Download Do Natural Disasters Lead to More Migration? Evidence

Do Natural Disasters Lead to More Migration? Evidence from
Indonesia∗
Chun-Wing Tse†
November 2012
Abstract
Using panel datasets of Indonesia, I examine how natural disasters affect household migration.
Specifically, the study analyzes the effects of earthquakes, volcanic eruptions and floods on
household tendency to migrate. Contrary to conventional wisdom, I find that all three kinds
of disasters significantly reduce migration rates. Nevertheless, the channels of impact are quite
different. Earthquakes reduce earnings and non-business assets, each of which tends to reduce
migration rates. Volcanic eruptions on the other hand raise the value of farmland, which, in turn,
induce households to stay. Floods have no significant impacts on household assets or earnings,
which can be the results from attenuation bias due to measurement errors .
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 Paserman
and Michael Manove for their advice and comments. I am also grateful to Ye Li, Jie Hou, Julian Chan, Hyo-Youn
Chu, Saori Chiba and seminar participants at Boston University. All errors are my own.
†
CHLR, Central University of Finance and Economics, Beijing, China ([email protected])
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1
Introduction
Given the rising losses from environmental calamities across the globe (Cameron and Shah 2010;
UNISDR 2007), the study of natural disasters has never been more crucial in our time. In the year
2010 alone, natural disasters of various kinds 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 study of environmental shocks 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 (Cavallo and Noy 2009;
Noy and Bang 2010; Ebeke and Combes 2010; Cavallo et al. 2010). Based on a descriptive study of
cross-country analysis, the International Organization for Migration (IOM) in 2009, suggests that
rising natural disasters can lead to more migration because poor households in developing countries
resort to migration to stay away from disaster-prone areas. Soaring climate change exacerbates the
problem of water shortages and agricultural failures. Seismic activities destroy industrial establishments and deter prospective investors from investing in quake zones. Having lost the livelihoods
after natural disasters, households have to make a living elsewhere and hence, move out (IOM 2009).
Natural disasters, however, can lower migration. Disasters destroy assets and consequently
reduce household financial resources to support migration. Disasters also present an aggregate shock
that hurts most households in affected villages, and therefore, it becomes more difficult to borrow
from others to pay migration costs (Yang 2008). They are not forced to migrate but forced to stay.
On the other hand, disasters can attract households to stay. Eruptions and floods enrich soil fertility
by lava ash and alluvial deposits (Ahmad 2011) respectively, which induces households to stay for
increased farming productivity. Furthermore, the marginal product of labor of some sectors can rise
after disasters and consequently lower migration. E.g., wages in the construction sector increase
after earthquakes because of a higher labor demand for village rebuilding. Regarding eruptions and
floods, both make farmlands more fertile and hence raise agricultural wages. Finally, some other
social factors such as stronger family ties and enhanced community bonding after disasters can also
draw households to stay.
2
By studying Indonesia, this paper attempts to understand the link between natural disasters and
migration. The study relies on two panel datasets, which are nationally representative. I conduct a
longitudinal study to account for household fixed effects and measure how time variation of disasters
alters household tendency to migrate. In particular, this paper highlights the effects of disasters
on two different forms of household migration: (1) split-household migration, in which part of the
household splits and migrates; and (2) whole-household migration, in which the entire household
relocates. Furthermore, disasters of various kinds occur in Indonesia frequently, and I investigate
each disaster individually. Specifically, this paper explores the effects of three most common kinds
of disasters in Indonesia: earthquakes, volcanic eruptions and floods.
The baseline empirical results show that all three kinds of disasters reduce household tendency
to move and the negative effects hold for both forms of migration. Nevertheless, the channels of
impact for each kind of disaster are quite different. Eruptions raise the value of farmland which
can be due to enrichment of soil fertility by lava ash. Households with more farm assets move
out less. Hence, eruptions lower household migration by increasing farm assets. On the other
hand, evidence shows that migration is less likely to occur if a household has lower earnings or nonbusiness assets. Earthquakes decrease earnings and non-business assets, and hence, reduce household
financial capability of supporting migration. Floods do not lead to reductions in household assets or
earnings and the negative impacts of floods remain unexplained. Yet the lack of significant effects
on households assets can be the results from attenuation bias since the data on floods are subject
to bigger measurement errors.
The economics literature on the link between natural disasters and migration is relatively new.
Naude (2008) uses panel analysis at the country level and finds that environmental shocks raise
migration because of increased conflicts. The cross-household studies by Halliday (2007), Ó Gráda
(1997) and Attzs (2008) analyze specific one-off deadly disasters and relate cross-section disaster
exposure with migration at the individual level. They find that individual migration increases after
massive disasters.
This study is most related to Yang’s (2008) paper, which is a panel study on El Salvador, examining how the massive earthquakes in 2001 affect household migration. Specifically, he highlights
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how the earthquakes affect household access to credit to finance migration, and discovers that the
earthquakes present a large aggregate shock across households in quake-affected villages. It becomes
more difficult to borrow from other households to pay fixed migration costs because most households in the villages are financially impaired. Hence, household tendency to migrate decreases. This
finding is similar to the overall result of my paper. In addition, I investigate a wide range of natural
disasters and a broad range of channels of impact, such as household size, total earnings, value of
farmland and non-business assets. The same outcome is obtained despite heterogeneous impacts of
different kinds of disasters on different types of assets.
Similar to Yang’s study, this paper relies on a longitudinal analysis and focuses on household
migration. However, I treat different disasters as heterogeneous shocks. Different disasters cause
differential changes in household asset composition and marginal product of labor in various sectors,
which can lead to very different migration decisions. Furthermore, I separately consider splithousehold and whole-household migration, because the determinants of these two forms of migration
can be very different. In split migration, the household aims to reduce risk by sending members to
other locations to diversify sources of income in anticipation of receiving remittances in the future.
However, whole-household migration is a risk-taking strategy which involves relocation of the entire
household to a new location. All the above facts point toward the need to treat different kinds of
disasters as heterogeneous shocks and to separately analyze various forms of household migration.
Finally, I do not consider the effects of some specific one-off massive disaster shocks but, instead,
investigate how time variation of disasters shapes the pattern of household migration. Given that
disasters of various kinds occur in some less-developed countries regularly, such as the Philippines,
Bangladesh and Pakistan, a longitudinal study of time variation of natural disasters is important.
The paper is organized as follows. Section 2 provides background information on Indonesia,
illustrating the demographics and disaster occurrence in the country. Section 3 outlines the data
used and give 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 checks for robustness and Section 8 concludes.
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2
Background
According to the UN Office for the Coordination of Humanitarian Affairs, Indonesia is the most
disaster-prone country of the world. Most parts of Indonesia are 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 the deforestation of massive scale. In 2009 alone, it experienced
469 earthquakes with a magnitude of 5 or higher. Sumatra, Java and Papua were especially hard
hit. According to the government data (BPS Indonesia), floods have accounted for about 40 percent
of Indonesia’s disasters in the past few years. Figure 1 shows the time-series patterns of earthquakes
and floods, and Figures 2 to 4 provide geographic snapshots on where earthquakes, eruptions and
floods occurred in the country between 1988 and 2000. Java and Sumatra Islands have always been
the black spots of disasters.
However, people do not stay away from disasters but continue to live with the environmental
risk. Figure 5 depicts the population density of Indonesia in 2000, with most dwellers crowded
in Java and Sumatra where disasters of different kinds occur frequently. People in the West Java
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 disaster occurrence within 50 years before 1993.
People are not driven away by disasters, but stay with the environmental risk instead.
It has always been claimed that communities in Indonesia stay near volcanic areas regardless
of the constant threat of eruptions. The regression of rice yield on eruptions at the province level
within the last 50 years shows that provinces with more eruptions can produce higher rice yield.
Lava ash from volcanoes enhances soil fertility to boost the farm yield, which may explain why
people settle near volcanic areas.
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3
Data and descriptive statistics
Data
This paper uses two datasets for the empirical analysis. The first one is a panel dataset from
the Indonesian Family Life Survey (IFLS), a nationally representative household 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) with three more waves 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, 94% 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
household migration. In this paper, I define two forms of household migration: (1) split-household
migration and (2) whole-household migration. For split-household migration, one or more household
members, but not including the head of household, leave and establish a new household in a new
location. On the other hand, if the entire household including the household head moves to a new
place, I call this whole-household migration.
Apart from a detailed section of household migration history, IFLS also asks several comprehensive sets of questions to obtain the economic variables of the sample households. Specifically, I focus
on household size, government aid received, remittances, total household earnings and amounts of
different types of assets, to study how natural disasters alter these economic variables to shape the
1
IFLS2+ was also carried out in 1998 to measure the impact of financial crisis. Yet only about 20% of the households
from IFLS2 were re-interviewed in IFLS2+.
2
I also test whether there exists non-random attrition and the findings are not sensitive to the treatment of
households, who dropped out from the samples.
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two forms of household migration.
The second dataset is the Indonesian DesInventar Database (DesInventar) administered by Data
& Informasi Bencana Indonesia. DesInventar aims to record every disaster happening in Indonesia
from the early 20th century. The details include location, date, fatalities, financial losses, damage
of infrastructure and other relevant information of the disasters. This paper looks at earthquakes,
volcanic eruptions and floods, which are the three most common kinds of natural disasters in
Indonesia.
DesInventar adopts a method of counting natural disasters, different from the traditional practice. First, a disaster is defined as “the set of effects caused by an event on human lives and economic
infrastructure on a geographic 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 geographic units, referred to as kecamatan (subdistrict), affected in the event.
Thus, DesInventar counts an earthquake event of extensive geographic coverage as multiple earthquake disasters. Thus, this makes statistics recorded 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 weight. DesInventar defines earthquakes, eruptions and
floods as follows:
Earthquakes - All movements in the earth’s 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).
This study retains households and their split-offs which exist in all four waves of the survey. In
addition, I only keep households with clear migration history between 1993 and 2007. Households
without information of some economic variables such as household size, earnings and assets are
discarded. This finally leaves the study with 8,217 households.
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Descriptive Statistics
Table 1 presents the descriptive statistics of the sample households. The disaster statistics record
the annual average number of each kind of disaster at the province level occurring between 1988
and 2000. Households, on average, experience 0.1 earthquakes annually between 1988 and 2000. In
other words, earthquake occurs in 10% of Indonesian provinces every year. Eruptions and floods
are also prevalent. The sample households are exposed to one eruption in every five years and more
than two floods in every three years. The mean annual exposure to any one of the three kinds of
disasters is 0.98. Table 1 also shows the migration statistics between 1993 and 2007. The overall
rate of migration combines both split-household migration and whole-household moving, and 1.4%
of the households in Indonesia move across provinces every year. Moving rates across kabupatens
(districts) and kecamatans (subdistricts) are respectively 3.2% and 4.6%, which are considerably
high. Yet most household migration is in the form of splits. More than 3.8% of households split
and relocate to a new subdistrict annually. Meanwhile, just less than 1% of households, as a whole,
move to a new subdistrict. Split-migration accounts for 83% of overall household moving.
Even though Indonesia is the most disaster-prone country, disaster is still a rare event, with
small mean numbers and relatively large standard deviation as shown in Table 1. Hence, I also
include the median and 90-percentile statistics. More than 50% of the households do not experience
any earthquake and eruption between 1988 and 2000. Only around 10% of the households are
affected by more than one earthquake in every three years. The occurrence is low for an individual
kind of disaster. However, more than 68% of households experienced one or more of three kinds of
disasters between 1993 and 2000, and more than half of the households are affected by more than
0.4 disasters every year.
3
Migration rate is also low in Indonesia. More than half of the households
have never moved beyond their sub-districts between 1993 and 2007, and more than 90% of the
households have never moved across provinces.
4
Table 1 also shows some other household statistics. There is an even proportion of urban and
3
I also restricted the sample to households, who have experienced at least one kind of disaster between 1993 and
2000 and the empirical findings do not change much.
4
Empirical study on rare events warrants the need of conditional analysis. Hence, I also use conditional logit model
to check the robustness and the findings do not change much. The results can be available upon request.
8
rural samples. Most of the household heads have only finished elementary education and about 15%
of the households are headed by females. Extended family is a common practice in Indonesia, 18%
of the households have extended family living in a single household.
Table 2 links household economic status in 2000, with migration decisions between 2000 and
2007. I separate the sample into three groups: (1) households which do not migrate between 2000
and 2007, (2) households which split and migrate across provinces but do not move out as a whole,
(3) households which move to a new province, as a whole. For statistics on assets, households which
migrate as a whole have significantly less farm assets than nonmigrant households. Whole-household
migrants have 3.2 million rupiahs of farm assets but the nonmigrants have twice more. On the other
hand, split-household migrants have more assets of all types, both business and non-business, than
the other two groups, and the differences in asset holdings are significant compared with nonmigrant
households. This suggests that the same effects on asset composition due to a disaster can lead
to very different impacts on split-household and whole-household migration. Considering total
earnings, split-household migrants also earn significantly more in year 2000. The amount of annual
earnings is more than 5.5 million rupiahs compared with less than 3 million rupiahs for nonmigrants.
Household size in year 2000 is also correlated with migration decisions. Households who split and
migrate have, on average, 7 people, significantly bigger than nonmigrant households and households
who move as a whole.
The above descriptive analysis illustrates how household size, asset composition and earnings
link with migration. Before presenting the empirical analysis, the paper first explains how disasters
affect migration. Since the empirical study emphasizes the differences 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
Migration can increase with natural disasters because households want to stay away from the risk
or they need to make a living elsewhere if their livelihoods are destroyed. Yet households exposed
to natural disasters may move out less. There exist three possible reasons: (1) increase in marginal
9
product of labor, (2) decrease in financial resources to pay for migration, and (3) strengthened social
bonding and mutual insurance.
(1) Increase in marginal product of labor (MPL)
Disasters destroy infrastructure and houses, which increases demand for labor to rebuild villages
afterward. Hence, the MPL of the reconstruction sector rises, which induces households to stay for
better employment opportunities. Regarding the agricultural sector, soil fertility can be enriched
by lava ash after eruptions and alluvial deposits after floods, which causes the farming productivity
to rise. Therefore, households may choose to stay.
(2) Decrease in financial resources to pay for migration
With assets destroyed and earnings reduced after disasters, households are less capable of affording migration. Thus, they are not forced to migrate but forced to stay. Moreover, households
find it more difficult to borrow from others to finance migration as disasters present an aggregate
shock and hurt most households living in the affected villages (Yang 2008). Disasters pose liquidity
constraints and, as a result, lower 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 may choose to
cope with disaster shock by accumulating social capital instead of moving out, which reduces the
tendency to migrate. Furthermore, households with houses destroyed by disasters need members to
stay to rebuild houses. Law and order may also break down after disasters and households should
remain to protect property and land rights.
This paper will empirically examine the first two reasons and leave out the third because the
relevant data are not available.
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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 location. In this study, the migration of a single individual
to set up a single-member household is also classified as split-household migration. Split-household
migration differs from individual migration in several ways: (1) in individual moving, the migrants
may just move out and enter another household in the new location; (2) individual migration tends
to be temporary and migrants may return after some time; and (3) individual migrants are, in
general, more attached to the original household. Yet split-off households are considered separate
from the original household.
Households may also regard 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 the human asset to various locations. The remittances received from
split-off households is also 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 to a new location. The insurance factor is much less significant, and the decision
is primarily based on the “push factors” of the origin and the “pull factors” of the destination, taking
into account of the total migration cost.
The above facts point toward the need to separately analyze these two forms of household migration. Yet it is first necessary to discuss the determinants of split and whole-household migration,
including household size, 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 locations for the purpose of risk diversification. On the other
hand, a household with more members is less likely to move out as a whole because migration cost
increases with the size of household.
Total earnings: Households with more earnings have a higher likelihood to split and migrate
because they have more financial resources to support the splits. Furthermore, split-household
migration can be a risky investment for the split-off households, and higher earnings lower the risk
11
for split-off households to relocate and make a higher income elsewhere. On the contrary, higher
earnings at the place of origin can reduce whole-household migration because the opportunity cost
of moving increases with 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, households 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. On the other hand, government aid received at the
place of origin, however, induces people to stay in order to obtain more aid money, which points
toward the issue of moral hazard.
Household assets: Households with more assets are better endowed financially to support splits.
Similar to the theory on total earnings, the risk of split-household migration is lower for households
with more assets. Hence, greater assets of various types increase split migration. On the other
hand, households with more assets are less likely to migrate as a whole because it is costly for
households to dispose of their assets and move out. This theory is particularly important for farm
assets. Households owning more farmland have a much higher cost to sell off their farm assets and
hence they have a lower tendency to move out as a whole.
5
Empirical Strategy
The empirical analysis starts with equation (1):
Mit = α0 + α1 Dct + θi + ρt + it
(1)
The LHS variable Mit is a migration dummy indicating whether household i moves out at a
given geographic level between time t and t + 1. The three different geographic 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 t − 1 and t.
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Hence, equation (1) studies how disaster occurrence in the last period affects household migration
in the following period. The panel survey spans from 1993 to 2007, with a total of four waves. The
specification includes t =1993, 1997 and 2000. I take t − 1 =1988 for t =1993 and t + 1 =2007 when
t =2000.
Equation (1) also controls for household fixed effect, θi . it captures idiosyncratic errors and
ρt denotes time dummies, which is essential because the panel dataset is unevenly spaced. The
specification is a linear probability model with controls for households fixed effects.5
There are two issues of concerns here: (1) number of disasters by itself cannot fully capture the
severity of disasters. (2) Disaster occurrence at the province level is too coarse for the analysis at
the household level. To address the first issue, I also use some other disaster measures, such as
deaths, casualties, and economic loss, as a robustness check. For the second issue, the province level
data are much more reliable with lower measurement errors. Furthermore, using disaster variable
at province level can also capture the spillover effect on households, who are not directly hit by
disasters. E.g., an earthquake occurring in a district can cause general equilibrium effects and
adversely affects the labor and property markets, which consequently reduce wages and housing
prices of the entire province. DesInventar also has disaster record at the kabupaten (district) level.
However, the data are less clean with missing values. The database can sometimes only identify
the province but not the district, where disasters occur. This issue renders the variable suffering
measurement errors, which may reduce the significance of the results. I also conducted the analysis
using district level data. The significance of the results is greatly reduced with some inconsistent
findings. Hence, I use the disaster variable at the province level instead.6
However, I first run a regression on equation (1) without including the household fixed effect and
conduct a simple OLS analysis. The OLS results shows how cross-household variation of disasters in
the last period correlates with migration in the following period. Such correlation gives the causal
impact only when Dct is uncorrelated with the combined error term, θi + 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
5
The migration variable is a dummy, which allows for logit or probit analysis. I conducted regressions using fixed
effect logit model and the findings are robust. Results can be available upon request.
6
The results can be available upon request.
13
will render the coefficients from a simple cross-section regression biased. Hence equation (1) also
accounts for household fixed effects, θi .
Household migration, Mit , consists of two different forms: (1) split-household migration and (2)
whole-household moving.
Splitit = β0 + β1 Dct + δi + ηt + eit
(2)
Allit = γ0 + γ1 Dct + µi + πt + εit
(3)
Splitit in equation (2) counts how many new households are formed between time t and t + 1
in splitting and moving. In equation (3), Allit is a migration dummy, indicating whether the entire
household i migrates to a new location between t and t + 1.
The above empirical specifications measure the total effects of disasters on these two forms
of migration, making up the first part of the analysis. The second part explains which channels
disasters operate, to bring about such effects. To do this, I modify equations (2) and (3) to control
for different economic variables, as shown in equations (4) and (5).
0
0
0
0
0
0
Splitit = β0 + β1 Dct + β2 Yit + δi + ηt + eit
0
0
0
0
0
0
Allit = γ0 + γ1 Dct + γ2 Yit + µi + πt + εit
(4)
(5)
Yit , consists of a list of economic variables, which includes household size, total earnings, external
transfer and household assets, 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 which economic channels disasters operate to shape split migration. I also
use the same approach to discover the channels for whole-household migration.
14
6
Results
In table 3, the dependent variable is the household-migration dummy between time t and t + 1,
combining both split-household migration and moving of an entire household. The explanatory
variables are the annual average number of earthquakes, eruptions and floods, happening between
time t − 1 and t. All standard errors are clustered at the province-time level.
7
The first three columns show the pooled-household analysis without controlling for household
fixed effects. Contrary to conventional wisdom, the probability for households to move out decreases
with disasters. Floods have significantly negative effects on household moving across provinces
and districts. The effects of eruptions are significantly negative at the 0.01 level for all three
geographic levels of migration. An additional eruption each year reduces the probability of moving
to another province by 0.024. Given the overall cross-province migration rate as 0.06, eruption
reduces household moving by 40%. Using the same computation, one more flood each year lowers
cross-province migration by 29%. Yet as suggested in Section 5, simple OLS may not account
for unobserved migration propensity. From now on, I control for household fixed effects in all
specifications to resolve this possible endogeneity. The regression coefficients now estimate how
time-variation of disaster occurrence affects the change in migration decisions.
According to columns (4) to (6), controlling for household fixed effects affirms the negative
impacts of disasters. Besides all the coefficients being negative, the effect of earthquakes is much
greater for all geographic levels of migration. Time variations of all three kinds of disasters do
significantly reduce household migration. Earthquakes decrease cross-province migration by 0.081,
which amounts to 13.4% for one more earthquake in every 10 years. An annual additional eruption
and flood also lower cross-province migration by 18% and 24% respectively.
This paper highlights the difference between split-household migration and whole-household
migration and I separately shows the results for the two forms of migration in Table 4. In columns (1)
to (3), the dependent variable counts the number of new households formed by splitting and moving
to a new location. In columns (4) to (6), the dependent variable is a dummy indicating whether
7
Given the clustering at the province-time level, there are 44 groups of cluster, which is greater than the benchmark
of 30 (Cameron et al. 2008). Hence, we can be less concerned about over-rejection of null hypothesis due to small
number of clusters and just rely on the standard technique of error clustering.
15
the entire household moves to a new location. Earthquakes significantly reduce split-migration at
all geographic levels. Splits to a new province fall by 0.068, which is 120% in percentage terms.
Eruptions also decrease cross-province splits by 31%. However, the effects of floods are just barely
significant on splits across provinces. Regarding whole-household migration, earthquakes have just
barely significantly negative effect on migration across provinces. The effect on moving across
subdistricts is even positive. These findings show a clear contrast with the effects on split migration.
Yet the differences are not quite remarkable for eruptions and floods. Eruptions significantly reduce
the tendency for an entire household to move at the district and subdistrict levels. Migration across
districts falls by 32% with one more eruption annually. The effects of flood are significantly negative
at all three geographic levels. Cross-province migration drops by 64% with one more flood every
year.
In percentage terms, the negative effect of disasters increases with the distance of migration. E.g.,
an additional earthquake in every 10 years reduces cross-province splits by 12%, but the decreases
in cross-district and cross-subdistrict splits are 6.3% and 4.4% respectively. Similar patterns are
observed for eruptions and floods, with greater effects on migration of longer distance.
Previous results show the reduced-forms effects of disasters. To explain why disasters lower
migration, I first examine the impacts of disasters on different economic variables, as the possible
channels which disasters operate. Equation (6) regresses various household economic variables on
disaster occurrence controlling for household fixed effects, ϑi , and time dummies, νt .
Yit = λ0 + λ1 Dct + ϑi + νt + eit
(6)
In Table 5, all the economic variables are measured in logs of real values except household size.
The stock variables include household size, non-business assets, farm assets and nonfarm-business
assets, recorded at time t. I highlight the effects on housing asset, as a category of non-business
assets. The table also shows how disasters affect the values of farmland, as a category of farm assets.
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, yet such data are not available.
16
Table 5 shows that earthquakes lower all economic variables except remittances received. An
additional earthquake each year significantly reduces household size by 0.35. The decrease is also
significant for non-business assets, which amounts to 69%. Housing assets significantly fall by 14%
for an additional earthquake in every 10 years. Households also suffer from losses in farm and
nonfarm-business assets even though the effects are not significant. Total household earnings drop
by 13% for one more earthquake in every decade.
While earthquakes reduce household economic status, eruptions significantly increase various
economic variables. An additional eruption raises the amount of farm assets by 55%. Lava ash
in eruptions can highly enrich soil fertility which plausibly increases soil fertility, as shown by an
increase in the value of farmland by 25%. Eruptions also significantly raise housing assets, which can
be due to the fact that relief money runs into affected areas for house rebuilding and consequently
helps boost the housing market. Furthermore, remittances received increase by 48% after eruptions.
Significant increase, however, is not observed for earthquakes. One possible explanation is that the
impacts of eruptions may only be geographically confined to the areas near volcanoes. Hence,
most households in the province are largely unaffected and they are still financially intact and
able to remit money to affected households. However, the damage of earthquakes can be much
more far reaching, adversely affecting most households in the province. Earthquakes constitute
aggregate shocks and do not lead to more financial support received, as non-household members are
also financially impaired. Unlike earthquakes, eruptions do not significant decrease household size
and total earnings. There exists no negative effect of eruption on household assets of any types.
Furthermore, earthquakes and eruptions do not cause households to receive more government aid.
Finally, Table 5 shows that floods significantly increase the value of farmland, plausibly due to
the better soil fertility by alluvial deposits. However, floods do not significantly affect household
well-being on any other measures. This may be due to bigger measurement errors of flood data,
which attenuate the effects on the economic variables.
Given the results of Tables 4 and 5, we can now explore the channels which disasters operate
to affect household migration. Tables 6 and 7 investigate the channels for split-household and
whole-household migration respectively.
17
In Table 6, I put the regression results without controls and with controls for economic variables,
side by side for ease of comparison. By adding economic variables, the magnitude of coefficients on
earthquakes has dropped for all three geographic levels of moving. From column (1), earthquakes
reduce split migration to a new province by 0.068 (120% in percentage terms), but the magnitude
falls to 0.058 (102%) after adding economic variables as shown in column (4). The drops in magnitude are even more noticeable for splits to a new district and subdistrict. This suggests that
earthquakes operate through some of the economic variables to reduce split migration.
From Table 6, households with bigger size and higher earnings split and migrate more, and
the positive effects are significant at all three geographic levels of moving. An additional household
member raises cross-province splits by 42%. One percentage rise in household earnings also increases
the number of new household formed in a new province by 0.00088, which amounts to an elasticity
of 1.5%. As shown in Table 5, earthquakes significantly reduce household size and total earnings.
Hence, earthquakes reduce split migration by decreasing household size and earnings.
Non-business assets just significantly increase cross-province splits. Yet the effects of business
assets of farm and nonfarm are significantly positive and the positive sign holds for migration at
all three geographic levels. From Table 5, earthquakes lower the two types of business assets,
although not significantly. Thus, the results suggest that earthquakes decrease split migration
through reducing both business and non-business assets.
Table 6 shows different findings for eruptions. All the negative signs for eruption remain and the
coefficients are even more negative after controlling for economic variables. Since households with
more farm assets split more and eruption raise farm assets, hence household splits increase with
eruptions and, hence, the coefficients on eruptions in columns (4) to (6) are even more negative. The
results invalidate the economic variables listed in Table 6, as the channels which eruptions operate
to reduce household splits.
The estimated effects of economic variables on split migration in Table 6 suffer the problem of
endogeneity. Hence, the results just provide suggestive evidence on the channels which disasters
operate. Yet the effects of the economic variables are mostly consistent across different geographic
levels of migration. Specifically, for household assets, the positive sign nearly always remains even I
18
separately consider assets of various types. Such robust results provide strong evidence that those
variables are plausible channels.
Table 7 presents the results for whole-household migration and we, in particular, focus on the
effects of eruptions and floods because earthquakes do not significantly affect whole-household migration. After adding economic variables, magnitude for the coefficients on eruptions drops substantially and the level of significance also falls. Coefficients for cross-district moving falls from -0.0094
to -0.0073, and the effects on cross-subdistrict migration reduces from -0.015 to -0.012. Table 7 also
shows that households with more farm assets move out less, as a whole, at all geographic levels.
The tendency of moving to a new district falls by 4% when farm asset increases by 1%. Eruptions raise the amount of farm assets, as shown in Table 5, which explains why eruptions reduce
whole-household migration.
Table 7 shows that the magnitude and significance of coefficients on floods do not change substantially. From Table 5, the estimated effects of floods on any of the economic variables are not
significant. Hence, including controls for the suggested variables will not reduce the magnitude of
the coefficients on floods. However, we should beware of higher measurement errors in counting
floods, which may attenuate the effects.
Tables 6 and 7 show some contrasting impacts of economic variables on split migration and
whole-household moving. Household size has significant but totally opposite effects on these two
forms of moving. Households with more members split and migrate more, but move out less as
a whole. Household asset also presents a contrasting picture. More assets of any type increases
split-migration to a new location, but reduces whole-household moving, which match the findings of
descriptive statistics in Table 2. These results suggest that households with more assets have greater
ability to support splits. However, most forms of assets, such as land and house are illiquid, and
accumulating assets makes the entire household more rooted in its village and less mobile to move
out. The contrasting effects also exist for earnings but the impact on whole-household migration is
not significant. Finally, remittances and government aid received have no significant effects on both
forms of migration.
I conduct a back-of-the-envelope calculation to measure the effects of disasters through eco-
19
nomic variables as different channels. From Tables 5 and 6, earthquakes decrease household size by
0.35, and an additional household member drives up splits across provinces by 0.023. Thus, earthquakes reduce cross-province splits by 0.0081 (0.35*0.023), which accounts for 15% of the decrease.
Using the same computation method, earthquakes lower earnings to decrease cross-province splits
by 0.0011, or 1.9%. For whole-household migration, eruptions increase farm assets by 55% and
consequently lower migration across provinces by 0.00020 (0.55*0.00036), or 2.1%.
We can also tell to what extent the suggested economic variables explain the negative impacts
of disasters. From Table 6, the coefficient on earthquakes for cross-province migration drops from
0.068 to 0.058, which amounts to a reduction of 15%. Thus, 15% of the negative impacts of
earthquakes is explained by economic variables. Similarly, economic variables explain 25% and 36%
of the decline in cross-district and cross-subdistrict splits, respectively. We use the same method
for whole-household migration. From Table 8, including economic variables explain 22% and 17%
of the cross-district and cross-subdistrict migration, respectively.
7
Robustness checks
The surveys are not conducted at a regular time interval and there is a seven-year gap between
the last two waves, IFLS3 (2000) and IFLS4 (2007). This time period is so long that the effects
of disasters in the previous period (1997-2000) may have substantially diminished well before 2007.
Furthermore, a huge tsunami happened in the province of Aceh in 2004, and resulted in massive
fatalities. Although the samples do not include any households from Aceh, the tsunami could have
forced Acehnese households to relocate to neighboring provinces, which may cloud the estimates.
To address this problem, I set a cut-off point in year 2004, and discarded all the households which
moved after 2004. Only households moving before 2004 are considered migrants, and the samples
are reduced to 7,843 households.
Table 8 shows the effects on split migration including the controls for economic variables. Most
of the negative coefficients still remain, but the magnitude drops. Earthquakes still reduce migration
rates, and the number of cross-province splits decreases by 9.1% for one more earthquake in every 10
years. More eruption also causes significantly less splits to districts and subdistricts. Furthermore,
20
the conclusions drawn in Section 6 still stand. The effects of earthquakes decrease after adding
economic variables. Also, the size of household and total earnings are still significant to raise
household splits, and the positive effects of household assets on splits are barely significant. Thus,
earthquakes lower household splits by reducing household size, earnings and assets, which is similar
to the findings in Table 6.
Table 9 shows the results for whole-household migration. The negative effects of eruption become
weaker and they are no longer significant, after controlling for economic variables. The coefficients
either become less negative or even positive. The previous reasoning still applies: more farm assets
lower whole-household moving, and eruptions increase farm assets, which leads to lower migration.
We can also observe, from Tables 8 and 9, some contrasting results with the revised samples,
similar to the findings from Tables 6 and 7. The size of household, on the one hand, increases
household splits, but on the other hand, lowers the migration of an entire household. More assets
of all types enhance the likelihood of household splits, but at the same time reduces the tendency
for an entire household to move.
All the above specifications use annual average number of disasters as the explanatory variables.
However, number by itself cannot fully capture the severity of disasters. A single massive deadly
catastrophe has far greater effects than a series of small-scale disasters of mild intensity. Hence, I use
alternative disaster measures in the specification, which include number of deaths, injuries, people
missing, financial loss and crop damage. Yet we should beware that those disaster measures are
subject to greater measurement errors, which can bias the estimated effects of disaster variables.
This suggests that number of disasters can be a better measure. The above alternative disaster
variables count the annual average of respective losses at the province level between time t − 1
and t. To simplify the exposition, I sum the losses from earthquakes, eruptions and floods as the
explanatory variables.
8
Table 10 shows consistent findings. Most of the significant effects are negative. Hence, greater the
severity of disasters, lower the tendency for household splits and migration of the entire households.
More deaths from disasters significantly reduce split migration across all geographic levels, even
8
Findings are quite robust when I separately consider the three kinds of disasters and the results are available upon
request.
21
though the effects on whole-household migration is not significant. The effects of financial losses
are more clear. Greater financial loss reduces both split and whole household migration, and the
negative effects of financial loss on split migration are significant for all three geographic levels of
moving. Regarding crop damage, the dataset just records the total farm loss due to floods. Table 10
shows the contrasting results on splits and whole household migration. Greater crop damage reduces
household ability to finance migration, but on the other hand, may push household members to leave
the family farmland and migrate to cities for non-agricultural employments, and consequently raise
split migration. The results using alternative disaster measure further support the finding that
disaster can reduce household migration through posing financial constraints or increasing farming
productivity.
7.1
Extension: Heterogeneous effects of disasters on household migration
The main analysis in Section 6 presents how disasters affect household migration on average.
Yet disasters can have heterogeneous impacts on household migration, depending on the household
economic variables at time t (Yit ).
To measure the heterogeneous effects of disasters, I add some interaction terms between disasters
and economic variables to equations (4) and (5).
Splitit = κ0 + κ1 Dct + κ2 Yit + κ3 Yit × Dct + ψi + νt + ωit
(7)
Allit = ϕ0 + ϕ1 Dct + ϕ2 Yit + ϕ3 Yit × Dct + χi + $t + ξit
(8)
The coefficients on the interaction term, κ3 and ϕ3 denote how disasters in the previous period
interact with economic variables at time t to affect household-migration decision in the next period. A positive coefficient suggests that after disasters, households with higher values of economic
variables are more likely to move out in the following period. To simplify the exposition, I sum
the number of earthquakes, eruptions and floods as the disaster measure to interact with various
household economic variables. I also consider total assets, adding up farm farm assets, nonfarm
22
business assets and non-business assets.
According to Table 11, there exist heterogeneous impacts of disaster for households with different
household sizes, amounts of government aid received, remittances and levels of total assets. For
household size, the effects on split across districts and subdistricts are significant, which suggests that
a bigger household is more likely to split and move in the following period after disasters. However,
the heterogeneous effects of aid received from government are totally different. Households, having
received more aid from the government, are less likely to move out in the following period. The effects
are significantly negative for both split and whole-household migration. These findings implies that
humanitarian aid, on the one hand, lessens the needs to find livelihoods elsewhere, but at the same
time, attracts households to stay in a disaster prone area, leading to an issue of moral hazard.
Similarly, more remittances received after disasters causes household to split and migrate less,
which implies more remittances lower household need for migration after disasters. However, such
negative effect is not observed for whole-household migration. Finally, for the heterogeneous effects
of earnings and total assets, the results are contrasting for split and whole-household migration.
Households with more earnings and assets split and move out more for all geographic levels of
migration even though the effects of assets are not significant. On the other hand, the heterogeneous
effects are negative on whole-household moving at all geographic levels. Households with more
earnings can better support splits but they have greater opportunity cost of moving out as a whole.
Households with more assets have higher costs of selling them off, which can also discourage wholehousehold migration.
8
Conclusion
This study examines whether natural disasters lead to more migration in Indonesia. It discovers
that more disasters result in less migration. The three most common kinds of disasters in Indonesia,
earthquakes, volcanic eruptions and floods, all lower household moving. I concentrate on household
migration, and separately consider split migration and whole-household migration. The results show
that disasters have negative impacts on both forms of migration. In particular, earthquakes cause
less household splits, and floods reduce whole-household migration. Eruptions decrease both forms
23
of migration.
The channels of impact for each kind of disaster are quite different. For split migration, earthquakes significantly reduce household size, total earnings and non-business assets. Smaller households are less likely to split, as are the households with less earnings and non-business assets,
which explains why earthquakes cause less split migration. For whole-household migration, eruptions increase the values of farm assets possibly because lava ash can raise farmland productivity.
Households with more farm assets are less likely to move out as a whole, which explains why eruptions lower whole-household migration. Finally, the reductions of whole-household migration due
to floods cannot be traced to household assets or earnings.
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 the fall in cross-province
splits, and account for 25% and 36% of the decline of cross-district and cross-subdistrict splits,
respectively. In the case of eruptions, economic variables explain 22% and 17% of the reduction in
cross-district and cross-subdistrict migration of an entire household, respectively.
This paper shows that the claim of more migration after natural disasters is not valid in Indonesia. Two important facts are ignored: (1) disasters can alter household economic well-being, which
may consequently lower household propensity to migrate as described in the study; and (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).
However, after adding economic variables to the regression, the negative coefficients still remain,
and the significance has not been fully eliminated. In terms of the negative effects of eruptions on
household splits, the magnitude of the coefficients even rises. Furthermore, none of the suggested
economic variables can explain how floods drive down whole-household migration.
Thus, the most plausible explanation is that the specification has not captured some other
variables which disasters also operate to affect migration. Since the regression has controlled for
time-invariant household fixed effects, those other possible variables should be time varying which
may include degree of risk aversion, health status of household heads, accumulation of social capital
and other social factors as described in Section 4. The negative causal relationship between disasters
24
and household migration warrants further research to better study how households in developing
countries determine migration decisions in the time of surging environmental calamities.
25
References
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Punjab, Pakistan,” Research Findings, International Potash Institute.
[2] Attzs, Marlene (2008), “Natural Disasters and Remittances: Exploring the Linkages between
Poverty, Gender, and Disaster Vulnerability in Caribbean SIDS,” WIDER Research Paper,
UNU-Wider.
[3] Cameron, Colin, Jonah Gelbach and Douglas Miller (2008), “Bootstrap-based Improvements
for Inference with Clustered Errors,” The Review of Economics and Statistics, Vol. 90.
[4] Cameron, Lisa and Manisha Shah (2010), “Risk Taking Behavior in the Wake of Natural
Disasters,” Working Paper.
[5] Cavallo, Eduardo and Ilan Noy (2009), “The Economics of Natural Disasters: A Survey,” IDB
Working Paper Series, No. IDB-WP-124.
[6] Cavallo, Eduardo, Sebastian Galiani, Ilan Noy and Juan Pantano (2010), “Catastrophic Natural
Disasters and Economic Growth,” IDB Working Paper Series, No. IDB-WP-183.
[7] DesInventar, http://dibi.bnpb.go.id/DesInventar/main.jsp?countrycode=id&lang=EN
[8] Ebeke, Christian and Jean-Louis Combes (2010), “Do remittances dampen the effect of natural
disasters on output growth volatility in developing countries?,” Working Paper Series, No.:
201031, CERDI
[9] Halliday, Timothy J. (2007), “Migration, Risk and the Intra-Household Allocation of Labor in
El Salvador,” Working Paper.
[10] Hsiao, Cheng (1986), “Analysis of Panel Data,” Econometric Society Monographs, Chapter 4.
[11] International Organization of Migration (2009), “Migration, Environment and Climate Change:
Assessing the evidence,” IOM publication.
26
[12] Naude, Wim (2008), “Conflict, Disasters, and No Jobs: Reasons for International Migration
from Sub-Saharan Africa,” Research Paper No. 2008/85, UNU-Wider.
[13] New York Times (Nov 02, 2010), http://www.nytimes.com/2010/11/02/world/asia/02indo.html
[14] Noy, Ilan and Tam-Bang Vu (2010), “The economics of natural disasters in a developing country: The case of Vietnam,” Working Paper series 200903, University of Hawaii at Manoa,
Department of Economics.
[15] Ó Gráda, Cormac (1997), “The Great Irish Famine : Winners and Losers,” No 97-23, Discussion
Papers from University of Copenhagen, Department of Economics.
[16] SEDAC - Gridded population of the World, http://sedac.ciesin.columbia.edu/
[17] UN International Strategy for Disaster Risk Reduction, (2007), “World experts unite to confront
growing risks of disasters,” Press release UN/ISR 2007/8, Geneva.
[18] US Federal Emergency Management Agency (2011), http://www.columbiamissourian.com/
stories / 2011 /01 /01 / 2010s-world-gone-wild-quakes-floods-blizzards/
[19] Wooldridge, Jeffrey (2001), “Econometric Analysis of Cross Section and Panel Data,” MIT
press.
[20] Yang, Dean (2008) “Risk, Migration, and Rural Financial Markets: Evidence from Earthquakes
in El Salvador”, Social Research Vol 75 : No 3.
27
Figure 1: Yearly occurrence of earthquakes and floods in Indonesia
number of floods
number of earthquakes
12
50
45
10
40
35
8
30
Eathquake
6
25
20
4
15
10
2
5
0
0
1950
1955
1960
1965
1970
1975
1980
Source: DesInventar Database
28
1985
1990
1995
2000
Flood
Figure 2: Spatial variation of number of earthquakes, 1988-2000
Figure 3: Spatial variation of number of eruptions, 1988-2000
Source: DesInventar Database
29
Figure 4: Spatial variation of number of floods, 1988-2000
Source: DesInventar Database
Figure 5: Population density of Indonesia in 2000
Source: Gridded Population of the World (GPWv3) – Socio-Economic Data and Application Center
30
Table 1: Descriptive Statistics of IFLS and DesInventar
Variable
Observation
Mean
Std. Dev.
Median
90-percentile
Earthquake
24651
0.099
0.18
0
0.33
Volcanic eruption
24651
0.20
0.52
0
0.5
Flood
24651
0.68
0.92
0.25
1.75
Disaster
24651
0.98
1.26
0.4
2.33
Migrate: province
24651
0.014
0.061
0
0
Migrate: district
24651
0.032
0.089
0
0.14
Migrate: subdistrict
24651
0.046
0.10
0
0.25
Whole move: province
24651
0.0022
0.023
0
0
Whole move: district
24651
0.0058
0.036
0
0
Whole move: subdistrict
24651
0.0096
0.046
0
0
Split move: province
24651
0.012
0.057
0
0
Split move: district
24651
0.027
0.084
0
0.14
Split move: subdistrict
24651
0.038
0.097
0
0.14
Notes: All The figures are annual statistics. The disaster variables, earthquake, eruption and flood, show
the annual average occurrence between 1988 and 2000. Migration statistics shows the annual rate of
migration across provinces, across districts and across sub-districts between 1993 and 2007. “Migrate:
province” is the migration rate across provinces combining both whole household (WH) migration and
split household (SH) migration. “Whole move: province” is the annual WH migration rate across provinces.
“Split move: province” is the corresponding statistics for SH migration.
31
Table 1 (Continued): Descriptive Statistics of IFLS and DesInventar
Variable
Observation
Mean
Std. Dev.
Household size
24651
5.50
2.57
Aid from government(‘000 rupiahs)
24651
360
18,000
Total earnings(‘000 rupiahs)
24651
2,288
14,600
Remittances(‘000 rupiahs)
24651
221
1,668
Farm asset(‘000 rupiahs)
24651
4,843
32,800
Nonfarm business asset(‘000 rupiahs)
24651
1,887
21,100
Non-business assets(‘000 rupiahs)
24651
18,300
60,900
Urban/Rural
24651
0.55
0.98
Education of household head
24651
1.85
1.16
Female headed
24651
0.15
0.36
Age of household head
24651
46.42
14.17
Extended family
24651
0.18
0.39
Notes: All measures are in real values. For Urban/Rural dummy, the value = 0 stands for urban household,
value=1 represents residence in rural area. Education head gives the education level of household head.
Female head shows whether the household is headed by a female.
32
Table 2: Comparing three sub-samples: 1. Households which do not move 2. Households split and migrate across provinces,
and, 3. Households which move across provinces as a whole between 2000 and 2007
Variable
Farm asset(‘000 rupiahs)
Nonfarm business asset(‘000 rupiahs)
Non-business asset(‘000 rupiahs)
Total household earnings(‘000 rupiahs)
household size
Number of households
No move at all
Mean
Split household migration across
provinces
Mean
T-test (1)
Whole household migration across
provinces
Mean
T-test (2)
11,000
(54,100)
3,800
(31,500)
34,400
(83,300)
3,000
(8,306)
5.42
(2.65)
7498
11,900
(36,100)
7,200
(46,800)
58,300
(143,000)
5,500
(2,400)
7.04
(2.74)
565
3,200
(17,600)
6,000
(55,800)
36,000
(90,100)
3,500
(6,644)
3.37
(2.34)
154
0.36
2.36***
6.16***
5.74***
14.01***
-1.79**
0.84
0.24
0.79
-9.50***
Notes: Standard errors in parentheses. All the measures are in real values. T-test (1): comparing the means between split-household migration and
those which do not move at all. T-test (2): compares the means between whole household migration and those which do not move at all.
33
Table 3: Baseline Results – Impacts of disasters on household migration in general (combining both split household (SH) and
whole household (WH) migration)
Dep. Variables
General household migration across
General household migration across
Province
District
Sub-district
Province
District
Sub-district
(1)
(2)
(3)
(4)
(5)
(6)
-0.0434*
-0.0207
0.000149
-0.0805***
-0.0821**
-0.0693*
(0.0245)
(0.0242)
(0.0382)
(0.0296)
(0.0312)
(0.0384)
-0.0237***
-0.0217***
-0.0304***
-0.0113
-0.0176**
-0.0242**
(0.00875)
(0.00537)
(0.00909)
(0.0101)
(0.00793)
(0.0104)
-0.0176***
-0.0137*
-0.0127
-0.0145**
-0.00726
-0.00807
(0.00612)
(0.00696)
(0.00990)
(0.00595)
(0.00647)
(0.00750)
Control for household
fixed effect
Observations
N
N
N
Y
Y
Y
24,651
24,651
24,651
24,651
24,651
24,651
R-squared
0.034
0.061
0.087
0.046
0.093
0.128
8,217
8,217
8,217
Earthquake
Eruption
Flood
Number of households
Notes: Robust standard errors in parentheses, adjusted for clustering at province-time level. All columns control for time dummies. Columns (1) – (3)
do not control for household fixed effects, columns (4) – (6) do. The dependent variables are the migration dummies between t and t+1 at various
geographic levels, which are province, district and subdistrict. Disaster variables measure the annual average number of disaster occurring at the
province between t-1 and t where household resides in. t=1993, 1997 and 2000. For t = 1993, t-1 =1998. For t =2000, t+1=2007.
(*** p<0.01, ** p<0.05, * p<0.1)
34
Table 4: Impacts of disasters on split-household (SH) migration and whole-household (WH) migration
Dep. Variables
Earthquake
Eruption
Flood
Observations
R-squared
Number of
households
Split household migration across
Whole household migration across
Province
(1)
District
(2)
Sub-district
(3)
Province
(4)
District
(5)
Sub-district
(6)
-0.0681**
(0.0277)
-0.0177
(0.0122)
-0.0120*
(0.00612)
-0.0823**
(0.0305)
-0.0199*
(0.0110)
-0.00494
(0.00834)
-0.0813**
(0.0330)
-0.0307**
(0.0126)
-0.00435
(0.00995)
-0.0208*
(0.0107)
0.000173
(0.00303)
-0.00684**
(0.00269)
-0.00898
(0.0148)
-0.00937**
(0.00359)
-0.00677**
(0.00276)
0.0182
(0.0243)
-0.0151**
(0.00631)
-0.0114***
(0.00393)
24,651
0.040
8,217
24,651
0.080
8,217
24,651
0.105
8,217
24,651
0.009
8,217
24,651
0.017
8,217
24,651
0.033
8,217
Notes: Robust standard errors in parentheses, adjusted for clustering at province-time level. All columns control for time dummies and household
fixed effects. The dependent variables of columns (1) to (3) measure household split, which count the number of new households formed by splitting
from the original households and then move to a new province, district and subdistrict between t and t+1. The dependent variables of columns (4) to
(6) are dummies indicating whether the entire household moves across provinces, districts and subdistricts between t and t+1.
(*** p<0.01, ** p<0.05, * p<0.1)
35
Table 5: Auxiliary regression - Impacts of disasters on household economic variables
Dep.
VARIABLES
Earthquake
Eruption
Flood
Household
size
Total
earning
Remittances
Aid money
House
Farmland
Farm asset
(7)
Nonbusiness
asset
(8)
(9)
Nonfarm
business
asset
(10)
(1)
(2)
(3)
(4)
(5)
-0.347***
(0.107)
0.0214
(0.0423)
0.0186
(0.0253)
-1.262***
(0.386)
-0.0716
(0.0774)
-0.0608
(0.0621)
0.310
(0.380)
0.484***
(0.162)
-0.0676
(0.109)
-0.0254
(0.257)
0.105
(0.106)
-0.0138
(0.0731)
-1.369***
(0.454)
0.324**
(0.126)
0.0984
(0.0747)
-0.517
(0.388)
0.246**
(0.117)
0.185*
(0.0935)
-0.689***
(0.223)
0.0571
(0.0508)
0.0354
(0.0274)
-0.169
(0.372)
0.554***
(0.120)
0.182
(0.120)
-0.430
(0.334)
0.149
(0.143)
-0.0753
(0.0751)
Observations 24,651
24,651
24,651
24,651
24,651
24,651
24,651
24,651
24,651
R-squared
0.055
0.055
0.030
0.099
0.045
0.024
0.279
0.035
0.043
Number of
8,217
8,217
8,217
8,217
8,217
8,217
8,217
8,217
8,217
households
Notes: Robust standard errors in parentheses, adjusted for clustering at province-time level. All columns control for time dummies and household
fixed effects. The dependent variables are different economic variables measured at time t. Total earnings, remittances and aid are the amounts
accumulated within one year before time t. Remittances are transfers from non-household family members. Aid money is the transfer received from
government, NGOs or some other parties. All the values except household size are transformed to log(1+Y). House is a category of non-business assets,
and farmland is a category of farm asset. Disaster variables measure the annual average number of disaster occurring at the province between t-1 and
t, where household resides in.
(*** p<0.01, ** p<0.05, * p<0.1)
36
Table 6: Impacts of disasters on Split Household (SH) migration with controls for
economic variables
Dep. Variables
Earthquake
Eruption
Flood
Split household migration across
Split household migration across
Province
District
Sub-district
Province
District
Sub-district
(1)
(2)
(3)
(4)
(5)
(6)
-0.0681**
-0.0823**
-0.0813**
-0.0581**
-0.0618**
-0.0513*
(0.0277)
(0.0305)
(0.0330)
(0.0255)
(0.0250)
(0.0279)
-0.0177
-0.0199*
-0.0307**
-0.0188
-0.0222**
-0.0343***
(0.0122)
(0.0110)
(0.0126)
(0.0114)
(0.0102)
(0.0109)
-0.0120*
-0.00494
-0.00435
-0.0123**
-0.00538
-0.00449
(0.00612)
(0.00834)
(0.00995)
(0.00574)
(0.00776)
(0.00928)
0.0233***
0.0383***
0.0611***
(0.00607)
(0.00624)
(0.0116)
0.000883**
0.00282***
0.00533***
(0.000407)
(0.000524)
(0.000770)
-0.000654
0.00104
0.00147
(0.000831)
(0.00113)
(0.00155)
0.00179*
0.00150
0.00190
(0.000900)
(0.00120)
(0.00170)
0.000221
0.00268*
-0.000477
(0.00118)
(0.00146)
(0.00148)
0.00102***
0.00101
0.00183**
(0.000347)
(0.000708)
(0.000819)
0.000942*
0.00113*
0.00239**
Household size
Total earnings
Remittances
Aid money
Non-business
asset
Farm asset
Nonfarm
business asset
(0.000527)
(0.000650)
(0.000892)
Observations
24,651
24,651
24,651
24,651
24,651
24,651
R-squared
0.040
0.080
0.105
0.071
0.132
0.182
Number of
8,217
8,217
8,217
8,217
8,217
8,217
households
Notes: Robust standard errors in parentheses, adjusted for clustering at province-time level. All columns
control for time dummies and household fixed effects. Dependent variables count the number of
households formed from split across provinces, districts and sub-districts. Disaster variables measure the
annual average number of disaster occurring at the province between t-1 and t where household resides
in. All other independent variables are measured at time t. Total earnings, remittances and aid money are
the amounts accumulated within one year before time t. All the values of economic variables except
household size are recorded in log(1+Y). Columns (1) – (3) do not control for economic variables and
columns (4) – (6) do. (*** p<0.01, ** p<0.05, * p<0.1)
37
Table 7: Impact of disasters on whole household (WH) migration with controls for
economic variables
Dep. Variable
Earthquake
Eruption
Flood
Whole household migration across
Whole household migration across
Province
District
Sub-district
Province
District
Sub-district
(1)
(2)
(3)
(4)
(5)
(6)
-0.0208*
-0.00898
0.0182
-0.0210**
-0.0113
0.0143
(0.0107)
(0.0148)
(0.0243)
(0.0104)
(0.0153)
(0.0252)
0.000173
-0.00937**
-0.0151**
0.00106
-0.00726*
-0.0124*
(0.00303)
(0.00359)
(0.00631)
(0.00298)
(0.00364)
(0.00659)
-0.00684**
-0.00677**
-0.0114***
-0.00713***
-0.00712***
-0.0114***
(0.00269)
(0.00276)
(0.00393)
(0.00262)
(0.00261)
(0.00376)
-0.00446***
-0.00952***
-0.0130***
(0.000704)
(0.00140)
(0.00187)
0.000189
-5.94e-05
-3.69e-05
(0.000159)
(0.000218)
(0.000269)
1.56e-05
-0.000123
0.000157
(0.000137)
(0.000261)
(0.000346)
0.000359
-0.000118
-0.000493
(0.000373)
(0.000579)
(0.000620)
-0.00135***
-0.00318***
-0.00461***
(0.000423)
(0.000634)
(0.000846)
-0.000362**
-0.00107***
-0.00194***
(0.000141)
(0.000223)
(0.000378)
-0.000458**
-0.00098***
-0.000546
Household
size
Total
earnings
Remittances
Aid money
Nonbusiness
asset
Farm asset
Nonfarm
business
asset
Observations
24,651
24,651
24,651
(0.000209)
24,651
(0.000269)
24,651
(0.000327)
24,651
R-squared
0.009
0.017
0.033
0.032
0.058
0.080
Number of
8,217
8,217
8,217
8,217
8,217
8,217
households
Notes: Robust standard errors in parentheses, adjusted for clustering at province-time level. All columns
control for time dummies and household fixed effects. Dependent variables are whole household
migration across provinces, districts and sub-districts. Disaster variables measure the annual average
number of disaster occurring at the province between t-1 and t where household resides in. All other
independent variables are measured at time t. Total earnings, remittances and aid are the amounts
accumulated within one year before time t. All the values of economic variables except household size are
recorded in log(1+Y). Columns (1) – (3) do not control for economic variables and columns (4) – (6) do.
(*** p<0.01, ** p<0.05, * p<0.1)
38
Robustness Checks
Table 8: Use 2004 as cut-off year – Impacts on split household migration
Dep. Variables
Earthquake
Eruption
Flood
Split household migration across
Split household migration across
Province
District
Province
District
Sub-district
(1)
(2)
Subdistrict
(3)
(4)
(5)
(6)
-0.052**
-0.054**
-0.056**
-0.0443**
-0.0409**
-0.0339
(0.0183)
(0.0190)
(0.0253)
(0.0167)
(0.0159)
(0.0250)
-0.011
-0.017**
-0.027*
-0.0115*
-0.0181***
-0.0284***
(0.00715)
(0.00664)
(0.0134)
(0.00559)
(0.00489)
(0.00965)
-0.0043
0.0024
0.0029
-0.00503
0.00156
0.00223
(0.00417)
(0.00486)
(0.0078)
(0.00384)
(0.00431)
(0.00727)
0.0213***
0.0334***
0.0576***
(0.00729)
(0.00640)
(0.0130)
0.000549
0.00225***
0.00473***
(0.000354)
(0.000494)
(0.000948)
-0.000871
0.000956
0.00140
(0.000969)
(0.00120)
(0.00209)
0.00141
0.00136
0.00169
(0.000858)
(0.000894)
(0.00180)
-0.000542
0.00207
-0.00124
(0.00160)
(0.00166)
(0.00188)
0.000964*
0.000614
0.00150
(0.000470)
(0.00105)
(0.00109)
0.000736
0.000711
0.00218*
(0.000670)
(0.000838)
(0.00115)
Household size
Total earnings
Remittances
Government aid
Non business asset
Farm asset
Nonfarm business
asset
Observations
23,529
23,529
23,529
23,529
23,529
23,529
R-squared
0.036
0.081
0.104
0.066
0.124
0.175
Number of
7,843
7,843
7,843
7,843
7,843
7,843
households
Notes: Robust standard errors in parentheses, adjusted for clustering at province-time level. All columns
control for time dummies and household fixed effects. Households migrated after 2004 are discarded
from the samples. Dependent variables count the number of households formed from split. Disaster
variables measure the annual average number of disaster occurring at the province between t-1 and t
where household resides in. All other independent variables are measured at time t. Total earnings,
remittances and aid are the amounts accumulated within one year before time t. All the values of
economic variables except household size are recorded in log(1+Y). Columns (1) – (3) do not control for
economic variables and columns (4) – (6) do.
(*** p<0.01, ** p<0.05, * p<0.1)
39
Table 9: Use 2004 as cut off year – Impacts on whole household migration
Dep. Variables
Earthquake
Eruption
Flood
whole household migration across
whole household migration across
Province
District
Province
District
Sub-district
(1)
(2)
Subdistrict
(3)
(4)
(5)
(6)
-0.019
-0.012
0.026
-0.0187
-0.0116
0.0231
(0.0133)
(0.0168)
(0.0314)
(0.0117)
(0.0160)
(0.0315)
0.0013
-0.0042
-0.015*
0.00193
-0.00257
-0.0131
(0.00200)
(0.00321)
(0.00755)
(0.00219)
(0.00371)
(0.00799)
-0.0058
-0.0064*
-0.0091*
-0.00618*
-0.00705**
-0.00844
(0.00347)
(0.00334)
(0.00514)
(0.00348)
(0.00322)
(0.00497)
-0.00407***
-0.00827***
-0.00968***
(0.000907)
(0.00203)
(0.00233)
0.000202
1.36e-05
-0.000139
(0.000206)
(0.000261)
(0.000265)
-6.38e-05
-0.000259
-0.000346
(9.82e-05)
(0.000220)
(0.000380)
0.000316
-8.88e-05
-0.00110
(0.000385)
(0.000641)
(0.000678)
-0.00144***
-0.00308***
-0.00415***
(0.000470)
(0.000651)
(0.000900)
-0.000182
-0.000786***
-0.00193***
(0.000124)
(0.000217)
(0.000372)
-0.000547***
-0.000985***
-0.000479
(0.000177)
(0.000274)
(0.000300)
Household size
Total earnings
Remittances
Government aid
Non business asset
Farm asset
Nonfarm business
asset
Observations
23,529
23,529
23,529
23,529
23,529
23,529
R-squared
0.006
0.009
0.053
0.027
0.046
0.093
Number of
7,843
7,843
7,843
7,843
7,843
7,843
households
Notes: Robust standard errors in parentheses, adjusted for clustering at province-time level. All columns
control for time dummies and household fixed effects. Household migrated after 2004 are discarded from
the samples. Dependent variables are whole-household migration. Disaster variables measure the annual
average number of disaster occurring at the province between t-1 and t where household resides in. All
other independent variables are measured at time t. Total earnings, remittances and aid are the amounts
accumulated within one year before time t. All the values of economic variables except household size are
recorded in log(1+Y). Columns (1) – (3) do not control for economic variables and columns (4) – (6) do.
(*** p<0.01, ** p<0.05, * p<0.1)
40
Table 10: Impacts of using alternative disaster measures on split migration and moving
of the entire household
Dep. Variables
Deaths
Injured
Missing
Financial loss
Crop damage
Observations
R-squared
Number of
household
Split household migration across
whole household migration across
Province
(1)
-0.000739**
(0.000278)
6.40e-05*
(3.54e-05)
-8.59e-05
(0.000628)
-0.0156***
(0.00318)
1.41e-06
(4.85e-06)
District
(2)
-0.000410*
(0.000233)
4.28e-05
(2.98e-05)
0.000200
(0.000541)
-0.0175***
(0.00393)
1.70e-05***
(6.07e-06)
Sub-district
(3)
-0.000466**
(0.000193)
9.91e-06
(2.62e-05)
-7.25e-05
(0.000607)
-0.0212***
(0.00560)
1.92e-05***
(6.49e-06)
Province
(4)
-0.000106
(6.74e-05)
1.09e-05
(7.05e-06)
8.30e-05
(0.000127)
-0.00247
(0.00175)
-3.97e-06
(2.98e-06)
District
(5)
0.000129
(0.000114)
3.88e-06
(1.85e-05)
-0.000541**
(0.000236)
-0.00582***
(0.00212)
-4.99e-06*
(2.85e-06)
Sub-district
(6)
0.000184
(0.000174)
1.14e-05
(2.80e-05)
-0.000604
(0.000369)
-0.00799*
(0.00403)
-7.38e-06**
(3.06e-06)
24,651
0.041
8,217
24,651
0.081
8,217
24,651
0.106
8,217
24,651
0.007
8,217
24,651
0.018
8,217
24,651
0.033
8,217
Notes: Robust standard errors in parentheses, adjusted for clustering at province-time level. All columns
control for time dummies and household fixed effects. Columns (1) – (3) are about split household
migration. Columns (4) – (6) are about whole household migration. Different disaster variables measure
various total human and economic losses due to earthquakes, eruption and floods at the province level
between time t-1 and t. Financial loss is recorded in log(1+Y).
(*** p<0.01, ** p<0.05, * p<0.1)
41
Table 11: Heterogeneous impacts of disasters on split migration and moving of the
entire household
Dep. Variables
Disaster
Household size
Earnings
Remittances
Aid money
Total asset
Disaster*hhsize
Disaster*aid
Disaster*earn
Disaster*remit
Disaster*asset
Observations
R-squared
Number of
household
Split household migration across
whole household migration across
Province
(1)
-0.0378**
(0.0160)
0.0241***
(0.00622)
0.000787*
(0.000407)
-0.000762
(0.000820)
-0.000118
(0.000975)
-0.000279
(0.00176)
-0.000912
(0.00181)
-0.00318**
(0.00138)
0.000232
(0.000231)
-0.000351
(0.000407)
0.00119
(0.000836)
District
(2)
-0.0508**
(0.0213)
0.0383***
(0.00676)
0.00261***
(0.000554)
0.000709
(0.00108)
-0.000748
(0.00130)
0.00221
(0.00208)
0.00670***
(0.00211)
-0.00323*
(0.00177)
0.000463
(0.000278)
-0.0021***
(0.000659)
0.00216
(0.00136)
Sub-district
(3)
-0.0543**
(0.0235)
0.0617***
(0.0124)
0.00492***
(0.000814)
0.00112
(0.00152)
-0.000340
(0.00197)
-0.000936
(0.00241)
0.00803**
(0.00370)
-0.00313
(0.00264)
0.000237
(0.000391)
-0.00214**
(0.000889)
0.00230
(0.00139)
Province
(4)
-0.00292
(0.00489)
-0.00461***
(0.000752)
0.000251
(0.000167)
1.61e-05
(0.000134)
-0.000122
(0.000429)
-0.00165**
(0.000651)
0.000292*
(0.000171)
-0.000872*
(0.000491)
-0.000205**
(9.50e-05)
0.000143**
(6.59e-05)
-0.000204
(0.000229)
District
(5)
0.0169**
(0.00649)
-0.00973***
(0.00143)
7.03e-05
(0.000209)
-0.000133
(0.000257)
-0.000373
(0.000604)
-0.00346***
(0.000786)
0.000540
(0.000548)
-0.000468
(0.000759)
-0.000273**
(0.000107)
0.000127
(0.000132)
-0.00149***
(0.000376)
Sub-district
(6)
0.0314**
(0.0120)
-0.0130***
(0.00186)
7.31e-05
(0.000270)
0.000179
(0.000346)
-0.000780
(0.000792)
-0.00456***
(0.00119)
8.47e-06
(0.000642)
-0.000521
(0.00104)
-0.000123
(0.000111)
0.000172
(0.000221)
-0.00258***
(0.000718)
24,651
0.072
8,217
24,651
0.138
8,217
24,651
0.188
8,217
24,651
0.032
8,217
24,651
0.059
8,217
24,651
0.080
8,217
Notes: Robust standard errors in parentheses, adjusted for clustering at province-time level. All columns
control for time dummies and household fixed effects. Columns (1) – (3) are about split household
migration. Columns (4) – (6) are about whole household migration. All the economic variables except
household size are recorded in log(1+Y).Disaster variable is the sum of earthquakes, eruptions and floods
between time t-1 and t. The interaction term interacts number of disasters with different economic
variables at time t.
(*** p<0.01, ** p<0.05, * p<0.1)
42