Tsukuba Economics Working Papers
No. 2012-001

Do natural disasters decrease the gender gap in schooling?
by
Yoshito TakasakiMarch 2012

UNIVERSITY OF TSUKUBA
Department of Economics
1-1-1 Tennodai
Tsukuba, Ibaraki 305-8571
JAPAN

Do natural disasters decrease the gender gap in schooling?
Yoshito Takasaki*
University of Tsukuba

March 5, 2012

Acknowledgements
I wish to thank my field team – Jonati Torocake, Viliame Manavure, Viliame Lomaloma, and 16
enumerators – for their advice, enthusiasm, and exceptional efforts on behalf of this project.
Special thanks are owed to the Fijians of the region who so willingly participated in the survey.
The Cakaudrove Provincial Office in Fiji offered valuable institutional support for this project.
This research has been made possible through support provided by the Sumitomo Foundation,
the Japan Society for the Promotion of Science, and the Ministry of Education, Culture, Sports,
Science and Technology in Japan. This paper benefited significantly from the insightfulcomments and suggestions of Jonna Estudillo and conference participants on an earlier versionof this work presented at Hakone Conference in Tokyo (2011, GRIPS). Any errors ofinterpretation are solely the author’s responsibility.
*Corresponding author. Faculty of Humanities and Social Sciences, University of Tsukuba, 1-11 Tennodai, Tsukuba, Ibaraki 305-8571 Japan, Tel./fax: +81 29 853 6280. E-mail address:
takasaki@sk.tsukuba.ac.jp.

1
Abstract
Rapidly decreasing gender gaps in schooling in developing countries can be a result of agendered division of child farm labor as a coping response to increased natural disasters. This
paper makes a case for this conjecture by analyzing original household survey data from ruralFiji. Boys, not girls, contribute to farming only among cyclone victims with dwelling damage,
independent of housing aid receipt. Boys’ school enrollment is significantly lower than girls’
only among victims who did not receive aid early enough. Boys with no elder brother and an
educated father are particularly vulnerable in their progression to higher-level schools.
Keywords: gender gap in schooling; child labor; natural disaster; disaster aid
JEL classification: O15; Q54

I.

Introduction
Eliminating gender disparity in education is crucial to promote gender equality andempower women. 1 Although the gender gap in education – measured by the difference in school
enrollments or educational attainments between men and women – is still significant, especiallyin South Asia and Sub-Saharan Africa, in most developing countries women’s schooling has
been increasing relative to men’s (Orazem and King 2008, World Bank 2011). In particular, inabout one third of developing countries (45), girls outnumbered boys in secondary education in2008 (World Bank 2011, p61). World Development Report 2012 (World Bank 2011) attributes
this remarkable progress to household responses to market and institutional changes – improved1

A strong reason for supporting women’s education is the important roles of maternal humancapital in reducing fertility, improving infant/child health, developing children's higher cognitive
attainment, and promoting their schooling (King and Mason 2001, Schultz 2008).

2
returns to education for females and reduced cost of schooling – fuelled by government policies,
such as school construction, free primary education, conditional cash transfer, and so forth. This
paper proposes an alternative mechanism underlying the relative progress in women’s educationthat has received virtually no attention – the gendered division of child farm labor as a coping
response to increased natural disasters.
Consider a case where men are more active in farming than women, because of their
distinct labor productivities across activities (including home work), labor-market conditions,
and/or social norms, as seen in many developing areas (FAO 2011). Suppose that poor farmers
rely on child farm labor to cope with adverse shocks. Then, boys at the secondary-school age
with physical maturity are more likely to drop out of school for farming than girls. When overall
secondary-school enrollment is low, this does not lead to a significant gender gap in schooling;
as secondary schooling becomes more and more common, however, it can have such an effect.
Then, as market and institutional changes promote schooling, the gender gap in education candecrease as a result of a household coping response that is independent of those changes. This
alternative mechanism can be more significant in the developing countries that have been
experiencing rapid education development without a comparable transformation in the
agricultural economy than in others (and developed countries) that have experienced (much)slower education development. The recent progress of education also coincides with increased
natural disasters, especially hydro-meteorological ones, such as floods, cyclones, and droughts(e.g., Cavallo and Noy 2009, Sawada 2007, Strömberg 2007), to which peasant farmers are
particularly vulnerable. Natural disasters may be a driving force to decrease the gender gap inschooling, especially in rural developing areas.

3
Analyzing original household survey data in rural Fiji, this paper makes a strong case thatthe gendered coping response to natural hazards is a main reason for the progress of women’s
education there. I show that (1) boys, not girls, contribute to farming, not non-farm activities,
among cyclone victims (households with dwelling damage), but not non-victims; and (2) boys’
school enrollment is significantly lower than girls’ among victims, but not non-victims.
I explore two critically important questions for policy. First, does disaster aid mitigate
school dropout and child labor among disaster victims? I reveal that disaster aid (housing
construction materials) mitigates boys’ school dropout, if the provision occurs early enough, but
not their labor use. In particular, although boys’ farm labor is independent of aid, a significant
gender gap in schooling exists only among aid non-recipients, and not recipients. de Janvry et al.
(2006) show that conditional cash transfers (Progresa) mitigate school dropout, but not childlabor, against various shocks, on which transfers are not conditional. My new finding means thatpublic transfers targeted toward victims, unconditional on schooling, have similar mitigating
effects.
Next, which boys are particularly vulnerable among non-recipient victims? I examinefour hypotheses: (1) Boys who enter higher-level schools at the same time as disasters and aid
are more vulnerable; (2) The oldest brothers are the most vulnerable (because of their physicalmaturity for farming, Fafchamps and Quisumbing 1999); 2 (3) Boys in poorer households withlimited coping capability are more vulnerable (as found in previous works cited shortly); and, (4)2Many extant works report similar birth-order effects – in both genders – not necessarily related

to shocks (e.g., Edmonds 2006, Emerson and Souza 2008, Lillard and Willis 1994, Parish andWillis 1993); Thomas et al. (2004) find the opposite after the 1998 Indonesian financial crisis.

4
Maternal education decreases boys’ vulnerability. I find strong evidence for the schoolprogression and birth-order effects, but not for the wealth and maternal effects.
The gender-risk nexus in schooling and child labor is related to two lines of literature. On
one hand, economists extensively study how gender disparity in schooling can be explained by
distinct returns to education and opportunity costs of schooling between boys and girls (e.g.,
Schultz 1987, 2002, 2001). Opportunity costs of schooling are mainly determined by returns tochild labor, which can significantly vary in the gender sphere depending on activities, including
home work (Edmonds 2008). In rural Ethiopia, for example, child farm labor is more common
among boys than girls (Guarcello, et al. 2006). On the other hand, many studies address thepotential role of school dropout and child labor as self-insurance. Forgone human capitaldevelopment for short-run coping responses can have a wide range of adverse consequences in
the long run. Supporting empirical evidence has been found in various locales (e.g., Beegle, et al.
2006, Duryea, et al. 2007, Jacoby and Skoufias 1997, Thomas, et al. 2004).
Empirical findings of distinct insurance roles between boys and girls are mixed. Whereas
Jensen (2000) finds no gender gap in schooling after rainfall shocks in Côte d’Ivoire, Cameronand Worswick (2001), Chaudhuri et al. (2006), and Skoufias and Parker (2006), respectively,
show bias against girls’ schooling corresponding to crop loss in Indonesia and Ethiopia andunemployment in Mexico; Duryea et al. (2007) report similar bias in child labor against
unemployment in urban Brazil. In rural Mexico, de Janvry et al. (2006) find bias against boys inschooling and labor use in response to unemployment and health shocks, respectively, and bias
against girls’ schooling following natural disasters (i.e., opposite to my conjecture).
The rest of the paper is organized as follows. Section II describes the study area,
livelihoods, and gender gaps in education and employment among adults. Section III describes

5
cyclone damage, aid, rehabilitation, and gender gap in schooling among youths; evidence forboys’ farm labor and school dropout against the damage is offered. Section IV presents the
econometric specification to test the alternative mechanism and the questions discussed above,
which is followed by the estimation results in Section V. The last section summarizes major
findings and offers implications for research and policy.
II.

Study Area, Livelihoods, Education, and Employment

A. Study Area and Livelihoods
In June-September 2005, I conducted a livelihood survey among 906 randomly selectedhouseholds in 43 native Fijian villages in Cakaudrove Province in the northern region of thecountry, which significantly lags behind the main island Viti Levu, where the state capital, twointernational airports, and most tourism businesses are situated. (Fiji is divided almost equally
between native Fijians and Indo-Fijians, and my study focuses on the former.) Virtually all
households in the sample employ traditional farming practices, using no mechanized equipment
or animal traction to produce taro, cassava, coconut, and kava plants. Rural land is communallyowned by clan (within-village kin group) and is privately used, and by law it cannot be sold.
Most households also engage in artisanal fishing and handicraft making. Whereas farming andfishing are conducted by both men and women (Fijian women are active fisherwomen, Chapman1987), handicrafts are made exclusively by women (Turner 1987). Farming, fishing, andhandicraft making, respectively, account for 66%, 11%, and 10% of income earned by sample
households in the past one month (the mean total income is F$1,583; F$1 = US$.60).
B. Education and Employment

6Fiji’s educational system consists of eight-year primary (Class 1-8; Class 1 begins at age6), which became compulsory in 1997, and four-year secondary (Form 3-6); 3 standardized
examinations are at the end of Class 8, Form 4, and Form 6 (Tavola 1992). Although girls weremuch less educated than boys during the British colonial period, girls’ secondary enrollments
quickly increased after 1970 independence and outnumbered boys’ by 1981; this reversed gender
gap has been persistent since that time (Ministry of Finance and National Planning 2004, Tavola
1992).
The progress of women’s education is confirmed among 2,115 working adults (age 20-59,
excluding a small number of adults still in school) in the sample (see Table 1). Education
attainments greatly improved over time. The largest gender gap – the simple difference ineducation attainments between men and women – exists in secondary complete or above, and it
reversed over time: Although men’s education was higher than women’s among old adults (age40-59) (4 percent gap), women surpassed men among young adults (age 20-39) (12 percent gap).

3

Almost all primary and secondary schools in the country are private schools managed bycommunity committees and religious organizations (Ministry of Education 2010). Following
independence, the Fijian government sought to introduce a new system of six-year primary/fouryear secondary/two-year college, but most schools did not make a shift to this new system, andmany junior-secondary schools expanded to full secondary schools by adding Forms 5-6. I treat
Forms 1 and 2 in this alternative system (which are uncommon in the sample) as Classes 7 and 8.

7
Although the gender gap in secondary education was not significant among old adults, it became
more common among young adults, a 10 percent gender gap emerged. 4
Fijian society is male dominant (e.g., Aucoin 1990), and women’s employment
opportunities, especially in rural areas, are weaker than men’s. Indeed, permanent employment isstrongly biased against women in the sample: 6.5% of working adults had been employed in thepast one year and men’s employment is almost three times women’s. In contrast to the reversed
gender gap in schooling, the gender gap in employment is persistent, though it decreases fromnear 10 percent among old adults to less than 4 percent among young adults. Adults with higher
education are much more likely to be employed. 5 As such, the rapid progress of women’s
education has not been accompanied with a comparable expansion of their labor-market
opportunities.
III.

Cyclone, Child Labor, and Schooling

A. Cyclone Damage and Aid
On 13 January 2003, i.e., at the beginning of the school year two years before the 2005
survey, Cyclone Ami swept over the northern and eastern regions of the Fiji Islands. According
4

Although over 90% of male adults were born in the current village, females’ marriage

migration is common: About 38% of female adults were born in other places. Education
attainments of adults who were born in the current village are very similar to those in Table 1.
5

The education level needed for securing employment differed between men and women overtime: Although old men with secondary incomplete could obtain employment, young men
needed secondary completion, as both old and young women did. Accordingly, a large gender
gap in employment has existed among individuals with high enough education, the level ofwhich increased over time.

8
to respondents’ subjective assessments, 62% of residents’ dwellings – a main house and/or freestanding units, such as the kitchen, shower, and toilet (not all households have such units, asthese facilities are often located inside the main house) – were damaged, and the mean value of
total dwelling damage in the whole sample was F$1,074 (see Table 2); the cyclone caused no
casualties, and permanent migration was virtually nonexistent after the disaster. The provision of
emergency relief (see Takasaki 2011c for details) was followed by housing reconstruction
programs. One quarter of households received construction materials, and the mean amountreceived in the whole sample was F$686. Although there was at least one victim (with dwelling
damage) in each village, a few villages had no recipients (of housing aid). Although almost allrecipients were victims (i.e., virtually no leakage), only 40% of victims were recipients (i.e.,large under-coverage) (see Takasaki 2011b for details).
None of the means of demographic factors measured at the time of interviews in 2005 –numbers of boys/girls (age 14-19), male/female working adults, children (age 0-13), and elderly(age 60 or above) as well as age of household head – and those of secondary education of any
working adults (dummy) and land holdings are significantly different between non-victims andvictims or between non-recipients and recipients among victims (see Table 3). This gives
evidence that households did not strongly adjust their migration and fertility decisions todwelling damage or aid receipt and that damage and aid are unlikely to be strongly correlatedwith these household factors at the time of the disaster, the data of which are lacking (though
adults’ education and holdings of communal land at that time must have been almost the same as
the current levels). In contrast, victims currently hold smaller non-land assets – mainly livestock,fishing capital, and consumer durables – than non-victims, though non-land assets among victims
are not strongly differentiated by aid. As discussed below, this pattern can be caused by pre-9disaster asset holdings, asset loss as a result of the cyclone, and/or (dis)investment in response to
shocks (these data are also lacking). I repeated these descriptive analyses for 355 householdswith youths, finding similar results.
B. Rehabilitation
Among victims with a completely destroyed main house (19% of households in thesample), more than half of recipients had built a new house by the time of interviews in 2005,
while 20% of non-recipients did so (Table 2); information about repairing is lacking. Thus,
provisions of housing aid greatly helped reconstruction, but those for new house building wereinsufficient and self-reconstruction was relatively common. Indeed, although aid receipt was themost common in 2004, full construction materials were provisioned mostly in 2005; the amountof aid received among recipients in 2005 was more than twice that in 2003 and 2004.
To examine how housing aid helped reconstruction over time, I regress house rebuilding(dummy) on housing aid receipt (dummy) among victims with main house completely destroyed,
controlling for village fixed effects, such as village-level aid received. OLS estimation results arereported in Table 4 (probit results are very similar); as villages with no variations in houserebuilding across households are dropped, the number of observations significantly decreases.
The probability of rebuilding among recipients in 2003-04 is higher by 0.24 than others (column
1). Adding household controls – demographic factors, working adults’ education, and landholdings discussed above – does not strongly alter the estimated aid effect (column 2)(estimation results of household controls are reported in Table A1). Despite the significant
increase in the amount of aid provisioned per recipient in 2005, the estimated effect onrebuilding of aid received in 2003-05 is almost the same as that in 2003-04 (column 3).
Qualitatively the same results hold for the amount of aid received (log) (columns 4-6). These10findings suggest that the aid in 2005 did not additionally help rebuilding, as non-recipients’ selfreconstruction had become common by then; alternatively, some recipients may not havecompleted rebuilding at the time of interviews.
C. Gendered Division of Adult and Child Labor and Child Labor as Insurance
My conjecture is that as men are more active in farming than women, child farm laborers
are mostly boys. Lack of data regarding labor input and time use precludes me from measuring
the participation of adults/youths in or the amount of their labor used for specific activities. As
an exception, individual-level data about wage labor show that teenagers’ employment – both
permanent and casual labor – is virtually nonexistent. Hence, child labor, if any, must be
exclusively for self-employment (as well as home work). Since hired-in labor is not common for
any livelihood activities, I show that male and female labor endowments influence farm and nonfarm income in distinct manners, as indirect evidence for the gendered division of labor.
OLS estimates of determinants of household income (log) in the past one month at thetime of interviews are reported in Table 5, where only results for numbers of male and female
working adults and youths, dwelling damage, and aid recipient in 2003-2005 are shown. Other
household controls are numbers of children and elderly, age and age squared of household head,working adults’ education, and land and non-land asset holdings (log), whose estimation results
are reported in Table A1, and village dummies, which fully control for environmental and market
conditions. A gendered division of adult and child labor is evident: In the whole sample (column1), male adult labor endowment contributes more to farm income than non-farm income, and theconverse holds true for female adults (active fisherwomen and craftswomen); boys strongly
contribute to farm income, but not non-farm income, and girls contribute to neither of them. It is11thus likely that although non-farm activities are mostly in the adult domain, boys are more active
in farming than girls.
Neither dwelling damage nor aid receipt significantly influences farm/non-farm income(and total income). Qualitatively the same results hold for aid receipt in 2003-2004 and for thevalue of dwelling damage and aid received (results not shown). Hence, more than two years after
the disaster, cyclone damage does not directly alter household livelihood patterns (agricultural
rehabilitation was intensive in 2003, Takasaki 2011c).
If boys drop out of school for farming in response to cyclone damage and their school
dropout is not temporary (as shown below), their current contribution to farming should be stilllarger among victims than non-victims. Estimating income equations separately for non-victims
and victims strongly confirms this pattern: Boys’ contribution to farming is significant among
victims, but not among non-victims (columns 2 and 3, respectively). This suggests that boys’
farming may have mostly emerged among victims as their coping response. Estimating income
equations separately for non-recipients and recipients (in 2003-2005) reveals that aid receipt does
not differentiate boys’ farming among victims (columns 4 and 5, respectively). This suggests thatdisaster aid does not strongly mitigate boys’ farm labor as a coping response (as found by deJanvry, et al. 2006 in Progresa). In contrast, neither dwelling damage nor housing aid strongly
alters the insignificance of girls’ labor or the gendered division of adult labor.
D. Youths’ Schooling
The sample contains 544 youths (in 355 households). A gender gap in youths’ schoolingis evident: Girls’ gross school enrollment rate is higher by about 8 percent than boys’; girls’
gross secondary school enrollment rate is higher by almost 12 percent than boys’ (see panel A of
Table 6). Although girls’ stronger school progression relative to boys’ is apparent across age

12groups, the most striking observation is that gender gaps in school enrollments are concentrated
on ages 15, 17, and 18. Although data of youths’ enrollment and grade in 2003-04 are lacking,youths at these ages are likely to have made decisions to enter junior-/senior-high schools in
2003-04 (when aid provision strongly affected housing rehabilitation); in contrast, among youths
at ages 14, 16, and 19, these two years mainly correspond to their grade progression in the sameschools. 6 This suggests that damage and aid mostly affected children’s progression to higherlevel schools, probably because of the larger cost of higher-level schooling, 7 school performanceadversely affected by shocks, and incentives to protect investments made in the current school. 8
School dropout against cyclone damage then is less likely to be temporary.

6

Consider youths at age 15, the majority of whom were in Form 1 or 2 (junior high) in 2005 andmust have been in Class 7 or 8 in 2003. Junior-high-school entry decisions were made in 2003 by
those in Class 8 who completed primary school in 2003 and in 2004 by those in Class 7 and 8
who completed primary school in 2004 (those in Class 8 who repeated the grade in 2003 andthose in Class 7 who made progress to Class 8 in 2003). In contrast, junior-high-school entry
decisions among youths at age 14 were made in 2004 only (among those in Class 7 in 2003 who
successfully advanced grades); decisions among youths at age 16 were made in either 2003 or
2004 only among those in Class 8 in 2003. Comparisons of other age groups are analogous.
7

With limited gendered schooling programs, such as scholarships, schooling costs in the sameschool should not be significantly different between boys and girls.
8Indeed, among working adults with secondary incomplete (Table 1), junior-high completion(Form 4) is more common than junior- or senior-high incomplete (Form 3 or 5).

13
As tertiary schooling is very uncommon for both boys and girls, 9 among youths not
currently in school, a significant gender gap exists in secondary-school completion at ages 18
and 19 (panel B): Girls are more likely to complete secondary school, as found among young
adults. Their secondary-school completion status in 2005 is determined by their enrollments (andperformance) in 2003-04. Thus, the combination of school enrollment – mostly primary or
secondary – and secondary-school completion at the time of interviews in 2005 (henceforth,adjusted school enrollment) captures the overall schooling patterns among youths after the 2003
cyclone. 10
The gender gap in the rate of adjusted school enrollment among youths is 9.4 percent (seeTable 7), almost the same as the gender gap in secondary education among young adults. As
found in the original school enrollment rates, gender gaps in adjusted enrollment rates at ages 15,
17, and 18 are much larger than others. Gender gaps in adjusted enrollment rates at ages 18 and
19 are larger than those in the original enrollment rates because of girls’ higher secondary-school
completion than boys’.
E. School Dropout as Insurance

9This is partly because youths who enter tertiary school migrate to cities. Although data about

those migrants are lacking, they should not be common because the numbers of youths in thesample are similar across age groups.
10

An alternative measure is the combination of secondary-school enrollment and completion

among youths who completed primary school. I repeated all analyses conducted below for thismeasure. Although selection bias is a potential concern because primary completion status ismeasured only in 2005, results are qualitatively the same as those reported below.

14
To capture how youths’ schooling is related to cyclone damage and aid, I introduce thefollowing measures of gaps in adjusted school-enrollment rates: victimization gap – simple
difference between disaster non-victims and victims – and aid-receipt gap – simple difference
between aid non-recipients and recipients among victims. Table 7 shows that among all youths,
victims’ schooling is lower than non-victims’, and among victims, schooling of non-recipients in
2003-04 is lower than that of recipients; the victimization and aid-receipt gaps, respectively, are
7.8 and 10.6 percent in magnitude, which are in a range similar to that of the gender gap. That is,schooling is as strongly correlated with cyclone damage and aid receipt as gender. Not
surprisingly, cyclone damage and aid receipt are uncorrelated with youths’ gender.
Now, how is the victimization/aid-receipt gap related with gender? How is the gender gap
related with victimization/aid receipt? First, boys’ victimization gap is larger than girls’ (10 vs. 5
percent) and victims’ gender gap is larger than non-victims’ (11 vs. 6 percent); for each gap
measure, only the larger gap is statistically significant, although the difference between thegender gap and the victimization gap (i.e., difference-in-differences) (5 percent) is statisticallynonsignificant. Thus, gender gap in schooling is mostly explained by the gender difference invictimization gap. Second, boys’ aid-receipt gap is much larger than girls’ (over 18 vs. less 2
percent) and non-recipients’ gender gap is much larger than recipients’ (over 15 vs. 1 percent); in
each gap measure, only the larger gap is statistically significant, and although the difference
between the gender gap and the aid-receipt gap among victims is large (almost 17 percent), it is
not statistically significant. Put differently, the victimization gap is significant only among nonrecipients.
The results for aid receipt in 2003-05 are different as follows. First, girls’ aid-receipt gap
is the opposite: Non-recipients’ schooling is higher than recipients’, though the difference is not15
statistically significant. Second, boys’ aid-receipt gap (about 10 percent) is smaller than that in
2003-04 and statistically nonsignificant. Third, as a result, the aid-receipt gap among all youths
is very small (about 3 percent). Consistent with the aid effects on housing rehabilitation foundabove, the aid in 2005 was probably too late to alter schooling decisions – especially aboutschool progression – made in the previous years.
Combined with the indirect evidence for child labor presented above, these findings
support my conjecture that the gender gap in schooling is the result of a gendered division ofchild farm labor against cyclone damage. Housing aid mitigates boys’ school dropout – school
non-progression – among victims (again, as found by de Janvry, et al. 2006), if the provisionoccurs early enough.
IV.

Econometric Specification

A. A Base Model
This section constructs an empirical model to estimate gender, victimization, and aidreceipt gaps in youths’ schooling. I employ the following model:‫ݏ‬௜௝ = ߚ଴ + ߚଵ ݃௜௝ + ߚଶ ݀௝ + ߚଷ ݃௜௝ ݀௝ + ߚସ ܽ௝ + ࢽ࢞࢏࢐ + ࢾࢠ࢐ + ࢜ + ߝ௜௝ ,(1)

where i and j stand for individual and household, respectively; sij is a dummy for adjusted school
enrollment defined above; gij, dj, and aj are dummies for girl, victimization, and aid receipt,
respectively; xij is a vector of individual controls, captured by youth’s age and siblingcomposition (two dummies for the availability of elder brother and sister at age 14-19 andnumbers of younger brothers and sisters); 11 zj is a vector of household controls, captured bynumbers of male and female working adults and elderly, age of household head, and land
11

In the youth sample, 46% are girls; 23% and 17% have an elder brother and sister,

respectively; and the mean numbers of younger brothers and sisters are 1.5 and 1.2, respectively.

16
holdings (log) (parental education is introduced as additional controls later); v is a vector ofvillage dummies, which fully control for school conditions (e.g., access, quality, cost, schooldamage and rehabilitation), as well as village-level covariate shocks and aid received; and ѓij isan error term. Following the empirical context in Fiji, I assume that there is no leakage oftargeting aid to victims and under-coverage of victims is significant, i.e., (dj, aj) = (0, 0), (1, 0),(1, 1); then, the aid dummy is effectively an interaction term of the damage and aid dummies, i.e.,
aj = djaj.
Equation (1) is estimated by OLS (probit results are very similar); villages with novariations in schooling across individuals are dropped. As damage and aid are measured at thehousehold level, standard errors are clustered by household.
B. Identification
In equation (1), is household-level dwelling damage exogenous as a determinant ofyouths’ schooling? Unobserved pre-disaster dwelling quality, which determines idiosyncratic
dwelling damage, needs to receive special attention: It is likely to be positively correlated withunobserved wealth at the time of disaster – other than land holdings controlled for – which canpositively influence schooling. Then, the estimated damage effect on schooling is biased
downward; in particular, the estimated negative effect can be an artifact of the omitted variablebias. If aid allocation targets the poor – according to wealth not observed by analysts – among
victims, the estimated positive aid effect is also biased downward, though it is qualitatively
robust.
To address this potential identification problem, I augment equation (1) by using nonland assets as an additional control as follows. Ignoring gender, individual/household controls,and village dummies for brevity, first consider the following model:

17‫ݏ‬௜௝ = ߚ଴ + ߚଶ ݀௝ + ߚସ ܽ௝ + ߜ‫ݓ‬௝‫ כ‬+ ߝ௜௝ ,(2)

where wj* denotes asset holdings right after the disaster, i.e., pre-disaster assets, minus assets lost
because of the disaster, which are proportional to pre-disaster holdings. Given that the household
cannot borrow (credit is very limited in the sample), current asset holdings w are w* after
depreciation (at a fixed rate Ĳ), plus investment m following the disaster, which is a function ofw*, dwelling damage value D, and the value of housing aid received A:‫( = ݓ‬1 െ ߬)‫ כ ݓ‬+ ݉(‫ כ ݓ‬, ‫ܦ‬, ‫)ܣ‬.

(3)

I assume that Pw* > 0, PD < 0, and P$ > 0; that is, the larger the damage on
assets/dwelling and the smaller the aid received, the more liquidation of or the less investment inassets. Assuming a linear investment function, equation (3) can be empiricized as follows:‫ݓ‬௝ = ߠ଴ + ߠଵ ‫ݓ‬௝‫ כ‬+ ߠଶ ‫ܦ‬௝ + ߠଷ ‫ܣ‬௝ + ߤ௝ ,(4)

where ș1 (> 0) captures one plus the rate of asset accumulation with no dwelling damage or aid;
ș2 (< 0) and ș3 (> 0) measure the asset effects of dwelling damage and aid, respectively; and ȝj isa residual. An alternative asset equation is‫ݓ‬௝ = ߨ଴ +ߨଵ ‫ݓ‬௝‫ כ‬+ ߨଶ ݀௝ + ߨଷ ܽ௝ + ߩ௝ ,(5)

where interpretation of ʌ1, ʌ2, and ʌ3 is analogous. 12
Substituting equation (5) into (2) yields
ଵ

గ

గ

ଵ

‫ݏ‬௜௝ = ߚ଴ + ߚଶ ݀௝ + ߚସ ܽ௝ + ߜ గ ‫ݓ‬௝ + ቂߝ௜௝ െ ߜ గమ ݀௝ െ ߜ గయ ܽ௝ െ ߜ గ ൫ߨ଴ + ߩ௝ ൯ቃ.
భ

12

భ

భ

భ

(6)

Regressing current non-land asset holdings (log) on dwelling damage and aid (either dummies

or log of values), as well as household controls and village dummies, confirms that the estimatedcoefficients for damage and aid are negative and positive, respectively; though only that fordamage value is statistically significant at a 10% significance level (results not shown).

18
In equation (6) with wj as a proxy for wj*, systematic measurement errors cause upward bias in
the estimated damage effect and downward bias in the estimated aid effect, if assets positively
affect schooling (i.e., į> 0). Then, in the augmented equation (1) with current non-land assets as
an additional control, both the estimated negative damage effect and the positive aid effect are atleast qualitatively robust. The estimated damage effects in the augmented and original equation(1), respectively, serve as the upper and lower bounds of the true effect. This is so even if
damage effects are distinct between boys and girls.
Although demographic factors at the time of disaster should be controlled for, using
current demographic factors as proxies in equation (1) is unlikely to involve a strong bias causedby systematic measurement errors, as discussed above. For a robustness check, I estimate
equation (1) without individual/household controls (results not shown); estimated coefficients forfour dummies for girl, victimization, girl-victimization interaction, and aid receipt are very
similar to (and statistically stronger than) those reported below.
For another robustness check, I also estimate the effects of the magnitude of damage andthe amount of aid received using the following model:‫ݏ‬௜௝ = ߙ଴ + ߙଵ ݃௜௝ + ߙଶ ‫ܦ‬௝ + ߙଷ ݃௜௝ ‫ܦ‬௝ + ߙସ ‫ܣ‬௝ + ߙହ ‫ܦ‬௝ ‫ܣ‬௝ + ࢽ࢞࢏࢐ + ࢾࢠ࢐ + ࢜ + ߝ௜௝ ,(7)

where Dj and Aj, respectively, are the value of dwelling damage and aid received (log), and theinteraction term DjAj allows their heterogeneous marginal effects. Potential bias in the estimateddamage and aid effects is analogous to equation (1) (based on equation 4). Distinct from damage
incidence and aid receipt in which recall errors are minor (Takasaki 2011b), measurement errors
in damage and aid values can be significant, causing attenuation bias.
C. Gender, Victimization, and Aid-receipt Gaps in Schooling19
Consider a standard enrollment equation with no disasters, i.e., a constrained equation (1)with ȕ2 = ȕ3 = ȕ4 = 0. The estimated ȕ1 captures the gender difference in the probability of school
enrollment; positive ȕ1 is the estimated gender gap in schooling against boys. Consider thedamage dummy as an additional control. The estimated ȕ2 captures the difference in theprobability of enrollment between victims and non-victims for both boys and girls; the negativeȕ2 is the estimated victimization gap in schooling. Adding the gender-victimization interaction
term allows heterogeneity in the damage and gender effects. In particular, with positive ȕ3, boys’
victimization gap is larger than girls’ in magnitude (ȕ2 < ȕ2 + ȕ3) and victims’ gender gap islarger than non-victims’ (ȕ1 < ȕ1 + ȕ3).
In the unconstrained equation (1) with the aid dummy as an additional control, theestimated ȕ4 captures the difference in the probability of enrollment between recipients and nonrecipients among victims (with no leakage) for both boys and girls. Positive ȕ4 is the estimatedaid-receipt gap in schooling; non-recipients’ victimization gap is larger than recipients’ inmagnitude (ȕ2 < ȕ2 + ȕ4 for boys). 13 By comparing aid receipts in 2003-04 and 2003-05, I also
test whether the aid in 2005 has no additional effect. In equation (7), the gender, victimization,
and aid-receipt gaps are estimated in a similar way.
D. Heterogeneity

13

The aid effect is assumed to be neutral to gender. With a relatively small number ofobservations in the Fijian data, an additional interaction term of gender and aid is not used (it iseffectively a triple interaction term of damage, aid, and gender). With richer data, capturing
gendered aid effects is straightforward. Introducing leakage is also a straightforward extension;
with significant leakage, the aid-receipt gap among non-victims can be also considered.

20
In equation (1), the homogenous damage effect on boys’ schooling among non-recipient
victims is assumed. To capture potentially heterogeneous effects, I augment equation (1) byadding an interaction term of dj and x1ij or z1j, where x1ij and z1j, respectively, are selectedindividual and household factors. Let Ȗ1, į1, and ȕ5, respectively, denote the estimatedcoefficients for x1ij, z1j, and djx1ij or djz1j. I test the following four hypothesized effects.
1) School-progression effect: Letting x1ij denote a dummy for ages 15, 17, and 18, negative ȕ5indicates that boys are more vulnerable in their progression to higher-level schools than in
their grade progression in the same schools, as found above (note that youths’ age isseparately controlled for).
2) Birth-order effect: Letting x1ij denote a dummy for the availability of elder brother (age 1419), positive ȕ5 indicates that the oldest brothers are more vulnerable than younger ones.
3) Wealth effect: Letting z1j denote land holdings or non-land asset holdings, positive ȕ5indicates that the richer the household, the less vulnerable are the boys.
4) Maternal effect: Letting x1ij denote maternal education in the augmented equation (1) withparental (maternal and paternal) education as additional controls, positive Ȗ1 and ȕ5 indicatethat maternal education promotes youths’ schooling and decreases boys’ vulnerability.
In equation (7), these effects can be tested in a similar way.
Since the data of parental education are available only for youths with parents currently
in the same household (i.e., information about deceased, divorced, and migrated parents arelacking), the numbers of observations significantly decrease. If results of variables other thanparental education are similar to those in the original models without parental education
controlled for, selection bias in this analysis is unlikely to be a major concern.
V.

Estimation Results

21
A. Gender, Victimization, and Aid-receipt Gaps in Schooling
Estimation results of four dummies for girl, victimization, girl-victimization interaction,
and aid receipt in equation (1) with current non-land assets controlled for are reported in columns(1)-(5) in panel A of Table 8. First of all, the signs of all estimated coefficients are the same as
those hypothesized above. The estimated gender gap is 9.5 percent (column 1) and the estimatedvictimization gap for all youths is almost 7 percent (with weak statistical significance) (column
2) (these are almost the same as the simple difference estimates obtained above). The estimatedcoefficient for the gender-victimization interaction is positive but statistically nonsignificant(column 3) (as in the difference-in-differences estimate obtained above). Boys’ victimization gap
is almost triple girls’ (with weak statistical significance), and victims’ gender gap (with statisticalsignificance) is more than twice non-victims’. The estimated aid-receipt gap for 2003-04 among
victims is about 7 percent with no statistical significance (column 4); boys’ victimization gap
among non-recipients (with statistical significance) is more than twice that among recipients.
Note that the estimated boys’ victimization gap among non-recipients is the same in magnitude
as the estimated victims’ gender gap (11.5 percent). The results for aid in 2003-05 (column 5) aresimilar to those for 2003-04, suggesting that only aid received in two years’ time affects
schooling. These results confirm that gender gap in schooling is mostly explained by thegendered victimization gap in schooling: Boys drop out of school against disaster damage only if
victims do not receive disaster aid early enough.
Estimation results of the unconstrained equation (1) without non-land assets controlled
for are reported in columns (6) and (7). Although the estimated victimization gaps increase inmagnitude from those in columns (4) and (5) as expected, their difference is very small for both

22boys and girls (about 1 percent). This suggests that potential bias in these estimated damage
effects are likely to be small. The estimated aid effects are also very similar to each other.
Estimation results of equation (7) with damage and aid value reported in panel B are
qualitatively the same as those in panel A. The estimated negative marginal effects of damage on
boys’ schooling among non-recipients are 0.14-0.18 in magnitude for a 10 percent increase indamage value (the results are statistically weak probably because of attenuation bias), and thecomparable estimates for girls are close to zero and statistically nonsignificant (columns 4-7).
B. Heterogeneity
Estimation results of heterogeneous damage effects on boys’ schooling among nonrecipients are reported in Table 9 (the maternal effect is discussed later). Only results forequation (1) with current non-land assets controlled for are reported; those for equation (1)without non-land assets and for equation (7) with and without non-land assets are qualitatively
the same. Column (1) replicates column (4) in panel A of Table 8 (results of the remaining
individual/household controls are reported in Table A1). The school-progression and birth-ordereffects are strong (columns 2 and 3): The victimization gap at ages 15, 17, and 18 (almost 15
percent) is near twice that at ages 14, 16, and 19 (only the former is statistically significant); thevictimization gap of the oldest brothers is also almost 15 percent, whereas that of younger ones isalmost zero. 14 In contrast, the wealth effects of land and non-land assets are weak (columns 4
and 5). The former is probably because among large landholders with high demand for farm

14

The direct birth-order effect among non-victims is the opposite: the probability of schooling ofyounger brothers is lower by over 0.13 than the oldest brothers. This probably reflects social
norms or inheritance patterns related to sons’ birth order.

23
labor, boys’ opportunity cost of schooling is high (Bhalotra and Heady 2003). Distinct from land,
non-land assets directly augment boys’ and girls’ schooling; I return to this result shortly.
Estimation results for schooling effects of parental education are reported in Table 10. To
capture potentially heterogeneous effects of parental human capital on boys and girls, twodummies for paternal and maternal secondary education are interacted with the girl dummy (inthe sample, 41% and 47% of youths, respectively, have a father and mother with secondary
education). Estimation results without damage and aid variables show that although maternal
education promotes boys’ schooling (the estimated direct maternal effect is about 0.15), its effecton girls is less than half and statistically nonsignficant (column 1); 15 in contrast, paternal
education is neutral to both boys’ and girls’ schooling.
With maternal education controlled for, the estimated coefficient for current non-landassets becomes smaller than that in Table 9 and is statistically nonsignificant. This suggests thatmaternal education is positively correlated with pre-disaster non-land assets, 16 and the significant
effect of current non-land assets found above mostly captures that of uncontrolled maternal
education. Combined with the nonsignficant effect of land holdings, this indicates that wealth is

15

Takasaki (2011a) shows that maternal education augments both male and female adults’

secondary education. Thus, after female secondary education surpassed that of males, maternal
human capital worked to balance human capital investments between genders.
16

In the patrilineal Fijian society, educated women may tend to marry wealthy men. Then,

female high returns to education in the marriage market could be another reason for thepersistent progress in women’s education. This conjecture deserves further research.

24
unlikely to strongly influence youths’ schooling (i.e., small į in equation 2), 17 buttressing
confidence that bias in the estimated damage effects is limited. All results other than non-landassets are very similar to those in the models without parental education controlled for; inparticular, all results on the gender, victimization, and aid-receipt gaps and the schoolprogression, birth-order, and wealth effects found above hold (results not shown).
Adding the damage and aid variables hardly changes these results at all (column 2). The
estimated coefficient for the interaction term of maternal education and damage is very small andstatistically nonsignificant (column 4), indicating that maternal human capital does not decrease
boys’ vulnerability (i.e., no interacted maternal effect).
In contrast, the estimated coefficient for the interaction term of paternal education anddamage is negative and large in magnitude (though it is not statistically significant), and boys’
victimization gap among non-recipients is larger with an educated father than without (over 18
vs. less than 6 percent, the former is statistically significant) (column 3). This result, which is
opposite to the hypothesized parental effect, can be interpreted as follows. The opportunity costof farming is high among educated fathers; in particular, those with permanent employment areunder severe time constraints. Then, educated fathers substitute boys’ labor for farming as a
coping response; 18 that is, paternal human capital instead increases boys’ vulnerability. With the17This result, which is distinct from empirical findings in other locales (World Bank 2011), may

reflect a relatively small wealth disparity within villages in the same province.
18

About 18% of households with youths have permanent employment, which accounts for about

7% of earned income. Consistent with the significant role of boys’ farm labor, permanent
employment does not strongly differentiate household farm income; households with permanent
employment earn higher non-farm income and thus have higher total income than others.

25
gendered division of farm labor and very limited female employment, this alternative channel is
not significant for mothers.
VI.

Conclusion
Rapidly decreasing gender gaps in schooling in developing countries can be a result of

the gendered division of child farm labor as a coping response to increased natural disasters. This
paper made a case for this conjecture by analyzing original household survey data in rural Fiji,
where girls’ secondary enrollments have been higher than boys’ since the 1980s. I have shownthat (1) boys, not girls, contribute to farming, not non-farm activities, among cyclone victims
with dwelling damage, independent of housing aid receipt, but not among non-victims, and (2) asignificant gender gap in school enrollment exists only among victims who did not receive aid in
two years’ time. Boys with no elder brother and an educated father (whose opportunity cost offarming is high) are particularly vulnerable in their progression to higher-level schools. Althoughmaternal education promotes boys’ schooling independent of shock, it does not decrease theirschool dropout as insurance. At the same time, however, boys’ vulnerability is neutral tohousehold wealth.
These findings lead to the following general implications for research and policy. First ofall, the recent progress in women’s education in developing countries can be at least partly
explained by household response to shocks, not to market and institutional changes (cf. WorldBank 2011). This alternative mechanism can be persistent for at least three reasons. First, as
maternal human capital does not weaken gendered coping responses, the progress in women’s
education does not help do so, either. Second, development of the female labor market increases
the relative opportunity cost of boys’ schooling. Third, without a breakthrough in safety-net
policies for poor farmers, they continue to be vulnerable to natural disasters. Research is needed

26
to see whether gendered coping responses play a significant role in decreasing the gender gap inschooling in other locales, especially in rural developing areas vulnerable to natural hazards.
Timely disaster aid can mitigate school dropout among disaster victims, even if it isneither targeted toward students nor conditional on enrollments. Effective post-disastermanagement is thus critically important for protecting children’s human capital. Policymakers
can target vulnerable children according to their attributes, such as gender, school grade, siblingcomposition, and parents’ occupation. Welfare targeting toward landless and small holders may
not be effective to combat child farm labor. A similar caveat is that agricultural rehabilitation inrural areas, which is important for food security and livelihood recovery, can at the same timeincrease child farm labor. Livelihood rehabilitation needs to include programs without involving
child labor, such as public work for community rehabilitation.
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31
Table 1. Working adults' education attainment and employment by gender.
Age 20-59
Male Female Gendergap(1) - (2)(1)(2)

Age 20-39
Male Female Gendergap(3) - (4)(3)
(4)

Age 40-59
Male Female Gendergap(5) - (6)
(5)
(6)

16.6
29.5
36.5
17.4

2.7 *
3.3 *
-0.5
-5.5 ***

14.8
23.0
40.5
21.7

10.6
17.0
38.6
33.8

4.2 **
6.0 ***
2.0-12.1 ***

19.1
38.8
30.8
11.3

18.5
39.7
34.8
7.1

0.6
-0.8
-4.0
4.2 **

Secondary education
53.9
59.9
-6.0 ***
Permanent wage employment by education attainment (percentage):
All
9.5
3.3
6.1 ***

62.2

72.4

-10.1 ***

42.1

41.8

0.2

7.3

3.7

3.7 ***

12.4

2.9

9.6 ***

Primary incomplete or below
Primary complete
Secondary incomplete
Secondary complete or above
No. observations* p < 0.1, ** p < 0.05, *** p < 0.01.

4.2
4.7
4.2
17.7
649

4.8
1.0
0.9
8.0
594

-0.6
3.7
3.3 **
9.8 ***

3.4
10.1
19.0
19.2
461

0.0
0.6
4.2
13.8
411

3.4
9.4 ***
14.8 ***
5.4

Education attainment (percentage):Primary incomplete or below
Primary complete
Secondary incomplete
Secondary complete or above

3.8
7.6
9.4
18.1
1110

13.8
26.3
37.0
22.9

2.2
0.8
2.2
8.7
1005

1.6
6.9 ***
7.2 ***
9.4 ***

32
Table 2. Household means of cyclone damage, aid, and rehabilitation.
Households
All households with youths(age 14-19)
Cyclone damage in January 2003:
Dwelling damage (victim) (0/1)
Dwelling damage value (F$)

0.62
1074 (2139)

0.66
1115 (2287)

Disaster aid through time:
Construction materials receipt (recipient ) (0/1)
2003
2004
2005
Construction materials received (F$)
2003
2004
2005
No. observations

0.25
0.05
0.14
0.06
686 (1984)
100 (782)
317 (1379)
267 (1310)
902

0.28
0.05
0.16
0.07
681 (1973)
71 (635)283 (1300)
321 (1437)
355

0.40
0.20
0.51

0.38
0.21
0.47

Rehabilitation at the time of interviews in 2005:
Main house rebuilding (0/1) among victims
with main house completely destroyed (0/1)
Non-recipients
Recipients
Note: Standard deviations are shown in parentheses.

33
Table 3. Means of household characteristics by cyclone damage and aid.
Allhouseholds

Non-victims

Victims(1)(2)(1) - (2)Nonrecipients(3)

Recipients(4)(3) - (4)

No. boys (age 14-19)
0.33 (0.64) 0.29 (0.65) 0.35 (0.64) -0.06
0.36 (0.67) 0.34 (0.59) 0.01
No. girls (age 14-19)
0.28 (0.54) 0.25 (0.50) 0.29 (0.56) -0.04
0.30 (0.57) 0.27 (0.54) 0.03
No. male working adults (age 20-59)
1.26 (0.93) 1.21 (0.88) 1.29 (0.97) -0.09
1.29 (0.99) 1.30 (0.94) -0.02
No. female working adults (age 20-59)
1.15 (0.76) 1.15 (0.78) 1.15 (0.75) 0.00
1.14 (0.78) 1.16 (0.69) -0.02
No. children (age 0-13)
1.93 (1.77) 1.83 (1.79) 1.99 (1.76) -0.16
1.92 (1.77) 2.10 (1.75) -0.17
No. elderly (age 60 or above)
0.54 (0.77) 0.52 (0.74) 0.55 (0.78) -0.03
0.56 (0.77) 0.55 (0.81) 0.01
Age of household head
51.4 (14.6) 51.6 (15.7) 51.4 (13.9) 0.19
51.4 (14.1) 51.3 (13.7) 0.15
Working adults' secondary education (0/1) 0.73 (0.44) 0.76 (0.43) 0.72 (0.45) 0.04
0.73 (0.44) 0.69 (0.46) 0.04
Land (acre)
2.86 (4.84) 2.83 (4.30) 2.87 (5.15) -0.04
2.73 (4.02) 3.09 (6.52) -0.36
Non-land assets (F$)
1846 (4457) 2266 (5428) 1588 (3720) 678 ** 1489 (3882) 1739 (3460) -249
No. observations
902
343
559
338
221
Note: Standard deviations are shown in parentheses. * p < 0.1, ** p < 0.05, *** p < 0.01.

Householdswith youths(age 14-19)
0.83 (0.80)
0.70 (0.66)
1.42 (0.99)
1.35 (0.80)
2.22 (1.86)
0.43 (0.70)
51.0 (11.8)
0.77 (0.42)
3.03 (6.11)
2193 (5438)
355

34
Table 4. Determinants of house rebuilding among victims with main house completely
destroyed.

Recipient in 2003-04 (0/1)
Recipient in 2003-05 (0/1)
Log of aid in 2003-04 (F$)(1)(2)
0.240 ** 0.273 **(0.120)(0.123)

(3)

(4)

(5)

(6)

0.264 *
(0.134)0.034 ** 0.036 **(0.016)(0.016)

0.036 **
(0.016)
Household controls
No
Yes
Yes
No
Yes
Yes
0.226
0.290
0.283
0.235
0.295
0.298
R-squared
135
134
134
131
130
130
No. observations
Notes: Dependent vaiables are a dummy for house rebuilding. OLS estimates are shown. Robust
standard errors are in parentheses. * p < 0.1, ** p < 0.05, *** p < 0.01. Household controls are no.
boys (age 14-19), no. girls (age 14-19), no. male adults (age 20-59), no. female adults (age 20-59), no.
children (age 0-13), no. elderly (age 60 or above), age of household head, working adults' secondary
Log of aid in 2003-05 (F$)

education (0/1), and log of land (m2). Village dummies and constant are also included in all columns.

35
Table 5. Determinants of household earned income.

A. Farm income
No. boys (age 14-19)
No. girls (age 14-19)
No. male adults (age 20-59)
No. female adults (age 20-59)
Victim (0/1)
Recipient (0/1)
R-squared
No. observations
B. Non-farm income
No. boys (age 14-19)
No. girls (age 14-19)
No. male adults (age 20-59)
No. female adults (age 20-59)
Victim (0/1)
Recipient (0/1)
R-squared
No. observations

All

Nonvictims

Victims

NonRecipientsrecipients

(1)

(2)

(3)

(4)

(5)

0.280 ***
(0.079)0.014(0.098)0.149 **
(0.062)0.029(0.077)-0.086(0.124)-0.016(0.137)
0.462
884

0.122
(0.131)0.088(0.177)0.204(0.132)0.125(0.152)0.305 ***
(0.094)-0.002(0.127)0.159 **
(0.071)-0.032(0.083)0.318 ** 0.313 *(0.133)(0.180)0.054-0.095(0.188)(0.203)0.173 *
0.130
(0.099)(0.115)0.067-0.109(0.110)(0.150)0.552
335-0.070(0.145)
0.456
544

0.531
326

0.412
212

0.051
(0.066)0.089(0.078)0.050(0.041)0.197 ***
(0.058)0.021(0.100)0.120(0.100)0.245
893

0.188 * -0.056(0.103)(0.083)0.082
0.050(0.147)(0.092)0.044
0.078(0.079)(0.052)0.243 ** 0.144 **(0.124)(0.061)-0.081(0.112)0.153(0.141)
0.052(0.071)0.141 *
(0.075)0.078(0.109)-0.073(0.115)0.117(0.086)0.224 *
(0.115)

0.271
333

0.434
212

0.350
337

0.064
(0.098)0.244
551

Notes: Dependent variables are log of monthly earned income (F$). OLS estimates are shown.
Robust standard errors are in parentheses. * p < 0.1, ** p < 0.05, *** p < 0.01. Controls not shown
here are no. children (age 0-13), no. elderly (age 60 or above), age of household head, working
adults' secondary education (0/1), log of land (m2), log of non-land assets (F$), village dummies,
and constant.

36
Table 6. Youths' school enrollment and attainment.
A. Enrollment rates by gender, age, and school (percentage)
Boys
No.
obs.

Not
enrolled(1)

Primary Secondary

Tertiary

Girls
No.
obs.

Not
enrolled(2)

Age 14-19
295
37.2
17.1
41.6
4.1
249
29.2
Age 14
56
3.6
64.3
32.1
0.0
44
2.3
Age 15
42
26.2
16.7
57.1
0.0
50
8.0
Age 16
57
26.8
7.1
64.3
1.8
40
25.0
Age 17
51
43.1
5.9
49.0
2.0
30
33.3
Age 18
44
68.2
0.0
22.7
9.1
47
51.1
Age 19
45
65.9
0.0
20.5
13.6
38
63.2
B. Education attainment among the not-enrolled by gender and age (percentage)
Boys
No.
obs.

SecondaryPrimary
Primary Secondary complete
incomplete
complete incomplete or above
or below
(3)

108
26.9
Age 14-192
100.0
Age 1411
36.4
Age 1515
26.7
Age 1621
23.8
Age 1730
26.7
Age 1829
20.7
Age 19* p < 0.1, ** p < 0.05, *** p < 0.01.

22.2
0.0
54.6
20.0
14.3
20.0
20.7

47.2
0.0
9.1
46.7
61.9
53.3
48.3

3.7
0.0
0.0
6.7
0.0
0.0
10.3

No.
obs.
72
1
4
10
10
23
24

Primary Secondary
15.8
56.8
24.0
5.0
0.0
0.0
0.0

53.4
40.9
68.0
70.0
66.7
48.9
26.3

Tertiary

Gendergap(1) - (2)

1.6
0.0
0.0
0.0
0.0
0.0
10.5

8.1 **
1.3
18.2 **
1.8
9.8
17.1 *
2.8

Girls

SecondaryPrimary
Primary Secondary complete
incomplete
complete incomplete or above
or below
(4)
18.1
100.0
50.0
20.0
20.0
13.0
12.5

23.6
0.0
50.0
40.0
20.0
21.7
16.7

48.6
0.0
0.0
40.0
60.0
56.5
50.0

9.7
0.0
0.0
0.0
0.0
8.7
20.8

Gendergap(3) - (4)-6.0 *
0.0
0.0
6.7
0.0
-8.7 *-10.5

37
Table 7. Youths' schooling by age, gender, cyclone damage, and aid.

No.
obs.

School enrollment or secondary school
completion in 2005 (0/1)
BoysGirls Gender gap
All(1) - (2)
(1)
(2)

544
68.4
Age 14-19100
97.0
Age 1492
83.7
Age 159774.2
Age 1681
60.5
Age 1791
42.9
Age 1883
45.8
Age 19Victims vs. non-victims:
Non-victims (a)185
73.5
Victims (b)359
65.7Victimization gap (a) - (b)7.8 *Recipients vs. non-recipients in 2003-04 among victims:
Non-recipients (c)257
62.7Recipients (d)101
73.3
Aid-receipt gap (c) - (d)-10.6 *
Recipients vs. non-recipients in 2003-05 among victims:
Non-recipients (e)223
64.6
Recipients (f)136
67.6
Aid-receipt gap (e) - (f)-3.1* p < 0.1, ** p < 0.05, *** p < 0.01.

64.1
96.4
73.8
73.7
56.9
31.8
42.2

73.5
97.7
92.0
75.0
66.7
53.2
50.0

-9.4 **-1.3-18.2 **
-1.3
-9.8
-21.4 **
-7.8

70.7
60.7
10.0 *

76.7
71.8
5.0

-6.0
-11.1 **
5.0

55.4
73.7
-18.3 **

71.2
72.7
-1.5

-15.8 ***
1.0
-16.7

56.7
67.1
-10.4

73.8
68.3
5.5

-17.1 ***
-1.2
-15.9

38
Table 8. Gender, victimization, and aid-receipt gaps in youths' schooling.

A. Dwelling damage
Girl (0/1)

(1)

(2)

(3)

(4)

(5)

(6)

(7)

0.095 **
(0.038)0.094 **
(0.038)-0.069(0.045)0.050(0.069)-0.101(0.065)0.067(0.084)

Yes

Yes

Yes

0.050
(0.069)-0.115 *(0.066)0.065(0.084)0.074(0.061)
2003-04
Yes

0.051(0.069)-0.115 *(0.067)0.066(0.084)0.047(0.056)
2003-05
Yes

0.052(0.070)-0.126 *(0.067)0.064(0.084)0.072(0.061)
2003-04
No

0.053(0.070)-0.125 *(0.068)0.065(0.084)0.044(0.056)
2003-05No

0.115 **
(0.047)-0.050(0.059)-0.041(0.079)0.288
524

0.117 **
(0.047)-0.049(0.060)-0.068(0.074)0.287
525

0.116 **
(0.046)-0.062(0.060)-0.054(0.080)0.281
524

0.118 **
(0.046)-0.060(0.061)-0.081(0.075)0.281
525(continued)

Victim (0/1)
Victim × Girl
Recipient (0/1)
Recipient in:
Non-land assets
Marginal effects:
Victimization among girls-0.034(0.058)0.117 **
(0.047)

Girl with victimization
Victimization among non-recipient girls
Victimization among recipient boys
R-squared
No. observations

0.281
525

0.284
525

0.285
525

39
B. Dwelling damage value
Girl (0/1)

0.095 **
(0.038)0.093 **
(0.038)-0.008(0.007)0.048(0.066)-0.013(0.009)0.011(0.012)

Yes

Yes

Yes

Log of damage (F$)
Log of damage × GirlLog of aid (F$)
Log of damage × Log of aid
Aid received in:
Non-land assets
Marginal effects:
Damage among girls-0.002(0.009)0.093 **
(0.038)Girl with mean damage
Damage among non-recipient girls
Damage among boys with mean aid
R-squared
No. observations

0.281
525

0.283
525

0.284
525

0.048
(0.065)-0.014(0.010)0.010(0.012)0.0022(0.039)0.0006(0.005)
2003-04Yes

0.047(0.065)-0.016(0.010)0.011(0.012)-0.017(0.036)0.0033(0.005)
2003-05Yes

0.049(0.066)-0.015(0.010)0.010(0.012)0.0011(0.039)0.0008(0.005)
2003-04No

0.048(0.066)-0.018 *(0.010)0.011(0.012)-0.020(0.036)0.0036(0.005)
2003-05No

0.089 **
(0.038)-0.004(0.009)-0.013(0.010)0.289
516

0.092 **
(0.039)-0.005(0.009)-0.011(0.011)0.289
517

0.090 **
(0.038)-0.005(0.010)-0.014(0.010)0.282
516

0.092 **
(0.039)-0.007(0.009)-0.012(0.011)
0.284
517

Notes: Dependent variables are a dummy for school enrollment or secondary school completion in 2005. OLS estimates are shown.
Standard errors clustered by household are in parentheses. * p < 0.1, ** p < 0.05, *** p < 0.01. Other controls not shown here are age,elder brother (age 14-19) (0/1), elder sister (age 14-19) (0/1), no. younger brothers, no. younger sisters, no. male adults (age 20-59), no.
female adults (age 20-59), no. elderly (age 60 or above), age of household head, log of land (m2), village dummies, and constant.

40
Table 9. School-progression, birth-order, and wealth effects on non-recipient boys'
victimization gap in schooling.
(1)
(2)
(3)
(4)
(5)
0.050
0.053
0.043
0.050
0.049
Girl (0/1)(0.069)(0.069)(0.069)(0.069)(0.069)-0.115 *-0.083-0.147 ** -0.095
0.031
Victim (0/1)(0.066)(0.068)(0.070)(0.242)(0.202)
Victim × Girl
0.065
0.063
0.070
0.065
0.066
(0.084)(0.084)(0.084)(0.084)(0.084)
Recipient in 2003-04 (0/1)
0.074
0.063
0.077
0.075
0.073
(0.061)(0.060)(0.060)(0.061)(0.060)-0.118 *** -0.115 *** -0.118 *** -0.118 *** -0.118 ***Age(0.011)(0.011)(0.011)(0.011)(0.011)-0.043-0.042-0.136**-0.043-0.043
With elder brother (age 14-19)(0.048)(0.048)(0.061)(0.048)(0.048)(0/1)
2-0.0009-0.001-0.0010.001
0.000
Log of land (m )(0.010)(0.010)(0.010)(0.024)(0.010)0.029 *
0.027 *
0.029 *
0.029 *
0.042 *
Log of non-land assets (F$)(0.015)(0.015)(0.015)(0.015)(0.024)-0.065Victim × At age 15, 17, 18 (0/1)(0.050)0.141 *
Victim × With elder brother(0.082)-0.002
Victim × Log of land
(0.027)-0.021
Victim × Log of non-land assets(0.027)
Non-recipient boys' victimization gap:
-0.148 **At age 15, 17, 18(0.073)-0.006
With elder brother(0.088)-0.115 *With mean land
(0.066)-0.113 *With mean non-land assets(0.066)
0.288
0.291
0.291
0.288
0.289
R-squared
524
524
524
524
524
No. observations
Notes: Dependent variables are a dummy for school enrollment or secondary school completion in2005. OLS estimates are shown. Standard errors clustered by household are in parentheses. * p <0.1, ** p < 0.05, *** p < 0.01. Other controls not shown here are elder sister (age 14-19) (0/1), no.
younger brothers, no. younger sisters, no. male adults (age 20-59), no. female adults (age 20-59),
no. elderly (age 60 or above), age of household head, village dummies, and constant.

41
Table 10. Effects of parental education on youths' schooling.

Father's secondary education (0/1)
Mother's secondary education (0/1)
Father's secondary education × Girl
Mother's secondary education × Girl
Girl (0/1)(1)-0.028(0.067)0.152 **
(0.067)0.026(0.094)-0.085(0.094)0.095(0.074)

Victim (0/1)
Victim × Girl
Recipient in 2003-04 (0/1)(2)-0.042(0.066)0.151 **
(0.067)0.042(0.094)-0.082(0.093)0.028(0.106)-0.116(0.074)0.088(0.099)0.031(0.067)

Victim × Father's secondary education

(3)
0.044
(0.099)0.142 **
(0.067)0.035(0.095)-0.072(0.094)0.031(0.106)-0.056(0.086)0.083(0.098)0.024(0.067)-0.127(0.097)

Victim × Mother's secondary education
Log of non-land assets (F$)
Marginal effects:
Father's secondary education for girls
Mother's secondary education for girls

0.021(0.017)0.017(0.017)-0.0017(0.063)0.067(0.068)0.0001(0.064)0.069(0.068)0.017(0.017)(4)-0.042(0.066)0.162(0.102)
0.043(0.094)-0.081(0.093)0.027(0.106)-0.108(0.094)0.088(0.099)0.029(0.068)-0.017(0.104)0.017(0.017)-0.183 **Victimization with father's secondary(0.089)education for non-recipient boys
-0.125Victimization with mother's secondary(0.087)
education for non-recipient boys
0.297
0.302
0.306
0.302
R-squared
396
396
396
396
No. observations
Notes: Dependent variables are a dummy for school enrollment or secondary school completion in2005. OLS estimates are shown. Standard errors clustered by household are in parentheses. * p <0.1, ** p < 0.05, *** p < 0.01. Other controls not shown here are age, elder brother (age 14-19)(0/1), elder sister (age 14-19) (0/1), no. younger brothers, no. younger sisters, no. male adults (age20-59), no. female adults (age 20-59), no. elderly (age 60 or above), age of household head, log ofland (m2), village dummies, and constant.

42
Appendix
Table A1. Determinants of household earned income, house rebuilding, and youths'
schooling not shown in Tables 4, 5, and 9.

Corresponding tables andcolumns
No. boys (age 14-19)
No. girls (age 14-19)
No. male adults (age 20-59)
No. female adults (age 20-59)
No. children (age 0-13)
No. eldery (age 60 or above)
Age of household headHouserebuilding(0/1)
Table 4
Column (2)-0.087(0.108)0.012(0.132)-0.065(0.066)-0.018(0.069)0.061 *
(0.032)-0.065(0.073)0.000(0.005)Age of household head squared
Working adults' secondary
education (0/1)
Log of land (m2)Log of non-land assets (F$)
Elder sister (age 14-19) (0/1)
No. younger brothers
No. younger sisters

0.093(0.111)-0.010(0.020)Household earned income
(F$)
Farm
Non-farm
Table 5 Column (1)Panel A
Panel B

Youths'schooling(0/1)
Table 9Column (1)0.008(0.023)-0.038(0.030)-0.004-0.008(0.033)(0.028)0.045
0.000(0.080)(0.079)0.045 **
0.014
(0.023)(0.024)-0.0005 ** -0.0002(0.0002)(0.0002)
0.105
0.202 **
(0.120)(0.096)0.472 ***
0.103 ***
(0.036)(0.029)-0.0480.040(0.030)(0.029)-0.110 ***(0.038)0.002(0.002)0.000(0.054)0.000(0.017)-0.035 *(0.018)Notes: OLS estimates are shown. Robust standard errors for household earned income and houserebuilding and standard errors clustered by household are in parentheses. * p < 0.1, ** p < 0.05, ***p < 0.01. Other controls not shown here are village dummies and constant, as well as those shownin the corresponding tables.