Natural Disasters and Migration Indonesia
Transcript of Natural Disasters and Migration Indonesia
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Do Natural Disasters Really Lead to More Migration? Evidence
from Indonesia
Chun-Wing Tse
October 18, 2011
Abstract
Do natural disasters lead to more migration? Using panel datasets of Indonesia, I discover
that the three most common types of disasters, earthquakes, volcanic eruptions and floods, in
fact reduce the likelihood for households to move out, contrary to our intuitive understanding and
findings of existing research. Yet, household migration can take various forms and in this study,
I consider split household migration which is defined as households splitting off and moving to a
new area. Together with whole household migration, I analyze the impacts of the three types of
disasters on these two different forms of migration separately and also find that they all suppress
these two different forms of household migration. The paper goes on to explain the results and
discovers that: (1) Earthquakes reduce household size, total earnings and non-business assets.
(2) Households with smaller size, lower total earnings and less non-business assets are less likely
to split. Thus, I conclude that earthquakes reduce split household migration by decreasing the
values of those economic variables. Meanwhile, eruptions drive up farm business assets and
consequently lead to less moving of the entire households since greater holdings of farm business
assets makes households less mobile to move out as a whole. On the other hand, floods do not
operate through human and economic assets to drive down household migration. Thus, the paper
concludes that the intuitive view of more migration after natural disasters is not well founded.
Keywords: Indonesia, natural disasters, migration
JEL codes: O15, Q54
I would like to thank Dilip Mookherjee for all his guidance and support. I also wish to thank Daniele Pasermanand Michael Manove for their advice and comments. I am also grateful to Ye Li, Jie Hou, Julian Chan, Hyo-YounChu, Saori Chiba and seminar participants at Boston University. All errors are my own.
Department of Economics, Boston University, 270 Bay State Rd., Boston, MA 02215 ([email protected])
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1 Introduction
Given the rising losses from environmental calamities across the globe (Cameron 2010), the
study of natural disasters has never been more crucial at our time. In just the year of 2010,
natural disasters of various types have killed at least a quarter million people, which exceeds the
number of people killed in terrorist attacks in the past 40 years combined (U.S. Federal Emergency
Management Agency). The research on environmental risk is even more important in development
economics given the fact that poor households have limited resources to deal with natural disasters,
which are highly unpredictable and aggregate in nature (Noy 2010 & Ebeke 2010). Among all the
coping mechanisms, it has been claimed that poor households in developing countries resort to out-
migration to stay away from disaster prone areas (International Organization for Migration (IOM)
2009). There is an increasing concern that rising natural disasters can drive toward more migration.
E.g. soaring climate change exacerbates the problem of water shortage and agricultural failures.
Increasing seismic activities destroy industrial establishments or threaten prospective investors away
from quake zones. Households having lost their livelihoods after natural disasters need to make a
living elsewhere and move out (IOM 2009).
However, do natural disasters really lead to more migration? General intuition and existing
theoretical research seem to answer yes to this question (Eeckhoudt 1996). Yet, empirical studies
on this topic are still emerging (Paxson 2008, Drabo 2011). Using Indonesia as the case country,
this paper attempts to understand the link between natural disasters and migration by answering
the following two questions: Do natural disasters really make households move out more? Why
natural disasters drive toward such household migration decision?
This study relies on two nationally representative datasets of Indonesia. Using the panel nature
of the datasets, I can conduct a longitudinal study to account for household fixed effects and measure
how time variation of disasters alters household migration. Also, the annual occurrence of disasters
of various kinds in Indonesia provides a natural experiment for the study. Given such environmental
context, I do not treat each type of disaster alike, but separately analyze the impacts of various
types of disasters on different geographical levels of moving. Specifically, the paper studies the three
most common types of disasters, earthquakes, volcanic eruptions and floods, to find out how these
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disasters affect household migration across provinces, districts and subdistricts in Indonesia. After
that, the second part of the study looks at different economic channels through which disasters
operate to shape household moving decision.
Another feature of this paper is to focus on household migration instead of just looking at
individual moving decision. However, household migration can take various forms. The conventional
form is whole household migration which involves relocation of the entire households. In this paper,
I also consider split household migration which is defined as households splitting off and moving to
a new area.
Contrary to the general intuition and results of existing literature, the baseline empirical results
show that natural disasters in fact lead to less migration at both household and individual levels.
Such findings stand for all three types of natural disasters at all three geographical levels of moving.
However, individually looking at (1) split household (SH) migration and (2) whole household (WH)
migration gives different findings. For whole household migration, earthquakes do not have any
significant impact, but eruptions and floods drive down the likelihood for the entire households
to move out. On the other hand, earthquakes lower split migration at all geographical levels and
more eruptions also reduce splits. Yet, floods do not cause significant decrease in splits. Thus, we
can conclude that earthquakes reduce migration primarily by suppressing splits and floods drive
down whole household migration. Meanwhile, eruptions are significant in decreasing both forms of
household moving.
The first part of the main analysis invalidates the claim of more migration after natural dis-
asters. But, why there exists such negative causation? The second part shows that earthquakes
and eruptions operate through different economic channels to shape the two forms of household
migration. Eruptions push up the values of farm business assets which can be due to enrichment of
soil fertility by lava ash. Furthermore, I also find that households with more farm business assets in
fact move out less. Therefore, eruptions suppress migration of the entire households by increasing
farm business assets. On the other hand, earthquakes drive down the household size, total earn-
ings and non-business assets. Because households with smaller size, lower total earnings and less
non-business assets are less likely to split, earthquakes make households split and migrate less by
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slashing household size, total earnings and non-business assets. Meanwhile, the paper finds that
floods do not operate through human and economic assets to reduce whole household migration.
The study discovers that if natural disasters alter the economic status such that households become
less likely to move out, the environmental shock can in fact lower migration as a result.
The economics literature on natural disasters is relatively new. Most prominent studies on
this topic look at some one-off deadly disasters (Noy 2009, Halliday 2007), and link up the cross-
section disaster exposure with household migration pattern. Panel studies (Yang 2003) adopt the
approach of difference in difference to study migration patterns before and after disasters. Existing
research also concentrates on individual migration (Halliday 2007) and treats each kind of disaster
as an homogeneous shock (Naude 2008). To my knowledge, this paper is the first longitudinal
study to consider different disasters as heterogeneous shocks and focus on household migration.
Given the fact that disasters of various types occur in some developing countries regularly, e.g.
Philippines, Bangladesh and Pakistan, a longitudinal study of time variation of natural disasters is
important. Furthermore, natural disasters present an aggregate human and economic risk to the
entire households, which usually trigger migration at both household and individual levels. This
explain why we should also focus on household migration. Yet, household migration can take various
forms and household split is common given the family structure in most developing countries. Also,
different natural disasters have contrasting impacts on household splits and moving of the entire
households. All the above facts point toward the need to separately analyze different forms of
household migration.
The paper is organized as follows. Section 2 presents the background of Indonesia, illustrating
the demographics and disaster occurrence in the country. Section 3 outlines the data used and gives
some descriptive statistics. Section 4 discusses the relationship between disasters and household
migration. Section 5 describes the empirical strategy and section 6 presents the main findings.
Section 7 shows some robustness checks and section 8 gives the extension of the main analysis.
Section 9 concludes.
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2 Background
Indonesia is the most disaster-prone country of the world, according to the UN Office for the
Coordination of Humanitarian Affairs. Most parts of Indonesia are right on the fault line of volcanic
origin, which gives rise to frequent outbreaks of massive earthquakes and volcanic eruptions. The
country is also regularly hit by floods due to its large scale deforestation and soaring global climate
change. In 2009 alone, it experienced 469 earthquakes with a magnitude of 5 or higher. Sumatra,
Java and Papua were especially hard hit. Floods have accounted for about 40 percent of Indonesias
disasters in the past few years, according to government data (BPS Indonesia). Figure 1 shows the
time series pattern of earthquakes and floods and figures 2 to 4 provides geographical snapshots on
where earthquakes, eruptions and floods occur in the country between 1988 and 2000. Java and
Sumatra Islands have always been the black spots of disasters due to its location along the fault
line and the ever worsening problem of deforestation.
However, people do not stay away from disasters but continue to live with the risk of increasing
environmental calamities. Figure 5 depicts the population density of Indonesia in 2000 and most
dwellers are crowded in Java and Sumatra where disasters of different kinds frequently strike there.
E.g. West Javanese people need to face the regular occurrence of floods and earthquakes. The
volcanoes in Yogyakarta pose a constant threat to the inhabitants there, where the eruption in 2010
destroyed numerous villages and killed more than 390 people (New York Times 2010). However,
population density of West Java well surpasses 1000 per km square and Yogyakarta has more than
980 people per same size of area (SEDAC) in 2000. Using simple cross province regressions, the
results show that population density in 1993 is not negatively correlated with disasters within 50
years before 1993. This implies that people are not driven away by disasters, but stay with the
environmental risk instead.
It has always been claimed that communities in Indonesia stay nearby volcanic areas regardless
of the constant threat of eruptions. The regression of rice yield on eruptions at province level within
the last 50 years show that provinces with more eruptions can produce a higher rice yield. This is
plausibly due to the fact that lava ash from the volcanoes enhances the soil fertility which helps
boost the farm yield. As a result, people settle and stay nearby volcanic areas.
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3 Data and descriptive statistics
The paper uses two datasets for the empirical analysis. The first one is a panel dataset from
Indonesian Family Life Survey (IFLS), a nationally representative survey covering both rural and
urban areas. This dataset gives a nation-wide sample of households spreading across 13 provinces
in the first wave of the survey in 1993 (IFLS1) and three more waves were also conducted in 1997
(IFLS2), 2000 (IFLS3) and 2007 (IFLS4).1 One prominent feature of this longitudinal survey is
the very high tracking rate. The survey did not just attempt to re-interview original households
sampled in 1993, but also all the migrant households and those split-off from the original households.
In IFLS4, 93.6% of IFLS1 households were re-contacted and this rate is as high as or even higher
than most longitudinal surveys in the United States and Europe. High re-interview rates contribute
significantly to data quality because this lessens the attenuation bias due to nonrandom attrition,
which is a critical issue of concern for studies of migration and natural disasters.2
A dummy variable indicating whether a household migrates between two successive survey years
is the main outcome of interest in the empirical study. But first, we need a clear definition of house-
hold out-migration. In this paper, I define two forms of household migration: (1) split household
(SH) migration and (2) whole household (WH) migration. In split household migration, one or more
household members, but not including the head of household, leave and establish a new household
at a certain geographical level. On the other hand, if the whole household including the household
head moves to a new area, I call this form of moving as whole household migration.
Apart from a detailed section of household migration history, IFLS also asks several compre-
hensive sets of questions to obtain the economic variables of the sample households. In particular,
I focus on household size, aid received, remittances, total household earnings and level of different
assets to study how natural disasters alter these variables to shape the two forms of household
migration.
The second dataset is Indonesian DesInventar Database (DesInventar) administered by Data
1IFLS2+ was also carried out in 1998 to measure the impact of financial crisis starting from 1997. Yet, only about20% of the households in IFLS2 were re-interviewed in that wave.
2I also test whether there exists non-random attrition in the analysis and the results are not sensitive to thetreatment of households which dropped out from the samples.
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& Informasi Bencana Indonesia. The aim of DesInventar is to record every disaster happening in
Indonesia from early 20th century. The details include geographical location, date, fatalities, finan-
cial losses, infrastructure damage and other relevant information of the disasters. The study looks
at earthquakes, volcanic eruptions and floods which are the three most common forms of natural
disasters occurring in Indonesia. For the empirical analysis, the most important explanatory vari-
able is the average annual number of the three types of disasters happening between two successive
survey years at the province level.
DesInventar adopts a method of counting natural disasters different from the traditional prac-
tice. First, a disaster is defined as the set of effects caused by an event on human lives and
economic infrastructure on a geographical unit of minimum resolution. (DesInventar) It imposes
no thresholds on the amount of damage for an environmental shock to be regarded as a disaster.
Furthermore, instead of treating a single event of environmental shock as one disaster, DesInventar
counts the number of minimal geographical units, which is kecamatan (subdistrict) in the database,
affected in the event. Thus, DesInventar counts an earthquake event of extensive geographical
coverage as multiple earthquake disasters. Thus, this makes statistics kept by DesInventar look
inflated compared with statistics kept under the traditional practice. Yet, such method is desirable
for this study as disaster of extensive coverage should receive more weights. DesInventar defines
earthquakes, eruptions and floods as follows:
Earthquakes - All movements in the earths crust causing any type of damage or negative effect
on communities or properties.
Volcanic eruptions - eruptions with disastrous effects: eruption and emission of gas and ashes,
stone falls (pyroclast), flows of lava, etc.
Floods - Water that overflows river-bed levels (riverine floods) and runs slowly on small areas
or vast regions in usually long duration periods (one or more days).
The study just retains households and their split-offs which exist in all four waves of the survey.
Certainly, I can only keep households with clear migration history between 1993 and 2007. House-
holds without information on some economic variables such as household size, earnings and assets
can only be discarded. This finally leaves the study with 8,217 households.
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Table 2 presents the descriptive statistics of the IFLS sample households. The disaster statistics
records the annual average number of each type of disaster at province level happening between
1988 and 2000. Households on average experience 0.099 earthquakes and 0.20 eruptions annually
between 1988 and 2000. Floods are more prevalent in Indonesia and the sample households are
exposed to more than two floods in every three years. The descriptive statistics also presents
the migration figures between 1993 and 2007. We first look at the annual rate of migration in
general combining both split household migration and whole household moving. On average, 1.43%
of the households move across provinces annually. The corresponding rates across kabupatens
(districts) and kecamatans (subdistricts) are respectively 3.24% and 4.60%, which are considerably
high. Yet, when we consider the two forms of household moving separately, the statistics shows that
most household migration are in the form of splits. More then 3.75% of households have split-off
households located in a new province. On the contrary, whole household migration is much less
frequent. Annually, just less than 0.1% of households move to a new province as a whole on average.
Table 2 also shows that there is a generally even proportion of urban and rural households.
Most of the household heads just finish elementary education and about 15% of the households are
female headed.
Table 3 links household economic well beings in 2000 with household migration between 2000
and 2007. We separate the entire samples into three groups: (1) households which are completely
intact without involving in split or whole household moving, (2) households which split and migrate
across provinces between 2000 and 2007 but do not move out as a whole, (3) households which move
to a new province as a whole. The mean statistics tells us that households which split and move
generally have a bigger size with higher earnings and assets of various kinds. On the other hand,
households which migrate as a whole are smaller and have less non business assets. The median
figures illustrate a much more clear picture. 50% of households which move out as a whole have
non business assets less than 4.7 million rupiah. But, the corresponding figure of for split migrant
households is 16.6 million rupiah. In general, households which migrate as a whole have less farm
business and non business assets compared with the other two groups.
The above descriptive analysis portrays the disparity in asset composition between migrant and
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non-migrant households which illustrates how household asset composition links with migration.
Before moving to the empirical analysis, the paper first explains how disasters may drive down
migration. Since the empirical study emphasizes the disparity between split migration and whole
household migration, the following section also describes how the two forms of migration differ.
4 How natural disasters drive down household migration
It is intuitive to conclude that natural disasters lead to more migration since households want
to stay away from the risk of disasters or they should make a living elsewhere if their livelihoods
are wiped out. However, households exposed to natural disasters can actually move out less. There
exists three possible reasons: (1) increase in marginal product of labor, (2) decrease in financial
resources to pay for migration and (3) strengthened social bonding and mutual insurance, which
can drive to less migration after disasters.
(1) Increase in marginal product of labor (MPL)
Natural disasters can cause recession, higher unemployment and lower wages in general. Yet,
the affected areas with infrastructure and houses destroyed have a high demand for labor to rebuild
villages. The MPL of reconstruction sector can go up as a result, which induces households to stay
after disasters for a better employment. In particular for farming, soil fertility can be enriched by
lava ash in eruptions and alluvial deposits in floods. This drives up the productivity of farming and
hence, households will choose to stay instead.
(2) Decrease in financial resources to pay for migration
With assets destroyed and earnings reduced, households are less capable to afford migration.
Thus, they are not forced to migrate but forced to stay. Also, households find it more difficult to
borrow from others to finance migration as disasters present an aggregate shock and affect most
households living nearby (Yang 2003). Disasters pose liquidity constraints to households and as a
result drive down migration.
(3) Strengthened social bonding and mutual insurance
Disasters can boost family ties and strengthen social bonding, especially in developing countries
since social capital plays a significant role in less developed economies. Households choose to cope
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with disaster shock by accumulating social capital instead of moving out. Thus, they are less likely
to migrate. Also, households with houses damaged by disasters require members to stay nearby
to repair the houses. Furthermore, law and order may break down after disasters and households
should remain to protect property and land rights.
The paper will empirically examine the first two reasons and leave out the third due to data
limitation.
4.1 Split household migration and whole household migration
Split household migration is a rarely studied concept, which involves not just household splits
but the split-off households moving to a new area. A single individual leaving and setting up a
single-member household is also classified as split household migration in this study. Split household
migration differs from individual migration in various aspects: (1) In individual moving, the migrant
individuals may just move out and enter another household in a new area. (2) Individual migration
tends to be temporary and migrants may return after some time. (3) Individual migrants are in
general more attached to the original household. Meanwhile, split-off households are considered
separate from the original household.
Households may also consider split migration as an insurance strategy. Considering household
members, especially the young and educated groups, as human asset, the head of household can
diversify risk by spreading out the asset to various geographical areas. Certainly, the remittances
received from split-off households is an important source of income, which enables the original
household to better mitigate the risk of future economic shocks.
Whole household migration is a completely different concept, which is defined as the moving of
the entire household across a certain geographical level. The insurance factor is much less significant
when the head of household decides to move out as a whole. The decision is rather based on the push
factors of the origin and the pull factors of the destination, taking into account the total migration
cost.
This paper tries to focus on these two forms of household migration separately and understand
how natural disasters shape these two different moving decisions. The rest of the sub-section will
discuss the following economic determinants of split and whole household migration: household size,
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total earnings, external transfer and household assets.
Household size: A bigger household will be more likely to split and migrate as it has more human
asset to allocate to various geographical locations for the purpose of diversifying risk. On the other
hand, household with more members are less likely to move out as a whole because migration cost
goes up with the size of household.
Total earnings: Households with more earnings have higher likelihood to split and migrate
because they have more financial resources to support the splits. Furthermore, considering split
household migration as a risky investment, the risk of the investment decreases with the income of
the households. Thus, higher earnings lower the risk for split-off households to move out and make
an even higher income elsewhere. On the contrary, the total effects of earnings on whole household
migration can be ambiguous. Households with higher earnings are better endowed financially to
pay for migration. Yet, the opportunity cost increases with the current earnings.
External transfer: External transfer such as remittances and government aid, is a positive factor
for split migration. Similar to the theory related to household earnings, household with more
external transfer have more financial resources to pay for split migration. Yet, remittances can have
totally different effects from government aid on whole household migration. Households receiving
more remittances can better afford migration. However, the government aid induces people to stay
for obtaining more aid money, which points toward Samaritans Dilemma.
Household assets: Households with more assets, in particular farm business assets, should be
less likely to split and migrate as they need members to stay and take part in the agricultural
business. They also need more people to protect the property and avoid eviction from their lands.
Yet, households with more assets are in fact better endowed financially to support splits. Also
similar to the theory on total earnings, the risk of split household migration falls with the wealth
of the households. Greater holdings of assets of various kinds lowers the risk of split household
migration. Hence, the likelihood of split migration can go up with household assets. On the other
hand, households with more assets are less likely to migrate as a whole because it is costly for
households to sell and dispose their assets to move out. More assets mean higher cost of whole
household migration.
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The above discussion suggests how disasters operate through a variety of economic channels to
shape different household migration decisions. The following sections empirically examine all the
above claims.
5 Empirical Strategy
The empirical analysis first starts with equation (1):
Mit = 0 + 1Dct + i + t + it (1)
The LHS variable Mit is the migration dummy indicating whether household i moves out at a
given geographical level between time t and t+ 1. The three different geographical levels are across
provinces, across kabupatens (districts) and across kecamatans (subdistricts). The most important
RHS variable is Dct, which uses the definition given by DesInventar to count the annual average
number of disasters happening in province c, where household i resides in between time t1 and t.
The panel survey spans from 1993 to 2007 with four waves altogether. The regression specification
includes t =1993, 1997 and 2000. I take t 1 =1988 for t =1993 and t + 1 =2007 when t =2000.
Earthquakes, eruptions and floods happen regularly in different provinces across time in Indone-
sia. Such environment acts as a natural experiment, which provides a sufficient degree of dispersion
for the RHS disaster variable, Dct.
Equation (1) also controls for household fixed effect, i and it captures idiosyncratic errors. t
denotes time dummies, which is essential because the panel data set is unevenly spaced.
Yet, I first run regression on equation (1) without including the household fixed effect and
conduct a simple OLS analysis. The OLS results tell us how the cross household variation of
disasters correlates with migration in the following period. Such analysis gives the causal impact
of disasters on household migration only when Dct is uncorrelated with the combined error term,
i + it. This assumption is arguably plausible given the random nature of disasters. However, it
could be possible that people with a high unobserved propensity to migrate tend to live in a disaster
prone province, which will render the coefficients from a simple cross-section regression biased. Thus
the paper takes advantage of the panel nature of the IFLS dataset and includes household fixed
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effects, i, in the equation. By controlling for household fixed effects, the coefficients on the disaster
variable, 1, can measure the causal impacts of disasters on migration in the next period.
Household migration, Mit, consists of split household migration and whole household moving.
Equations (2) and (3) give the regression specification respectively on these two different forms.
Splitit = 0 + 1Dct + i + t + eit (2)
Allit = 0 + 1Dct + i + t + it (3)
Splitit in equation (2) counts how many new households formed between time t and t + 1 by
splitting and moving. In equation (3), Allit is a migration dummy, representing whether the entire
household i migrates to a new location.
The above empirical analysis enables us to measure the total effects of disasters on these two
forms of migration, which makes up the first part of the analysis. The second part goes on to
explain through which channels disasters operate to bring about such effects. To do this, I modify
equations (2) and (3) to include controls for different economic variables, as shown in equations (4)
and (5).
Splitit =
0+
1Dct +
2Yit +
i+
t+ e
it(4)
Allit =
0 +
1Dct +
2Yit +
i +
t +
it (5)
Yit, consists of a list of economic variables of household i at time t. By comparing the coefficients
on disaster variables, Dct, in equations (2) and (4) and also the coefficients on economic variables,
Yit in equation (4), we can tell through which economic channels disasters work on to affect split
migration. We can use the same approach to find out the channels for whole household migration.
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6 Results
Table 3 presents the results of the linear probability model. The dependent variable is the
household migration dummy between time t and t + 1, combining both split household migration
and moving of the entire households. The explanatory variables are the annual average number of
earthquakes, eruptions and floods, happening between time t 1 and t. All specifications allow for
clustering of standard errors at the province-time level.
The first three columns do not control for household fixed effects, which give the analysis of
cross-household variation. Contrary to general intuition, probability for households to move out in
fact goes down with more disasters. Furthermore, floods significantly drive down household moving
across provinces and kabupatens (districts). The effect of eruptions on all three geographical levels
of migration is negatively significant at 0.01 level. When an additional eruption occurs annually,
the probability for households to move to another province falls by 0.0237. Given that the overall
migration rate across provinces is 0.06, eruptions drive down cross-province migration by 39.5%.
By the similar token, one more flood each year lowers cross-province migration by 29.3%. Yet, as
suggested in section 5, simple OLS cannot account for unobserved household migration propensity.
From now on, I control for household fixed effects in all specifications to address this possible
endogeneity.
In columns (4) to (6), the results present an even more negative impact of disasters. Apart from
all the coefficients being negative, the effect of earthquakes is much greater for all geographical levels
of migration. Time variation of all three types of disasters does significantly drive down household
migration. When an additional earthquake strikes annually in a province, households will be 0.024
less likely to migrate to another province in the next period. Translated into percentage terms,
the fall amounts to a whopping 134%. One more eruption and flood each year also push down
cross-province migration by respectively 18% and 24% even though the impacts of eruptions are
not significant.
We now separately consider the two different forms of household moving, split household and
whole household migration. Columns (1) to (3) of table 4 list the results for split household migra-
tion. The dependent variable counts the number of new households formed by splitting and moving
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to a new area. Meanwhile, columns (7) to (9) are about whole household migration. The dependent
variable is a dummy indicating whether the entire household relocates to a new residence. The
main analysis uses count variable for split household migration and dummy for whole household
migration. To enhance comparability, I also include columns (4) to (6) which use split migration
dummy as the dependent variable. The dummy denotes whether the household splits and migrates
to a new area.
Table 4 shows a clear difference between the two forms of migration. For split migration,
earthquakes are significant to drive down all geographical levels of migration. Splits to a new
province goes down by 0.0681, which is 120% in percentage terms. Eruptions also significantly
reduce splits at all geographical levels. Cross-province splits go down by 0.018 when one more
eruption happens annually, which is 31% in percentage terms. However, the effects of floods are not
statistically significant except for splits across provinces.
We now move to whole household migration. As shown in columns (7) to (9), earthquakes do
not significantly drive down whole household migration at any geographical level. These findings
contrast with the results of split migration. Yet, eruptions cause the entire households to move
out less. Cross-district migration goes down by 0.0094, which amounts to 32%. Meanwhile, all
geographical levels of household moving decrease significantly when one more flood occurs each
year. Cross-province migration drop by 0.0068, which is a considerable fall of 64%.
Table 4 shows that floods do not significantly reduce split household migration, but drives down
whole household migration at all geographical levels. On the other hand, earthquakes lower splits
at all levels but have no effects on moving of the entire households. Meanwhile, eruptions cause
both forms of household migration to fall. These contrasting findings tell us that it is crucial to
study each form of migration separately.
The negative impacts of disasters do not just apply to household moving, but also migration
at individual level. From table 5, all types of disasters drive down individual migration at every
geographical level even though the effects of floods are not significant. Earthquakes reduce cross-
province migration by 0.031. This is a large effect (121%), given that the mean of cross-province
migration is just 0.026. The negative impacts of eruptions are also considerable. When an additional
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eruption takes place, cross-province migrations falls by 0.0098, which is a decrease of 15.4%. Thus,
the analysis on migration at both household and individual levels shows that disasters make people
move out less. From now on, the paper will shift the focus back to household migration because
I will explain how disasters operate through economic variables to shape migration. The datasets
just provide economic variables at household level rather than individual level.
The results in tables 4 and 5 clearly invalidate the claim that natural disasters lead to more
migration. However, why is there a negative causation? To answer this question, it is necessary to
first understand how different disasters affect a variety of economic variables. This can be done by
running an auxiliary regression on the following equation. Regression on equation (6) tells us the
impacts of disasters on different economic variables of household i at time t controlling for household
fixed effects, i, and time dummies, t.
Yit = 0 + 1Dct + i + t + eit (6)
Table 6 shows the impacts of disasters on the economic variables. All the variables except house-
hold size are real values in natural logs. The stock variables include household size, non business
assets, farm business assets and nonfarm business assets, which are measured at time t. Non-
business assets are further categorized into land holdings, housing and financial assets. Meanwhile,the flow variables include total household earnings, remittances and financial aid received within
one year before time t. It would be ideal to have the average annual measures of flow variables
between time t 1 and t. But it is not feasible due to data limitations.
Table 6 indicates that earthquakes significantly drive down economic well beings on various
measures. An additional earthquake each year reduces household size by 0.347. Earthquakes also
slash non-business assets substantially by 68.9%. Financial asset, a category of non-business asset,
goes down by 79.4% when one more earthquake takes place annually. This implies that householdsmay drain financial resources to cope with earthquakes. As expected, earthquakes damage housing
assets, driving down the values by 13.7% if one more earthquake happens in every 10 years. House-
holds also suffer from losses in farm and nonfarm business assets but the effects are not significant.
Furthermore, earthquakes also lower total household earnings and the fall amounts to 126%. One
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possible explanation is the worsening of macroeconomic conditions or destruction of factories, which
may altogether reduce the employment prospects. On the other hand, remittances and aid received
do not go up significantly with more earthquakes.
While earthquakes give some negative impacts on household economic status, eruptions increase
different measures of economic variables. An additional eruption raises the amount of farm business
assets substantially by 55%. Lava ash in eruptions can highly enrich soil fertility which plausibly
increases the value of farm business assets. Interestingly, eruptions also increase housing assets. This
can be because relief money runs into affected areas for house rebuilding, which consequently helps
boost the housing market. Furthermore, households receive significantly more remittances with the
rise as 48.4%. However, such significant increase is not observed for earthquakes and floods. One
possible explanation is that the impacts of eruptions can be very limited geographically, confined to
the areas nearby volcanoes. Hence, most households in the province are largely unaffected and they
are still financially intact to remit money to affected households. However, the damage of floods and
earthquakes can be much more far reaching, adversely affecting most households in the province.
Earthquakes and floods may constitute aggregate shocks, causing households not to receive more
financial support as non-household members are also financially impaired.
To recap, earthquakes reduce non-business assets and in particular, the values of financial and
housing assets go down as a consequence. Total household earnings and household size also fall
with more earthquakes. Meanwhile, eruptions drive up farm business assets and the amount of
remittances received. Floods in general do not affect any measure of household economic well
beings. Given the results of tables 5 and 6, we can now explore the channels which disasters operate
to affect the two different forms of household migration. I will focus on split household and whole
household migration one by one.
Table 7 presents the findings for split household migration. I put the regression results without
controls and with controls for economic variables side by side. By including controls for economic
variables, the magnitude of coefficients on earthquakes has dropped for all three geographical levels
of moving. From column (1), earthquakes reduce household splits to a new province by 0.068 (120%
in percentage terms), but the magnitude falls to 0.058 (102%) after adding economic variables as
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shown in column (4). The drop in magnitude is even more noticeable for splits to a new kabupaten
(district). Furthermore, the coefficients on split migration to kecamatan (subdistrict) is no longer
significant after adding controls. This suggests that earthquakes operate through some of the
included economic variables to reduce split migration.
Table 7 also shows that household size and total earnings are significant positive factors for
split migration. An additional household member increases cross-province splits by 0.024 or 42.4%
in percentage terms. Also, one percent increase in household earnings raises the number of new
household formed in a new province by 0.00085, which amounts to an elasticity of 1.5%. We have
known from table 6 that earthquakes significantly reduce household size, total earnings. Combining
these findings, we can conclude that earthquakes slash household earnings and household size to
drive down split migration as a result.
However, the findings on non-business assets do not give us a clear conclusion. Table 7 tells us
that non-business assets do not significantly drive up split migration and the coefficient of cross-
subdistrict splits is even negative. Yet, we can also consider farm and nonfarm business assets and
both types of business assets significantly increase split migration. As shown in table 6, earth-
quakes lower the two types of business assets though insignificantly. Thus, the results suggest that
earthquakes decrease split migration through reducing business and non-business assets.
On the other hand, amount of remittance received is not a significant factor for split migration
at all three geographical levels and aid money from the government is just barely significant in
driving up splits across provinces. Hence, we can conclude that the effects of external transfer
are insignificant. Furthermore, earthquakes in fact do not significantly affect remittances and aid
received. We can discard these two variables which earthquakes operate to reduce split migration.
Yet, table 7 presents a completely different story for eruptions. All the negative signs just remain
and the coefficients are even more negative after controlling for economic variables. Households with
more farm business assets split more and farm business assets go up with eruptions. Hence, eruptions
cause more household splits as a result. This explains why the coefficients on eruptions in columns
(4) to (6) of table 7 are even more negative. Since the coefficients are even more negative after
including controls, we can reject all the economic variables listed in table 6 as the channels which
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eruptions work on to suppress household splits.
We now shift our focus to whole household migration. Table 8 shows how disasters and various
economic variables affect moving of the entire households. In particular, we just need to consider
the effects of eruptions and floods because earthquakes are not significant in affecting whole house-
hold migration. After adding economic variables, there is a substantial drop in magnitude for the
coefficients on eruptions. Coefficients for cross-district moving goes down from -0.0094 to -0.0073
and including controls even completely wipes out the significant impacts on migration across sub-
districts. This is because households with more farm business assets are less mobile to move as
a whole. The likelihood of migration to a new district falls 0.0012 when farm business asset goes
up by 1%, with the elasticity as 4.0%. Since eruptions raise the amount of farm business assets,
eruptions drive down whole household migration by increasing farm business assets.
Table 8 shows that the magnitude and significance of coefficients on floods do not change sub-
stantially which implies that the suggested economic variables are not the channels which floods
operate to reduce migration. From table 6, floods in fact do not cause significant impacts on any of
the economic variables, therefore the negative significance remains after including those variables
in the specification. We can conclude that floods do not alter human and economic assets to reduce
whole household moving.
Tables 7 and 8 together tell us some contrasting impacts of economic variables on split migration
and moving of the entire households. The size of household has totally opposite effects on these
two forms of moving. Households with more members split and migrate more , but less likely to
move out as a whole. Similarly, more assets enable households to split and move to a new location,
but drag down migration of the entire households. These results are in line with the discussion in
section 4. Households with more assets have greater ability to support splits. However, most forms
of assets, e.g. land and house are illiquid, accumulating assets in fact makes the entire households
more rooted in its village and less mobile to move out. More assets on the one hand increase
household splits but on the other makes the household less mobile to move out as a whole.
As a summary, when earthquakes, eruptions and floods occur, households move out less in the
following period. But after breaking down the analysis into two different forms of migration, we
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observe that earthquakes only reduce household splits and floods have negative impacts only on
migration of the entire households. Meanwhile, eruptions drive down both forms of migration.
Earthquakes decrease household splits through decreasing household size, household earnings and
non-business assets. On the other hand, eruptions increase farm business assets and consequently
make households move out less as a whole. Floods do not alter human and economic assets to lower
whole household migration.
To quantitatively assess the impacts of disasters on household migration through economic vari-
ables, we can conduct a simple back-of-the-envelope calculation. From tables 6 and 7, an additional
earthquake decreases household size by 0.35 and an additional household member drives up splits
across provinces by 0.024. Thus, earthquakes reduce cross-province splits by 0.0085 (0.35*0.024),
which amounts to 15%. Using the similar method, earthquakes lower earnings to decrease cross-
province splits by 0.0011 or 1.9%. For whole household migration, eruptions increase farm business
assets by 55.4% and consequently drives down moving of the entire households across provinces by
0.00022 (0.554*0.000405), or 2.1%.
We can also tell to what extent the economic variables explain the negative impacts of disasters
on household migration. From table 7, the coefficient on earthquakes for cross-province drop from
0.0681 to 0.0581, which is a 15% drop. Thus, 15% of the negative impacts of earthquakes is
explained by economic variables. Similarly, economic variables explain respectively 25% and 36%
the decrease in cross-district and cross-subdistrict splits. We use the same method to calculate how
much economic variables account for the fall in whole household migration due to eruptions. From
table 8, including economic variables respectively explain 22% and 17% for the cross-district and
cross-subdistrict moving of the entire households.
7 Robustness checks
First, to affirm the negative impacts of disasters on the two forms of migration, the study takes
a placebo test on the migration data. The analysis alters the time interval for the disaster variables.
Instead of using the yearly average number of disasters within the immediate last period, I push the
time period 14 years backward to set up a placebo time frame. E.g., for the regression of migration
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between 1997 and 2000, the time period for disaster variables is from 1983 to 1986. Hence, the
specification uses the number of disasters in the placebo time frame and check whether disasters in
that period have any effects on the two forms of migration.
Table 9 shows that the coefficients on disasters in the placebo time frame are mostly insignificant.
Earthquakes only have barely significant effects on split migration at district level and whole house-
hold moving at province level. Floods are just marginally significant in affecting cross-province
splits. Hence the placebo test affirms the negative relationship between disasters and migration
within the immediate last period.
The surveys are not conducted at a regular time interval and there is a seven-year gap between
the last two waves, IFLS3 (2000) & IFLS4 (2007). Such time period is too long that the effects
of disasters in the previous period (1997-2000) have substantially diminished well before 2007.
Furthermore, a huge tsunami happened in the province of Aceh in 2004, which resulted in massive
fatalities. Though the samples do not include any households from Aceh, tsunami can force Acehnese
households to relocate to neighboring provinces, which may cloud the estimates. To address this
problem, I set a cut-off point at year 2004 and discarded all the sample households which moved
after 2004. Only households moving before 2004 are considered migrants.
Tables 10 and 11 present the results of the revised specification. For split migration, most of
the negative coefficients still remain, but the magnitude and significance drop. Earthquakes still
primarily reduce split migration. The number of cross-province splits decrease by 0.052, or 90.9%.
An additional eruption also causes significantly less splits to district and subdistrict. Furthermore,
the conclusions drawn in section 6 still stand. For household splits, the coefficients on earthquakes
fall in magnitude after adding economic variables. Meanwhile, the size of household and total
earnings are still significant to drive up household splits. Also, household assets have marginally
significant impacts on increasing splits. Thus, earthquakes suppress household splits by reducing
household size, earnings and assets. Such results are similar to the findings in table 7.
For whole household migration, eruption is no longer a significant negative factor at all after
controlling for economic variables. The coefficients either become less negative or even positive.
Following the results that more farm assets lower whole household moving, we can conclude that
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eruptions reduce the likelihood of migration by increasing farm business assets.
Table 10 and 11 show us some contrasting results which we also observe in tables 7 and 8.
The size of household on the one hand increases household splits but on the other hand suppresses
the migration of the entire households. More assets of different kinds enhance the likelihood of
household splits, but at the same time lowers the geographical mobility for the entire households to
move.
The specification regresses migration decision between time t and t + 1 on disaster occurrence
from time t 1 to t. One may be concerned that some earlier disasters have driven out households
with high propensity of moving well before time t. E.g., the disasters in 1988 have prompted
households with high propensity of moving to migrate well before 1993. Thus, the households
left behind in 1993 are more inclined to stay. This may cause the negative relationship between
disasters and household migration. To test whether such bias exists, I shorten the time interval and
count disasters between time t and one year before time t. Shortening time interval eliminates the
possibility that households migrate after disasters and before time t. Table 12 presents the results
for revised specification and the negative impacts of disasters on household migration still remain.
The conclusions drawn from section 6 still stand.
All the above specifications use annual average number of disasters as the explanatory variables.
However, number by itself cannot fully gauge the severity of disasters. A single massive deadly
catastrophe has far much greater effects than a series of small scale disasters of mild intensity.
Hence, I use other disaster variables in the specification, which include number of deaths, injuries,
people missing and houses destroyed. These variables count the average annual number of respective
losses at the province level between time t1 and t. The list also includes the logged value of financial
losses and the tonnes of crop damage due to disasters in the last period.
Table 12 shows some mixed findings. On the front of human losses, earthquakes and eruptions
are just marginally significant to reduce the two forms of household migration. More deaths due to
floods in fact push up split migration and whole household moving, but more injuries from floods
make the entire households less likely to migrate. Number of missing people caused by floods is
another important factor lowering both forms of household migration.
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For economic losses, the effects of disasters on migration are mostly negative. Households
residing in the province with more houses destroyed by earthquakes are significantly less likely to
migrate. Similarly, when floods damage more houses in a province, households are also less likely
to relocate. Financial losses and crop damage from floods also lower the likelihood for households
to split and move.
8 Extension: Heterogeneous effects of disasters on household mi-
gration
The main analysis in section 6 tells us how disasters affect household migration in general. How-
ever, when disasters happen, different households can take different migration decisions, depending
on their economic well beings at time t (Yit). Before examining empirically the heterogeneous im-
pacts, I first give a brief discussion on why different households may respond differently to disasters.
A bigger household is more likely to split and move out after disasters. The environmental shock
acts as a trigger and households with more members have greater propensity to move out in the
following period. On the contrary, whole household migration is less likely for a bigger household
as the migration cost increases with its size.
Households receiving more external transfer and having higher income should tend to move out
less whether in the form of split or whole household moving. They are more financially endowed to
cope with the shock of disasters with less needs to find a living elsewhere.
On the other hand, households with more assets are more capable to support split moving.
Thus, disasters act as a trigger for households to split and migrate. Yet, there should be less whole
household migration since households with more assets need to incur a higher cost to dispose assets
and move out altogether. Also, assets act as a strong buffer against adverse impacts of disasters. In
particular, eruptions increase the value of farmland and households with more farm assets are less
likely to move out as a whole after eruptions.
To empirically analyze the heterogeneous impacts of disasters, I add some interaction terms
between disasters and economic variables to equations (4) and (5).
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Splitit = 0 + 1Dct + 2Yit + 3Yit Dct + i + t + it (7)
Allit = 0 + 1Dct + 2Yit + 3Yit Dct + i + t + it (8)
The coefficients on the interaction term, 3 and 3 denote how disasters in the previous period
interact with economic variables at time t to shape the household migration decision in the next
period. A positive significant coefficient implies that households with higher values of economic
variables are more likely to move out in the following period after disasters. The disaster and
economic variables in the interaction terms are first grand-mean centered such that the results are
comparable to the main results in tables 7 and 8.
Table 13 presents the results on equations (7) and (8). In general the heterogeneous impacts are
minimal and households with different economic status do not have significantly different migration
responses. We consider each of the economic variables individually. For the size of household, a
bigger household will not be more likely to split or move out as a whole given the mean number
of earthquakes in the last period. Floods give some similar findings and the coefficients on the
interaction terms are not significant at all three geographical levels. Eruptions interacting with
household size have negative impacts which are just barely significant. Also the sign flips from
one geographical level of migration to another. Thus, we can conclude that disasters do not have
heterogeneous impacts on households of different sizes.
For receipt of aid, the interaction terms with earthquakes and floods do not give any significant
impacts on household migration. However, eruptions have heterogeneous impacts on households
receiving different amount of aid and the effects are significantly negative. Given the average number
of eruptions, split migration to a new district goes down by 0.0034 with a percentage increase of
aid received. On the other hand, the probability for the entire households to move to a new district
falls by 0.0011.
The rest of the two flow variables, household earnings and remittances received also do not give
rise to any heterogeneous impacts. Households with more earnings or more remittances do not react
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differently in migration in the next period.
For asset variables, the picture is not much different and most of the coefficients on interaction
terms are not statistically significant. However, floods interacting with non-business assets lead to
contrasting impacts on the two forms of migration. Households with more non-business assets will
split and move to a new province and district more. On the contrary, households with more non-
business assets will be less likely to move out as a whole. Households with more non-business assets
are more capable to support splits and floods trigger households to split more. Yet, non-business
assets also act as a buffer against the environmental shock.
However, the above mentioned effects are just barely significant statistically and we can conclude
that disasters do not cause substantially different responses in migration for households with different
levels of economic well beings.
9 Conclusion
Using Indonesia as the case country, the study examines whether natural disasters will lead to
more migration. It discovers that increasing disasters in fact cause households to move out less. The
three most common types of disasters in Indonesia, earthquakes, eruptions and floods, all lead to less
household and individual migration. For household migration, the paper separately considers split
migration and whole household migration. The study shows that disasters have negative impacts
on both. In particular, earthquakes reduce migration primarily through suppressing household
split and floods drive down whole household migration. Meanwhile, eruptions lower both forms of
migration at all geographical levels.
The above analysis enables us to invalidate the claim of more migration after disasters. The paper
then moves on to explain why there exists such negative causation. For split migration, earthquakes
suppress household splits through a variety of economic channels. Earthquakes significantly slash
household size, total earnings and holding of non-business assets. Since smaller households are less
likely to split, so do the households with less earnings and non-business assets, earthquakes cause
less split migration by decreasing the values of those economic variables.
For whole household moving, eruptions increase the values of farm business assets possibly by
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enhancing soil fertility through lava ash. Households with more farm assets are less mobile to move
out as a whole. Thus, eruptions lower whole household migration by driving up values of farm
business assets. Finally, floods do not operate through human and economic assets to reduce whole
household migration.
I also quantitatively assess the explanatory power of the economic variables for the negative
impacts of disasters. For earthquakes, the economic variables explain 15% of cross-province splits.
The economic channels can also account for 25% and 35% for the fall of cross-district and cross-
subdistrict splits repetitively. Meanwhile, for eruptions, economic variables respectively explain
22% and 17% of cross-district and cross-subdistrict moving of the entire households.
This paper shows that the usual claim of more migration after natural disasters is not valid for
Indonesia. The hypothesis in fact ignores two important facts: (1) Disasters can alter household
economic well-beings, which may consequently lower their propensity to migrate as described in
the study. (2) Given the regular occurrence of disasters, households may resort to a variety of
adaptation mechanisms instead of simply moving out of disaster prone areas (IOM 2009). The
adaptation behaviors of households facing increasing natural disasters are not covered in this paper.
The makes a promising avenue of research to further examine the theory of disasters and migration.
Yet, even after adding economic variables in the regression, the negative coefficients still remain
and the significance has not been fully wiped out. For the effects of eruptions on household splits, the
magnitude of the coefficients even goes up. Indonesian communities develop their heritage nearby
volcanic areas which may give rise to positive correlation between eruptions and population density.
But this reason cannot explain the findings on the empirical specification of this paper. Because
the regression controls for household fixed effects, the coefficients measure how the variation of the
eruptions across time alters household migration pattern. Increasing eruptions should not induce
households to stay. Furthermore, none of the suggested economic variables can explain how floods
drive down whole household migration.
Thus the most possible explanation is that the specification has not fully captured some other
variables through which disasters operate to affect migration. Since the regression has controlled
for time invariant household fixed effects, those other possible variables should be time varying
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which may include but not limited to degree of risk aversion, health status of household heads,
accumulation of social capital and other sociological factors as described in section 4. The strong
negative causal relationship of disasters on household migration warrants further research to better
study how households in developing countries determine migration decisions at our time of surging
environmental calamities.
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References
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2010 in Punjab, Pakistan
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[3] Attzs, Marlene (2008), Natural Disasters and Remittances: Exploring the Linkages
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[4] Blaikie, Piers (1994), At Risk: Natural Hazards, Peoples Vulnerability and Disasters
[5] Cameron, Lisa; Shah, Manisha (2010), Risk Taking Behavior in the Wake of Natural
Disasters
[6] Cavallo, Eduardo; Noy, Ilan (2009), The Economics of Natural Disasters: A Survey
[7] Cavallo, Eduardo; Galiani, Sebastian; Noy, Ilan & Pantano, Juan (2010),Catastrophic
Natural Disasters and Economic Growth
[8] Drabo, Alassane (2011), Climate Change, Natural Disasters and Migration: An Em-
pirical Analysis in Developing Countries
[9] Ebeke, Christian; Combes, Jean-Louis (2010), Do remittances dampen the effect of
natural disasters on output growth volatility in developing countries?
[10] Halliday, Timothy J. (2007), Migration, Risk and the Intra-Household Allocation of
Labor in El Salvador
[11] Hsiao, Cheng (1986), Analysis of Panel Data
[12] International Organization of Migration (2009), Migration, Environment and Climate
Change: Assessing the evidence
[13] Naude, Wim. (2008), Conflict, Disasters, and No Jobs: Reasons for International
Migration from Sub-Saharan Africa
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[15] Noy, Ilan; Vu, Tam Bang (2010), The economics of natural disasters in a developing
country: The case of Vietnam
[16] O Grada, Cormac (1997), The Great Irish Famine : Winners and Losers
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[17] Paxson, Christina; Cecilia Elena Rouse (2008), Returning to New Orleans after Hur-
ricane Katrina
[18] SEDAC - Gridded population of the World, http://sedac.ciesin.columbia.edu/
[19] Skidmore, Mark; Toya, Hideki (2005), Economic Development and the Impacts of
Natural Disasters
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to confront growing risks of disasters, Press release UN/ISR 2007/8, Geneva
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http://www.columbiamissourian.com/stories/2011/01/01/2010s-world-gone-wild-
quakes-floods-blizzards/
[22] Wooldridge, Jeffrey (2001), Econometric Analysis of Cross Section and Panel Data
[23] Yamamura, Eiji (2011), Institution, economic development, and impact of natural
disasters
[24] Yang, Dean (2008) Risk, Migration, and Rural Financial Markets: Evidence from
Earthquakes in El Salvador
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30
Fig.1:YearlyoccurrenceofearthquakeandfloodinIndonesia
Source:DesInventarDatabase
0
2
4
6
8
10
12
1950 1955 1960 1965 1970 1975 1980 1985 1990 199
no.ofearthquake
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Fig.2:Sp
Fig.3:Sp
Source:De
atialvariati
atialvariati
sInventarDat
onofnumb
onofnumb
base
rofearthq
roferuptio
31
ake,1988
n,198820
000
0
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Fig4:Sp
S
Fig.5Po
tialvariati
urce:DesInve
ulationde
Sourc
nofnumbe
ntarDatabase
sityofIndo
:GriddedPop
rofflood,1
esiain200
ulationofthe
32
9882000
0
World(GPWv )SocioEco omicDataan Application enter
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33
Table1:DescriptiveStatisticsofIFLSandDesInventar
Variable Obs Mean Std.Dev. Median 90 percentile
Earthquake 24651 0.099 0.176 0 0.333
Volcaniceruption 24651 0.195 0.524 0 0.5
Flood
24651
0.680
0.924 0.25 1.75Move_prov 24651 0.0142 0.0609 0 0
Move_kabu 24651 0.0324 0.0889 0 0.143
Move_kec 24651 0.0460 0.103 0 0.25
All_move_prov 24651 0.00218 0.0225 0 0
All_move_kabu 24651 0.00582 0.0362 0 0
All_move_kec 24651 0.00963 0.0457 0 0
Split_move_prov 24651 0.0122 0.0573 0 0
Split_move_kabu 24651 0.0271 0.0835 0 0.143
Split_move_kec 24651 0.0375 0.0966 0 0.143
Notes:All
The
figures
are
annual
statistics.
The
disaster
variables,
earthquake,
eruption
and
flood,
show
the
annual
averagerateofoccurrencebetween1988and2000. Migrationstatisticsshowstheannualrateofmigrationacross
provinces(prov),acrossdistricts(kabu)andacrosssubdistricts(kec)between1993and2007.Move_provisthe
annualaveragemigrationrateacrossprovincescombiningbothwholehousehold(WH)migrationandsplit
household(SH)migration.Allmove_provistheannualWHmigrationrateacrossprovinces.Split_move_provisthe
correspondingstatisticsforSHmigration.
Variable Obs Mean Std.Dev. median 75percentile
HHsize 24651 5.50 2.57 5 7
Aidfromgovt
(000rupiahs)
24651 360 18,000 0 0
Totalearnings
(000rupiahs)24651
2,288
14,600 25 2,070
Remittances
(000rupiahs)
24651 221 1,668 0 0
Farmbizassets
(000rupiahs)
24651 4,843 32,80 0 1,129
Nonfarmbiz
asset
(000rupiahs)
24651 1,887 21,100 0 40
Nonbizassets
(000rupiahs)
24651 18,300 60,900 3,440 12,800
Urban/rural
24651
0.55
0.98 1 1
Educationhead 24651 1.85 1.16 1 2
Femalehead 24651 0.15 0.36 0 0
Agehead 24651 46.42 14.17 45 57
Notes:Allmeasuresareinrealvalues.ForUrban/ruraldummy,thevalue=0standsforhouseholdresidingin
urbanarea,value=1representsresidenceinruralarea.Educationheadgivestheeducationlevelofhousehold
head.Femaleheadshowswhetherthehouseholdisfemaleheaded.
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34
Table2:Comparingthreegroupsofsamples:1.Householdswhichdonotmove,2.Householdssplitand
3.Householdswhichmoveacrossprovincesasawhole,between2000and2007
Variable Nomoveatall Splithousehold migrationacross
provinces
Wholehousehold migrationacross
provinces
Mean
median Mean median T test (1) Mean median T
test (2
Farmbizasset
(000rupiahs)
11,000
(54,100)
0 11,900
(36,100)
0 0.36 3,197
(17,600)
0 1.79**
Nonfarmbizasset
(000rupiahs)
3,800
(31,500)
0 7,186
(46,800)
0 2.36*** 6,019
(55,800)
0 0.84
Nonbizasset
(000rupiahs)
34,400
(83,300)
11,700 58,300
(143,000)
16,600 6.16*** 36,000
(90,100)
4,675 0.24
TotalHHearnings
(000rupiahs)
2,961
(8,306)
400 5,525
(2,400)
900 5.74*** 3,495
(6,644)
850 0.79
HHsize 5.42
(2.65)
5 7.04
(2.74)
7 14.01*** 3.37
(2.34)
3 9.50**
N
7498
565 154
Notes:Standarderrorsinparentheses.Allthemeasuresareinrealvalues. Ttest(1):comparingthemeansbetweenSHmig
moveatall.Ttest(2):comparethemeansbetweenWHmigrationandthosewhichdonotmoveatall.
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Table3:BaselineResultsImpactsofdisastersonhouseholdmigrationingeneral(combiningbothsplit
household(WH)migration)
DepVariables Generalhouseholdmigrationacross General householdmigrationac
Province District Subdistrict Province District Sub
(1)
(2) (3) (4) (5) (6)
Earthquake 0.0434 0.0207 0.000149 0.0805*** 0.0821** 0.0
(0.0297) (0.0302) (0.0425) (0.0224) (0.0328) (0.0
Eruption 0.0237*** 0.0217*** 0.0304*** 0.0113 0.0176** 0.0
(0.00815) (0.00475) (0.00813) (0.00845) (0.00656) (0.0
Flood 0.0176*** 0.0137** 0.0127 0.0145** 0.00726 0.0
(0.00591) (0.00607) (0.00868) (0.00669) (0.00624) (0.0
ControlforHHfixed
effect
N N N Y Y Y
Observations 24,651 24,651 24,651 24,651 24,651 24,
Rsquared 0.034 0.061 0.087 0.046 0.093 0.1
Numberofhhid 8,217 8,217 8,2
Notes:Robuststandarderrorsinparentheses,adjustedforclusteringatprovincetimelevel.Allcolumnscontrolfortimedu
controlforHHfixedeffects,columns(4)(6)do.Thedependentvariablesarethemigrationdummiesbetweentandt+1at
areprovince,districtandsubdistrict.Disastervariablesmeasuretheannualaveragenumberofdisasterhappeningatthepr
householdresidesin.t=1993,1997and2000.Fort=1993,t1=1998.Fort=2000,t+1=2007.
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Table4:BaselineResultsImpactsofdisastersonindividualmigration
DepVariables Individualmigrationacross
Province District Subdistrict
(1) (2) (3)
Earthquake 0.0310* 0.0251 0.0146
(0.0174) (0.0217) (0.0327)
Eruption 3.52e05 0.00984** 0.0175**
(0.00278) (0.00396) (0.00784)
Flood 0.00447 0.00477 0.00733
(0.00324) (0.00334) (0.00521)
sex 0.000549 0.00211** 0.00254**
(0.000800) (0.000889) (0.00116)
age 0.000888*** 0.00189*** 0.00261***
(0.000172) (0.000159) (0.000205)
grade
0.000393* 0.000467 0.00110***
(0.000199) (0.000328) (0.000364)
Observations 55,647 55,647 55,647
Rsquared 0.014 0.026 0.039
Numberofhhid 9,990 9,990 9,990
Notes:Robuststandarderrorsinparentheses,adjustedforclusteringatprovincetimelevel.Allcolumnscontrolfortimedu
Thedependentvariablesareindividualmigrationdummiesbetweentandt+1atvariousgeographicallevels,,whicharepro
Disastervariablesmeasuretheannualaveragenumberofdisasterhappeningattheprovincebetweent1andtwherehous
2000.Fort=1993,t1=1998.Fort=2000,t+1=2007.
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Table5:Impactsofdisastersonsplithousehold(SH)migrationandwholehousehold(WH)migration
Dependentvariablesascountvariables Dependent variablesasdummyva
DepVariables Splithouseholdmigrationacross Split householdmigrationacross Wholeho
Province District Subdistrict Province District Subdistrict Province
(1)
(2)
(3) (4) (5) (6)
(7)
Earthquake 0.0681*** 0.0823** 0.0813** 0.0613*** 0.0729** 0.0835** 0.0208
(0.0215) (0.0307) (0.0344) (0.0206) (0.0259) (0.0300) (0.0145)
Eruption 0.0177* 0.0199** 0.0307** 0.0120 0.0112* 0.0145 0.000173
(0.00888) (0.00851) (0.0122) (0.00750) (0.00578) (0.00835) (0.00215)
Flood 0.0120* 0.00494 0.00435 0.00791 0.000683 0.000988 0.00684*
(0.00620) (0.00807) (0.0103) (0.00485) (0.00535) (0.00562) (0.00371)
Observations 24,651 24,651 24,651 24,651 24,651 24,651 24,651
Rsquared 0.040 0.080 0.105 0.044 0.093 0.130 0.009
Numberof
hhid
8,217
8,217
8,217 8,217 8,217 8,217
8,217
Notes:Robuststandarderrorsinparentheses,adjustedforclusteringatprovincetimelevel.Allcolumnscontrolfortimedu
Thedependentvariablesofcolumns(1)to(3)measurehouseholdsplit,whichcountthenumberofnewhouseholdsformed
householdsandthenmovetoanewprovince,districtandsubdistrictbetweentandt+1.Thedependentvariablesofcolum
whetherthehouseholdhasinvolvedinsplittoanewprovince,districtandsubdistrictbetweentandt+1.Thedependentva
dummiesindicatingwhethertheentirehouseholdhasmovedacrossprovinces,districtsandsubdistrictsbetweentandt+1.
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Table6:Auxiliaryregression Impactsofdisastersonhouseholdeconomicvariables
Dep.
VARIABLES
Household
size
Total
earning
Remittance Aid from
govt
House Land Financial
asset
Nonbiz
asset
(1) (2) (3) (4) (5) (6) (7) (8)
Earthquake 0.347*** 1.262** 0.310 0.0254 1.369** 0.457 0.794*** 0.689**
(0.116) (0.468) (0.382) (0.290) (0.532) (1.470) (0.264) (0.254)
Eruption 0.0214 0.0716 0.484*** 0.105 0.324*** 0.115 0.125 0.0571
(0.0563) (0.0992) (0.101) (0.137) (0.0801) (0.316) (0.134) (0.0332)
Flood 0.0186 0.0608 0.0676 0.0138 0.0984 0.0716 0.0527 0.0354
(0.0312) (0.0789) (0.0699) (0.101) (0.0766) (0.169) (0.127) (0.0308)
Observations 24,651 24,651 24,651 24,651 24,651 24,651 24,651 24,651
Rsquared 0.055 0.055 0.030 0.099 0.045 0.021 0.011 0.279
Numberofhhid 8,217 8,217 8,217 8,217 8,217 8,217 8,217 8,217
Notes:Robuststandarderrorsinparentheses,adjustedforclusteringatprovincetimelevel.Allcolumnscontrolfortimedu
The
dependent
variables
are
different
economic
variables
measured
at
time
t.
Total
earnings,
remittances
and
aid
are
the
ayearbeforetimet.Remittancesaretransfersfromnonhouseholdfamilymembers.Aidisthetransferfromgovernment,NG
valuesexcepthouseholdsizearerecordedinlog(1+Y).House,landandfinancialassetaresubcategoriesofnonbizassets.D
numberofdisasterhappeningattheprovincebetweent1andtwherehouseholdresidesin.
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Table7:ImpactsofdisastersonSplitHousehold(SH)migration
Dep.Variables Splithouseholdmigrationacross Split householdmigrationacross
Province District Subdistrict Province District Subdistrict
(1) (2) (3) (4) (5) (6)
Earthquake 0.0681*** 0.0823** 0.0813** 0.0581*** 0.0621** 0.0516
(0.0215) (0.0307) (0.0344) (0.0190) (0.0248) (0.0302)
Eruption 0.0177* 0.0199** 0.0307** 0.0188** 0.0219** 0.0340***
(0.00888) (0.00851) (0.0122) (0.00722) (0.00821) (0.00937)
Flood 0.0120* 0.00494 0.00435 0.0124** 0.00552 0.00468
(0.00620) (0.00807) (0.0103) (0.00547) (0.00654) (0.00850)
HHsize 0.0241*** 0.0411*** 0.0648***
(0.00701) (0.00614) (0.0122)
Totalearnings 0.000847* 0.00270*** 0.00518***
(0.000453) (0.000551)
(0.000957)Remittances 0.000562 0.00134 0.00187
(0.000935) (0.00127) (0.00210)
Aid 0.00180* 0.00154 0.00195
(0.00101) (0.00118) (0.00195)
Farmasset 0.00112** 0.00136 0.00229*
(0.000472) (0.00114) (0.00124)
Nonfarmbizasset 0.000938 0.00112 0.00237*
(0.000672) (0.000808) (0.00113)
Nonbizasset 0.000294 0.00292 0.000158
(0.00157) (0.00184) (0.00196)
Observations 24,651 24,651 24,651 24,651 24,651 24,651
Rsquared 0.040 0.080 0.105 0.071 0.132 0.182
Numberofhhid 8,217 8,217 8,217 8,217 8,217 8,217
Notes:Robuststandarderrorsinparentheses,adjustedforclusteringatprovincetimelevel.Allcolumnscontrol
fortimedummiesandhouseholdfixedeffects.Dependentvariablescountthenumberofhouseholdsformedfrom
splitacrossprovinces,districtsandsubdistricts.Disastervariablesmeasurethenumberofdisasterhappeningat
theprovincebetweent1andtwherehouseholdresidesin.Allotherindependentvariablesaremeasuredattime
t.Totalearnings,remittancesandaidaretheamountsaccumulatedwithinoneyearbeforetimet.Allthevaluesof
economicvariablesexcepthouseholdsizearerecordedinlog(1+Y).Columns(1)(3)donotcontrolforeconomic
variablesandcolumns(4)(6)do.
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Table8:Impactofdisastersonwholehousehold(WH)migration
Dep.Variables Wholehouseholdmigrationacross Wholehouseholdmigrationacross
Province District Subdistrict Province District Subdistrict
(1) (2) (3) (4) (5) (6)
Earthquake 0.0208 0.00898 0.0182 0.0210 0.0112 0.0146
(0.0145) (0.0195) (0.0309) (0.0130) (0.0188) (0.0311)
Eruption 0.000173 0.00937** 0.0151* 0.00103 0.00732* 0.0126
(0.00215) (0.00339) (0.00771) (0.00239) (0.00396) (0.00853)
Flood 0.00684* 0.00677* 0.0114** 0.00711* 0.00708* 0.0114**
(0.00371) (0.00371) (0.00525) (0.00373) (0.00359) (0.00507)
HHsize 0.00480*** 0.0103*** 0.0138***
(0.000947) (0.00222) (0.00286)
Totalearnings 0.000203 2.78e05 3.38e06
(0.000194) (0.000278)
(0.000366)Remittances 2.13e05 0.000203 7.15e05
(0.000124) (0.000204) (0.000381)
Aid 0.000355 0.000128 0.000502
(0.000472) (0.000746) (0.000808)
Farmasset 0.000405** 0.00116*** 0.00204***
(0.000159) (0.000272) (0.000392)
Nonfarmbizasset 0.000457** 0.000974** 0.000541
(0.000203) (0.000337) (0.000483)
Nonbizasset 0.00138** 0.00325*** 0.00468***
(0.000507) (0.000638) (0.000775)
Observations 24,651 24,651 24,651 24,651 24,651 24,651
Rsquared 0.009 0.017 0.033 0.032 0.058 0.080
Numberofhhid 8,217 8,217 8,217 8,217 8,217 8,217
Notes:Robuststandarderrorsinparentheses,adjustedforclusteringatprovincetimelevel.Allcolumnscontrol
fortimedummiesandhouseholdfixedeffects.Dependentvariablesarewholehouseholdmigrationacross
provinces,districtsandsubdistricts.Disastervariablesmeasurethenumberofdisasterhappeningattheprovince
betweent1andtwherehouseholdresidesin.Allotherindependentvariablesaremeasuredattimet.Total
earnings,remittancesandaidaretheamountsaccumulatedwithinoneyearbeforetimet.Allthevaluesof
economicvariablesexcepthouseholdsizearerecordedinlog(1+Y)..Columns(1)(3)donotcontrolforeconomic
variablesandcolumns(4)(6)do.
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RobustnessChecks
Table9:PlaceboTestimpactsonmigrationofnaturaldisasterswithinplacebotimeinterval
Dep.variables Splithouseholdmigration Wholehouseholdmigration
Province
District
Sub
district Province District Sub
district
(1) (2) (3) (4) (5) (6)
Earthquake 0.0282 0.0538* 0.0580 0.0122** 0.00941 0.0114
(0.0227) (0.0257) (0.0367) (0.00571) (0.0135) (0.0165)
Eruption 0.0247 0.00624 0.0125 0.00972 0.00481 0.00582
(0.0205) (0.0179) (0.0194) (0.00609) (0.00809) (0.0109)
Flood 0.0511* 0.0298 0.00975 0.00134 0.0211 0.0371
(0.0285) (0.0286) (0.0316) (0.00544) (0.0169) (0.0307)
Observations 24,651 24,651 24,651 24,651 24,651 24,651
Rsquared 0.039 0.079 0.104 0.006 0.016 0.031
Numberof
hhid8,217
8,217
8,217 8,217 8,217 8,217
Notes:Robuststandarderrorsinparentheses,adjustedforclusteringatprovincetimelevel.Allcolumnscontrol
fortimedummiesandhouseholdfixedeffects.Dependentvariablesofcolumns(1)to(3)countthenumberofsplit
offhouseholdsformedacrossprovinces,districtsandsubdistricts.Dependentvariablesofcolumns(4)to(6)are
dummiesofwholehouseholdmigrationacrossprovinces,districtsandsubdistricts.Disastervariablesarethe
numberofdisastersmeasuredintheplacebotimeframe.Theplacebotimeframeforeachtimeinterval:(1)1988
1993,placebotimeframe:19741979;(2)19931997,placebotimeframe:19791983;(3)19972000,placebotime
frame:1983 1986
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