4    

In developed countries, insurance and formal government aid play a large role in

supporting communities following a disaster as in Coffman and Noy (2011). The

presence of efficiently distributed aid on a sufficient scale could prevent disasters from

having any noticeable impact on poverty. In developing countries, such transfers are

often less significant compared to total losses and are often informal (Carter et al. 2007,

Morris et al. 2002), focusing on family, religious, and other social groups. Large disasters

may draw inflows of foreign aid, but regular smaller-scale disasters, such as those

experienced by Indonesia, do not attract the same level of foreign attention.

Section II of this essay contains a description of the data used to address the

following questions:

1) To what degree have natural disasters affected poverty in Indonesia between

2003 and 2010?

2) Is there any evidence for heterogeneity in the effects of disasters on poverty

across different types of damages?

3) Is there evidence supporting the idea that disasters increase inequality?

The answer to the first question will indicate whether existing transfer/coping

mechanisms in Indonesia are sufficient to prevent disasters from exacerbating poverty or

if they need to be supplemented by new policies. The answer to the second question will

suggest the most important targets of any new policies. Section III describes the

methodology used and summarizes the results. Section IV discusses the implication of

the results for our understanding of the relationship between disasters and poverty, and

Section V concludes the essay.

I I . Data

To investigate the influence of disasters on poverty in Indonesia, two datasets are

employed: one covering poverty in Indonesia at the district level and one reporting the

consequences of disasters in Indonesia at the district level. The data on poverty are

(Sub Direktorat

Analisis Statistik Lintas Sektor 2003 2010). The data on natural disasters are assembled

by the DesInventar Disaster Information Management System developed by, among

others, the United Nations Office for Disaster Risk Reduction (DesInventar Project Team

5    

2013). Exchange rate and GDP data used for calculations are from the World Bank

(2013).

Poverty

The first year for which poverty data are available is 2003, and the most recent year

included in this research is 2010. The poverty data are reported at the district level for the

following measures: number of poor, poor as a percentage of the population (PL), two

measures of poverty severity, and the poverty line (in monthly terms in local currency

and adjusted annually for inflation). The definition of poverty is based on the level of

consumption. The measures of poverty severity are described below, and summary

statistics can be found in Table 1.1.

The first measure is the normalized poverty gap (P1). This measures, as a

percentage of the poverty line, how much each person in the region would have to

contribute to compensate for the amount by which the expenditures of the poor fall short

of the poverty line. A larger value of P1 indicates a more severe poverty burden adjusted

for population size. If P1 rises, this means that the expenditure shortfall of the poor

relative to total population is rising. This implies that the total expenditure shortfall

(poverty gap) is rising for the district (poor people are getting poorer, or more people are

poor, or both), that the population of the district has fallen, or some combination of the

above.

The second measure is the inequality-weighted poverty gap (P2). P2 is basically

the same as P1 except that the ratio of the difference between monthly consumption by

the household and the poverty line is squared, giving more weight to extreme values. This

means that P2 can behave differently than P1. Ceteris paribus, an increase in the poverty

gap will raise P2, but P2 can also rise as a result of an increase in inequality in the

distribution of poverty. Thus, it is possible for P2 to remain unchanged even when the

poverty gap rises if this increase in the poverty gap is accompanied by a sufficient

increase in the equality of the poverty distribution. In summary, while changes in P1

indicate changes in the relative poverty burden, changes in P2 can indicate either a

change in the relative poverty burden, a change in poverty inequality, or some

combination of the two.

6    

In 2003, data are reported for 30 provinces and 416 districts. In order to maintain

consistency across time, data from any province or district created after 2003 are

organized based on 2003 geographical boundaries. Additionally, for some years there are

districts for which data are not reported; these districts are dropped. In this study there are

data on 397 districts available for 2003 2010.

Data on poverty are reported by region and year in Figure 1.1 and Table 1.1. For

all regions there is a significant decrease in the percentage of the population with

expenditures below the poverty line. For the country as a whole, the percentage of the

population classified as being in poverty fell by approximately 25 percent. The best

performing region was Kalimantan, where the proportion of the population in poverty fell

by just over 35 percent. The worst performer was Irian Jaya, where the percentage in

poverty fell by just over 16 percent. Within this universal downward trend there is also a

temporary increase in the relative number of poor that takes place between 2004 and

2006, which is followed by a steady decrease in the percentage poor in later years. This

deviation from the trend is true for all regions except Sumatra (where the increase takes

place between 2005 and 2006) and Sulawesi (where the increase continues into 2007).

For the normalized poverty gap, all regions improved over the period studied (see

Figure 1.1 and Table 1.1). Overall, the normalized poverty gap fell by just under 33

percent. The most improved region is Kalimantan (42.9 percent), and the least improved

region is Irian Jaya (7 percent). Irian Jaya is the only region that saw a significantly

greater reduction in the poor as a share of its population compared to the reduction in its

normalized poverty gap. Aside from Maluku (which reduced both by an almost identical

percentage), the other regions all saw substantially larger reductions in the normalized

poverty gap than in the relative number of poor individuals.

Within the downward trend, there is more fluctuation in the direction of

movement of P1. For the majority of regions (four out of seven), P1 falls in 2004, rises in

2005, falls in 2006 and 2007, rises in 2008, and falls in 2009 and 2010. The region with

the greatest departure from this pattern is Irian Jaya, where P1 rises in 2004 and 2005,

falls in 2006, rises in 2007, falls in 2008 and 2009, and rises in 2010. For all regions, P1

and P2 share identical patterns with the exception of Irian Jaya where P2 fell in 2004

while P1 rose (see Figure 1.1). The consistent observation of an increase in poverty

7    

immediately after 2004 at first appears to be an effect of the 2004 Indian Ocean tsunami.

However, real GDP per capita in Indonesia rose in every year of the period of study

(World Bank 2013), and the direct impact of the tsunami, while severe, was highly

localized.

Disasters

The data on disasters are obtained from DesInventar (DesInventar Project Team 2013).

Between 2003 and 2010, the majority of disaster events in Indonesia were categorized as

floods or strong winds followed by droughts and landslides. Indonesia is subject to

frequent natural disasters.

experience at least one natural disaster, and many experience multiple disasters. In

Figures 1.2 and 1.3, three basic measures of disaster impact have been selected: deaths,

injuries, and damage to housing resulting from disasters. These figures show the

prevalence of disasters by region and over time. In the full dataset, there are data on a

much larger number of effects including damage to crops, damaged roads, population

affected, damaged manufacturing facilities, damaged/disrupted educational facilities, and

disrupted hospitals, among others*. Summary statistics for the disaster measures used in

this paper can be found in Table 1.3.

As can be seen in Figures 1.2 and 1.3, deaths and destruction of housing from the

As the primary goal of this essay is to investigate the impact of Indo

disaster experience, the regressions will use a sample that does not include the province

most affected by the tsunami. However, identical regressions will be estimated using the

full sample, and noteworthy differences will be discussed.

The severity of a disaster is not something that can be measured on the surface but

time of the disaster. Having access to a wide variety of measures presents the interesting

opportunity to examine not just the impact of disasters on poverty, but the possibility that

different types of disaster impacts may have different effects on poverty. The maps in

                                                                                                                     *A small number of disaster measures have a parallel at the national level in the EM-DAT database published by the Center for Research on the Epidemiology of Disasters (CRED). Of the comparable measures from the data, estimated real losses are the least consistent with the data from CRED.  

8    

Figure 1.4 represent the regional distribution of some disaster impacts, and the next

section explains the methodology used and the results of the regressions employed.

I I I . Methodology and Results

The purpose of this research is to investigate the influence of natural disasters on poverty.

From the literature, the expectation is that as the severity of disasters increases, the level

of poverty will also increase, all else equal. I assume that by controlling for common time

trends and constant differences between districts, I will be able to isolate the nature of the

relationship between disasters and poverty in Indonesia over the time period in question.

F ixed E ffects

To begin, the impact of different types of disaster damage on poverty will be explored

using a basic regional fixed effects model with time dummies as follows:

(1)

Where P is the level of poverty in district i at time t, D represents the disaster impact in

district i at time t relative to the population, c represents a fixed effect for each district,

is a set of time dummies, and u is the error term. A separate regression is conducted for

each type of disaster damage. As stated earlier, there are three measures of poverty

provided by the data: the percentage of the population below the poverty line (PL), the

poverty gap (P1), and the inequality-weighted poverty gap (P2). Results can be found in

Table 1.4 and are organized by poverty measure and type of damage. For discussion of

the interpretation of the results, please see Section IV. The disaster damage types can be

divided into the following broad categories: harm to individuals, population disrupted,

damage to housing, damage to human/social capital institutions, damage to agriculture,

damage to infrastructure, damage to production facilities, and real losses (reported in

local currency and adjusted for inflation).

PL

The results indicate that deaths, population missing, population affected, destruction of

housing, damage to housing, disrupted education centers, disrupted hospitals, disrupted

religious buildings, damage to irrigation, damage to manufacturing facilities, and real

losses are all statistically significant and positively associated with the percentage of the

9    

population in poverty. There are three damage measures that are statistically significant

and negatively associated with poverty: submerged houses, damage to crops, and damage

to roads.

The largest statistically significant coefficients are associated with the proportion

of the population missing, disruption of hospitals, and damage to manufacturing;

however, comparing the raw results may be misleading as these coefficients may be

larger only because the associated type of damage requires a larger disaster. For example,

hospitals and manufacturing facilities may be built to a higher structural standard than the

average private home. As a result, a more severe disaster would be required to destroy a

hospital than to destroy a home. In order to account for this possibility, the estimated

coefficients will be used to calculate the marginal impact of one-standard deviation of

each damage type. These resulting normalized impact estimates (arranged by category),

along with bars indicating plus and minus two standard errors, can be found in Figure 1.5.

Figure 1.5 illustrates that destroyed and damaged housing, disrupted hospitals,

and real losses are the largest contributors to the proportion of the population in poverty.

The standard errors leave some doubt where the relative importance of disrupted

hospitals and destroyed housing are concerned, but the importance of real losses and

damaged housing seems relatively clear. Damage to roads and submerged housing seem

to be the most important disaster impacts that are associated with a lower number of

people in poverty. These results will be addressed in the discussion section that follows.

Another way to look at the results would be to compare the average estimated impact of

statistically significant damage measures by category. This is shown in Figure 1.8. Real

losses and disruption of human/social capital institutions are on average the largest

contributors to increased poverty. Damage to infrastructure has the greatest role in a

reduction of the number of people below the poverty line.

P1

When comparing the results for P1 to those for PL, fewer damages are statistically

significant. Submerged houses, disrupted hospitals, damage to crops, damage to

irrigation, and damage to roads are no longer statistically significant. One measure

damaged office buildings is statistically significant and positively related to poverty

when no such significance was found for PL. Only one of the statistically significant

10    

coefficient estimates affected population is negatively related to poverty. When

comparing the normalized impact estimates (Figure 1.6), disrupted religious buildings,

destroyed houses, and deaths seem to be most important; however, the standard errors are

large enough to make this uncertain. What seems more certain is that real losses are

relatively less important in connecting disasters with a higher normalized poverty gap.

When comparing damage categories (Figure 1.8), one difference is that all categories

having statistically significant results are on average positively related to the poverty gap

(P1). Disruption of human/social capital institutions and harm to individuals appear to be

the most important categories, but not by a large margin. Real losses are of much smaller

importance than the other types of damage, which contrasts with the results for PL.

P2

When considering the impact of disasters on the inequality-weighted poverty gap (Figure

1.7), deaths, number missing, damage to housing, disrupted education centers, disrupted

religious buildings, damage to office buildings, and damage to manufacturing facilities

are all statistically significant and positively associated with P2. When considering

damage categories (Figure 1.8), damage to human/social capital institutions seems to be

most important followed by harm to individuals and disruption of the productive

economy. In comparison to the results for P1, the estimated coefficients for destroyed

housing and population affected are no longer statistically significant. Interestingly, the

estimated impact of real losses reverses in sign. This result seems puzzling at first, and it

will be interesting to see if it survives the more complex econometric approach applied

below.

System G M M

The data available for this research present a potential problem as the outcome of interest,

poverty, is arguably dependent on its previous realization, and disaster impact may be

related to poverty and thus not strictly exogenous. To address this concern, an equation

that includes the previous level of poverty on the right hand side is estimated as follows:

(2)

11    

Where , represents q lags of poverty for district i. To estimate this equation, an

Arellano-Bover (1995)/Blundell-Bond (1988) dynamic panel estimation method known

as system GMM is employed. This estimation was implemented in STATA using the

xtabond2 command developed by Roodman (2009). This estimator is designed for use

when the panel has a large number of individuals but a short time period, the independent

variable of interest may not be strictly exogenous, and the dependant variable depends in

part on its past realization (Roodman 2009). Under these conditions, and when the

estimations satisfy the Hansen test of over-identifying restrictions, the system GMM

method yields superior results to a standard fixed effects model. The results reported

below are obtained using two-step robust system GMM regressions including four lags of

poverty and the Windmeijer (2005) finite-sample correction to counter the downward

bias in standard errors.

Table 1.5 reports the estimated coefficient, robust corrected standard errors, and

results from the Hansen test for joint exogeneity of instruments. Of the important patterns

that appear in the results, one is the failure of the regressions examining the effect of

disaster impacts on PL to pass either the Arellano-Bond test for autocorrelation in levels

or the Hansen test. Additionally, the regressions examining the impact of disasters on P1,

while passing the test for autocorrelation in levels, fail the Hansen test. As a result, the

only reliable system GMM estimates are those for P2, the inequality-weighted poverty

gap.

P2

Of the twenty-one disaster measures used, thirteen are statistically significant at 10

percent or below (Table 1.5). Proportion of the population killed, proportion of the

population missing, destroyed housing, damage to housing, impacted education centers,

impacted hospitals, impacted religious buildings, damage to office buildings, and damage

to manufacturing facilities are all positively associated with P2. Only four types of impact

are negatively associated with P2: irrigation damage, damage to kiosks, proportion of the

population affected, and real losses.

The largest magnitude statistically significant coefficients are associated with the

proportion of the population missing, impact on hospitals, damage to office buildings,

and damage to manufacturing. For the disaster impacts that are positively related to

12    

poverty, the normalized impact estimates found in Figure 1.9 reduce the difference

between estimated impacts. However, for those impacts negatively associated with

poverty, the exercise served to widen the gap between estimated impacts. In fact, the

estimated impact of real losses becomes much more negative than the others. This result

confirms what was found in the basic fixed effect model and will be discussed in more

detail in Section IV of this essay. For the measures positively related to P2, estimated

impacts are all close to 0.01, though the impact of deaths and damage to office buildings

appears to be higher. In terms of damage categories (Figure 1.10), real losses continue to

stand out as the largest magnitude impact and are related to lower levels of P2. Harm to

individuals and damage to housing are the next largest impacts and both relate to a higher

level of P2.

Robustness

In order to check whether excluding the tsunami-affected province significantly alters the

results, the equations were re-estimated using a sample that includes the province most

affected by the Indian Ocean tsunami. When the results above are compared to those

obtained when including the province most affected by the tsunami, what stands out is

that a smaller number of damage measures are statistically associated with poverty, and

those that are statistically significant are smaller in magnitude than those found when the

tsunami province is excluded. This pattern holds for all three poverty measures and their

associated estimation techniques. The most likely explanation comes from the fact that

the immediate relationship between disasters and poverty can be difficult to identify in

the data if major disasters occur at the end of a year. The tsunami occurred at the very

end of the year, and the poverty data were most likely collected for a period of time

preceding the tsunami.

The main regressions are also re-estimated after dropping the provinces with the

highest and lowest initial levels of poverty. These test the sensitivity of the results to the

inclusion of the districts expected to be least and most able to handle disasters well. For

PL, the results are essentially identical. For P1, the only noteworthy difference is that the

estimated coefficient for disruption of hospitals is twice as large. For P2, the results are

broadly similar except that the magnitude of the estimated coefficient for real losses is

reduced by almost half, and damage to roads as well as damage to plantations/forests are

13    

statistically significant and negatively associated with P2. For the discussion of the

results in the following section, I will rely on the regression results described in Tables

1.2 and 1.3.

I V . Discussion

Percentage of the Population in Poverty (PL)

To begin discussing the impact of natural disasters on the percentage of the population in

poverty, a range of numerical examples provides some context for the results. The

highest estimated normalized impact was for damaged houses. The results suggest that a

one-standard deviation increase in damage to housing (3,784 houses in the average

district) results in the percentage of people in poverty rising by 0.13 percentage points.

For the average district in the sample with a population of 577,991 and poverty rate of

16.11 percent, this implies a 0.81 percent increase in the poverty rate or 757 additional

people in poverty (about one additional impoverished person for every five houses

damaged). A similar exercise for disruption to educational centers (the estimated impact

of which was more typical of damage measures) yields a 0.63 percent increase in the

poverty rate (587 people for an additional 90 centers disrupted). Finally, submerged

houses (4,776 houses), the impact most associated with lower poverty, produces a

decrease in the poverty rate of 0.41 percent (382 people).

When reviewing the categories of damage (Figure 1.8), damaged/destroyed

housing, disrupted hospitals, and real losses seem to be most associated with an increase

in PL. Damage to/destruction of housing and real losses both indicate that the disaster has

diminished consumption and higher measured poverty. This reduced consumption is

reinforced by efforts to rebuild the home and restore other lost capital. Interestingly,

injuries do not show up as statistically significant for PL, yet disruption of hospitals does.

Disruption of hospitals may be so highly associated with poverty because of the

importance of treatment in the immediate aftermath of a disaster. If hospitals are not

functioning, more injured people will remain less productive for a longer period of time,

increasing their chances of entering poverty. Interestingly, the proportion of the

population affected does not seem to be very important in increasing poverty. This is

14    

most likely because of the generality of the measure. People are affected in so many

different ways that the measure does not capture very much useful information.

Damage to roads was negatively associated with PL. The relationship between

damage to roads and poverty fits appealingly into a story of transfers following the

disasters. Roads are an important part of public infrastructure, and, if funding (and jobs)

to repair the roads quickly follows any damage, the negative impact of the disaster on

poverty may be counteracted. The results for submerged houses and damage to crops,

which were also negatively associated with poverty, are intriguing, and it is not clear

what mechanism could relate these to reduced poverty. It is possible that differential

effects of different disaster types have something to do with this if, for example, flooding

tends to attract more government aid than other types of disasters. This would be an

interesting question to pursue in further research.

The Normalized Poverty Gap (P1)

Disrupted/damaged religious buildings is the damage measure most associated with an

increase in the normalized poverty gap (P1). The results suggest that disruption of an

additional eighty-eight religious buildings in the average district results in an increase of

2.28 percent in the monthly poverty gap. A measure with a smaller impact on the poverty

gap, proportion of the population missing, implies that an additional eight people missing

increases the poverty gap by 1.37 percent in the average district. The measure most

negatively associated with the poverty gap indicates that an increase in the affected

population of 130,136 will lower the poverty gap by 0.15 percent.

When considering damage categories and individual measures of damage, there is

no clearly dominant type of disaster impact. However, what is interesting in comparison

to the results for PL is the relatively minor role played by real losses. While real losses

are one of the damage types most associated with an increase in the number of people

below the poverty line, they are not as relatively important in increasing the poverty gap.

This may be evidence that real losses are primarily experienced by (or measured for)

those above the poverty line. If this were the case, real losses would be associated with

pushing previously non-poor households into poverty, but would not be as associated

with the increased poverty of the majority of those already in poverty.

15    

Inequality-W eighted Poverty (P2)

The type of disaster damage most associated with the inequality-weighted poverty gap is

damage to office buildings. For the average district, damage to an additional forty office

buildings raises P2 by 2.27 percent. Disruption of an additional eighteen hospitals

increases P2 by 1.19 percent. An increase in reported real losses of $1,072 is associated

with a 4 percent reduction in P2. Unfortunately, because of the way P2 is calculated, it is

very difficult to interpret it in any conclusive way. An increase in P2 can indicate either

an increase in the normalized poverty gap or an increase in consumption inequality

among the poor.

One way to extract information from P2 is to compare the way the relative

importance of different types of damage changes between P1 and P2. In the results of this

research the patterns are very similar, although from a superficial examination it seems

that damage to office buildings is relatively more important for the inequality-weighted

poverty gap than for P1, and that disruption of religious buildings is relatively less

important for P2 compared to P1. The result for office buildings is puzzling. One would

be tempted to interpret this as a higher impact of disrupted economic activity on the

poorest, but this is not confirmed by the relative importance of disruption to

manufacturing which would seem the most logical place for such an effect to show up, as

work in office buildings is likely less open to the very poor. The result for disruption to

religious buildings could indicate that the aid religious networks provide is not targeted at

the poorest of the poor, and the disruption of that network increases the vulnerability of

the less poor more than that of the poorest.

The only clear interpretive opportunities are when the results for P1 and P2 are of

opposite sign. If P1 rises, but P2 falls, that can only be the result of a decrease in

inequality that dominates the overall increase in the poverty gap. This is observed with

respect to real losses. The declining role of real losses for increases in poverty already

observed with P1 continues with P2 to the point that its association is reversed. While

real losses are associated with an increase in the normalized poverty gap, they are also

associated with a decrease in the inequality-weighted poverty gap. The implication is that

asset loss can actually lead to a reduction in the poverty of the very poor. This reinforces

                                                                                                                     Dollar  amounts  reported  are  in  constant  2005  US  dollars.  

16    

the conclusion from the comparison between PL and P1 that real losses have different

effects on different members of the poverty distribution. If the very poor do not have

significant assets for disasters to destroy, asset destruction would mainly reduce the

consumption of those at the top of the poverty distribution. If the asset destruction attracts

aid that benefits the poor generally, the aid may increase consumption of the poorest but

not completely make up for the asset loss of the less poor. This would be consistent with

the observation that the number of poor and overall poverty rise with reported losses, but

consumption inequality falls among the poor.

Another notable observation apparent in a comparison of the role of disasters in

inequality is the differing magnitudes of the normalized impact estimates for PL, P1, and

P2. The statistically significant disaster effects positively associated with PL are

estimated to increase the percentage poor between 0.1 and 0.81 percent with an average

increase of 0.36 percent. However, while the estimated impact of real losses on the

poverty gap is low at 0.12 percent, the impacts of the other nine measures positively

associated with the poverty gap are estimated to be between 1.37 to 2.28 percent, and the

average (including the result for real losses) is 1.35 percent. The ratio of the two averages

(P1/PL) is 3.75.

Based on this ratio, the impact of disasters on the poverty gap is almost four times

their influence on the percentage of people poor. This would seem to suggest that

disasters impact the poorest more than those at or just above the poverty line, but this is

not certain. Because of the way it is calculated, the increase in the poverty gap will

naturally be larger than the increase in the percentage poor even if effects are distributed

evenly. To provide context, the average ratio when comparing changes in P1 to changes

in PL for all districts and years in the sample in which the two poverty measures rise

together is 8.54 (with a standard deviation of 21.08). When looking at the corresponding

ratio of normalized impact estimates for P2/P1, it is only 0.36 (even if the damage

measures having negative associations with poverty are excluded, the ratio only rises to

1.03). This can be compared to a ratio of 2.00, which was the average for all districts and

years in which P1 and P2 both increased.

This evidence does not suggest that disasters in general raise poverty more for the

poorest than the less poor. Disasters appear to have a larger impact on the normalized

17    

poverty gap than the inequality-weighted poverty gap. The method by which the

inequality-weighted poverty gap is measured means that, even in a case where the

poverty gap rises by increasing poverty for all households equally, the percentage

increase in the inequality-weighted poverty gap can easily be up to twice that seen in the

normal poverty gap. Thus, this increase in the normalized poverty gap of greater

magnitude than the increase in the inequality-weighted poverty gap (echoed by the low

relative magnitude of the normalized impact estimate ratio for P2/P1 when compared

with that found in the data) suggests that disasters may reduce the consumption of those

at the top of the poverty distribution by more than those at the bottom, thus reducing

consumption inequality among the poor. The reasoning behind this may be similar to that

previously discussed with respect to real losses. It is important to note that these results

cannot say what effect disasters have on inequality when considering the general

income/consumption distribution in Indonesia, as the measures employed are only

relevant for the poor.

An issue of concern in this research is the role of the poverty line in driving the

poverty measures. The poverty lines used in Indonesia differ across districts, allowing

districts to take into account local conditions when defining the desired minimum level of

consumption. In theory, over the period in question these poverty lines are only supposed

to change with inflation. Since all the poverty measures are based on the poverty line, it is

important that disasters not affect the poverty line. To test this possibility, the regional

fixed effects model found in Equation 1 above were estimated with the poverty line on

the left-hand side. Interestingly, there was evidence that real losses, damage to bridges,

and damage to crops were all statistically significant and negatively associated with the

poverty line. For the vast majority of disaster measures, there was no evidence of an

association between disasters and the poverty line. However, for the three named above,

the negative association would suggest that the results may be downward biased when

estimating the impact of disasters on poverty because more disaster damage of these

types is associated with greater difficulty in being recognized as poor. This may

contribute to the unusual result found with respect to real losses and P2, but it seems

unlikely that this downward bias is the driving force behind the pattern of results for real

losses over all the poverty measures discussed above. Why these disaster impacts would

18    

affect the poverty line is not clear. It is easier to find reasons why damage to bridges and

crops would raise local prices rather than lower them, although some types of emergency

food policies could produce a lower price level. Higher real losses in areas without

financial services may result in a lower price level due to the decrease in wealth and

associated lower economic activity. This would be an interesting question to pursue in the

future.

V . Conclusion

This research set out to explore the recent experience of Indonesia with respect to natural

disasters and poverty. Over the last decade, Indonesia has made impressive gains in

poverty reduction throughout all its regions. Indonesia is made up of a large number of

Ring of

F As such, the country is subject to frequent and diverse natural disasters. This

makes Indonesia an interesting subject in the quest to understand how natural disasters

influence poverty.

This essay examined the short-term impact of natural disasters of a wide range of

types and severity. The results suggest that disasters do lead to higher poverty, though

there are some disaster effects, such as damage to roads, which may not lead to an

increase in poverty. For damages positively associated with poverty, the increase in the

consumption shortfall of the poor resulting from an increase of one-standard deviation in

disaster damage ranges between 1.3 and 2.3 percent, depending on the type of disaster

damage. When comparing measures of poverty, the inequality-weighted poverty gap is

less sensitive to disasters than the normalized poverty gap. This suggests that disasters

may be reducing inequality among the poor. It would be interesting to examine any

interaction between the local nd disaster impacts. Extending

this research to examine the issue of disasters and inequality more directly may prove

enlightening. It is worth noting that although others have made a compelling stylized case

for the need to pay more attention to the damage disasters cause to infrastructure

(Freeman 2000), there is no evidence here that damage to infrastructure has an immediate

role in increasing poverty. The impact of extreme events such as the Indian Ocean

tsunami has not been addressed, and further investigation and comparison of extreme to

19    

normal events would be useful. Based on the results of this research, damage to

human/social capital institutions, housing, and production facilities may be relatively

more important conductors of poverty creation than others, but an analysis incorporating

dynamic effects may provide more definitive results as differences between damage types

in poverty exacerbation may take time to be revealed.

The results of this research suggest that additional effort should be made in

Indonesia to prevent disasters from generating poverty. Increased housing quality,

protection of commercial facilities, and availability of medical treatment following a

disaster would be useful policy goals for Indonesia and aid agencies seeking to reduce the

negative impact of disasters. Developing a compelling argument for targeted policy

intervention may make Indonesia more attractive to outside aid agencies interested in

reducing the negative impact of disasters. Concerning poverty support structures, this

research suggests that religious organizations may need to review their approach to target

the most vulnerable. Additionally, further research on whether Indonesia is unique in its

ability to handle natural disasters would be valuable, as would assessment of any

institutional role played in this resilience, as it could provide lessons for countries that

find the struggle against disasters to be more difficult. Understanding more about the way

disasters influence poverty in Indonesia should provide an opportunity for development

policy to target not only disaster prevention and mitigation efforts, but also to provide

insight into how general development policy might be modified to work better in the

presence of frequent disasters.

 

20    

Table 1.1. Summary Statistics for District Poverty Measures

Poverty Measure

Minimum Maximum Mean Standard Deviation

2003 2010 2003 2010 2003 2010 2003 2010 PL 2.54% 2.21% 50.31% 45.69% 18.44% 13.70% 9.78 7.06 P1 0.33% 0.19% 17.32% 16.72% 3.47% 2.34% 2.48 1.71 P2 0.06 0.02 7.78 6.84 0.98 0.65 0.93 0.64 Table 1.2. Percentage Poor, P1, and P2 by Region* and Year

Region 2003 2004 2005 2006 2007 2008 2009 2010 Sumatra (%)

P1 P2

18.23 3.38 0.93

17.47 3.10 0.82

17.23 3.43 1.00

17.55 3.05 0.83

16.46 2.71 0.71

14.96 3.01 0.85

13.56 2.18 0.57

13.07 2.14 0.56

Java (%) P1 P2

16.73 2.97 0.79

15.97 2.65 0.69

16.02 3.17 0.90

17.36 2.93 0.76

16.22 2.56 0.64

14.95 3.27 0.95

13.56 2.12 0.53

12.57 1.99 0.50

Kalimantan (%) P1 P2

11.84 2.07 0.57

11.00 1.85 0.51

11.02 2.01 0.54

11.78 1.89 0.48

10.37 1.55 0.38

8.72 1.84 0.54

7.51 1.14 0.29

7.65 1.18 0.30

Sulawesi (%) P1 P2

17.56 3.29 0.93

16.69 3.04 0.83

16.86 3.23 0.92

17.81 3.09 0.84

18.17 3.05 0.79

15.32 3.70 1.13

13.88 2.26 0.60

13.13 2.18 0.59

Irian Jaya (%) P1 P2

38.21 10.19 3.72

37.48 10.78 3.64

40.07 11.67 4.69

40.64 11.00 4.24

39.90 11.20 4.64

32.51 10.21 4.23

31.12 7.69 2.95

32.01 9.45 3.69

Maluku (%) P1 P2

25.05 4.90 1.37

24.02 4.66 1.27

24.44 5.45 1.78

24.50 5.47 1.81

23.16 4.74 1.46

21.70 5.06 1.58

20.21 4.34 1.43

19.37 3.80 1.17

Tenggara (%) P1 P2

21.58 4.01 1.12

20.86 3.58 0.98

21.24 4.33 1.32

22.22 4.02 1.12

20.65 3.50 0.90

19.21 5.06 1.71

17.59 3.04 0.84

16.91 2.97 0.80

Indonesia (%) P1 P2

17.30 3.15 0.86

16.53 2.84 0.76

16.56 3.30 0.96

17.59 3.04 0.82

16.47 2.68 0.69

15.01 3.32 0.98

13.62 2.19 0.57

12.90 2.12 0.56

*As a table with all 30 provinces would be unwieldy, I have grouped the provinces geographically into seven regions. See Appendix A for a list of the provinces in each region.

21    

Table 1.3. Means and Standard Deviations of Reported District Level Disaster Damage

Damages per 1,000 people

Mean Standard Deviation

Minimum Maximum

Deaths 0.04 0.26 0.0003 5.84

Injuries 0.69 0.36 0.0002 44.30

Missing 0.02 0.07 0.0002 0.73

Affected 67 635 0.0004 8,765

Evacuated 15 52 0.0008 645

Destroyed Houses 1.4 8.9 0.0005 184

Damaged Houses 2.7 14 0.0004 260

Submerged Houses 6.1 17 0.002 197

Education Centers 0.1 0.36 0.0002 4.34

Hospitals 0.04 0.11 0.0003 0.80

Religious Centers 0.1 0.37 0.0002 3.14

Crops (Hectares) 4 26 0.0001 570

Plantation/Forest (Hectares)

4.7 35 0.0008 567

Irrigation 0.15 0.56 0.0007 3.80

Ponds 1.3 3 0.0006 20.27

Real Losses (Indonesian rupiah)

32 193 0.0004 3,362

Office Buildings 0.08 0.26 0.0003 2.62

Kiosks 0.23 0.99 0.0002 10.28

Manufacturing Facilities

0.04 0.23 0.0004 1.70

Roads (Meters) 0.45 3.79 0.0000006 50.37

Bridges 0.05 0.23 0.0003 3.28

22    

F igure 1.1. Poverty over Time by Region

0  5  

10  15  20  25  30  35  40  45  

2003   2004   2005   2006   2007   2008   2009   2010  

Percentage  of  the  Population  Below  the  Poverty  Line  by  Region  

Sumatra  (%)  

Java  (%)  

Kalimantan  (%)  

Sulawesi  (%)  

Irian  Jaya  (%)  

Maluku  (%)  

Tenggara  (%)  

Indonesia  (%)  

0  

2  

4  

6  

8  

10  

12  

2003   2004   2005   2006   2007   2008   2009   2010  

Normalized  Poverty  Gap  by  Region    (as  a  Percentage  of  the  Poverty  Line)  

0  

1  

2  

3  

4  

5  

2003   2004   2005   2006   2007   2008   2009   2010  

Inequality-­‐Weighted  Poverty  Gap  by  Region  

23    

 

F igure 1.2. Disaster Outcome by Region (2003 2010)

Note: Sumatra is the region most affected by the Indian Ocean tsunami.

0  

100,000  

200,000  

300,000  

400,000  

500,000  

600,000  

700,000  

800,000  

900,000  

1,000,000  

Absolute  Number  of  Disaster  Outcomes  

Deaths  

Injured  

Houses  Destroyed/Damaged  

0  

50  

100  

150  

200  

250  

Disaster  Outcomes  Relative  to  Population  

Deaths  per  10,000  

Injured  per  10,000  

Destroyed/Damaged  Houses  per  10,000  People  

24    

Deaths by Year Injuries by Year

Houses Damaged/Destroyed by Year

F igure 1.3. Damage Incidence by Year (2003 2010) (DesInventar 2013)

25    

Deaths by District, 2003 2010

Injuries by District, 2003 2010

Damaged/Destroyed Houses by District, 2003 2010

F igure 1.4. Maps of Disaster Severity by District (DesInventar 2013)

26    

Table 1.4. Estimated Disaster Damage Coefficients (Standard Errors) from F ixed E ffects Regressions

Damage  Category  

Harm  to  Individuals   Population  Disruption   Housing  Damage  

Disaster  Measure  

Deaths   Injuries   Missing   Affected   Evacuated   Destroyed  Houses  

Damaged  Houses  

Submerged  Houses  

PL   0.53**  (0.24)  

0.02        (0.02)  

6.8***  (1.66)    

0.00007  ***  (0.00002)  

0.0003  (0.001)  

0.02***  (0.006)  

0.02***  (0.004)  

-­‐0.008*    (0.004)  

P1   0.36**  (0.15)  

0.01        (0.01)  

2.82***  (1.14)  

-­‐0.00002**  (0.00001)  

0.0003  (0.0005)  

0.01*        (0.006)  

0.007***  (0.002)  

-­‐0.004        (0.002)  

P2   0.17*  (0.09)  

0.004  (0.009)  

1.22**  (0.57)  

-­‐0.000006  (0.000007)  

0.0001  (0.0002)  

0.006      (0.004)  

0.002**  (0.001)  

-­‐0.002        (0.001)  

Damage  Category  

Damage  to  Human/Social  Capital  Institutions  

Damage  to  Agriculture   Real  Losses  

Disaster  Measure  

Education  Centers  

Hospitals   Religious  Buildings  

Crops   Plantation/  Forest  

Irrigation   Ponds   Real  Losses  

PL   0.65***  (0.23)  

3.86***  (1.11)  

0.5**      (0.21)  

-­‐0.004***  (0.002)  

-­‐0.002  (0.004)  

0.38*            (0.22)  

0.007  (0.08)  

0.000007***  (0.0000001)  

P1   0.29**  (0.12)  

0.8          (0.81)  

0.45**  (0.2)  

-­‐0.0002  (0.0006)  

0.000007  (0.002)  

-­‐0.07            (0.09)  

0.02  (0.05)  

0.0000002***  (0.00000004)  

P2   0.14*  (0.08)  

0.12        (0.56)  

0.28**  (0.14)  

0.00003  (0.0002)  

0.0002  (0.001)  

-­‐0.05            (0.03)  

0.01  (0.03)  

-­‐0.0000004***  (0.00000002)  

Notes: Estimates obtained using separate regional fixed effect regressions with time dummies and robust clustered standard errors. *,**,*** Indicate statistical significance at 10 percent, 5 percent, and 1 percent, respectively. PL is the percentage of the population below the poverty line, P1 is the normalized poverty gap, and P2 is the inequality-weighted poverty gap. All disaster variables are measured relative to the population.

27    

Table 1.4. (Continued) Estimated Disaster Damage Coefficients (Standard Errors) from F ixed E ffects Regressions

Damage  Category   Damage  to  Production  Facilities  

Disaster  Measure   Office  Buildings   Kiosks   Manufacturing  Facilities  

PL   0.65                                          (0.63)  

0.11                                          (0.14)  

2.65***                                      (0.44)  

P1   0.73*                                              (0.39)  

0.04                                        (0.12)  

1.52***                                              (0.12)  

P2   0.46*                                              (0.26)  

0.05                                      (0.08)  

0.57***                                          (0.1)  

Damage  Category   Damage  to  Infrastructure    

Disaster  Measure   Roads   Bridges    

PL   -­‐0.05***                                        (0.008)  

-­‐0.56                                      (0.69)  

 

P1   -­‐0.008                                          (0.006)  

-­‐0.2                                  (0.25)  

 

P2   -­‐0.001                                          (0.003)  

-­‐0.08                                    (0.15)  

 

Notes: Estimates obtained using separate regional fixed effect regressions with time dummies and robust clustered standard errors. *,**,*** Indicate statistical significance at 10 percent, 5 percent, and 1 percent, respectively. PL is the percentage of the population below the poverty line, P1 is the normalized poverty gap, and P2 is the inequality-weighted poverty gap. All disaster variables are measured relative to the population.

28    

F igure 1.5. Product of Estimated Coefficient and One Standard Deviation in Impact on PL (Fixed Effect Regressions)

Notes: *,**, and *** indicate statistical significance at 10 percent, 5 percent, and 1 percent, respectively. Circle represents product of estimated coefficient and one standard deviation in disaster impact. Lines extend two standard errors above and below. The estimated impact can be interpreted as the percentage point change in the population below the poverty line associated with one standard deviation of disaster damage.

-­‐1.5  

-­‐1  

-­‐0.5  

0  

0.5  

1  

29    

F igure 1.6. Product of Estimated Coefficient and One Standard Deviation in Impact on P1 (Fixed Effect Regressions)

Notes: *,**, and *** indicate statistical significance at 10 percent, 5 percent, and 1 percent, respectively. Circle represents product of estimated coefficient and one standard deviation in disaster impact. Lines extend two standard errors above and below. P1 represents the per capita poverty burden when expressed as a percentage of the monthly poverty line. The estimated impact can be interpreted as the percentage point change (not percentage change) in this burden.

-­‐0.6  

-­‐0.5  

-­‐0.4  

-­‐0.3  

-­‐0.2  

-­‐0.1  

0  

0.1  

0.2  

0.3  

30    

F igure 1.7. Product of Estimated Coefficient and One Standard Deviation in Impact on P2 (Fixed Effect Regressions).

Notes: *,**, and *** indicate statistical significance at 10 percent, 5 percent, and 1 percent, respectively. Circle represents product of estimated coefficient and one standard deviation in disaster impact. Lines extend two standard errors above and below. P2 represents the sum of squared deviations from the poverty line for households below the poverty line. It is difficult to distinguish whether the estimated impact represents a change in the distribution of poverty among the poor or if it represents an increase in the overall poverty burden.

-­‐0.3  

-­‐0.25  

-­‐0.2  

-­‐0.15  

-­‐0.1  

-­‐0.05  

0  

0.05  

0.1  

0.15  

0.2  

31    

PL P1

P2

F igure 1.8. Estimated Impact by Damage Category on PL (Fixed Effect Regressions)

Note: For each category, the average of the products of the estimated coefficients and one standard deviation are reported for those impacts that have statistically significant coefficients.

-­‐0.1  

-­‐0.05  

0  

0.05  

0.1  

0.15  

0  

0.01  

0.02  

0.03  

0.04  

0.05  

0.06  

-­‐0.01  -­‐0.005  

0  0.005  0.01  

0.015  0.02  

0.025  0.03  

0.035  

32    

Table 1.5. Estimated Disaster Damage Coefficients (Standard Errors) from System GMM Regressions

Damage  Category  

Harm  to  Individuals   Population  Disruption   Housing  Damage  

Disaster  Measure  

Deaths   Injuries   Missing   Affected   Evacuated   Destroyed  Houses  

Damaged  Houses  

Submerged  Houses  

PL   0.11            (0.08)  

0.1***        (0.02)  

1.1*                          (0.6)  

0.00005**  (0.00002)  

-­‐0.006***  (0.002)  

-­‐0.003    (0.006)  

-­‐0.007  (0.006)  

-­‐0.002                    (0.005)  

P1   0.195***    (0.07)  

0.007      (0.015)  

0.36      (0.88)  

-­‐0.00004***  (0.00001)    

-­‐0.002  (0.001)  

0.004      (0.003)  

0.007***  (0.001)  

-­‐0.002*    (0.001)  

P2   0.11***      (0.02)  

0.002        (0.009)  

0.63**  (0.29)  

-­‐0.00002***  (0.000005)  

-­‐0.0003  (0.0007)  

0.002*  (0.001)  

0.002***  (0.0005)  

-­‐0.0009  (0.0007)  

Damage  Category  

Damage  to  Human/Social  Capital  Institutions  

Damage  to  Agriculture   Real  Losses  

Disaster  Measure  

Education  Centers  

Hospitals   Religious  Buildings  

Crops   Plantation/  Forest  

 Irrigation   Ponds   Real  Losses  

PL   -­‐0.42    (0.28)  

-­‐0.89    (1.56)  

-­‐0.43      (0.41)  

-­‐0.003    (0.02)  

-­‐0.0005  (0.001)  

0.17                        (0.28)  

0.07      (0.1)  

-­‐0.000004***  (0.0000003)  

P1   0.14*      (0.08)  

0.64        (0.62)  

0.13*    (0.07)  

-­‐0.01    (0.009)  

-­‐0.0007  (0.0005)  

-­‐0.23**    (0.10)  

-­‐0.15*  (0.08)  

-­‐0.000002  (0.0000004***)  

P2   0.07**  (0.03)  

0.33*    (0.18)  

0.06**  (0.03)  

-­‐0.004  (0.004)  

-­‐0.0005  (0.0004)  

-­‐0.09**    (0.04)  

-­‐0.04      (0.05)  

-­‐0.000002***  (0.0000002)  

Notes: Estimates obtained from 1,508 observations using separate two-step robust System GMM regressions with the Windmeijer finite sample correction to standard errors. *,**,*** Indicate statistical significance at 10 percent, 5 percent, and 1 percent, respectively. PL is the percentage of the population below the poverty line, P1 is the normalized poverty gap, and P2 is the inequality-weighted poverty gap. All disaster variables are measured relative to the population.

33    

Table 1.5. (Continued) Estimated Disaster Damage Coefficients (Standard Errors) from System GMM Regressions

Damage  Category  

Damage  to  Production  Facilities  

Disaster  Measure  

Office  Buildings   Kiosks   Manufacturing  Facilities  

PL   -­‐1.74                                    (2.56)  

0.19***          (0.07)  

-­‐1.71**                                  (0.75)  

P1   0.71*                                          (0.39)  

-­‐0.06*            (0.03)  

1.17***                                  (0.22)  

P2   0.29***                                          (0.1)  

-­‐0.02*                          (0.01)  

0.31**                                      (0.14)  

Damage  Category  

Damage  to  Infrastructure   Specification  Test  

Disaster  Measure  

Roads   Bridges   Range  of  Hansen  Test  p-­‐values  

PL   -­‐0.03***              (0.007)  

-­‐1.03**          (0.46)  

[0.00,  0.00]  

P1   -­‐0.02**                            (0.008)  

0.01                  (0.27)  

[0.001,  0.001]    

P2   -­‐0.007                                    (0.004)  

0.0007          (0.08)  

[0.164,  0.26]  

Notes: Estimates obtained from 1,508 observations using separate two-step robust System GMM regressions with the Windmeijer finite sample correction to standard errors. *,**,*** Indicate statistical significance at 10 percent, 5 percent, and 1 percent, respectively. PL is the percentage of the population below the poverty line, P1 is the normalized poverty gap, and P2 is the inequality-weighted poverty gap. The Hansen test has the null hypothesis that the instruments are jointly endogenous thus a higher p-value indicates more reliable results. For all results, a satisfactory result for the Hansen test was accompanied by a satisfactory result when testing for autocorrelation in levels. All disaster variables are measured relative to the population.

34    

F igure 1.9. Product of Estimated Coefficient and One Standard Deviation in Impact on P2 (System GMM)

Notes: Circle represents product of estimated coefficient and one standard deviation in disaster impact. Lines extend two standard errors above and below. P2 represents the sum of squared deviations from the poverty line for households below the poverty line. It is difficult to distinguish whether the estimated impact represents a change in the distribution of poverty among the poor or if it represents an increase in the overall poverty burden.

-­‐0.1200  

-­‐0.0700  

-­‐0.0200  

0.0300  

0.0800  

35    

F igure 1.10. Average Estimated Impact by Damage Category on P2 (System GMM)

Note: For each category, the average of the products of the estimated coefficients and one standard deviation are reported for those impacts that have statistically significant coefficients.

 

-­‐0.04  

-­‐0.03  

-­‐0.02  

-­‐0.01  

0  

0.01  

0.02  

Harm  to  Individuals  

Disruption  to  Population  

Damage  to  Housing  

Damage  to  Human/Social  Capital  Inst.  

Damage  to  Agriculture  

Damage  to  Production  

Real  Losses  

36    

Chapter 2. The Impact of Natural Disasters on Education in Indonesia

I . Introduction

Economic research on the influence of natural disasters on economies has been growing rapidly.

This area of research has been motivated by the occurrence of extreme natural events, such as the

large tsunamis that have affected Southeast Asia and Japan. Population growth and increases in

capital stocks mean that the economic significance of these extreme events is growing over time

(Freeman, Keen, and Mani 2003; van den Berg 2010). Natural disasters result in

disproportionately greater damages and deaths in poorer countries, making their impact on

economies an important topic in development economics (Henderson 2004; Freeman, Keen, and

Mani 2003; Noy 2009; van den Berg 2010). Research has described the potential natural

disasters have to reduce employment, disrupt production, and decrease income in the affected

countries (Morris et al. 2002, Narayan 2003, Noy and Vu 2010, Coffman and Noy 2011).

A substantial and growing amount of research has been conducted on the impact of

natural disasters on multiple aspects of decision-making at the household level, including

decisions related to education (Janvry, Finan, Sadoulet, and Vakis 2006; Kim and Prskawetz

2010; Deuchert and Felfe 2013; Bustelo 2011; Frankenberg et al. 2008; Gitter and Barham 2006;

Yamauchi, Yohannes, and Quisumbing 2009). Most of these papers deal with research on

specific events. Non-wealthy, credit-constrained households affected by natural disasters often

find that the losses and labor market disruption created by disasters put pressure on consumption,

which can make it increasingly difficult to accept the opportunity cost of enrolling children in

school (Sawada and Shimizutani 2008; Mechler 2009; Sigurdsen, Berger, and Heymann 2011).

This concern becomes even more important when considering poorer households facing the

possibility of a poverty trap (Carter, Little, Mogues, and Negatu 2007). Education is a priority in

developing countries because of the importance placed on human capital accumulation for

growth, and the potential of disasters to disrupt this investment is one of the major concerns

expressed in the literature on disasters and development (Cuaresma 2010; Lopez 2009; Skidmore

and Toya 2002; Toya, Skidmore, and Robertson 2010).

The household response is likely related to their wealth, with richer households able to

use assets to maintain consumption (Berloffa and Modena 2013), which may allow them to

maintain investment in human capital. Poorer households are often observed to alter

37    

consumption to preserve physical assets and/or avoid poverty traps (Berloffa and Modena 2013;

Carter, Little, Mogues, and Negatu 2007; Dercon 2004; Jakobsen 2012; Shoji 2010), and it is

reasonable to believe they will lower investment in human capital as well. Disasters can also

influence education by way of negative impacts on child health as suggested by the experience of

Belarus following the Chernobyl nuclear disaster (Yemelyanau, Amialchuk, and Ali 2012).

There has also been a limited amount of cross-country research on disasters and

education (Cuaresma 2010; Toya, Skidmore, and Robertson 2010; Skidmore and Toya 2002), as

well as literature examining the relationship between macroeconomic shocks and education

(Ferreira and Schady 2009, Jones and Hagul 2001, Maio and Nandi 2013, Sparrow 2007). While

there has been a significant amount of empirical research at the cross-country, national, and

household level, as well as some theoretical exploration of the impact of natural disasters on

education (Pörtner 2008; Lopez 2009; Yamauchi, Yohannes, and Quisumbing 2009; Ferreira and

Schady 2009), there is a lack of research in between these levels of analysis. Much has been

learned from household level studies of specific large disaster events, but we do not yet know

much about how the ongoing, typical disaster experience of an area translates to regional

outcomes. This research will contribute to that gap by using Indonesian disaster data and data on

enrollment and literacy broken down by district. This will allow exploration of the question

using the diversity of experience of the different regions within one country. In this essay I

explore how natural disasters can influence development at a level that better corresponds to

national and regional development policy targets.

When it comes to disasters and enrollment, the theory discussed above suggests that it is

important to consider the impact of disaster risk as well as the impact of the event. The

theoretical treatments of the impact of disaster risk on education are mixed with different

theoretical contexts suggesting different results. Risk may lead to increased investment in

education if it raises the relative return to human versus physical capital (Pörtner 2008, Skidmore

and Toya 2002), or if migration is an important coping technique (Pörtner 2008). However, the

opposite could be true if disasters substantially increase mortality risk (Pörtner 2008) or lower

the return to education through worsening economic conditions (Pörtner 2008; Jang, Wong, and

Huh 2008; Lopez 2009). Unfortunately, empirical assessment has not achieved clarity on this

issue either as there is evidence that higher disaster risk can increase investment in education

38    

(Pörtner 2008, Skidmore and Toya 2002), as well as evidence that higher risk reduces investment

in education (Cuaresma 2010, Fitzsimons, 2007).

Considering the impact of a disaster event on education, the theory is generally

supportive of the hypothesis that disaster events reduce investment in education (Pörtner 2008;

Lopez 2009; Sigurdsen, Berger, and Heymann 2011; Yamauchi, Yohannes, and Quisumbing,

2009). Still, there are theoretical arguments suggesting that in some cases disasters could

increase education investment (Lopez 2009, Bustelo 2011, Ferreira and Schady 2009).

Empirically, there is some evidence that macroeconomic shocks in relatively wealthy countries

will result in increased investment in education (Ferreira and Schady 2009), but for developing

countries the evidence from national studies and household level research thus far has indicated

that disasters reduce investment in education (Pörtner 2008; Janvry, Finan, Sadoulet, and Vakis

2006; Yamauchi, Yohannes, and Quisumbing 2009; Gitter and Barham 2006; Deuchert and Felfe

2013; Bustelo 2011; Maio and Nandi 2013; Funkhouser 1999; Ferreira and Schady 2009).

Regarding evidence from Indonesia specifically, data from major macroeconomic shocks

suggest that nationwide shocks reduce enrollment (Sparrow 2007; Jones and Hagul 2001) and

that major natural disasters can disrupt access to schools in the affected area (Frankenberg et al.

2008). In potential contrast to these, there is some research that suggests disasters increase

expenditures on education (Kim and Prskawetz 2010). The following empirical research

contributes to the neglected area of how disasters in general, not just large-scale specific events,

influence economic development over time. From the literature it is expected that disasters will

generally have a negative impact on enrollment in the short term, although impact in the medium

term may be more difficult to predict. Additionally, it is expected that this impact on enrollment

will be more severe for secondary school students than for primary school students (Janvry,

Finan, Sadoulet, and Vakis 2006; Jones and Hagul 2001; Sparrow 2007). This paper also

contributes to the literature in that disasters are explored using a large variety of measures of

disaster damage. This variety will facilitate the development of a much more detailed picture of

the mechanisms connecting disasters to enrollment than is possible with less specific disaster

measures.

39    

I I . Data

In order to investigate the influence of disasters on education in Indonesia, two datasets are

employed, one covering school enrollment in Indonesia at the district level and one reporting the

consequences of disasters in Indonesia on the district level from 2002 to 2010. The data on

enrollment (Sub

Direktorat Analisis Statistik Lintas Sektor 2003-2010). The data on natural disasters are

assembled by the DesInventar Disaster Information Management System developed by, among

others, the United Nations Office for Disaster Risk Reduction (DesInventar Project Team 2013).

The data on enrollment report the percentage of children enrolled in school in two age

groups, seven to twelve and thirteen to fifteen, which correspond to primary and lower secondary

school respectively. Data are

districts. During the period in question, implementation of education policy was in the hands of

lization of education policy in 2001. While enrollment in

both primary and lower secondary school has been legally compulsory since 1994, Indonesia

struggled to achieve universal enrollment, especially in lower secondary school, leading to a

renewed government initiative toward this goal in 2006 (UNESCO 2010).

Enrollment in primary school is higher than that for secondary school for all regions in all

years except for Irian Jaya in 2006 (see Figure 2.1 and Table 2.2). In 2003 average enrollment in

primary percent, while enrollment in secondary school

averaged 81.9 percent. In 2010, enrollment in primary school was almost unchanged at 96.1

percent, while average enrollment in secondary school had fallen to 76 percent. The only region

where enrollment in primary school grew between 2003 and 2010 was Tenggara, where the

enrollment rate increased by 1 percent. Enrollment in primary school fell most in Maluku,

decreasing by 2 percent. The changes in secondary school enrollment over the period were

significantly larger. Enrollment in secondary school is reported to have grown by 10 percent in

Irian Jaya, which is the only region to host an increase in secondary school enrollment between

2003 and 2010. in Java, where it fell by

11 percent. When examining the movement in enrollment rates, the trend is generally positive

, when enrollment rates drop almost universally. In light of

the major international economic crisis that began around that time, this suggests that enrollment

is falling in response to worsening economic conditions within Indonesia.

40    

The DesInventar disaster database reports impacts of disasters on a local level for a large

number of countries. DesInventar works with a network of institutions to support a common

methodology that participating countries use to report disaster effects. The United Nations

International Strategy for Disaster Reduction (UNISDR), the United Nations Development

Program (UNDP), the Ministry of Disaster Management in Sri Lanka, and the Indonesian

National Disaster Management Agency are among the institutions that worked to develop

DesInventar. Summary statistics for the disaster damages studied in this paper can be found in

Table 2.3.

floods and strong winds followed by droughts and landslides. Indonesia is no stranger to natural

disasters. In

disaster, and many experience more. In Figure 2.2, the geographic prevalence of three types of

disaster damage deaths, injuries, and damage to housing is shown. DesInventar provides data

on a large number of damages including damage to crops, damaged roads, population affected,

damaged manufacturing facilities, damaged/disrupted educational facilities, and disrupted

hospitals .

The Indian Ocean tsunami caused damages that dwarf all other disaster events in

experience. As the goal of this essay is to investigate the impact of

more typical experience with disasters, the regressions that follow use a sample that

excludes the province most affected by the Indian Ocean tsunami. A disaster is not something

that can be measured on the surface but is nature interacting with the social environment present

when the disaster hits. Access to a variety of measures presents the interesting opportunity to

explore how different types of disaster damage may be more or less important in influencing

enrollment in education. The next section explains the methodology used and summarizes the

results.

                                                                                                                     A few of the disaster measures have an EM-DAT database counterpart (CRED). Among the measures that have

such a counterpart, estimated real losses is the measure that is least consistent with the data from CRED.  

41    

I I I . Methodology and Results

Regional F ixed E ffects

This section will explain the regressions employed as well as describe the results obtained. A

discussion of the results and their interpretation can be found in the next section of the paper.

The impact of different types of disaster damage on enrollment in Indonesia is investigated using

a regional fixed effects regression of the following form:

(1)

Enrollment in either primary or lower secondary school is represented by E for district i at time t,

is the measure of disaster impact adjusted for the population of the district, is a set of dummy

variables representing year, which should account for the increased government efforts to

increase lower secondary school enrollment after 2006, c is the fixed effect for each district, and

u is the error term. The twenty-one different disaster damages are divided into the following

categories: harm to individuals, population disrupted, damage to housing, damage to

human/social capital institutions, damage to agriculture, damage to infrastructure, damage to

production facilities, and real estimated losses (reported in local currency and adjusted for

inflation). Results for the fixed effects regression can be found in Table 2.3 with robust clustered

standard errors.

Enrollment in Primary School

There are seven types of disaster damage that are statistically significant in their relationship

with enrollment in primary school. Of the seven, six are estimated to be negatively associated

with primary school enrollment: deaths, number missing, number evacuated, damage to kiosks,

damage to manufacturing facilities, and damage to roads. Number of people affected is the one

disaster damage positively associated with enrollment. Considering the categories of damage,

harm to individuals, damage to production, and damage to infrastructure are negatively

associated with primary school enrollment, while the results for disrupted population are mixed.

The types of statistically significant damage with the largest estimated coefficients are number

missing and damage to manufacturing facilities. The impacts can be explored by providing an

idea of what the average experience would be like. The estimated coefficients suggest that an

42    

additional twelve people reported missing in the average district§ was associated with a decrease

in enrollment of 0.2 percent, while damage to twenty-nine manufacturing facilities was

associated with a 0.06 percent decrease in enrollment. A disaster that affects 43,570 people is

associated with a 0.02 percent increase in primary school enrollment.

The coefficient estimates for number of people reported missing and damage to

manufacturing facilities are quite a bit higher than the other four results. This may not be due to

these specific damages being significantly more detrimental for enrollment, but rather because

they are only triggered by extreme events. It is likely that only disasters that significantly disrupt

the social infrastructure will result in people being reported missing. If a disaster is relatively

manageable, people will be dead, injured, or unhurt, but it is unlikely that anyone will be

missing. Manufacturing facilities may be more resilient than other buildings and thus require a

more severe disaster to experience damage. The large estimated coefficients on these outcomes

may be partly because they are capturing a more general larger disaster effect.

As an exercise to reduce this influence, a plot of the estimated coefficients multiplied by

is generated (see Figure 2.3). When adjusted, the

impact of people being reported missing remains much larger than any other disaster damage,

but the relative importance of damage to manufacturing facilities is reduced and is more similar

to the other types of damage (deaths, damaged kiosks, evacuated population). Damage to roads

seems have the lowest estimated relationship with enrollment. From the normalization exercise it

appears that direct impacts on the population in terms of deaths or people being reported missing

are the most important disaster impacts associated with lower enrollment followed by impact on

production.

Secondary School Enrollment

There are also seven damage types that are statistically significant in their association with

secondary school enrollment. Of these relationships, six are estimated to be negatively associated

with enrollment: affected population, destroyed houses, disrupted education centers, damaged

religious buildings, damage to plantations/forests, and damaged bridges. Damage to cropland is

positively related to enrollment in lower secondary school. For lower secondary school, the

damage categories associated with lower enrollment are population disruption, housing damage,                                                                                                                      §The  average  district  in  the  sample  time  period  had  a  population  of  approximately  646,000,  primary  school  enrollment  rate  of  96.69  percent,  and  lower  secondary  school  enrollment  rate  of  81.88  percent.  

43    

damage to social capital, and damage to infrastructure. The nature of the relationship between

damage to agriculture/nature and enrollment is mixed. The largest statistically significant

estimated coefficient is associated with damage to bridges. The estimated coefficients for

disruption of religious buildings and disruption of schools are second and third. The coefficients

suggest that for an average district, damage to thirty bridges or sixty-six religious buildings is

associated with enrollment falling by 0.21 percent, and disruption of sixty-one educational

centers with a reduction in enrollment of 0.19 percent. Damage to 2,545 hectares of cropland is

associated with an increase of enrollment in lower secondary school of 0.05 percent.

In Figure 2.4, the estimated relationships between disaster damages and lower secondary

school enrollment are normalized as in Figure 2.3. The most notable differences are with respect

to impact of damage to bridges and plantations/forests. Damage to bridges loses much of its

relative importance when normalized, while damage to plantations/forests becomes the largest

magnitude impact. This suggests that damage to 22,323 hectares of plantation/forest is associated

with a 1.69 percent decrease in enrollment in the average district. Damage to bridges and

disruption of religious buildings seem to be the second and third most important damages for

lower secondary school enrollment.

L iteracy

The preceding regression (Equation 1) is relatively simple. One factor that could influence the

results is the tendency for enrollment to be related to social factors that have not been included.

In order to explore this idea, the fixed effects regressions were carried out again but this time

(L) for each district as in Equation 2.

(2)

These results, which use literacy to control for the level of basic education, can be found in Table

2.4. The intention is that the literacy rate will serve as a useful proxy for the social factors that

influence the basic attitude toward education, with a more literate population likely more

supportive of enrolling children in school.

Primary School

In all regressions relating disaster damage to primary school enrollment, the estimated

coefficient for literacy is positive and statistically significant. Districts with a higher rate of

44    

literacy have a higher rate of primary school enrollment. When considering the damages that

were associated with enrollment in the original regression method, the only noteworthy

difference when including the literacy rate is that evacuations are no longer associated with

enrollment. Additionally, when including literacy, damage to religious buildings, damage to

crops, and damage to office buildings are all statistically significant and negatively associated

with enrollment. Damage to plantations/forests and damage to irrigation are statistically

significant and positively associated with primary school enrollment.

Secondary School

statistically significant in any of the regressions relating

disaster damage to secondary school enrollment. Interestingly, the literacy rate does not seem to

be a significant factor in secondary school enrollment decisions. The way the inclusion of

literacy alters the regression results is similar to that observed with primary school enrollment.

One damage type, damage to crops, is no longer statistically significant, and a few damage types

that were not statistically significant in the original regressions now are. These include damage

to ponds and damage to office buildings, which are negatively associated with enrollment, and

the number missing, damage to irrigation, and damage to manufacturing, which are positively

related to secondary school enrollment.

Interaction

What if literacy interacts with disasters in shaping enrollment? To address this possibility

another set of regressions are conducted following Equation 3. The results of the literacy

interaction regressions can be found in Table 2.5.

(3)

Primary School

There are six types of damage for which the interaction between damage and literacy is

statistically significant for enrollment in primary school. The proportion of the population

affected and damage to irrigation both have positive terms, which suggests that as the level of

literacy rises, the effect of damage to irrigation and affected population on enrollment becomes

more positive/less negative. Evacuations, damage to hospitals, real losses, and damage to office

45    

buildings all have negative terms, suggesting that a higher rate of literacy makes the impact of

these disasters on enrollment in primary school more negative.

Secondary School

There are eight types of damage for which the interaction term is statistically significant for

enrollment in secondary school. The terms for injuries, destroyed houses, real losses, damaged

kiosks, and damaged roads are positive, indicating that higher literacy reduces the negative

impact of these types of damage. The proportion of the population missing, proportion affected,

and damage to plantations/forests appear to have a more negative impact on enrollment as

literacy rises.

Poverty

Another way to account for the social environment that is shaping attitudes toward education

would be to control for poverty. In order to do this, the fixed effects regressions are conducted

(P) for each district as in Equation 4. Note

that to avoid the investigation becoming overly complex, and because in this research literacy

and poverty are both serving as proxies for the same thing, the measure of literacy is not included

in these regressions. The results are reported in Table 2.6.

(4)

Primary School

the estimated coefficients for the disaster damages that are statistically significant in the original

regressions. However, as in the case where literacy was included, a number of disaster damages

that were not statistically significant originally become so when including poverty. These include

damage to hospitals, religious buildings, crops, and office buildings, which are negatively

associated with primary school enrollment. Also, damage to plantations/forests and damage to

irrigation are positively associated with enrollment. Including poverty in the secondary school

enrollment regressions yields results that are essentially the same as those obtained when

controlling for literacy.

46    

Interaction

As was done with the literacy rate, the interaction between poverty level and disaster damages is

explored as in Equation 5, and the results are reported in Table 2.7.

(5)

Damage to hospitals, damage to irrigation, and damage to roads all have negative and

statistically significant terms with respect to enrollment in primary school. This suggests that as

poverty rises, these types of damage have a larger negative impact on primary school enrollment.

Only the on real losses is positive, suggesting that higher poverty makes the impact of real

losses on enrollment less negative. Only real losses and damage to kiosks have statistically

significant interaction terms with respect to enrollment in lower secondary school. Both results

suggest that a higher level of poverty aggravates the negative impact of disasters on enrollment.

The following section will focus on the interpretation of the results summarized above.

While the regressions including literacy and poverty raise interesting points for discussion, they

are also considered less reliable for the purpose of the discussion because of the way poverty,

and to a lesser extent literacy, may help determine the way disasters affect communities in the

first place. For this reason, in the discussion that follows the greatest weight will be given to the

results from the original regressions, with the regressions including literacy being considered

more reliable than those including poverty.

I V . Discussion

Primary School

The evidence supports the argument that natural disasters can have an impact on primary school

enrollment. This effect seems to be negative in general, as one would expect. Deaths can reduce

.

Damage to kiosks and manufacturing facilities may have a similar effect through reducing

employment in the district. Damage to roads may make it more difficult for students to get to

school and thus reduce enrollment, in addition to any impact road damage may have on the local

economy. From the above, harm to individuals, damage to production, and damage to

infrastructure reduce enrollment in primary school. From the normalization exercise (Figure 2.3),

it appears that damage to production facilities is as important as deaths in reducing enrollment.

47    

Interestingly, the measures that deal with the size of the disasters impact on the

population provide mixed results. More people missing and more people evacuated are

associated with lower enrollment. If being reported missing or being evacuated is associated with

being a member of a wealthier household, it is possible that disasters generating these types of

damage are disproportionately affecting the children who were most likely to be enrolled and

promoting a more negative impact on enrollment. Even the normalized impact of the number

missing is substantially larger than any other impact. This could be an indication that

normalization does not remove all of the big disaster effect that the measure captures. It is also

possible that having people go missing produces more social disruption than deaths, as people

seek to find those who are missing. Perhaps the longer-term nature of the disruption caused by

people going missing creates a strong negative effect on enrollment.

The result that stands out is that obtained for the proportion of the population affected. A

higher percentage of the population being affected by disasters is associated with higher

enrollment in primary school. The normalized magnitude of the impact is also striking, as it

suggests that the impact of the proportion affected is approximately as positive as the impact of

deaths or damage to manufacturing is negative. An interesting possible explanation for this result

has to do with the way different disaster damages may attract aid differently. If the proportion of

the population affected is an unusually strong driver for aid from the government or foreign

sources then it is possible that the aid drawn is enough to improve overall economic conditions

and promote primary school enrollment as well. Another possibility could be that certain types of

disasters, such as drought, are associated with affecting large portions of the population. If the

damage caused by these types of disasters also tends to eliminate the alternative opportunities of

primary school students perhaps farm labor in the case of drought it makes households more

likely to enroll their children in school. In this context it is interesting to note that in the

regressions controlling for literacy (Table 2.4), damage to plantations and damage to irrigation

(although not damage to cropland) are positively associated with enrollment in primary school.

Secondary School

The literature suggests (Janvry, Finan, Sadoulet, and Vakis 2006; Jones and Hagul 2001;

Sparrow 2007) household enrollment decisions are not identical for primary and lower secondary

school students. Older children can contribute more both in terms of labor within and outside of

the home, thus making the opportunity cost of their enrollment higher. This, combined with

48    

possibly decreasing returns to education, is one explanation for the lower enrollment found for

students eligible for lower secondary school. The differences in opportunities related to age make

it entirely reasonable that the way disasters influence enrollment could be different for secondary

school students. Additionally, due to stronger policy support for primary school enrollment, it

would be reasonable to predict that enrollment in secondary school would be more vulnerable to

shocks such as disasters than enrollment in primary school (Jones and Hagul 2001, Sparrow

2007). A direct comparison between the results for primary and secondary school enrollment is

difficult due to the absence of overlap between the statistically significant disaster measures.

However, when comparing the normalized impact of those measures that are statistically

significant, the impact of disaster damage on secondary school enrollment is generally estimated

to be higher than the impact of disaster damage on primary school enrollment. This is interesting

considering that both levels of education are compulsory during this period. These results point

to districts and households placing a larger emphasis on implementing the universal education

policy for primary school than for lower secondary school.

The results for secondary school enrollment are similar to those for primary school in that

they are generally supportive of a negative relationship between disasters and enrollment with

six of the seven statistically significant results having a negative sign. The difference between

the decisions to enroll in primary and enroll in secondary school are reflected in that the results

for the two enrollment rates show significant variation in the types of damage that are

statistically significant. In fact, only one type of damage, population affected, is statistically

significant for both, and the sign is reversed in the case of secondary school. When considering

the normalized effects, the impact of different types of damage is largely consistent with the

exception of damage to agriculture, which has both the most negative and most positive

normalized impact for lower secondary school while it is not statistically significant for primary

school enrollment.

The only type of damage that is positively associated with enrollment in secondary

school is damage to crops. This may be a case where the disaster eliminates the most important

alternative to education. If the crops are destroyed and it is not time to replant, their labor is no

longer required, which encourages enrolling children in school. Damage to crops would also

have a negative impact on income, which should reduce enrollment, but this may either be

dominated by the previous effect, or the impact on income may be delayed depending on when

49    

in the agricultural cycle the disaster occurred. Damage to crops was not statistically significant

for enrollment in primary school. This difference could be driven by the lower potential

contribution for younger children to agricultural work. Because younger children are less useful

than older children, agricultural disasters more clearly affect enrollment of the older children.

Destruction to housing creates an immediate and critical demand for additional labor

being allocated to the household. A disaster that destroyed your house would significantly

increase the opportunity cost of sending your older children to school, and thus districts with

more housing destroyed will tend to enroll fewer children in lower secondary school. Damage to

plantations is also associated with lower enrollment in secondary school. It is likely that damage

to plantations increases demand for the labor of students who would be enrolled in secondary

school and reduces their participation in education. Why the impact of plantation damage would

be opposite that of crop damage is uncertain. It likely has to do with differences in the pattern of

labor use and the way disasters affect management of the respective activities. In both the

reconstruction of housing and plantations, younger children will be less useful for this work, and

so damage of these types will not be as important for primary school enrollment.

When considering infrastructure, damage to roads is not statistically significant for

enrollment in secondary school, but damage to bridges is associated with lower enrollment.

There are most likely fewer secondary schools than primary schools in a region. As a result, most

secondary school students will have to travel farther to school than primary school students and

are more likely to encounter bridges. Additionally, damage to bridges is more likely to serve as a

serious hindrance to travel for secondary school students than damage to roads, which should

impede older students less than younger.

Disruption of education centers and religious buildings are both associated with lower

enrollment in secondary school. This is the most straightforward result of them all. Few would

be surprised that a reduction in the number of classrooms/schools available will decrease

enrollment rates. Additionally, religious institutions are important providers of secondary school

education in Indonesia (Jones and Hagul 2001). As religious institutions are primarily active in

supplying secondary school education, it is not surprising that damage to religious buildings is

not clearly associated with lower enrollment in primary school. What is more puzzling is why

damage to education centers is not more clearly associated with a reduction in primary school

enrollment. If districts view primary education as a priority, they may engage in efforts to

50    

cushion negative impacts on primary school provision by transferring resources from the

production of secondary education. Also, as primary schools are generally more numerous more

opportunity exists to find a substitute facility following a disaster.

While damage to production in the form of kiosks and manufacturing facilities was

associated with primary school enrollment, these damages are not statistically significant for

secondary school enrollment. While the disruption of employment would lower household

incomes and discourage enrollment, older students may experience an offsetting substitution

effect. Because employment in the productive sector is an alternative to education, the reduction

in productive activity from the disaster damage may create a substitution effect toward education

that offsets the income effect. Since employment outside the household is less of an option for

younger children, the income effect dominates for primary school.

The impact of population disruption is an issue that is hard to explain. In contrast to the

results for primary school enrollment, the proportion of the population missing and the

proportion evacuated are not statistically significant for secondary school enrollment, and though

the proportion of the population affected is statistically significant, it is associated with lower

enrollment rather than higher. Why this is the case is not readily apparent. Also difficult to

explain is the absence of statistical significance of deaths for secondary school enrollment.

Interactions

Literacy

It was assumed that districts with a higher rate of literacy would be more predisposed toward

education and more resilient in their social institutions. This would suggest that a higher literacy

rate would reduce the impact of disasters on enrollment. The regressions that explore interactions

between literacy and disaster damage for primary school enrollment yield mixed results (Table

2.5). Only two of the six statistically significant interaction terms (those for proportion affected

and damage to irrigation) are positive as predicted. The other four (proportion evacuated,

damage to hospitals, real losses, and damage to office buildings) are negative, suggesting that a

higher rate of literacy increases the negative impact of disasters on enrollment. Of these terms,

only the proportion affected and proportion evacuated are found to be related to primary school

enrollment in the initial regressions. This suggests that the positive impact of the proportion of

51    

the population affected on enrollment and the negative impact of evacuations on primary school

enrollment both grow with literacy rates.

For secondary school enrollment, the only literacy interaction terms that are statistically

significant and correspond to damage types found to be relevant in the original regressions are

destroyed houses, proportion affected, and damaged plantation/forests. For both destroyed

houses and proportion affected, higher literacy reduces the negative impact of damage on

enrollment, while for damaged plantations/forests, higher literacy exacerbates the negative

effects on enrollment. Why the literacy rate should have such a variety of interactions is unclear

and may indicate that literacy is an imperfect measure of social enthusiasm for education.

Poverty

As poverty can reduce the resilience of a community as well as make enrollment extremely

financially difficult for households, a higher rate of poverty is assumed to increase the negative

impact of disasters on enrollment. For enrollment in primary school, the only statistically

significant interaction with poverty that is associated with a damage type found relevant in the

original regressions is the interaction between damage to roads and poverty (Table 2.7). This

interaction is consistent with the prediction and implies that as poverty rises, the negative

influence on enrollment from damage to roads grows. None of the statistically significant

interaction terms correspond with a type of damage found significant in the original regressions.

V . Conclusion

Natural disasters have a profound impact on households and can significantly alter the

constraints they face when making decisions about the education of children. Additionally, a

disaster is not a monolithic event but can have a variety of impacts depending on the specific

environment affected. As a developing country, Indonesia has a strong interest in making

continued progress toward universal primary and lower secondary education. As the country

seeks to accomplish this goal, it faces frequent and various natural disasters. Using an

international dataset that breaks disasters down by region and type of damage caused, this essay

explored the nature of the relationship between damage from natural disasters and enrollment in

education.

The results obtained from this examination are broadly supportive of the idea that deaths,

destruction of housing, and destruction of productive institutions (among others) caused by

52    

natural disasters reduce enrollment. In addition, there are a number of types of disaster damage

found to be significant that are not commonly examined in the natural disaster literature related

to education, including damage to infrastructure, production facilities, and religious institutions.

This suggests that more research needs to be done to examine different types of disaster damage

in greater detail. Only when we understand the mechanisms that translate a natural disaster into

the change in the enrollment rate can we adequately design policy to deal with common

disasters.

Notably, the results reported in this essay suggest that there may be some types of

disaster damage proportion of the population affected and damage to crops which may

actually result in higher enrollment. The evidence also points to major differences in the way

disasters affect primary versus secondary school enrollment. Secondary school enrollment is

more sensitive to damage caused by disasters. This is evidence that Indonesia is correct in its

decision to increase efforts to implement the compulsory education policy for lower secondary

school (UNESCO 2010). Secondary school enrollment responds to different types of disaster

damage. In fact, there is no evidence of shared relationships with disaster damage between these

two enrollment levels. Indonesia needs to consider the different options and constraints facing

households deciding on enrollment in different levels of education. The results suggest that

education policies responding

disruption need to be tailored to the level of schooling concerned. Disasters will continue to

change the environment faced by households and children, but further work to understand the

mechanisms connecting natural disasters to development outcomes should help communities

foresee and navigate these changes.

 

53    

F igure 2.1. Enrollment in Primary and Lower Secondary School by Region

54    

Deaths by District, 2003 2010

Injuries by District, 2003 2010

Damaged/Destroyed Houses by District, 2003 2010

F igure 2.2. Maps of Disaster Severity by District (DesInventar 2013)

55    

Table 2.1. Means and Standard Deviations of Reported District Level Disaster Damage

Damages per 1,000 People

Mean Standard Deviation

Minimum Maximum

Deaths 0.04 0.26 0.0003 5.84

Injuries 0.69 0.36 0.0002 44.30

Missing 0.02 0.07 0.0002 0.73

Affected 67 635 0.0004 8,765

Evacuated 15 52 0.0008 645

Destroyed Houses 1.4 8.9 0.0005 184

Damaged Houses 2.7 14 0.0004 260

Submerged Houses 6.1 17 0.002 197

Education Centers 0.1 0.36 0.0002 4.34

Hospitals 0.04 0.11 0.0003 0.80

Religious Centers 0.1 0.37 0.0002 3.14

Crops (Hectares) 4 26 0.0001 570

Plantation/Forest (Hectares)

4.7 35 0.0008 567

Irrigation 0.15 0.56 0.0007 3.80

Ponds 1.3 3 0.0006 20.27

Real Losses (Indonesian Rupiah)

32 193 0.0004 3,362

Office Buildings 0.08 0.26 0.0003 2.62

Kiosks 0.23 0.99 0.0002 10.28

Manufacturing Facilities 0.04 0.23 0.0004 1.70

Roads (Meters) 0.45 3.79 0.0000006 50.37

Bridges 0.05 0.23 0.0003 3.28

56    

Table 2.2. Summary Statistics for District Enrollment Rates in Primary and Lower Secondary School

Mean Standard Deviation

Minimum Maximum

2003 2010 2003 2010 2003 20010 2003 2010 Primary Enrollment

96.26% 96.12% 3.57 4.92 67.91% 55.26% 100% 100%

Secondary Enrollment

81.90% 76% 10.77 14.62 47.97% 22.22% 100% 100%

57    

Table 2.3. Estimated Disaster Damage Coefficients (Standard Errors) from F ixed E ffects Regressions

Primary Secondary Primary Secondary

Harm to Individuals

Hospitals 1.25 (3.22)

0.58 (5.05)

Deaths -0.95*** (0.33)

0.77 (1.30)

Religious Centers -0.37 (0.28)

-1.71** (0.84)

Injuries 0.01 (0.03)

0.009 (0.07)

Damage to Agriculture

Population Disruption

Crops -0.001 (0.003)

0.01*** (0.004)

Missing -10.68*** (1.07)

10.95 (7.90)

Plantation/Forest 0.004 (0.003)

-0.04*** (0.01)

Affected 0.0003 *** (0.00002)

-0.001*** (0.00008)

Irrigation 0.46 (0.33)

0.75 (0.50)

Evacuated -0.004** (0.002)

-0.003 (0.005)

Ponds -0.14 (0.20)

-0.66 (0.41)

Housing Damage

Damage to Infrastructure

Destroyed Houses

0.0007 (0.007)

-0.06*** (0.02)

Roads -0.02*** (0.007)

-0.07 (0.05)

Damaged Houses

-0.004 (0.006)

-0.03 (0.02)

Bridges 0.26 (0.77)

-3.71*** (1.12)

Submerged Houses

-0.003 (0.009)

-0.03 (0.02)

Damage to Production

Real Losses 0.001 (0.0009)

-0.002 (0.002)

Office Buildings -0.37 (0.60)

-1.23 (2.18)

Human/Social Capital

Manufacturing Facilities

-1.27*** (0.17)

1.49 (0.96)

Education Centers

0.03 (0.33)

-1.62** (0.90)

Kiosks -0.24*** (0.06)

-0.21 (0.24)

Notes: Estimates obtained using a regional fixed effect regression with time dummies and robust clustered standard errors. *,**,*** Indicate statistical significance at 10%, 5%, and 1%, respectively. Primary is the percentage of the relevant age group enrolled in primary school. Secondary is the percentage of the relevant age group enrolled in lower secondary school. All disaster variables are measured relative to the population.

58    

F igure 2.3. Normalized Estimated Impact of Disaster Damage on Primary School Enrollment Rates (Basic Fixed Effect Regressions)

Notes: Circle represents product of estimated coefficient and one standard deviation in disaster impact. Lines extend two standard errors above and below. *,**,*** Indicate statistical significance at 10 percent, 5 percent, and 1 percent, respectively.

-­‐2  

-­‐1.5  

-­‐1  

-­‐0.5  

0  

0.5  

1  

59    

F igure 2.4. Normalized Estimated Impact of Disaster Damage on Lower Secondary Enrollment Rates (Basic Fixed Effect

Regressions)

Notes: Circle represents product of estimated coefficient and one standard deviation in disaster impact. Lines extend two standard errors above and below. *,**,*** Indicate statistical significance at 10 percent, 5 percent, and 1 percent, respectively.

-­‐5  

-­‐4  

-­‐3  

-­‐2  

-­‐1  

0  

1  

2  

3  

60    

Table 2.4. Estimated Disaster Damage Coefficients (Standard Errors) from F ixed E ffects Regressions Controlling for Literacy Education Level

Harm to Individuals Population Disruption Housing Damage

Deaths Injuries Missing Affected Evacuated Destroyed Houses

Damaged Houses

Submerged Houses

Primary -0.69* (0.37)

0.02 (0.04)

-9.57*** (1.50)

0.0003 *** (0.00002)

-0.002 (0.002)

0.005 (0.009)

0.001 (0.006)

-0.003 (0.009)

Secondary 1.02 (1.45)

-0.003 (0.07)

15.34*** (2.65)

-0.001*** (0.00009)

-0.003 (0.005)

-0.07*** (0.03)

-0.02 (0.03)

-0.03 (0.02)

Education Level

Damage to Human/Social Capital Institutions

Damage to Agriculture Real Losses

Education Centers

Hospitals Religious Buildings

C rops Plantation/ Forest I r r igation Ponds Real Losses

Primary -0.007 (0.32)

-1.84 (1.32)

-0.40* (0.21)

-0.01** (0.004)

0.005** (0.002)

0.63* (0.33)

-0.14 (0.19)

0.001 (0.001)

Secondary -1.78* (0.92)

-5.15 (1.47)

-2.06*** (0.75)

0.007 (0.02)

-0.04*** (0.01)

0.91* (0.49)

-0.72* (0.42)

-0.002 (0.002)

Notes: Estimates obtained using a regional fixed effect regression with time dummies, the lagged district literacy rate, and robust clustered standard errors. *,**,*** Indicate statistical significance at 10 percent, 5 percent, and 1 percent, respectively. Primary is the percentage of the relevant age group enrolled in primary school. Secondary is the percentage of the relevant age group enrolled in lower secondary school. All disaster variables are measured relative to the population. The estimated coefficient for lagged literacy is always positive, ranges between 0.04 and 0.07, and is statistically significant at 10 percent for primary school enrollment but not statistically significant for secondary school enrollment.

61    

Table 2.4. (Continued) Estimated Disaster Damage Coefficients (Standard Errors) from F ixed E ffects Regressions Controlling for Literacy

Education Level Damage to Production F acilities

Office Buildings K iosks Manufacturing Facilities

Primary -1.28* (0.69)

-0.24*** (0.07)

-0.42** (0.19)

Secondary -5.45** (2.64)

-0.38 (0.29)

2.74*** (1.04)

Education Level Damage to Infrastructure

B ridges

Primary -0.02*** (0.007)

-0.13 (0.14)

Secondary -0.08 (0.05)

-3.91*** (0.48)

Notes: Estimates obtained using a regional fixed effect regression with time dummies, the lagged district literacy rate, and robust clustered standard errors. *,**,*** Indicate statistical significance at 10 percent, 5 percent, and 1 percent, respectively. Primary is the percentage of the relevant age group enrolled in primary school. Secondary is the percentage of the relevant age group enrolled in lower secondary school. All disaster variables are measured relative to the population. The estimated coefficient for lagged literacy is always positive, ranges between 0.04 and 0.07, and is statistically significant at 10 percent for primary school enrollment but not statistically significant for secondary school enrollment.

62    

Table 2.5. Estimated Coefficients for the Interaction between Disaster Damage and Literacy (Standard Errors) from F ixed E ffects Regressions

Primary School Enrollment Secondary School Enrollment

Damage Coefficient

Interaction with Literacy

Damage Coefficient

Interaction with Literacy

Harm to Individuals

Deaths 14.80 (16.17)

-0.17 (0.18)

-4.42 (39.12)

0.06 (0.43)

Injuries 0.34 (0.73)

-0.003 (0.008)

-2.50*** (0.91)

0.03*** (0.01)

Population Disruption

Missing -198.18 (469.33)

2.09 (5.19)

2307.91* (1210.77)

-25.37* (13.41)

Affected -0.03 (0.01)

0.0003 *** (0.0001)

0.14*** (0.04)

-0.001*** (0.0004)

Evacuated 0.03*** (0.01)

-0.0004*** (0.0001)

-0.08 (0.05)

-0.0009 (0.005)

Housing Damage

Destroyed Houses 0.51 (0.35)

-0.005 (0.004)

-1.02** (0.46)

0.01** (0.004)

Damaged Houses 0.15 (0.29)

-0.002 (0.003)

-0.17 (0.57)

0.002 (0.006)

Submerged Houses -0.23 (0.22)

0.002 (0.002)

0.41 (0.50)

-0.005 (0.005)

Real Losses 0.02*** (0.002)

-0.0002*** (0.00003)

-0.03 (0.003)

0.0003*** (0.00004)

Human/Social Capital

Education Centers -2.33 (6.48)

0.02 (0.07)

-35.75* (20.67)

0.35 (0.22)

63    

Table 2.5. (Continued)  

Primary School Enrollment Secondary School Enrollment

Damage Coefficient

Interaction with Literacy

Damage Coefficient

Interaction with Literacy

Hospitals 40.14 (24.44)

-0.44* (0.26)

-159.28 (101.80)

1.61 (1.07)

Religious Centers 1.75 (4.24)

-0.02 (0.05)

-16.47 (19.48)

0.15 (0.21)

Damage to Agriculture

Crops -0.39 (0.26)

0.004 (0.003)

-0.28 (1.14)

0.003 (0.01)

Plantation/Forest -0.06 (0.10)

0.0008 (0.001)

0.29 (0.44)

-0.004*** (0.005)

Irrigation -32.04*** (9.12)

0.34*** (0.09)

1.17 (24.55)

-0.003 (0.25)

Ponds -2.93 (2.56)

0.03 (0.03)

-4.39 (4.80)

0.04 (0.05)

Damage to Infrastructure

Roads -2.48 (1.56)

0.03 (0.02)

-17.86*** (5.26)

0.18*** (0.05)

Bridges 6.51 (4.40)

-0.07 (0.05)

-5.14 (12.95)

0.01 (0.14)

Damage to Production

Office Buildings 15.40* (8.08)

-0.18** (0.09)

-35.52 (45.29)

0.33 (0.52)

Manufacturing Facilities -40.62 (89.80)

0.43 (0.97)

-566.22 (697.04)

6.11 (7.49)

Kiosks 1.04 (1.93)

-0.01 (0.02)

-19.04*** (7.23) 0.19***

(0.07) Notes: Estimates obtained using a regional fixed effect regression with time dummies, the lagged district literacy rate, and robust clustered standard errors. *,**,*** Indicate statistical significance at 10 percent, 5 percent, and 1 percent, respectively. Primary is the percentage of the relevant age group enrolled in primary school. Secondary is the percentage of the relevant age group enrolled in lower secondary school. All disaster variables are measured relative to the population.

64    

Table 2.6. Estimated Disaster Damage Coefficients (Standard Errors) from F ixed E ffects Regressions Controlling for Poverty Education Level

Harm to Individuals Population Disruption Housing Damage

Deaths Injuries Missing Affected Evacuated Destroyed Houses Damaged Houses

Submerged Houses

Primary -0.74** (0.34)

0.02 (0.03)

-9.79*** (1.60)

0.0003*** (0.00002)

-0.003* (0.002)

0.004 (0.01)

-0.0004 (0.006)

-0.004 (0.009)

Secondary 1.22 (1.61)

-0.007 (0.07)

18.42*** (5.86)

-0.002*** (0.00009)

-0.002 (0.005)

-0.07*** (0.03)

-0.03 (0.03)

-0.03 (0.02)

Education Level

Damage to Human/Social Capital Institutions

Damage to Agriculture Real Losses

Education Centers

Hospitals Religious Buildings

C rops Plantation/ Forest I r r igation Ponds Real Losses

Primary -0.04 (0.31)

-1.90* (1.15)

-0.43** (0.21)

-0.009** (0.004)

0.005*** (0.002)

0.58* (0.34)

-0.15 (0.20)

0.001 (0.001)

Secondary -1.81* (0.93)

-5.26 (3.39)

-2.11*** (0.75)

0.006 (0.02)

-0.04*** (0.01)

0.90* (0.50)

-0.73* (0.43)

-0.002 (0.002)

Notes: Estimates obtained using a regional fixed effect regression with time dummies, the lagged district poverty gap, and robust clustered standard errors. *,**,*** Indicate statistical significance at 10 percent, 5 percent, and 1 percent, respectively. Primary is the percentage of the relevant age group enrolled in primary school. Secondary is the percentage of the relevant age group enrolled in lower secondary school. All disaster variables are measured relative to the population

65    

Table 2.6. (Continued) Estimated Disaster Damage Coefficients (Standard Errors) from F ixed E ffects Regressions Controlling for Poverty Education Level Damage to Production F acilities

Office Buildings K iosks Manufacturing Facilities

Primary -1.42** (0.68)

-0.24*** (0.07)

-0.62*** (0.18)

Secondary -5.63** (2.69)

-0.42 (0.30)

2.58** (1.03)

Education Level Damage to Infrastructure

Roads B ridges

Primary -0.02*** (0.007)

-0.12 (0.13)

Secondary -0.07 (0.05)

-4.02*** (0.52)

Notes: Estimates obtained using a regional fixed effect regression with time dummies, the lagged district poverty gap, and robust clustered standard errors. *,**,*** Indicate statistical significance at 10 percent, 5 percent, and 1 percent, respectively. Primary is the percentage of the relevant age group enrolled in primary school. Secondary is the percentage of the relevant age group enrolled in lower secondary school. All disaster variables are measured relative to the population.

66    

Table 2.7. Estimated Coefficients for the Interaction Between Disaster Damage and Poverty (Standard Errors) from F ixed E ffects Regressions

Primary School Enrollment Secondary School Enrollment

Damage Coefficient

Interaction with Poverty

Damage Coefficient

Interaction with Poverty

Harm to Individuals

Deaths -2.24 (2.30)

0.33 (0.52)

1.27 (6.14)

-0.01 (1.37)

Injuries -0.01 (0.08)

0.01 (0.03)

0.18 (0.11)

-0.07* (0.04)

Population Disruption

Missing -125.06 (130.37)

23.84 (26.88)

-240.63 (235.52)

53.57 (48.70)

Affected 0.0004*** (0.00007)

-0.00009 (0.0001)

-0.001*** (0.0002)

-0.0004 (0.0003)

Evacuated 0.0004 (0.003)

0.0004 (0.0008)

0.006 (0.009)

-0.002 (0.002)

Housing Damage

Destroyed Houses -0.02 (0.01)

0.007 (0.006)

-0.06 (0.04)

-0.003 (0.01)

Damaged Houses -0.02 (0.03)

0.007 (0.01)

-0.01 (0.07)

-0.006 (0.02)

Submerged Houses 0.02** (0.007)

-0.009 (0.001)

-0.05** (0.03)

0.008 (0.009)

Real Losses -0.0005 (0.0004)

0.0004*** (0.00005)

0.001 (0.0009)

-0.0007*** (0.0001)

Human/Social Capital

Education Centers 0.48 (0.40)

-0.27 (0.17)

-1.64 (1.29)

0.09 (0.49)

67    

Table 2.7. (Continued)

Primary School Enrollment Secondary School Enrollment

Damage Coefficient

Interaction with Poverty

Damage Coefficient

Interaction with Poverty

Hospitals 2.09 (2.71)

-1.77* (1.03)

-15.04*** (5.62)

4.34 (2.76)

Religious Centers 0.15 (0.54)

-0.14 (0.14)

-4.77*** (1.77)

0.63 (0.39)

Damage to Agriculture

Crops -0.008 (0.01)

-0.0008 (0.008)

0.01 (0.08)

-0.004 (0.05)

Plantation/Forest 0.02 (0.02)

-0.002 (0.004)

-0.12 (0.10)

0.02 (0.02)

Irrigation 1.15*** (0.23)

-0.99* (0.55)

0.91 (0.61)

-0.03 (1.05)

Ponds -0.65 (0.57)

0.14 (0.13)

-1.40 (1.05)

0.19 (0.25)

Damage to Infrastructure

Roads 0.09** (0.04)

-0.04** (0.01)

-0.15 (0.12)

0.03 (0.05)

Bridges -0.13 (0.61)

0.004 (0.20)

-2.75 (2.37)

-0.46 (0.88)

Damage to Production

Office Buildings -2.47 (1.51)

0.29 (0.31)

-4.86 (7.65)

-0.21 (1.46)

Manufacturing Facilities 1.62 (5.85)

-0.66 (1.70)

39.43 (52.42)

-10.85 (15.27)

Kiosks -0.25* (0.14)

0.006 (0.03)

0.11 (0.20)

-0.21*** (0.07)

Notes: Estimates obtained using a regional fixed effect regression with time dummies, the lagged district poverty gap, and robust clustered standard errors. *,**,*** Indicate statistical significance at 10% percent, 5 percent, and 1 percent, respectively. Primary is the percentage of the relevant age group enrolled in primary school. Secondary is the percentage of the relevant age group enrolled in lower secondary school. All disaster variables are measured relative to the population.

68    

Chapter 3. Rural Households, Education, and the Impact of El

I . Introduction

This research explores the short-run impact of the 2001 earthquakes on enrollment and

expenditures on education in El Salvador. Households invest in education through enrolling

students in educational programs and paying the expenses related to that enrollment. If

households are optimizing in a world with diminishing returns to capital investment, we would

expect an earthquake that destroys primarily physical capital to increase the relative marginal

return to physical capital compared to human capital. In this case we should observe households

reducing enrollment in order to use more labor in the reconstruction effort or to have more

household members working outside the home to increase monetary income in order to fund

reconstruction and maintain consumption levels (Pörtner 2008; Lopez 2009; Sigurdsen, Berger,

and Heymann 2011; Yamauchi, Yohannes, and Quisumbing 2009). We would expect the same to

In order to invest in physical

capital while cushioning the impact on consumption, spending on education would decline.

There are theoretical arguments that contend that in some cases disasters could increase

education investment (Lopez 2009, Bustelo 2011, Ferreira and Schady 2009). In an extreme case

we might see this

perceptions of their vulnerability to such disasters. If the disaster raised expectations of disasters

in the future, people may reevaluate the relative returns to physical and human capital investment

and invest more in human capital, which is relatively less vulnerable to disasters (Pörtner 2008,

Skidmore and Toya 2002). However, the opposite could be true if disasters substantially increase

mortality risk (Pörtner 2008) or lower the return to education through worsening economic

conditions (Pörtner 2008; Jang, Wong, and Huh 2008; Lopez 2009). Unfortunately, empirical

assessment has not achieved clarity on this issue as there is evidence that higher disaster risk can

increase investment in education (Pörtner 2008, Skidmore and Toya 2002), as well as evidence

that higher risk reduces investment in education (Cuaresma 2010, Fitzsimons 2007).

Empirically, there may be some evidence that macroeconomic shocks in relatively

wealthy countries will result in increased investment in education (Ferreira and Schady 2009);

for developing countries the evidence from national studies and household level research thus far

69    

has indicated that disasters reduce investment in education (Pörtner 2008; Janvry, Finan,

Sadoulet, and Vakis 2006; Yamauchi, Yohannes, and Quisumbing 2009; Gitter and Barham

2006; Deuchert and Felfe 2013; Bustelo 2011; Maio and Nandi 2013; Funkhouser 1999; Ferreira

and Schady 2009; Sparrow 2007; Jones and Hagul 2001) and that major natural disasters can

disrupt access to schools in the affected area (Frankenberg et al. 2008). In potential contrast to

these there is some research that suggests disasters increase expenditures on education. Using the

Indonesian Family Life Survey and a household fixed effects regression to examine a wide range

of household responses to economic hardship, Kim and Prskawetz (2010) conclude that natural

disasters raised expenditures on education.

From the literature it is hypothesized that earthquakes will have a negative

impact on enrollment and expenditures on education in the short term. Greater earthquake

damages should be associated with reduced enrollment/expenditures as households allocate more

child labor to earning wages rather than attending school and shift funding to within household

recovery activities and away from education investment. In a 2006 paper studying the same

event and using the same data, Halliday investigated the impact in El Salvador of agricultural

shocks and the 2001 earthquakes. Halliday employed household survey data collected through

the BASIS program** to explore the relationship between negative shocks, household migration,

and remittances. The results suggest that a negative shock to harvest or livestock significantly

increases the probability that the household sends migrants to the United States or Canada and

increases the value of remittances received from migrants abroad. In contrast, earthquake

damage significantly decreases the probability that a household will send migrants abroad and

decreases the value of remittances (Halliday 2006). This may suggest that the major impact of

the earthquake was to create a greater need for labor than for funding and that the earthquake

will be more important for enrollment than expenditures on education.

Micro-level survey data of households in El Salvador before and after the earthquakes of

2001 will be used to determine if there is any evidence of changing investment behavior in

human capital as a result of the earthquakes. This paper will address the short-run implications of

the earthquakes and provide an insight into ways that earthquakes may influence household

decisions.

                                                                                                                     **See  data  section  for  further  description  of  this  data.  

70    

The Event

According to The International Disaster Database maintained by the Center for Research on the

Epidemiology of Disasters, El Salvador has experienced nine earthquakes between 1900 and

2001 (EM-DAT 2009). Two of the most severe of those earthquakes took place exactly one

month apart. The BBC reports that on Saturday afternoon, January 13, 2001, El Salvador

experienced an earthquake measuring 7.6 on the Richter scale and lasting 40 seconds. Exactly

one month later, El Salvador experienced another earthquake. This quake measured 6.1 on the

Richter scale and lasted approximately 20 seconds. The first quake struck in the Pacific Ocean,

60 miles southwest of San Miguel. The second quake was centered 15 miles east of San

Salvador, the capital city (USGS). It is estimated that between the two disasters over 1,000

people were killed, and nearly 1.5 million were affected (EM-DAT 2009).

economic damages were estimated at close to $2 billion US (EM-DAT 2009). Table 3.1 contains

The earthquakes affected at least 22 percent of the

population and caused damages valued at 14 percent of GDP.

The earthquakes in El Salvador present on opportunity to examine the impact of damage

to the capital stock on decisions regarding education investment. This essay is focused on the

short-term impact of earthquake damage on educational investments in rural households.

I I . Data and Methodology

Data

This essay uses data from the BASIS project . This project focuses on understanding the

dynamics of poverty, especially in relation to poverty traps, asset accumulation, and access to

markets. The BASIS project includes a number of projects in multiple countries. One of these

projects was a survey of rural households in El Salvador conducted every two years. This essay

uses panel data from household surveys conducted in 2000 and 2002. Since the earthquakes

occurred very early in 2001, these surveys captured the short-term impact of the earthquake.

Table 3.2 summarizes the impact of the earthquakes on the households surveyed.

                                                                                                                     Depending  on  the  measure  of  impact,  the  January  2001  earthquake  is  ranked  between  the  most  and  the  third  

most  severe  earthquake  the  country  has  experienced  since  1900,  while  the  February  2001  earthquake  is  either  third  or  fourth.      This  is  the  same  data  set  used  in  Halliday  (2006)  where  the  reader  can  find  additional  details  about  the  data.  

71    

The household survey includes§§ information on household education, health, work,

earthquake damage, coping behaviors, and agricultural production. For example, within the

section on education, individuals are asked about their literacy, whether or not they are studying

in the current year, highest grade level completed, and if they studied during the previous year. If

they did not study during the previous year, the reason for this is requested. Coded responses

include reasons related to: illness, working, finances, assisting in the home, lack of further

educational opportunities in the community, and the earthquakes.

Halliday (2006) assessed the importance of attrition for the BASIS panel. He found that

attrition during the period covered here was 4 percent. In terms of basic household

characteristics, the only significant predictor of attrition was land holdings, which will be

controlled for in our estimations. Experiencing earthquake damage is not a significant predictor

of attrition for the period being discussed here.

Earthquakes

The data allow us to measure earthquake impact by using the estimated value of damages to a

household caused by the earthquake. These estimates were collected by the survey in 2002.

There are 672 households that report education data in both survey years (2002 and 2000). Of

these households, 444 (66 percent) report earthquake damage and 345 (51 percent) report

earthquake damage of at least 500 Salvadoran colón. This equates to $57 US, which was

approximately 2.5 percent The maximum reported

earthquake damage was $18,857, and the mean damage was $480. For households without any

land, mean earthquake damage was $445. For households with land, mean damages were

reported at $508. For the half of households with the least land, average earthquake damage was

valued at $445, while the average reported damages for households holding the most land was

$571. Damages appear to be higher for the households with the most land. This is reasonable as

households with more land are likely to have more assets that are vulnerable to damage from the

earthquake. In fact, it is interesting that the damage estimates are not more different for those

with the least and most amount of land. Land holdings will be used as a control variable in our

estimations in the next section.

                                                                                                                     §§For  a  complete  list  of  sections  and  questions  used  in  this  analysis  see  Appendix  B.  

72    

Education Investment

This essay seeks to understand the influence of the 2001 earthquakes on education investment by

rural households. The survey provides two ways to measure education investment by each

household. The data contain information on which members of the household were enrolled in an

educational institution in 2000, 2001, and 2002 and information about the amount of education-

related expenditures made by the household in 2000 and 2002. Each household is asked to report

how much they spent on several categories of education-related expenses. In the research that

follows, the sum of expenditures across categories is used as the measure of education

expenditures. In the period of this study, nine years of primary education were legally

compulsory (Ley 1996).

Enrollment

In 2000, 69.6 percent of households had at least one member enrolled in an educational program.

In 2001 (the year of the earthquakes), 69.8 percent of households report having at least one

member enrolled in an educational institution. In 2002, 72.6 percent of households reported

having at least one member studying. The households can be divided into two groups, those that

reported damages from the earthquakes and those that did not. The percentage of households that

did not report earthquake damage and did report at least one member enrolled in school is 71

percent, 69 percent, and 70 percent in 2000, 2001, and 2002 respectively. For those households

that did report damage the enrollment rates for the same years are 69 percent, 70 percent, and 74

percent.

It is also reasonable to divide the households into two groups based not on whether or not

they reported any amount of damage, but rather on whether or not their reported damages reach a

threshold level. Households with more than $57 of reported damage had enrollment ratios of 68

percent, 70 percent, and 74 percent for the same years. This line of observation can be continued

by calculating these rates for roughly the top third of households by earthquake damage (240

households reporting at least $228 of damage). For these households the rates are similar at 68

percent, 72 percent, and 75 percent. These trends are illustrated in Figure 3.1. From looking at

the above statistics it appears that households that experience earthquake damage may have

slightly lower pre-earthquake enrollment levels. Enrollment levels in the year of the earthquake

are similar for treated (affected by the earthquake) and untreated (not affected by the earthquake)

households except in the case of the most affected households, which are more likely to enroll

73    

than unaffected households. In the year following the earthquakes, enrollment levels are higher

for the treated households.

The preceding data are rough and do not take into account possible changes in the

number of people enrolled in each household. For all households, the average number of

household members enrolled in an educational institution was 1.74, 1.72, and 1.83 in 2000, 2001,

and 2002 respectively (Table 3.3). If households are divided based on reporting any damage

from the earthquakes, households without damage enrolled an average of 1.79, 1.79, and 1.83

people in the same years. For households experiencing damage the corresponding averages are

1.71, 1.68, and1.84. For households experiencing damage of at least $57, the average enrollment

figures are 1.66, 1.66, and 1.83. Finally, for households experiencing more that $228 of damage

average enrollment figures are 1.73, 1.74, and 1.88. These data are illustrated in Figure 3.2.

These trends reinforce the observation that the number enrolled prior to the earthquake is lower

among households that will be affected by the earthquake. The trend differs in that the number

enrolled by the affected households is lower than the number enrolled by unaffected households

in the year of the earthquake and, except in the case of the most affected households (who enroll

more), enrollment numbers are similar for both groups in the year after the disaster.

A more useful measure is the proportion of the household enrolled. For all households the

average proportion of the household enrolled in 2000 is 0.26, in 2001 it is 0.26, and in 2002 it is

0.28. For households not affected by the disaster, the average proportions of the household

enrolled are 0.27, 0.25, and 0.28 in 2000 and 2002 respectively. For those households affected by

the earthquakes the average figures are 0.26, 0.26, and 0.27. When comparing households based

on the $57 threshold, households affected beyond the threshold report proportions of 0.24, 0.25

and 0.27 for the same years. Finally, the most affected households (those with damage greater

than $228) report proportions of 0.25, 0.27 and 0.27. Again, there may be some evidence of

lower pre-earthquake enrollment among households who would be more affected by the

earthquake, but the hypothesis that the enrollment rates are in fact the same cannot be rejected

with confidence*** except for the case of the half of households most affected. Post-earthquake

enrollment is similar for affected and unaffected households with the exception of the most

affected households for which the enrollment rate is higher.

                                                                                                                     ***Based  on  a  t-­‐test  at  5  percent  significance.  

74    

All the preceding enrollment data superficially suggest that the enrollment behavior of

households after the earthquakes differed between the households that had been (more) affected

by the earthquakes and those that had not been (more) affected. However, the data also suggest

that households that were affected by the disaster may have been less likely to enroll household

members in an educational institution prior to the earthquakes. Thus, some of the increased

enrollment following the earthquakes in these households may be due to factors encouraging a

process of enrollment convergence among households that was proceeding despite the

earthquakes. The data studied here is not sufficient to provide information about what trends may

have been present before the earthquake. If there is a difference in enrollment behavior between

affected and unaffected households, and it is not accounted for by the household characteristics I

introduce as controls in the regression estimations, this would most likely result in an upward

bias in the estimated relationship between the earthquake and enrollment. If a negative

relationship is estimated, this will not affect the broad conclusions, but if a positive relationship

is estimated, it will make the results less certain.

Expenditure

Another angle from which to view education investment is in terms of expenditures on

education. Both the 2000 and 2002 surveys provide data on annual education spending by

household in El Salvador. In 2001 El Salvador adopted the US dollar as a legal currency and

pegged the value of the Salvadoran colón at 8.75 per dollar. The survey waves used in this

research report expenditures in Salvadoran colón. For all households, average annual expenditure

on education was $258 in 1999 and $544 in 2001. For the group of households unaffected by

the earthquakes either directly or indirectly, annual expenditure on education was $485 before

the earthquakes and $608 afterward. For the households directly affected by the earthquakes

these figures were $248 and $584. Households reporting damage above the threshold reported

expenditures of $243 and $531 for the same years. This data on expenditures confirms the basic

observations developed from the data on enrollment. We see the same pattern of rising

investment in education, and the size of the increase in investment continues to be higher for

affected households. The difference is large with the households that experienced neither direct

nor indirect effects increasing enrollment by around 25 percent, and those households affected

                                                                                                                     Figures  for  1999  are  adjusted  for  inflation  using  data  from  the  World  Bank  and  converted  to  2001  dollars  at  the  

official  2001  exchange  rate  reported  by  the  World  Bank.  

75    

increasing by more than 100 percent . Table 3.3 includes summary statistics regarding

expenditures on education in local currency.

Methodology

The purpose of this research is to estimate the short-run impact of the 2001 earthquakes on

household investment in human capital. The regressions are run using two measures of human

capital investment: enrollment and expenditures. While expenditures are only reported for two

years, enrollment status is reported for more periods. As a result, two enrollment regressions can

be run: one examining the impact of the earthquakes on enrollment in the same year as the

earthquakes, and the other examining the impact of earthquake damage on enrollment in the

following year. The regressions include a number of control variables related to the region, age

structure of the household, gender of household members, and characteristics of the household

head. In many cases, land held under title by the household is included as a control variable to

proxy for wealth.

Difference in Differences (DID)

The initial investigation of the data is through the use of a difference-in-differences specification

to estimate the effect of treatment (where treatment is defined as being affected by the

earthquake either at all or beyond a defined threshold) on the education investment of the

household. The model estimated in this paper follows the estimation approach described by

Imbens and Wooldridge (2009):

                                                    (1)

Where Hi, is the investment in human capital by household i. This is regressed on a treatment

dummy (Q , equal to unity if affected by the earthquakes), a year dummy (Y) equal to one in

2002, which is the first observation following the earthquakes, and the difference-in-differences

estimator (the interaction term Q*Y). Regressions concerning expenditures are carried out using

                                                                                                                     The  increase  in  untreated  households  is  still  quite  large  in  light  of    result  in  section  III  showing  

increased  spending  for  treated  households.  Table  3.4  summarizes  expenditures  broken  down  by  type  for  a  subsample  of  households.  For  the  untreated  group,  the  most  significant  increase  in  expenditures  are  those  related  to  spending  on  food  for  the  children  while  at  school  (9  percent)  and  on  fees  (108  percent).  This  may  reflect  an  increase  in  local  food  prices  due  to  earthquake  disruptions.  The  observed  increase  in  fees  may  reflect  an  indirect  impact  from  a  decrease  in  the  supply  of  education  following  the  earthquake  as  households  in  regions  affected  by  the  earthquake  seek  to  maintain  enrollment  by  competing  with  unaffected  households  for  a  smaller  number  of  slots.  

76    

the natural log of expenditures. X is a vector of household characteristics, and Z represents region

dummies. The final term, u, represents unobservable characteristics of the households and is

assumed to be independent of earthquake experience. The DID regression above is estimated

using two definitions of treatment. In the first definition, a household is considered treated if it

reported any earthquake-related damage. The second definition considers the household treated

only if the reported damage is above a threshold level.

The preceding regression focuses on the demand response of households for human

capital investment. It is also possible that the earthquake disrupts the capacity of a region to

supply human capital through destruction of schools or disruption of the labor supply to

educational facilities (Jones and Hagul 2001, Frankenberg et al. 2008). In order to explore this

possibility, the DID regression is estimated again, but the definition of treatment changes.

Treatment is assigned based on average reported household damage by households in that region.

Thus, under the broad definition of treatment, a household is considered treated if it is located in

a region where earthquake damage was reported. There is also a corresponding threshold

definition, which considers a household treated if it is located in a region where average reported

damage per household in the sample was above a threshold. The level of damage experienced by

the household will be included as a control. The results of these regressions should indicate

whether or not there is an additional effect on education beyond the household specific effect.

The following section discusses the results by dependent variable.

I I I . Results

Expenditure

The impact of treatment on education expenditures the year of the earthquake is estimated as

being positive (see Table 3.5). The OLS DID regression using the natural log of expenditures as

the dependent variable returns statistically significant results for households damaged by the

quake beyond the threshold level, whether or not any control variables are incorporated. The

estimate for the treatment effect is 0.73 without controls and 0.82 with controls§§§ included on

the right-hand side. This suggests that experiencing damage beyond the threshold is

associated with an 82 percent increase in expenditures on education. The wider definition of

                                                                                                                     §§§Land  ownership,  age  structure,  number  of  female  household  members,  age  of  the  household  head,  region,  and  sex  of  the  household  head.  

77    

treatment (any damage) is statistically significant only when controlling for land, and the

estimated coefficient is smaller at 0.64. An increase in expenditures on education seems contrary

to what would be expected following a disaster, but it is consistent with the findings of Kim and

Prskawetz (2010). Household members aged seven to eighteen, more female household

members, and more land are associated with higher expenditures on education. The estimates

based on allocating treatment by region rather than household did not provide any evidence that

simply

(Table 3.6).

Enrollment

The surveys allow us to look at enrollment in two periods following the quakes: 2001 and 2002.

A DID regression is employed to compare the change in enrollment between 2000 and 2001

between affected (more affected) and unaffected (less affected) households. The same is done to

compare changes in enrollment between 2000 and 2002. Regressions employing a definition of

treatment at the household level fail to provide any evidence for a household response to its own

earthquake damages (Tables 3.7 and 3.9). However, there is evidence that the earthquake caused

households in the most heavily affected regions to reduce enrollment in 2001 (see Table 3.8).

The estimated coefficient for treatment at the threshold (defined as being located in a region

experiencing average damages above the threshold) is -0.34. This result suggests slightly lower

enrollment after the earthquake for those households located in regions significantly affected by

the disaster. In 2000 the average number of household members enrolled in education was 1.74.

Thus, the results suggest treated households have lower enrollment due to the earthquakes, equal

to approximately 19 percent of the 2000 average enrollment. This may suggest some local

spillover effects due to disruption in supply of education. In the above results, additional

household members and land held with title (when included as a control) are both associated

with higher enrollment in both years.

I V . Interpretation

The evidence available suggests that in 2001, the year the earthquakes occurred, household

expenditures on education rose while enrollment declined. The result that the earthquake had a

negative impact on school enrollment is consistent with other research on this question (Pörtner

2008). To a certain extent, an increase in expenditures on education when earthquake damage

78    

rises is surprising though consistent with the descriptive statistics. It may reinforce the

implication from Halliday (2006) that the earthquake created a greater shortage of labor than

income. We would generally expect the change in the relative marginal return from investing in

human versus physical capital brought about by the negative shock to lead people to reduce

expenditures on education in favor of reinvesting in physical capital. The evidence presented

above suggests a different story. The spending increases combined with the reduction in

enrollment can be accounted for in more than one way. First, if the earthquakes resulted in

significant damage to the educational system, this could have the effect of reducing the supply of

education. This reduction in the supply of education would result in fewer students being

enrolled and an increase in the cost of enrolling each student. If demand for education is inelastic

due to the compulsory primary school enrollment policy, then the presence of an increased

spending effect would be less surprising as households switch to more expensive education

options following disasters.

Another possible explanation for both phenomena is based on costs complementary to

education. The cost of enrolling a household member in school is not confined to tuition but

includes many other related costs: transportation, school supplies, uniforms, food, and other

costs. If these complementary costs rise due to the earthquake, then a possible result would be

the observed increase in household expenditure and simultaneous reduction in enrollment.

The problem with both of these explanations is that relationship between earthquake

treatment and household expenditures is only found to be present when treatment is determined

specifically by household, while the enrollment impact is associated with regional treatment.

There is no evidence to suggest that a household that is located in a treated region but not itself

affected by the earthquake will have higher spending on education. The explanations offered

above fit a situation where the overall regional impact is affecting all households, and the higher

spending and lower enrollment are being driven by forces external to the household. However,

this is not exactly what the data suggest.

The fact that evidence for a regional treatment impact is found for enrollment, rather than

evidence for a household treatment impact, suggests that the supply of education in the aftermath

of a disaster is of more concern than the demand. The destruction of schools and roads, loss of

power, and interruption of teacher availability may all be contributing to a persistent reduction in

enrollment. If the earthquakes created an increased demand for local labor, as suggested by

79    

Halliday (2006), the regional effect on household enrollment may also indicate that child (or

teacher) time is being reallocated away from education toward the labor market. It is interesting

that there is no evidence for the earthquakes having any effect on enrollment in 2002 (Tables 3.9

and 3.10). This suggests that the earthquakes impact on enrollment was not permanent.

The result with respect to household spending on education is not what one would expect

but is quite economically significant. It is difficult to come up with any reasonable explanation

using forces internal to the household for higher household damages resulting in higher

education expenditure. One possibility that fits is that there is some kind of change in

expectations that is driving a new pattern in human capital investment at the same time that they

need more household members contributing through work or other means, but this paper cannot

offer any serious evidence in support of changes in expectations.

Table 3.12 provides the distribution of reported reasons for not enrolling in an

educational institution by degree of earthquake impact. There is no report of household members

not being enrolled in school because the local school was destroyed by the earthquake. Thus we

find no support for the idea that there was a reduction in the supply of education. However, the

data above do not eliminate the possibility that schools were damaged and not destroyed. There

is evidence that the earthquakes

increasing importance of fear of future earthquakes as households are located in regions more

affected by the earthquakes. Admittedly, the share is extremely small, yet if it were indicative of

education by households affected by the earthquakes may have something to do with changed

expectations about the value of education in the vein of the human capital theories of disaster and

growth.

The distribution of causes for not enrolling household members does suggest that

households in regions affected by the earthquakes were more likely to remove people from

school in order to work, with households in heavily affected regions most likely to substitute

work for education. This fact might shed light on the surprising pattern that households in

affected regions seem much less likely to remove individuals from school in order to help in the

home and that households in affected regions do not seem to have a higher tendency to remove

household members due to lack of money. As far as the issue of helping in the household goes, it

may be that households find that they need less household help and more money to reinvest in

80    

their physical capital. Thus, in addition to those switching from school to work due to the

earthquakes, some individuals who were substituting helping out in the home for education are

now shifted toward work outside the home to earn cash for reconstruction funding. This

suggestion is consistent with the pattern found by Halliday (2006), in which the earthquakes

increased the local demand for labor in El Salvador.

If this is indeed the case****, and the share of household members with outside

employment is rising, it could explain why income level is not driving lower school attendance.

When considering the earlier result that household spending on education rises with disaster

impact, some of this increased earning outside the household may be going to pay for the higher

education spending. Households may be prioritizing education for those with the highest

marginal gain from education. Making this substitution encourages them to increase their

investment in a smaller number of individuals with a higher return to education.

The other causes for not attending school are reported not because they are particularly

informative in determining what is happening with respect to the earthquake, but because they

seem to be associated with earthquake damage i Illness

becomes less of a factor in not attending school as earthquake damage rises. Not attending school

because you have received enough schooling is much more likely for households in unaffected

regions. Some kind of discouraged student effect (not attending school due to grade repetition)

These patterns, if accurately

reflecting what happened in these regions, may suggest that there are underlying differences

between affected and unaffected regions that contribute to these specific patterns.

V . Conclusion

This essay contributes to the discussion concerning disasters and investment in human capital by

using household level data. The results are focused on the short-run response of household

education investment behavior to the two major earthquakes El Salvador experienced in early

2001. The results indicate that the earthquakes are associated with a reduction in enrollment for

affected households in 2001. The analysis also suggests a positive relationship between

                                                                                                                     ****Of  course,  the  distribution  of  causes  for  non-­‐enrollment  is  not  enough  to  tell  us  what  is  happening  to  earning  levels  or  even  the  number  of  individuals  involved  in  certain  activities.  What  we  do  know  is  that  the  number  of  household  members  enrolled  goes  down  enrollment  follows  the  pattern  suggested  by  the  distribution  data  then  the  explanations  may  be  reasonable.  

81    

earthquake damage and household spending on education. While this result is unusual, it is not

unprecedented. Unfortunately, the data available are not able to provide a satisfactory

explanation for this phenomenon, and it awaits a more direct analysis elsewhere.

The reduction in enrollment associated with the earthquake is consistent with the

relationship found between earthquakes and education in Skidmore and Toya (2002) and

Cuaresma (2010). Both these papers suggest that earthquakes and climatic disasters have

different impacts on education in the long run. This essay agrees that earthquakes have a

negative immediate impact on enrollment, but this impact dissipates quickly. The results related

to spending, if accurate, complicate the picture, suggesting that the earthquakes do not have a

wholly negative impact on education investment. In fact, the results in this essay do not

contradict an explanation that involves households in El Salvador changing their expectations of

disasters in the future and changing human capital investment behavior as a result. Determining

the overall impact of the lower enrollment and higher spending on education would require more

research using data across a longer time period. Extending this case study into a medium and/or

long-term framework would be the next natural extension. Regardless of the explanation for the

observed increase in educational expenditures, the reduction of enrollment is troubling and

provides an opportunity for policy-makers. The negative association between disasters and

enrollment that this research has confirmed comes with a natural policy recommendation for

developing countries. For those countries concerned with maintaining enrollment, conditional

cash transfer schemes are becoming increasingly popular (Janvry et al. 2006, Gitter and Barham

2006). Including a structure within these transfer mechanisms that encourages households to

maintain children in school following significant shocks would aid in reducing the negative

 

82    

Table 3.1. National Impact of 2001 Earthquakes

Event Deaths* Deaths/ Population

Number A ffected*

A ffected/ Population

Damages* (Millions of Dollars)

Damages/ G DP**

January 844 0.01% 1,334,529 22.45% 1500 11.42% February 315 <0.01% 256,021 4.31% 348.5 2.65% Total 1,159 0.02% 1,848.5 14.07% * Source: EM-DAT, OFDA/CRED International Disaster Database **Source: World Bank data for previous year (2000) Since some people may have been affected by both disasters, the total number of people affected likely overstates

the number of people affected and is not particularly useful.

Table 3.2. Summary of Losses (Colón) Experienced by Sample Households

Proportion of HHs

Affected

Mean Loss

Median Loss

Minimum Loss

Maximum Loss

Standard Deviation

66% 6428.85 2000 1 165,000 14,157.48

Table 3.3. Education Enrollment and Real Expenditures (2001 Colón)

Household Treatment Means

Regional Treatment Means

Untreated Sample

Any Losses

Threshold Losses

Any Losses

Threshold Losses

Mean Mean Min Max

Expenditure 1999 (Colón)

2,172 2,126 2,109 2,151 4,245 2,261 0 112,001

Expenditure 2001

5,114 4,642 4,740 5,098 5,325 4,759 0 397,220

Enrollment 2000

1.71 1.66 1.72 1.75 1.93 1.74 0 12

Enrollment 2001

1.68 1.66 1.70 1.69 2.09 1.72 0 11

Enrollment 2002

1.84 1.83 1.83 1.83 2 1.83 0 10

83    

Table 3.4. Expenditures by Type for Different Household Groups (2001 Colón) Tuition Uniforms Materials Fees Food Transporta-

tion 1999 2001 1999 2001 1999 2001 1999 2001 1999 2001 1999 2001 Sample 73

64 222 229 177 170 262 194 2987 1002 697 242

Untreated 34

48 310 284 158 172 192 400 3099 3384 167 76

HH Threshold Treatment

63

57

222

194

168

148

286

154

2777

819

763

250

Regional Threshold Treatment

78

67

235

228

186

175

290

176

3302

852

720

260

Note: Only 565 households report expenditures by category. In contrast 672 households report total expenditures.

84    

F igure 3.1. Enrollment Rates by Group (2000-2002)

67  

68  

69  

70  

71  

72  

73  

74  

75  

76  

2000   2001   2002  

Percen

t  of  H

ouseho

lds  En

rolling

 

Enrollment  Rates  by  Year  and  Group  

Not  Treated  

Treated  

Treated  at  $57  

Treated  at  $228  

1.65  

1.7  

1.75  

1.8  

1.85  

1.9  

2000   2001   2002  

Num

ber  o

f  Hou

seho

ld  M

embe

rs  

Enrolled  

Average  Enrollment  by  Year  and  Group    

Not  Treated  

Treated  

Treated  at  $57  

Treated  at  $228  

85    

DID Expenditure Regressions

Table 3.5. OLS DID Treatment by Household: Dependent Variable is Natural Log of Expenditure

lnE X DID§ 0.51

(1.20) 0.62

(2.20)**

DID T§ 0.66 (1.47)

0.66 (2.20)**

Year -0.31 (0.90)

-0.31 (0.90)

-0.33 (1.38)

-0.32 (1.29)

T reated -0.19 (0.66)

-0.32 (1.04)

0.01 (0.03)

-0.16 (0.47)

Age: 0-6 -0.05 (0.42)

0.001 (0.01)

Age: 7-14 1.13 (9.46)***

1.06 (8.28)***

Age: 15-18 0.67 (4.60)***

0.69 (4.80)***

Age: 19-30 0.01 (0.09)

0.03 (0.20)

Age: >30 0.008 (0.06)

-0.03 (0.20)

Number of F emales

0.22 (2.25)**

0.25 (2.23)**

Male H H H ead

0.25 (0.73)

0.31 (0.81)

Age H H H ead

-0.01 (1.49)

-0.02 (1.38)

lnLand 0.29 (1.68)*

0.36 (1.82)*

Controls No No Yes Yes Region No No Yes Yes E rrors C lustered by M un.

No No Yes Yes

N 1344 1146 1338 1144 Region is controlled for at the municipal level. Absolute value robust t-statistics are in parenthesis *,**, and *** indicate significance at 10 percent, 5 percent, and 1 percent respectively. EX is the measure of expenditure using the sum of the reported expenditures in each expenditure category.

§DID is the estimate where treatment is defined as reporting any earthquake damage. DIDT is the estimate where treatment is defined as reporting earthquake damage greater than the threshold.

Controls indicates that the regression incorporated controls for age structure, age of the head of household, sex of the head of household, and the number of female individuals in the household.

Land measures land owned accompanied by a title.

86    

Table 3.6. OLS DID Treatment by Region: Dependent Variable is Natural Log of Expenditure

lnE X DID§ 0.10

(0.13) -0.04

(0.07)

DID T§ 0.09 (0.12)

-0.07 (0.54)

Year -0.04 (0.05)

-0.04 (0.05)

0.11 (0.17)

0.15 (0.23)

T reated -0.94 (1.79)*

-0.88 (1.68)*

-0.93 (1.77)*

-0.77 (1.36)

Age: 0-6 -0.02 (0.20)

-0.07 (0.54)

Age: 7-14 0.99 (9.10)***

0.93 (8.14)***

Age: 15-18 0.76 (6.06)***

0.82 (5.76)***

Age: 19-30 0.10 (1.06)

0.05 (0.54)

Age: >30 0.08 (0.68)

0.01 (0.09)

F emales§§ 0.24 (2.75)***

0.28 (2.95)***

Male Household H ead

0.01 (0.04)

0.12 (0.36)

Age: Household H ead

-0.02 (2.11)**

-0.02 (2.35)**

Land 0.33 (2.42)**

0.36 (2.40)**

Region No No Yes Yes E rrors C lustered by M un.

No No Yes Yes

N 1338 1150 1338 1150 Region is controlled for at the municipal level. Numbers in parentheses are robust absolute t-values. *,**, and *** indicate significance at 10 percent, 5 percent, and 1 percent respectively. EX is the measure of expenditure using the reported total.

§DID is the estimate where treatment is defined as reporting any earthquake damage. DIDT is the estimate where treatment is defined as reporting earthquake damage greater than the threshold.

Age controls refer to the number of household members in the age category. §§Females measures the number of female household members in 2002.

Land measures land owned accompanied by a title.

87    

O LS DID Enrollment Regressions Table 3.7. OLS DID Treatment by Household

Region is controlled for at the municipal level. Numbers in parentheses are absolute robust t-statistics. *,**, and *** indicate significance at 10 percent, 5 percent, and 1 percent respectively.

EN2 is the number of household members enrolled in 2002. §DID is the estimate where treatment is defined as reporting any earthquake damage. DIDT is the estimate where treatment is defined as reporting earthquake damage greater than the threshold.

Controls indicates that the regression incorporated controls for age structure, age of the head of household, sex of the head of household, and the number of female individuals in the household.

Age controls refer to the number of household members in the age category. §§Females measures the number of female household members in 2002.

Land measures land owned accompanied by a title.

E N1 DID§ -0.03

(0.14) -0.02

(0.19)

D ID T§ 0.006 (0.03)

-.02 (0.21)

Year -8.84e-16 (0.00)

3.66 e-15 (0.00)

-0.03 (0.47)

-0.03 (0.46)

T reated -0.08 (0.56)

-0.13 (0.94)

-0.10 (0.95)

-0.17 (1.40)

Age: 7-14 0.77 (16.96)***

0.75 (14.50)***

Age: 15-18 0.54 (7.95)***

0.54 (7.40)***

Age: 19-30 0.08 (1.91)*

0.11 (2.14)**

F emales§§ 0.04 (1.08)

0.02 (0.52)

Age: Household H ead

-0.005 (1.50)

-0.006 (1.72)*

Land 0.02 (4.69)***

0.02 (4.44)***

Region No No Yes Yes Controls No No Yes Yes Clustered E r rors by M un.

No No Yes Yes

N 1344 1146 1338 1144

88    

Table 3.8. OLS DID Treatment by Region

E N1 DID§ -0.18

(0.54) -0.29

(1.53)

D ID T§ -0.21 (0.62)

-0.34 (1.77) *

Year 0.15 (0.47)

0.15 (0.47)

0.22 (1.21)

0.23 (1.25)

T reated -0.21 (1.03)

-0.19 (0.89)

-0.18 (1.51)

-0.15 (1.13)

Age: 0-6 0.06 (1.09)

0.07 (1.21)

Age: 7-14 0.77 (20.49)***

0.78 (19.14)***

Age: 15-18 0.54 (9.09)***

0.58 (8.61)***

Age: 19-30 0.09 (2.74)***

0.07 (2.04)**

Age: >30 0.04 (0.99)

0.005 (0.13)

F emales§§ 0.04 (1.21)

0.04 (1.15)

Male Household H ead

0.04 (0.39)

0.09 (0.90)

Age: Household H ead

-0.004 (1.76) *

-0.004 (1.63)

Land No No 0.02 (4.98)***

0.02 (5.77) ***

Controls No No Yes Yes Region No No Yes Yes Clustered E r rors by M un.

No No Yes Yes

N 1338 1150 1338 1150 Region is controlled for at the department level. Numbers in parentheses are absolute t-values. *,**, and *** indicate significance at 10 percent, 5 percent, and 1 percent respectively.

EN2 is the number of household members enrolled in 2002. §DID is the estimate where treatment is defined as being in a region reporting any earthquake damage. DIDT is the estimate where treatment is defined as being in a region where average household damage is above a threshold.

Controls indicates that the regression incorporated controls for age structure, age of the head of household, sex of the head of household, and the number of female individuals in the household.

Age controls refer to the number of household members in the age category. §§Females measures the number of female household members in 2002.

Land measures land owned accompanied by a title.

89    

Table 3.9. OLS DID Treatment by Household

E N2 DID§ 0.08

(0.43)

0.06 (0.57)

D ID T§ 0.13 (0.66)

0.06 (0.55)

Year 0.04 (0.28)

0.04 (0.28)

-0.03 (0.38)

-0.03 (0.43)

T reated -0.08 (0.56)

-0.13 (0.94)

-0.08 (0.80)

-0.15 (1.31)

Age: 0-6 0.23 (4.09)***

0.28 (4.60)***

Age: 7-14 0.80 (19.27)***

0.79 (16.83)***

Age: 15-18 0.53 (7.97)***

0.53 (7.35)***

Age: 19-30 0.07 (1.73)*

0.10 (2.25)**

Age: >30 0.05 (1.28)

0.07 (1.49)

Land No No 0.02 (5.03)***

0.02 (5.02)***

Controls No No Yes Yes Region No No Yes Yes Clustered E r rors by M un.

No No Yes Yes

N 1344 1146 1338 1144 Numbers in parentheses are absolute t-statistics. *,**, and *** indicate significance at 10 percent, 5 percent, and 1 percent respectively.

EN2 is the number of household members enrolled in 2002. §DID is the estimate where treatment is defined as reporting any earthquake damage. DIDT is the estimate where treatment is defined as reporting earthquake damage greater than the threshold.

Controls indicates that the regression incorporated controls for age structure, age of the head of household, sex of the head of household, and the number of female individuals in the household.

Age controls refer to the number of household members in the age category. §§Females measures the number of female household members in 2002.

Land measures land owned accompanied by a title.

90    

Table 3.10. OLS DID Treatment by Region

Region controlled for at the department level. Numbers in parentheses are absolute t-values. *,**, and *** indicate significance at 10 percent, 5 percent, and 1 percent respectively.

EN2 is the number of household members enrolled in 2002. §DID is the estimate where treatment is defined as reporting any earthquake damage. DIDT is the estimate where treatment is defined as reporting earthquake damage greater than the threshold.

Controls indicates that the regression incorporated controls for age structure, age of the head of household, sex of the head of household, and the number of female individuals in the household.

Age controls refer to the number of household members in the age category. §§Females measures the number of female household members in 2002.

Land measures land owned accompanied by a title.

E N2 DID§ -0.04

(0.13)

-0.12 (0.88)

DID T§ 0.01 (0.05)

-0.17 (1.22)

Year 0.07 (0.22)

0.07 (0.22)

0.12 (0.91)

0.12 (0.93)

T reated -0.21 (1.03)

-0.19 (0.89)

-0.19 (1.61)

0.12 (0.93)

Age: 0-6 0.24 (4.63)***

0.27 (4.86)***

Age: 7-14 0.80 (22.32)***

0.82 (21.35)***

Age: 15-18 0.52 (9.16)***

0.55 (8.80)***

Age: 19-30 0.09 (2.69)***

0.08 (2.44)**

Age: >30 0.07 (1.88)*

0.04 (1.13)

Age: Household H ead

-0.004 (1.47)

-0.003 (1.09)

Land No No 0.02 (4.98)***

0.02 (6.42)***

Controls1 No No Yes Yes Region No No Yes Yes E rrors C lustered by M un.

No No Yes Yes

N 1338 1150 1338 1150

91    

Table 3.11. Estimated Impact of Treatment Household

T reatment Regional T reatment

Enrollment 2001 * 19% Decrease

Enrollment 2002 * *

Expenditures 2001 62-66% Increase *

     

Enrollment Estimates are based on regional treatment, and expenditure estimates are based on household treatment. Percentage changes in enrollment are based on the estimated change relative to the average for that group in the year prior to the earthquake.

92    

Table 3.12. Reasons for Non-Enrollment Reason for Not A ttending School

Unaffected Regions

L ightly A ffected Regions

Moderately A ffected Regions

H eavily A ffected Regions

To Work

18.95%

21.05%

19.29%

24.07%

To Help in House

13.16%

7.91%

6.92%

8.16%

Lack of Money

33.16%

30.56%

33.75% 29.20%

School destroyed by Earthquakes

0% 0% 0% 0%

Fear of Earthquakes

0% 0% 0.10% 0.40%

Illness 2.63% 2.03% 1.99% 1.91% Received Sufficient Instruction

4.21% 1.28% 1.26% 2.42%

Repeated Grade Too Many Times

0.52% 0.96% 1.47% 1.81%

Other 27.37% 36.21% 35.22% 32.03% Total Instances of Provided Reasons

190 936 954

993

Number of Regions in Category

20 56 43 41

                                                                                                                     specific  reason  for  not  being  enrolled  

in  an  educational  institution.  Reasons  are  given  for  each  individual,  but  sometimes  multiple  reasons  are  given  for  the  same  individual.    

93    

Appendix A: Provinces Contained in Regions

For the purposes of presenting data in the first two essays without overwhelming the reader, I

have sometimes grouped the thirty provinces used in this research into seven regions as follows:

Sumatra (1)

Nanggroe Aceh Darussalam, Sumatera Utara, Sumatera Barat, Riau, Jambi, Sumatera Selatan,

Bengkulu, Bangka Belitung, and Lampung.

Java (2)

Jawa Barat, Banten, DKI Jakarta, Jawa Tengah, Yogyakarta, and Jawa Timur.

K alimantan (3)

Kalimantan Barat, Kalimantan Timur, Kalimantan Tengah, and Kalimantan Selatan.

Sulawesi (4)

Sulawesi Utara, Sulawesi Tengah, Sulawesi Selatan, Sulwesi Tenggara and Gorontalo.

I r ian Jaya (5)

Papua

Maluku (6)

Maluku and Maluku Utara.

T enggara (7)

Nusa Tenggara Barat, Nusa Tenggara Timur, and Bali.

94    

Appendix B: BASIS Survey

Question Categories:

1. DATOS DE IDENTIFICACIÓN Identification Information 2. INFORMACIÓN GENERAL SOBRE LA FAMILIA General Information about the Family 3. EDUCACIÓN Education 4. SALUD Health 5. TRABAJO DE LOS INTEGRANTES DEL HOGAR Type of Work among Household Members 6. OTRAS ACTIVIDADES REMUNERADAS O ther Paid Activities 7. TERREMOTOS Y OTROS ACONTECIMIENTOS Earthquakes and Other Events 8. REDES DE AYUDA Aid and Aid Organizations 9. CARACTERÍSTICAS DE LA VIVIENDA Characteristics of the House 10. ENTORNO DE LA COMUNIDAD Environment of the Community 11. INFORMACIÓN SOBRE LA TIERRA Information about the Land 12. USOS DE LA TIERRA Uses of the Land 13. PRODUCCIÓN AGRÍCOLA Agricultural Production 14. ACTIVIDADES PECUARIAS                                                                                                                      

All information in this appendix corresponds to the 2002 survey.

95    

Agricultural Activities, Specifically Animals (Cows, Pigs, Ranching)   15. OTROS ASPECTOS DE LA PRODUCCIÓN AGROPECUARIA O ther Agricultural Factors 16. LUGAR DE ORIGEN DE LA FAMILIA

17. MIGRACIÓN Migration   18. TRANSACCIONES FINANCIERAS F inancial Transactions 19. DEUDA ANTERIOR A 2001 Debt Previous to 2001 20. AHORROS Savings 21. OTROS INGRESOS Y GASTOS O ther Deposits and Expenditures 22. DIRECCIÓN FUTURA Future Direction 23. PREGUNTAS PARA EL ENTREVISTADOR Questions for the Interviewer 24. OBSERVACIONES Observations

Education:

9. Answer provided in complete sentence for each person. ¿Sabe leer y escribir _____? 1. Sí

2. No Can the subject read and write? 10. Answer numerical from 0-11 ¿Cuál es el último grado que completó? (Anotar el grado) What is the highest grade completed?

01. Parvularia

96    

Nursery School 02. Primaria Primary 03. Tercer ciclo 7th to 9th grade 04. Carrera corta/ vocacional

Technical degree 05. Bachillerato Academic secondary school course for ages 14 17 06. Superior no universitario

Some university level 07. Técnico universitario Technical university 08. Universitario University 09, Programa a distancia Program from a distance 10. Otro (Especifique) O ther (Specify) 11. No sabe

11. Numerical 1 or 2 ¿Estudió _____ el año pasado (2001)?

1. Sí 2. No

12. Answer in sentence. ¿Estudia _____ algo ahora (2002)?

1. Sí ..... Si tambien estudió en 2001 Pase a la pregunta 14 2. No

13. Numerical ¿Por qué no estudia / dejó de estudiar/ no estudió? (NO LEER, MARCAR LAS QUE APLICAN) Why aren't you studying?/Why did you stop going to school?/Why did you never go to school?

01. Se casó o acompañó Got married or moved in with your partner

02. Por enfermedad Because of illness 03. Por embarazo Because of pregnancy 04. Por trabajar In order to work 05. Para ayudar en la casa

97    

In order to help in the house 06. Por no tener dinero para gastos Due to lack of money for expenses 07. Ya tenía suficientes studios Has sufficient studies 08. Porque repitió muchos grados

Because I repeated the same grade numerous times 09. La escuela no tenía más grados

The school I attended did not have higher grades 10. Está muy pequeño Is very small 11. Se cayó la escuela por los terremotos

The school disappeared due to the earthquakes 12. Les dio miedo por los terremotos

We were afraid of more earthquakes 13. Los papás no quisieron Parents did not want to send me anymore 14. Se mudaron donde no había escuela cerca Moved to a place where there was no school close by 15. Otros (Especifique) O ther (Specify)

14. Numerical ¿Qué está estudiando ahora _____? (Anotar el grado) What/where are you studying now?

01. Parvularia Nursery School 02. Primaria Primary 03. Tercer ciclo 7th to 9th grade 04. Carrera corta/ vocacional

Technical degree 05. Bachillerato Academic secondary school course for ages 14-17 06. Superior no universitario

Some university level 07. Técnico universitario Technical University 08. Universitario University 09. Programa a distancia Program from a distance 10. Otro (Especifique)

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O ther (Specify) 11. No sabe

15. ¿Cuánto gasta por año en: How much did you spend each year on:

1. Matrícula (al año) Tuition (per year)

2. Uniformes (al año) Uniforms (per year)

3. Utiles (al año) School materials, i.e. writing materials (per year)

4. Cuota mensual Monthly fee

5. Refrigerios (a la semana) Snacks (per week)

6. Transporte (a la semana) Transportation (per week)

7. Otros gastos relacionados (Especifique, e indique la periodicidad) O ther related expenses (specify and indicate the frequency)

Earthquakes:

68. ¿Fallecieron algunos de sus familiares (aunque no vivieran con ustedes) durante el primero o el segundo de los terremotos, o por cualquier causa durante el año pasado (2001)? Did any family member die (whether or not they live with you) during the first or second of the earthquakes or for any cause during the past year (2001)?

Sí ____________¿Cuántos? ___________ Yes How many? No (Pase a la pregunta 77) No (Pass to question 77)

69. ¿Murió a causa del terremoto? Did they die due to the earthquake?

1. Sí 2. No

74. ¿Formaba parte de su grupo familiar, es decir, vivía con ustedes? Did they live with you?

2. No

76. ¿A qué se dedicaba ______? (marque las que aplican) What did he/she do?

1. A las labores agrícolas, en terrenos de la familia Farming on the family land

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2. Actividades no agropecuarias por cuenta propia o de la familia Non-agricultural activities for themselves or the family 3. Asalariado agropecuario Agricultural wage earner 4. Asalariado no agropecuario Non-agricultural wage earner 5. A las labores de su casa (cuidar niños, limpiar, cocinar, etc.) Work at home 6. A estudiar Studied 7. No trabajaba Did not work 8. Otros (especifique_____________________) O ther (specify)

77. ¿Tuvieron algunos de los siguientes acontecimientos familiares durante el año 2001? Did any of the following events occur within the family in 2001?

Respuesta Answer 1= Sí, 2= No ¿Fue por causa de los terremotos? Because of the earthquakes? 1= Sí, 2= No ¿Fue por otras causas? Because of other causes? 1= Sí, 2= No

01. Nacimiento en la familia Birth in the family 02. Alguien se casó o acompañó Any marriage or moving in with a partner 03. Alguien se divorció o separó Any divorce or separation 04. Algún o algunos miembros del hogar se fueron a vivir a otra parte de El

Salvador Any member of the household moved to another part of El Salvador 05. Algún o algunos miembros del hogar se fueron a vivir a otro país Any member of the household moved to another country 06. Tuvieron ustedes, como grupo familiar, que irse a vivir a otro lado aunque haya sido temporalmente (por lo menos una semana) Did you, as a family group, have to go live in another place even if it was temporary (for at least a week) 07. Algunos miembros del hogar se enfermaron, lesionaron o tuvieron algún accidente que no les permitió trabajar por lo menos una semana

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Was any member of the household sick, injured or experienced an accident that kept them from work for at least a week 08. Tuvieron el año pasado incrementos significativos en los gastos medicos (en comparación con el año 2000) Did they have a significant increase in expenditures in 2001 compared with 2000 09. Algún o algunos miembros del hogar perdieron el empleo o no pudieron encontrar trabajo (pero lo buscaron) por lo menos durante una semana

Did any members of the household lose their job or were unable to find work (but they looked) for at least a week

10. Algún pariente en EEUU que les enviaba ayuda fue afectado por los ataques terroristas o por la crisis económica por lo que disminuyó o suspendió lo que les enviaba

Was any relative in the United States that sent you aid affected by the terrorist attacks or the economic crisis and reduced or stopped their aid

78. ¿Tuvieron ustedes o algún miembro de la familia alguna pérdida, durante el año 2001? Did you or any member of your family experience loss, theft, or destruction of the following in 2001?

Pérdida, robo o destrucción Loss, theft, or destruction.

1= Sí 2= No

¿Fue por causa de los terrmotos? Was it due to the earthquakes?

1= Sí 2= No

¿Fue por otras causas? Was it due to other causes?

1= Sí 2= No

¿En cuánto estima el costo de lo que perdió? What is the estimate of the value of the loss?

01. Daños menores a la vivienda Minor damage to the house

02. Daños severos a la vivienda, pero era habitable Severe damage to the house, but it was still habitable

03. La vivienda quedó inhabitable The house was uninhabitable

04. Destrucción total de la vivienda The house was totally destroyed

05. Daños a otras construcciones (especifique _________) Damage to other buildings (specify)

06. Daños en el terreno (especifique _____________) Damage to the fields (specify)

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07. Muebles del hogar Household furnishings

08. Electrodomésticos (refrigeradora, TV, radio, etc.) Appliances (refrigerator, TV, radio, etc.)

09. Maquinaria agrícola Farm equipment

10. Herramientas y otros utensilios para producción agrícola Tools used for agricultural production

11. Maquinaria no agrícola Non-agricultural equipment

12. Herramientas y otros utensilios para producción no agrícola Tools used for non-agricultural production

13. Mercadería para la venta Merchandise for sale

14. Granos que estaban almacenados Stored grains

15. Se le murieron animales de crianza (aves, ganado, etc.) Did any of the animals you raised die

16. Otras pérdidas (especifique_______________) O ther losses (specify)

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