Do Natural Disasters Lead to More Migration? Evidence from
Indonesia∗
Chun-Wing Tse†
November 2011
Abstract
Using Indonesian panel datasets, I examine how earthquakes, volcanic eruptions and floods
affect household migration. The study separately analyzes the impact of these natural disasters
on the tendency of entire households to migrate, as well as for part of the household to split off
and migrate. Contrary to conventional wisdom, I find that all three types of disasters significantly
reduce migration rates. Nevertheless, the channels of impact are quite different. Earthquakes
reduce household size, 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, reduces
migration. Floods have no significant impacts on household assets or earnings, and their effect
remains unexplained.
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.
†
Department of Economics, Boston University, 270 Bay State Rd., Boston, MA 02215 ([email protected])
1
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 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 (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 drive toward 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 or deter prospective investors from investing in quake zones.
Having lost their livelihoods after natural disasters, households need to make a living elsewhere and
thus move out (IOM 2009).
Natural disasters, however, can actually lower migration. With their assets destroyed by disasters, households become financially constrained which discourages 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 finance migration (Yang 2008). They are not forced to migrate but
forced to stay. Disasters can also alter household asset composition to lower migration; eruptions
and floods enrich soil fertility with lava ash and alluvial deposits (Ahmad, 2011) respectively, which
causes households to be less willing to dispose all the farm assets and move out. Furthermore,
the marginal product of labor of some sectors can rise after disasters and lead to less migration.
Wages in the construction sector increase after earthquakes due to a higher labor demand for village
rebuilding. With respect to eruptions and floods, both make farmlands more fertile and hence drive
up agricultural wages. Finally, some other social factors such as stronger family ties and enhanced
community bonding after disasters can all lower migration.
2
Using Indonesia as the case country, this paper attempts to understand the link between natural
disasters and migration. The study relies on two nationally representative datasets. Given the
panel nature of the datasets, I am able to conduct a longitudinal study to account for household
fixed effects and measure how time variation of disasters alters migration at both the household and
individual level. Moreover, disasters of various types occur in Indonesia frequently and I do not treat
each type of disaster alike, but rather separately analyze the impacts of various types of disasters on
different geographic levels of moving. Specifically, this paper studies the three most common types
of disasters in Indonesia, earthquakes, volcanic eruptions and floods, to find out how these disasters
affect migration across provinces, districts and subdistricts. In particular, this paper concentrates
on household migration. I separately examine the effects of these disasters on migration of the entire
household and split-household migration, which is defined as part of the household splitting from
the original households to move to a new location. This study then examines the different economic
channels through which disasters operate to shape household-moving decisions.
The baseline empirical results show that all three types of disasters drive down migration at both
the household and individual level. Nevertheless, the channels of impact on household migration
are quite different. Eruptions push up the values of farm assets which can be due to enrichment of
soil fertility by lava ash. Households with greater farm business assets are also less likely to move
out. Hence, eruptions suppress household migration by increasing farm assets. On the other hand,
evidence shows that migration is less likely to occur if a household is smaller, has lower earnings or
non-business assets. Earthquakes decrease household size, earnings and non-business assets, which
explains why they lower migration rates. Floods, however, do not lead to reductions in household
assets or earnings and the negative impacts of floods on household migration remain unexplained.
The economics literature on the link between natural disasters and migration is relatively new.
Naude (2008) adopts a panel analysis at the country level and finds that environmental shocks drive
up migration through increasing 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 goes up after
massive disasters.
3
This study is most related to Yang’s (2008) paper, which is a panel study on El Salvador, examining how the massive one-off event of earthquakes in 2001 affects household migration. Specifically,
he highlights 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.
Hence, it becomes more difficult to borrow from other households to pay fixed migration costs as
most households in the villages are financially impaired, which actually drives down migration. This
finding is similar to the overall result of my paper. I investigate a wide range of natural disasters
and a broad range of channels of impact, such as household size, earnings, value of farmland and
non-business assets. The same outcome is obtained despite heterogeneous impacts of different types
of disasters on different kinds of assets.
Similar to Yang’s study, this paper also adopts 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 result in different migration decisions. Furthermore, I separately examine the impacts
of these natural disasters on split-household and whole-household migration, which are two contrasting decisions. 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 displacing the entire
household to a new location. Hence, the above facts point toward the need to treat different types of
disasters as heterogeneous shocks and to separately analyze various forms of household migration.
Finally, I do not just consider specific one-off events but account for time variation of disasters.
Given the fact that disasters of various types occur in some developing 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 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
4
main findings. Section 7 checks for robustness and Section 8 concludes.
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 its large scale deforestation. 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 risk of increasing
environmental calamities. Figure 5 depicts the population density of Indonesia in 2000, with most
dwellers crowded in Java and Sumatra where disasters of different types frequently occur. 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 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 and boosts the farm yield, which explains why people
5
settle and stay near volcanic areas.
3
Data and descriptive statistics
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 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 on 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 the new
location. On the other hand, if the whole 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, aid received, remittances, total household earnings and levels of different assets,
to study how natural disasters alter these variables to shape the two forms of household migration.
The second dataset is the Indonesian DesInventar Database (DesInventar) administered by Data
1
IFLS2+ was also carried out in 1998 to measure the impact of financial crisis starting from 1997. Yet only about
20% of the households of IFLS2 were re-interviewed in IFLS2+.
2
I also test whether there exists non-random attrition and the results are not sensitive to the treatment of households
which dropped out from the samples.
6
& Informasi Bencana Indonesia. The aim of DesInventar is 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 types of natural
disasters occurring 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 a 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 just retains households and their split-offs which exist in all four waves of the survey.
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.
Table 1 presents the descriptive statistics of the sample households. The disaster statistics record
the annual average number of each type of disaster at province level occurring between 1988 and
7
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 every three years. Table 1 also presents the migration figures between 1993
and 2007. I first consider the annual rate of migration in general, combining both split-household
migration and whole-household moving. On average, 1.4% of the households annually move across
provinces. The corresponding rates across kabupatens (districts) and kecamatans (subdistricts) are
respectively 3.2% and 4.6%, which are considerably high. Yet when we examine the two forms
of household moving separately, the statistics shows that most household migration is in the form
of splits. More than 3.8% 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 1 also shows that there is a
generally even proportion of urban and rural households. Most of the household heads have only
finished elementary education and about 15% of the households are headed by females.
Table 2 links household economic well-being in 2000, with household migration between 2000
and 2007. I separate the entire samples into three groups: (1) households with no migration, (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. Households which split 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 clearer picture. 50% of households which move out as a whole have non-business assets less
than 4.7 million rupiah. Yet the corresponding figure for split migrant households is 17 million
rupiah. In general, households which migrate as a whole have less farm and non-business assets
compared with the other two groups.
The above descriptive analysis portrays the disparity in asset composition between migrant and
non-migrant households, which illustrates how household asset composition links with migration.
Before presenting the empirical analysis, the paper first explains how disasters may drive down
migration. Since the empirical study emphasizes the disparity between split migration and wholehousehold migration, the following section also describes how the two forms of migration differ.
8
4
How natural disasters drive down household migration
Migration can increase with natural disasters because households want to stay away from the
risk of disasters or they need to make a living elsewhere if their livelihoods are eliminated. Yet
households exposed to natural disasters can actually be less likely to move out. There exist 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.
(1) Increase in marginal product of labor (MPL)
Natural disasters can cause recession, higher unemployment and lower wages. Yet the affected
areas with infrastructure and houses destroyed have a high demand for labor to rebuild villages.
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 in eruptions and alluvial deposits in floods, which increase the productivity of farming. Hence, households
may choose to stay.
(2) Decrease in financial resources to pay for migration
With assets destroyed and earnings reduced, 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. Thus, they are less
likely 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
9
remain to protect property and land rights.
This paper will empirically examine the first two reasons and leave out the third due to data
limitations.
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. Splithousehold migration differs from individual migration in various aspects: (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 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 the 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 at a certain geographic level. The insurance factor is much less significant
when the head of household decides to relocate the entire household. The decision is based on the
“push factors” of the origin and the “pull factors” of the destination, taking into account the total
migration cost.
This paper separately examines how disasters shape these two forms of household migration. The
rest of the subsection will discuss the following economic determinants of split and whole-household
migration: 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 diversifying risk. On the other
hand, a household with more members is less likely to move out as a whole because migration cost
10
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, considering splithousehold 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, higher earnings can drive down whole-household
migration because the opportunity cost of moving 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, 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. Government aid received, however, induces people
to stay in order to obtain more aid money, which points toward Samaritan’s Dilemma.
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 falls with the wealth of
the households. Greater assets of various kinds lower the risk and hence, drive up 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 sell and dispose of their assets to move out. Cost for whole-household
migration rises with the amount of assets.
The above discussion suggests how disasters operate through a variety of economic channels
to shape different household-migration decisions. The following sections empirically examine the
above claims.
5
Empirical Strategy
The empirical analysis first starts with equation (1):
Mit = α0 + α1 Dct + θi + ρt + it
(1)
The LHS variable Mit is the migration dummy indicating whether household i moves out at a
11
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.
The panel survey spans from 1993 to 2007, with a total of four waves. The regression specification
includes t =1993, 1997 and 2000. I take t − 1 =1988 for t =1993 and t + 1 =2007 when t =2000.
In Indonesia, earthquakes, eruptions and floods occur regularly in different provinces across
time. This environmental context provides a sufficient degree of dispersion for the RHS disaster
variable, Dct .
Equation (1) also controls for household fixed effect, θi and it captures idiosyncratic errors. ρt
denotes time dummies, which is essential because the panel dataset is unevenly spaced.
However, I first run a 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 correlation gives the causal impact
of disasters on household migration 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 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
effects, θi , in the equation.
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 = 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
12
by splitting and moving. In equation (3), Allit is a migration dummy, indicating 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, making up the first part of the analysis. The second part explains 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).
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 through which economic channels disasters operate to affect split migration.
We can also use the same approach to discover the channels for whole-household migration.
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 an entire household. 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 cross-household
analysis. The results show that the probability for households to move out goes down with more
disasters. Furthermore, floods significantly drive down household moving across provinces and
kabupatens (districts). The effect of eruptions on all three geographic levels of migration is negatively
significant at the 0.01 level. With respect to eruptions, the probability of moving to another province
13
falls by 0.024. Given the overall migration rate across provinces as 0.06, an additional eruption
annually drives down cross-province migration by 40%. By the similar token, one more flood each
year leads to a fall of 29% in cross-province migration. 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. Besides all
the coefficients being negative, the effect of earthquakes is much greater for all geographic levels of
migration. Time variation of all three types of disasters does significantly lower household migration.
Earthquakes cause cross-province migration to fall by 0.024, which amonts to 134%. Similarly,
an annual additional eruption and flood also reduce cross-province migration by 18% and 24%
respectively, even though the impacts of eruptions are not significant.
I now separately consider the two different forms of household moving. Columns (1) to (3) of
Table 4, list the results for split-household migration. The dependent variable counts the number
of new households formed by splitting and moving to a new location. Columns (7) to (9) list the
results for whole-household migration. The dependent variable is a dummy indicating whether the
entire household relocates to a new residence. The main analysis uses a count variable for splithousehold migration and dummy for whole-household migration. To enhance comparability, I also
include columns (4) to (6), which use a split-migration dummy as the dependent variable.
Table 4 shows a clear difference between the two forms of migration. 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 not statistically significant except for splits across provinces.
On whole-household migration, earthquakes do not significantly reduce the moving of the entire households, as shown in columns (7) to (9). These findings contrast with the results of split
migration. Yet eruptions cause an entire household to move out less. Cross-district migration falls
by 32%. Furthermore, household moving decreases significantly at all geographic levels when one
more flood occurs each year, with cross-province migration decreasing by 64%.
Table 4 shows that floods do not significantly reduce split-household migration, but reduces
14
whole-household migration at all geographic levels. Earthquakes lower splits at all levels but have
no effect on moving of the entire households. Eruptions cause both forms of household migration
to fall.
The negative impacts of disasters do not just apply to household moving, but also migration at
the individual level. From Table 5, all types of disasters decrease individual migration even though
the effects of floods are not significant. Earthquakes reduce cross-province migration by 121%, or
12% for an additional earthquake in every 10 years. Similarly, when one more eruption takes place,
cross-province migration decreases by 15%. Thus, the analysis on migration at both the household
and individual level 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 provide economic variables at the household
level rather than at the individual level.
To understand why disasters lower migration, it is necessary to first examine 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, ϑi , and time
dummies, νt .
Yit = λ0 + λ1 Dct + ϑi + νt + eit
(6)
Table 6 lists all the economic variables, which are measured in natural logs of real values except
household size. The stock variables include household size, non-business assets, farm assets and
nonfarm-business assets, which are recorded at time t. Non-business assets are further categorized
into land holdings, housing and financial assets. On the other hand, 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, which is
not feasible due to data limitations.
Table 6 shows that earthquakes significantly lower economic well-being on various measures. An
additional earthquake each year reduces household size by 0.35. Earthquakes also slash non-business
15
assets by 69%. Financial asset, a category of non-business asset, declines by 79% when one more
earthquake takes place annually. This implies that households may drain financial resources to cope
with earthquakes. Earthquakes damage housing assets, decreasing the values by 14% if one more
earthquake happens in every 10 years. Households also suffer from losses in farm and nonfarmbusiness assets but the effects are not significant. One more earthquake in every decade also lowers
total household earnings by 13%. One possible explanation is the worsening of macroeconomic
conditions or destruction of factories, which may reduce the employment prospects. On the other
hand, remittances and aid received do not go up significantly with more earthquakes.
While earthquakes have negative impacts on household economic status, eruptions increase
different measures of 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 the value of
farm assets. Eruptions also increase 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, households receive significantly more remittances with the rise of 48%. Such significant
increase, however, is not observed for earthquakes and floods. 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 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 to not receive more financial support, as nonhousehold members are also financially impaired.
To recap, earthquakes reduce non-business assets and specifically, the values of financial and
housing assets fall. Total household earnings and household size also decrease with more earthquakes. Eruptions raise farm assets and the amount of remittances received. Floods, in general, do
not affect any measure of household economic well-being. Given the results of Tables 4 and 6, we
can now explore the channels through which disasters operate to affect the two different forms of
household migration.
Table 7 presents the findings for split-household migration. I put the regression results without
16
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 geographic 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
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) are 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 42%. One percentage
increase in household earnings also raises the number of new household formed in a new province
by 0.00085, which amounts to an elasticity of 1.5%. We know from Table 6, that earthquakes
significantly reduce household size and total earnings. Combining these findings, I conclude that
earthquakes decrease household earnings and household size to drive down split migration.
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 increase split migration and that the coefficient of crosssubdistrict splits is even negative. However, I also consider farm and nonfarm business assets and
both types of business assets significantly raise split migration. As shown in table 6, earthquakes
lower the two types of business assets, although insignificantly. Thus, the results suggest that
earthquakes decrease split migration through reducing business and non-business assets.
Table 7 presents different findings for eruptions. All the negative signs remain and the coefficients
are even more negative after controlling for economic variables. Households with more farm assets
split more, and farm assets rise with eruptions, which explains why eruptions cause more household
splits and the coefficients on eruptions in columns (4) to (6) of Table 7 are even more negative.
Hence, I reject all the economic variables listed in Table 6, as the channels through which eruptions
operate to suppress household splits.
We now shift our focus to whole-household migration. Table 8 shows how disasters and economic
variables affect the moving of an entire household. We need only to consider the effects of eruptions
17
and floods because earthquakes are not significant in affecting whole-household migration. After
adding economic variables, there is a substantial drop in magnitude for the coefficients on eruptions.
Coefficients for cross-district moving falls from -0.0094 to -0.0073, and including controls completely
eliminates the significant impacts on migration across subdistricts. Households with more farm
assets are less mobile to move as a whole. The likelihood of moving to a new district drops by 4%
when farm business asset goes up by 1%. Eruptions raise the amount of farm assets as shown in
Table 6, which explains why eruptions drive down whole-household migration.
Table 8 shows that the magnitude and significance of coefficients on floods do not change substantially, which implies that the suggested economic variables are not the channels through which
floods operate to reduce migration. From Table 6, floods do not cause significant impacts on any
of the economic variables. Thus, I conclude that the reduction of whole-household moving because
of floods is not related to household asset or earnings.
Tables 7 and 8, together, show some contrasting impacts of economic variables on split migration
and moving of an entire household. The size of the household has totally opposite effects on these
two forms of moving. Households with more members split and migrate more, but are less likely to
move out as a whole. Similarly, more assets enable households to split and move to a new location,
but reduce migration of an entire household. 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, such as land and house, are illiquid, accumulating assets makes the entire household more
rooted in its village and less mobile to move out.
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
observe that earthquakes only reduce household splits, and floods have negative impacts only on
migration of an entire household. Eruptions drive down both forms of migration. For the channels of
impacts, earthquakes lower household splits through decreasing household size, household earnings
and non-business assets. On the other hand, eruptions increase farm assets and consequently make
households move out less as a whole. Reduction of whole-household migration due to floods is not
related to any of the suggested economic variables.
18
I conduct a simple back-of-the-envelope calculation to quantitatively assess the impacts of disasters on household migration through economic variables. From Tables 6 and 7, earthquakes decrease
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 assets by 55% and consequently drives down moving of an entire household across provinces by 0.00022 (0.55*0.00041), or
2.1%.
We can also tell to what extent the suggested economic variables explain the negative impacts
of disasters on household migration. From Table 7, the coefficient on earthquakes for cross-province
migration drops from 0.068 to 0.058, which is a 15% fall. Thus, 15% of the negative impacts of
earthquakes is explained by economic variables. Similarly, economic variables explain the 25% and
36% decline in cross-district and cross-subdistrict splits respectively. We use the same method
to explain the decrease in whole-household migration due to eruptions. According also to Table 8,
including economic variables explain 22% and 17% of the cross-district and cross-subdistrict moving
of the entire households, respectively.
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. For instance, the regression
of migration 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 checks 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 have only barely significant effects on split migration at the district level and wholehousehold moving at province level. Floods are just marginally significant in affecting cross-province
19
splits. Hence, the placebo test affirms the negative relationship between migration and disasters
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) and IFLS4 (2007). This time period is so 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, 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 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. With respect to 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 91%. An
additional eruption also causes significantly less splits to districts and subdistricts. Furthermore,
the conclusions drawn in Section 6 still stand. The coefficients on earthquakes fall in magnitude
after adding economic variables. Furthermore, the size of household and total earnings are still
significant to raise household splits. Household assets also 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.
Regarding 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
eruptions reduce the likelihood of migration by increasing farm assets.
Tables 10 and 11 show us some contrasting results which are also observed in Tables 7 and
8. The size of household, on the one hand, increases household splits, but on the other hand, it
suppresses the migration of an entire household. More assets of different kinds enhance the likelihood
of household splits, but at the same time lowers the likelihood for an entire household to move out.
All the above specifications use annual average number of disasters as the explanatory variables.
20
However, number by itself cannot fully gauge 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 other disaster measures 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 t − 1 and t. The list also includes the logged value of financial
losses and 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 raise split migration and whole-household moving, but more injuries from floods make an
entire household less likely to migrate. The number of missing people caused by floods is another
important factor for lowering both forms of household migration.
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 less likely to
relocate. Financial losses and crop damage by floods also lower split-household migration.
7.1
Extension: Heterogeneous effects of disasters on household migration
The main analysis in Section 6 tells us how disasters affect household migration on average. Yet
when disasters happen, different households can make different migration decisions, depending on
their economic status at time t (Yit ).
To empirically analyze the heterogeneous impacts 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 shape the household-migration decision in the next
21
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 findings. In general, the heterogeneous impacts are minimal and households
with different economic status do not have significantly different migration responses. There exist
negative significant impacts for eruptions interacting with household receipt of aid. 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 an entire household to move to a new
district falls by 0.0011. Furthermore, 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 out more, but are less likely to move out as a whole. Households can rely on non-business
assets to finance splits when floods occur. Yet non-business assets also act as a buffer against the
environmental shock, which lowers the need for an entire household to relocate.
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-being.
8
Conclusion
Using Indonesia as the case country, this study examines whether natural disasters lead to more
migration. It discovers that more disasters actually result in less migration. The three most common
types of disasters in Indonesia, earthquakes, volcanic eruptions and floods, all lower household and
individual migration. Regarding household migration, the paper separately considers split migration
and whole-household migration, and finds that disasters have negative impacts on both. Specifically,
earthquakes reduce migration primarily through suppressing household splits, and floods drive down
whole-household migration. Eruptions lower both forms of migration at all geographic levels.
The above results invalidate the claim that disasters cause more migration. The paper then
moves on to explain this negative relationship. For split migration, earthquakes significantly reduce
22
household size, total earnings and holding of 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 business assets possibly by enhancing soil fertility through lava ash. Evidence shows that
households with more farm assets are less mobile 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 crossprovince splits. The economic channels can also account for 25% and 36% of the decline of crossdistrict and cross-subdistrict splits, respectively. In case of eruptions, economic variables explain
22% and 17% of the reduction of 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 for Indonesia. The hypothesis of the claim ignores two important facts: (1) disasters can alter household
economic well-being, which may consequently lower people’s 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 in the regression, the negative coefficients still remain,
and the significance has not been fully eliminated. In terms of the effects of eruptions on household
splits, the magnitude of the coefficients goes up. Indonesian people develop their communities
near volcanic areas which may give rise to a positive correlation between eruptions and population
density. This reason, however, cannot explain the findings given the empirical specification of this
paper. The regression controls for household fixed effects, hence, the coefficients measure how the
variation of the eruptions across time alters the 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 plausible explanation is that the specification has not captured some other
23
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
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 and household migration warrants further research to better study how households in
developing countries determine migration decisions in our time of surging environmental calamities.
24
References
[1] Ahmad, Z. (2011), “Impact of Alluvial Deposits on Soil Fertility during the Floods of
2010 in 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, Lisa and Manisha Shah (2010), “Risk Taking Behavior in the Wake of Natural
Disasters”, Working Paper.
[4] Cavallo, Eduardo and Ilan Noy (2009), “The Economics of Natural Disasters: A Survey”, IDB Working Paper Series, No. IDB-WP-124.
[5] Cavallo, Eduardo, Sebastian Galiani, Ilan Noy and Juan Pantano (2010), “Catastrophic
Natural Disasters and Economic Growth”, IDB Working Paper Series, No. IDB-WP183.
[6] DesInventar, http://dibi.bnpb.go.id/DesInventar/main.jsp?countrycode=id&lang=EN
[7] 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
[8] Halliday, Timothy J. (2007), “Migration, Risk and the Intra-Household Allocation of
Labor in El Salvador”, Working Paper.
[9] Hsiao, Cheng (1986), “Analysis of Panel Data”, Second Edition, Chapter 4.
[10] International Organization of Migration (2009), “Migration, Environment and Climate
Change: Assessing the evidence”, IOM publication.
[11] Naude, Wim. (2008), “Conflict, Disasters, and No Jobs: Reasons for International
Migration from Sub-Saharan Africa”, Research Paper No. 2008/85, UNU-Wider.
25
[12] New York Times (Nov 02, 2010), http://www.nytimes.com/2010/11/02/world/asia/02indo.html
[13] 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.
[14] Ó Gráda, Cormac (1997), “The Great Irish Famine : Winners and Losers”, No 97-23,
Discussion Papers from University of Copenhagen, Department of Economics.
[15] SEDAC - Gridded population of the World, http://sedac.ciesin.columbia.edu/
[16] UN International Strategy for Disaster Risk Reduction, (2007), “World experts unite
to confront growing risks of disasters,” Press release UN/ISR 2007/8, Geneva.
[17] US
Federal
Emergency
Management
Agency
(2011),
http://www.columbiamissourian.com/stories/2011/01/01/2010s-world-gone-wildquakes-floods-blizzards/
[18] Wooldridge, Jeffrey (2001), “Econometric Analysis of Cross Section and Panel Data”,
MIT press.
[19] Yang, Dean (2008) “Risk, Migration, and Rural Financial Markets: Evidence from
Earthquakes in El Salvador”, Social Research Vol 75 : No 3.
26
Fig. 1: Yearly occurrence of earthquakes and floods in Indonesia
no. of earthquakes
no. of floods
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
27
1985
1990
1995
2000
Flood
Fig. 2: Spatial variation of number of earthquakes, 1988-2000
Fig. 3: Spatial variation of number of eruptions, 1988-2000
Source: DesInventar Database
28
Fig 4: Spatial variation of number of floods, 1988-2000
Source: DesInventar Database
Fig. 5 Population density of Indonesia in 2000
Source: Gridded Population of the World (GPWv3) – Socio-Economic Data and Application Center
29
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
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 rate of 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 annual average
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.
Variable
Observation
Mean
Std. Dev.
Median
75-percentile
Household size
24651
5.50
2.57
5
7
Aid from government
(000 rupiahs)
Total earnings
(000 rupiahs)
Remittances
(000 rupiahs)
Farm asset
(000 rupiahs)
Nonfarm business asset
(000 rupiahs)
Non-business assets
(000 rupiahs)
Urban/Rural
24651
360
18,000
0
0
24651
2,288
14,600
25
2,070
24651
221
1,668
0
0
24651
4,843
32,80
0
1,129
24651
1,887
21,100
0
40
24651
18,300
60,900
3,440
12,800
24651
0.55
0.98
1
1
Education head
24651
1.85
1.16
1
2
Female headed
24651
0.15
0.36
0
0
Age head
24651
46.42
14.17
45
57
Notes: All measures are in real values. For Urban/Rural dummy, the value = 0 stands for household residing in
urban area, 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.
30
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
Split household migration across
provinces
Whole household migration across
provinces
Mean
median
Mean
median
T-test (1)
Mean
median
T-test (2)
11,000
(54,100)
3,800
(31,500)
34,400
(83,300)
2,961
(8,306)
5.42
(2.65)
7498
0
11,900
(36,100)
7,186
(46,800)
58,300
(143,000)
5,525
(2,400)
7.04
(2.74)
565
0
0.36
0
-1.79**
0
2.36***
0
0.84
16,600
6.16***
4,675
0.24
900
5.74***
850
0.79
7
14.01***
3,197
(17,600)
6,019
(55,800)
36,000
(90,100)
3,495
(6,644)
3.37
(2.34)
154
3
-9.50***
0
11,700
400
5
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.
31
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)
-0.043
-0.021
0.00015
-0.081***
-0.082**
-0.069
(0.0297)
(0.0302)
(0.0425)
(0.0224)
(0.0328)
(0.0449)
-0.024***
-0.022***
-0.030***
-0.011
-0.018**
-0.024*
(0.00815)
(0.00475)
(0.00813)
(0.00845)
(0.00656)
(0.0122)
-0.018***
-0.014**
-0.013
-0.015**
-0.0073
-0.0081
(0.00591)
(0.00607)
(0.00868)
(0.00669)
(0.00624)
(0.00795)
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 happening 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)
32
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
Dependent variables as count variables
Split household migration across
Province
District
Sub-district
(1)
(2)
(3)
Dependent variables as dummy variables
Split household migration across
Whole household migration across
Province
District
Sub-district Province
District
Sub-district
(4)
(5)
(6)
(7)
(8)
(9)
-0.068***
(0.0215)
-0.018*
(0.00888)
-0.012*
(0.00620)
-0.082**
(0.0307)
-0.020**
(0.00851)
-0.0049
(0.00807)
-0.081**
(0.0344)
-0.031**
(0.0122)
-0.0044
(0.0103)
-0.061***
(0.0206)
-0.012
(0.00750)
-0.0079
(0.00485)
-0.073**
(0.0259)
-0.011*
(0.00578)
-0.00068
(0.00535)
-0.084**
(0.0300)
-0.015
(0.00835)
0.00099
(0.00562)
-0.021
(0.0145)
0.00017
(0.00215)
-0.0068*
(0.00371)
-0.0090
(0.0195)
-0.0094**
(0.00339)
-0.0068*
(0.00371)
0.018
(0.0309)
-0.015*
(0.00771)
-0.011**
(0.00525)
24,651
0.040
8,217
24,651
0.080
8,217
24,651
0.105
8,217
24,651
0.044
8,217
24,651
0.093
8,217
24,651
0.130
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 household involved in a split to a new province, district and subdistrict between t and t+1. The dependent variables of columns (7) to (9) are
dummies indicating whether the entire household has moved across provinces, districts and subdistricts between t and t+1.
(*** p<0.01, ** p<0.05, * p<0.1)
33
Table 5: Baseline Results – Impacts of disasters on individual migration
Dep. Variables
Province
(1)
Earthquake
Eruption
Flood
Female
Age
Education
Observations
R-squared
Number of households
Individual migration across
District
Sub-district
(2)
(3)
-0.031*
(0.0174)
-3.52e-05
(0.00278)
-0.0045
(0.00324)
-0.00055
(0.000800)
-0.00089***
(0.000172)
0.00039*
(0.000199)
-0.025
(0.0217)
-0.0098**
(0.00396)
-0.0048
(0.00334)
-0.0021**
(0.000889)
-0.0019***
(0.000159)
0.00047
(0.000328)
-0.015
(0.0327)
-0.018**
(0.00784)
-0.0073
(0.00521)
-0.0025**
(0.00116)
-0.0026***
(0.000205)
0.0011***
(0.000364)
55,647
0.014
9,990
55,647
0.026
9,990
55,647
0.039
9,990
Notes: The dependent variables are dummies indicating whether an individual from a household moves between t and t+1 at various geographic levels, which
are province, district and subdistrict. Robust standard errors in parentheses, adjusted for clustering at province-time level. All columns control for time
dummies and household fixed effects. Disaster variables measure the annual average number of disaster happening 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 6: Auxiliary regression - Impacts of disasters on household economic variables
Dep.
VARIABLES
Earthquake
Eruption
Flood
Household
size
Total
earning
Remittance
House
Land
Financial
asset
(3)
Aid from
government
(4)
(1)
(2)
-0.35***
(0.116)
0.021
(0.0563)
0.019
(0.0312)
-1.26**
(0.468)
-0.072
(0.0992)
-0.061
(0.0789)
Farm asset
Nonfarm
business asset
(7)
Non
business
asset
(8)
(5)
(6)
(9)
(10)
0.31
(0.382)
0.48***
(0.101)
-0.068
(0.0699)
-0.025
(0.290)
0.11
(0.137)
-0.014
(0.101)
-1.37**
(0.532)
0.32***
(0.0801)
0.098
(0.0766)
0.46
(1.470)
0.12
(0.316)
-0.072
(0.169)
-0.79***
(0.264)
0.13
(0.134)
0.053
(0.127)
-0.69**
(0.254)
0.057
(0.0332)
0.035
(0.0308)
-0.17
(0.449)
0.55***
(0.143)
0.18
(0.152)
-0.43
(0.423)
0.15
(0.174)
-0.075
(0.0717)
Observations
24,651
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
0.021
0.011
0.279
0.043
0.035
Number of
8,217
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 is the transfer from government, NGOs or some other parties. All the
values except household size are recorded in log(1+Y). House, land and financial asset are sub-categories of non-business assets. Disaster variables measure
the annual average number of disaster happening at the province between t-1 and t where household resides in.
(*** p<0.01, ** p<0.05, * p<0.1)
35
Table 7: Impacts of disasters on Split Household (SH) migration with controls for economic
variables
Dep. Variables
Earthquake
Eruption
Flood
Split household migration across
Province
District
(1)
Split household migration across
Province
District
Sub-district
(2)
Subdistrict
(3)
(4)
(5)
(6)
-0.068***
-0.082**
-0.081**
-0.058***
-0.062**
-0.052
(0.0215)
(0.0307)
(0.0344)
(0.0190)
(0.0248)
(0.0302)
-0.018*
-0.020**
-0.031**
-0.019**
-0.022**
-0.034***
(0.00888)
(0.00851)
(0.0122)
(0.00722)
(0.00821)
(0.00937)
-0.012*
-0.0049
-0.0044
-0.012**
-0.0055
-0.0047
(0.00620)
(0.00807)
(0.0103)
(0.00547)
(0.00654)
(0.00850)
0.024***
0.041***
0.065***
(0.00701)
(0.00614)
(0.0122)
0.00085*
0.0027***
0.0052***
(0.000453)
(0.000551)
(0.000957)
-0.00056
0.0013
0.0019
(0.000935)
(0.00127)
(0.00210)
0.0018*
0.0015
0.0020
(0.00101)
(0.00118)
(0.00195)
0.00029
0.0029
-0.00016
(0.00157)
(0.00184)
(0.00196)
0.0011**
0.0014
0.0023*
(0.000472)
(0.00114)
(0.00124)
0.00094
0.0011
0.0024*
(0.000672)
(0.000808)
(0.00113)
Household size
Total earnings
Remittances
Aid
Non-business asset
Farm asset
Nonfarm business asset
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 households
8,217
8,217
8,217
8,217
8,217
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. 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
happening 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)
36
Table 8: Impact of disasters on whole household (WH) migration with controls for economic
variables
Dep. Variables
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)
-0.021
-0.0090
0.018
-0.021
-0.011
0.015
(0.0145)
(0.0195)
(0.0309)
(0.0130)
(0.0188)
(0.0311)
0.00017
-0.0094**
-0.015*
0.0010
-0.0073*
-0.013
(0.00215)
(0.00339)
(0.00771)
(0.00239)
(0.00396)
(0.00853)
-0.0068*
-0.0068*
-0.011**
-0.0071*
-0.0071*
-0.011**
(0.00371)
(0.00371)
(0.00525)
(0.00373)
(0.00359)
(0.00507)
-0.0048***
-0.010***
-0.014***
(0.000947)
(0.00222)
(0.00286)
0.00020
-2.78e-05
-3.38e-06
(0.000194)
(0.000278)
(0.000366)
-2.13e-05
-0.00020
7.15e-05
(0.000124)
(0.000204)
(0.000381)
0.00036
-0.00013
-0.00050
(0.000472)
(0.000746)
(0.000808)
-0.0014**
-0.0033***
-0.0047***
(0.000507)
(0.000638)
(0.000775)
-0.00041**
-0.0012***
-0.0020***
(0.000159)
(0.000272)
(0.000392)
-0.00046**
-0.00097**
-0.00054
(0.000203)
(0.000337)
(0.000483)
Household size
Total earnings
Remittances
Aid
Nonbusiness asset
Farm asset
Nonfarm business asset
Observations
24,651
24,651
24,651
24,651
24,651
24,651
R-squared
0.009
0.017
0.033
0.032
0.058
0.080
Number of households
8,217
8,217
8,217
8,217
8,217
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. Dependent variables are whole household migration across
provinces, districts and sub-districts. Disaster variables measure the annual average number of disaster happening
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)
37
Robustness Checks
Table 9: Placebo Test – impacts on migration of natural disasters within placebo time interval
Dep. variables
Earthquake
Eruption
Flood
Split household migration
Whole household migration
Province
District
Sub-district
Province
District
Sub-district
(1)
(2)
(3)
(4)
(5)
(6)
0.028
(0.0227)
-0.025
(0.0205)
0.051*
(0.0285)
0.054*
(0.0257)
-0.0062
(0.0179)
0.030
(0.0286)
0.058
(0.0367)
-0.013
(0.0194)
0.0098
(0.0316)
-0.012**
(0.00571)
-0.0097
(0.00609)
0.0013
(0.00544)
-0.0094
(0.0135)
-0.0048
(0.00809)
-0.021
(0.0169)
-0.011
(0.0165)
-0.0058
(0.0109)
-0.037
(0.0307)
Observations
24,651
24,651
24,651
24,651
24,651
24,651
R-squared
0.039
0.079
0.104
0.006
0.016
0.031
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 of columns (1) to (3) count the number of
split-off households formed across provinces, districts and subdistricts. Dependent variables of columns (4) to (6)
are dummies of whole-household migration across provinces, districts and subdistricts. Disaster variables are the
annual average number of disasters measured in the placebo time frame. The placebo time frame for each
migration time period: (1) 1988-1993, placebo time frame:1974-1979; (2) 1993-1997, placebo time frame: 19791983; (3) 1997-2000, placebo time frame: 1983 – 1986.
(*** p<0.01, ** p<0.05, * p<0.1)
38
Table 10: Use 2004 as cut-off year – impacts on split household migration
Dep. Variables
Earthquake
Eruption
Flood
Split household migration across
Province
District
(1)
Split household migration across
Province
District
Sub-district
(2)
Subdistrict
(3)
(4)
(5)
(6)
-0.052**
-0.054**
-0.056**
-0.044**
-0.041**
-0.034
(0.0183)
(0.0190)
(0.0253)
(0.0167)
(0.0159)
(0.0250)
-0.011
-0.017**
-0.027*
-0.012*
-0.018***
-0.028***
(0.00715)
(0.00664)
(0.0134)
(0.00559)
(0.00489)
(0.00965)
-0.0043
0.0024
0.0029
-0.0050
0.0016
0.0022
(0.00417)
(0.00486)
(0.00782)
(0.00384)
(0.00431)
(0.00727)
0.021***
0.033***
0.058***
(0.00729)
(0.00640)
(0.0130)
0.00055
0.0023***
0.0047***
(0.000354)
(0.000494)
(0.000948)
-0.00087
0.00096
0.0014
(0.000969)
(0.00120)
(0.00209)
0.0014
0.0014
0.0017
(0.000858)
(0.000894)
(0.00180)
-0.00054
0.0021
-0.0012
(0.00160)
(0.00166)
(0.00188)
0.00096*
0.00061
0.0015
(0.000470)
(0.00105)
(0.00109)
0.00074
0.00071
0.0022*
(0.000670)
(0.000838)
(0.00115)
Household size
Total earnings
Remittances
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 households
7,843
7,843
7,843
7,843
7,843
7,843
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 across provinces, districts and subdistricts.
Disaster variables measure the annual average number of disaster happening 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 11: 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
Sub-district
Province
District
Sub-district
(1)
(2)
(3)
(4)
(5)
(6)
-0.019
-0.012
0.026
-0.019
-0.012
0.023
(0.0133)
(0.0168)
(0.0314)
(0.0117)
(0.0160)
(0.0315)
0.0013
-0.0042
-0.015*
0.0019
-0.0026
-0.013
(0.00200)
(0.00321)
(0.00755)
(0.00219)
(0.00371)
(0.00799)
-0.0058
-0.0064*
-0.0091*
-0.0062*
-0.0071**
-0.0084
(0.00347)
(0.00334)
(0.00514)
(0.00348)
(0.00322)
(0.00497)
-0.0041***
-0.00827***
-0.0097***
(0.000907)
(0.00203)
(0.00233)
0.00020
1.36e-05
-0.00014
(0.000206)
(0.000261)
(0.000265)
-6.38e-05
-0.00026
-0.00035
(9.82e-05)
(0.000220)
(0.000380)
0.00032
-8.88e-05
-0.0011
(0.000385)
(0.000641)
(0.000678)
-0.0014***
-0.0031***
-0.0042***
(0.000470)
(0.000651)
(0.000900)
-0.00018
-0.00079***
-0.0019***
(0.000124)
(0.000217)
(0.000372)
-0.00055***
-0.00099***
-0.00048
(0.000177)
(0.000274)
(0.000300)
Household size
Total earnings
Remittances
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 households
7,843
7,843
7,843
7,843
7,843
7,843
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 across provinces, districts and sub-districts. Disaster variables
measure the annual average number of disaster happening 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 12: Impacts of using alternative disaster measures on split migration and moving of the
entire household
Dep. Variables
Split household migration across
Province
District
Sub-district
(1)
(2)
(3)
Whole household migration across
Province
District
Sub-district
(4)
(5)
(6)
Death_earth
-0.13**
(0.0587)
-0.021
(0.0128)
0.024***
(0.00447)
0.016***
(0.00359)
-0.035
(0.0450)
-0.0043***
(0.00113)
-5.06e-05
-0.079
(0.0829)
-0.023
(0.0171)
0.022***
(0.00711)
0.0067
(0.00540)
-0.040
(0.0608)
-0.0039*
(0.00204)
-5.33e-05
-0.068
(0.0884)
-0.031
(0.0184)
0.014
(0.00944)
0.0045
(0.00841)
0.0036
(0.0670)
-0.0021
(0.00248)
-6.45e-05
0.027
(0.0735)
-0.0051
(0.00523)
0.0055**
(0.00210)
0.0020
(0.00188)
-0.12***
(0.0266)
-0.0016**
(0.000579)
-0.00012**
0.034
(0.0691)
-0.0012
(0.00555)
0.0068***
(0.00223)
-0.0023
(0.00273)
-0.13***
(0.0291)
-0.0027***
(0.000595)
-0.00012**
-0.037
(0.0982)
0.0037
(0.00685)
0.011**
(0.00415)
0.0025
(0.00676)
-0.12***
(0.0334)
-0.0031***
(0.00102)
-0.00012*
(4.66e-05)
0.00031
(5.77e-05)
0.00018
(5.64e-05)
0.00016
(5.30e-05)
0.00054
(4.98e-05)
0.00062*
(6.37e-05)
0.00099**
(0.000218)
-0.065***
(0.000318)
-0.058**
(0.000301)
-0.064*
(0.000343)
-0.014
(0.000318)
-0.013
(0.000437)
-0.049
(0.0186)
-0.00011**
(0.0228)
-5.42e-05
(0.0312)
-5.92e-05
(0.0151)
1.42e-05
(0.0154)
2.56e-05
(0.0306)
-1.59e-05
Death_erupt
Death_flood
Injured_earth
Injured_flood
Missing_flood
House_destroyed_
earth
House_destroyed_
flood
Financial
loss_flood
Crop
damage_flood
(4.02e-05)
(5.68e-05)
(5.97e-05)
(4.63e-05)
(4.29e-05)
(5.61e-05)
Observations
24,651
24,651
24,651
24,651
24,651
24,651
R-squared
0.051
0.084
0.109
0.048
0.036
0.046
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. Columns (1) – (3) are about split household migration. Columns (4)
– (6) are about whole household migration. Different disaster variables measure various areas of human and
economic losses. Financial loss is measured in log(1+Y)..
(*** p<0.01, ** p<0.05, * p<0.1)
41
Table 13: Heterogeneous impacts of disasters on split migration and moving of the entire
household
Dep. Variables
Split household migration across
Province
District
Sub-district
(1)
(2)
(3)
whole household migration across
Province
District
Sub-district
(4)
(5)
(6)
Earthquake
-0.053**
(0.0183)
-0.025**
(0.00937)
-0.017***
(0.00553)
0.024***
(0.00711)
0.00084*
(0.000419)
-0.00061
(0.000918)
4.24e-05
(0.000900)
0.00097
-0.062***
(0.0200)
-0.030**
(0.0108)
-0.013**
(0.00584)
0.040***
(0.00692)
0.0026***
(0.000561)
0.0011
(0.00129)
-0.0017
(0.00118)
0.0039**
-0.051*
(0.0276)
-0.047***
(0.0143)
-0.013
(0.00836)
0.064***
(0.0133)
0.0051***
(0.00102)
0.0016
(0.00207)
-0.0019
(0.00138)
0.00051
-0.018*
(0.00933)
0.00088
(0.00261)
-0.0080*
(0.00379)
-0.0048***
(0.00103)
0.00020
(0.000175)
-3.68e-05
(0.000124)
-0.00019
(0.000338)
-0.0014**
-0.0096
(0.0146)
-0.013**
(0.00458)
-0.0054
(0.00327)
-0.010***
(0.00216)
-1.89e-05
(0.000268)
-0.00020
(0.000190)
-0.00081*
(0.000395)
-0.0036***
0.015
(0.0249)
-0.018*
(0.0100)
-0.0086*
(0.00437)
-0.014***
(0.00271)
5.53e-06
(0.000359)
8.46e-05
(0.000382)
-0.0012**
(0.000557)
-0.0052***
(0.00119)
0.0012**
(0.000478)
0.00090
(0.00174)
0.0013
(0.00112)
0.00096
(0.00170)
0.0023*
(0.00123)
0.0023*
(0.000579)
-0.00044**
(0.000176)
-0.00046**
(0.000729)
-0.0011***
(0.000286)
-0.00096***
(0.000750)
-0.0019***
(0.000385)
-0.00053
(0.000712)
5.25e-05
(0.00813)
-0.0047**
(0.00164)
-0.00030
(0.00273)
0.0043
(0.00846)
-0.0020
(0.00308)
-0.0040**
(0.00163)
-0.0042
(0.00248)
0.0011***
(0.000364)
0.00019
(0.000363)
-0.0016
(0.00268)
0.00022
(0.000775)
0.016
(0.0180)
0.0062
(0.00429)
0.0056
(0.00372)
-0.0061
(0.0128)
-0.014**
(0.00563)
-0.0020
(0.00226)
-0.0032
(0.00301)
0.00030
(0.000544)
0.00079
(0.000697)
-0.0034
(0.00550)
-0.000911
(0.00114)
0.011
(0.0271)
0.0059
(0.00554)
0.0079
(0.00594)
-0.0027
(0.0164)
-0.019**
(0.00679)
-0.0022
(0.00267)
-0.0015
(0.00252)
0.00029
(0.000462)
0.00033
(0.000512)
-0.0015
(0.00865)
0.00067
(0.000212)
0.00020
(0.00139)
0.00078*
(0.000395)
0.00030
(0.000207)
0.00093
(0.00358)
-0.0018
(0.00170)
-0.00083*
(0.000448)
-0.00010
(0.000605)
5.94e-05
(0.000126)
-0.00030**
(0.000131)
-2.20e-05
(0.000505)
-5.48e-05
(0.000329)
-0.0043*
(0.00241)
-0.0014
(0.000873)
0.0015
(0.000938)
-0.0055
(0.00435)
-0.0044*
(0.00218)
0.00013
(0.000824)
-0.0021
(0.00124)
-0.00011
(0.000237)
-0.00020
(0.000151)
-0.00013
(0.000869)
4.32e-05
(0.000474)
-0.0038
(0.00552)
-0.0024*
(0.00128)
0.0012
(0.00128)
-0.011*
(0.00576)
-0.0046
(0.00295)
0.00046
(0.000973)
-0.0030
(0.00181)
5.94e-05
(0.000274)
2.48e-06
(0.000196)
-0.0023
(0.00231)
-0.00049
Eruption
Flood
household size
Total earning
Remittances
Aid
Non business
asset
Farm asset
Nonfarm
business asset
Earth*hh_size
Erupt*hh_size
Flood*hh_size
Earth*aid
Erupt*aid
Flood*aid
Earth*earning
Erupt*earning
Flood*earning
Earth*remit
Erupt*remit
42
Flood*remit
Earth*nonbiz
Erupt*nonbiz
Flood*nonbiz
Earth*farm
Erupt*farm
Flood*farm
Earth*nonfarm
Erupt*nonfarm
Flood*nonfarm
(0.000549)
-0.00035
(0.000770)
-0.0032
(0.00423)
-0.00092
(0.000938)
0.0033**
(0.00148)
-0.0025
(0.00190)
0.00052
(0.000476)
-0.00068*
(0.000345)
-0.0030*
(0.00162)
0.00033
(0.000430)
-7.33e-05
(0.000510)
(0.00159)
-0.0024*
(0.00125)
-0.0052
(0.0114)
-0.0026
(0.00182)
0.0058**
(0.00251)
4.45e-05
(0.00186)
0.00064
(0.000605)
-0.0013
(0.000756)
0.0012
(0.00313)
-0.00022
(0.000882)
0.00044
(0.000700)
(0.00150)
-0.0032**
(0.00126)
-0.00062
(0.0127)
-0.00018
(0.00193)
0.0041
(0.00285)
0.0012
(0.00268)
-0.00032
(0.000787)
-0.00091
(0.000696)
0.0024
(0.00438)
-0.00049
(0.00119)
-0.00034
(0.000887)
(0.000106)
0.00022*
(0.000113)
-0.00093
(0.00248)
-0.00035
(0.000970)
-0.00033
(0.000432)
0.0019**
(0.000832)
-0.00018
(0.000219)
0.00015
(0.000144)
0.00013
(0.000471)
-0.00021
(0.000121)
0.00014**
(6.45e-05)
(0.000144)
0.00021
(0.000227)
-0.0029
(0.00378)
0.00089
(0.000913)
-0.0019**
(0.000878)
0.0016*
(0.000821)
-0.00035
(0.000250)
-0.00018
(0.000236)
0.00016
(0.000759)
1.87e-05
(0.000210)
0.00018
(0.000247)
(0.000305)
0.00069*
(0.000368)
-0.0065
(0.00505)
0.00074
(0.000959)
-0.0029**
(0.00114)
0.0014
(0.00268)
-0.00094*
(0.000502)
-9.66e-06
(0.000456)
-0.0028**
(0.000962)
0.00045
(0.000278)
0.00034
(0.000316)
Observations
24,651
24,651
24,651
24,651
24,651
24,651
R-squared
0.074
0.136
0.185
0.034
0.060
0.082
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. Columns (1) – (3) are about split household migration. Columns (4)
– (6) are about whole household migration. All the economic variables except household size are measured in
log(1+Y).. The interaction terms interact number of disasters with different economic variables.
(*** p<0.01, ** p<0.05, * p<0.1)
43