natural disasters and migration indonesia

7/31/2019 Natural Disasters and Migration Indonesia

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Do Natural Disasters Really Lead to More Migration? Evidence

from Indonesia

Chun-Wing Tse

October 18, 2011

Abstract

Do natural disasters lead to more migration? Using panel datasets of Indonesia, I discover

that the three most common types of disasters, earthquakes, volcanic eruptions and floods, in

fact reduce the likelihood for households to move out, contrary to our intuitive understanding and

findings of existing research. Yet, household migration can take various forms and in this study,

I consider split household migration which is defined as households splitting off and moving to a

new area. Together with whole household migration, I analyze the impacts of the three types of

disasters on these two different forms of migration separately and also find that they all suppress

these two different forms of household migration. The paper goes on to explain the results and

discovers that: (1) Earthquakes reduce household size, total earnings and non-business assets.

(2) Households with smaller size, lower total earnings and less non-business assets are less likely

to split. Thus, I conclude that earthquakes reduce split household migration by decreasing the

values of those economic variables. Meanwhile, eruptions drive up farm business assets and

consequently lead to less moving of the entire households since greater holdings of farm business

assets makes households less mobile to move out as a whole. On the other hand, floods do not

operate through human and economic assets to drive down household migration. Thus, the paper

concludes that the intuitive view of more migration after natural disasters is not well founded.

Keywords: Indonesia, natural disasters, migration

JEL codes: O15, Q54

I would like to thank Dilip Mookherjee for all his guidance and support. I also wish to thank Daniele Pasermanand Michael Manove for their advice and comments. I am also grateful to Ye Li, Jie Hou, Julian Chan, Hyo-YounChu, Saori Chiba and seminar participants at Boston University. All errors are my own.

Department of Economics, Boston University, 270 Bay State Rd., Boston, MA 02215 ([email protected])

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1 Introduction

Given the rising losses from environmental calamities across the globe (Cameron 2010), the

study of natural disasters has never been more crucial at our time. In just the year of 2010,

natural disasters of various types have killed at least a quarter million people, which exceeds the

number of people killed in terrorist attacks in the past 40 years combined (U.S. Federal Emergency

Management Agency). The research on environmental risk is even more important in development

economics given the fact that poor households have limited resources to deal with natural disasters,

which are highly unpredictable and aggregate in nature (Noy 2010 & Ebeke 2010). Among all the

coping mechanisms, it has been claimed that poor households in developing countries resort to out-

migration to stay away from disaster prone areas (International Organization for Migration (IOM)

2009). There is an increasing concern that rising natural disasters can drive toward more migration.

E.g. soaring climate change exacerbates the problem of water shortage and agricultural failures.

Increasing seismic activities destroy industrial establishments or threaten prospective investors away

from quake zones. Households having lost their livelihoods after natural disasters need to make a

living elsewhere and move out (IOM 2009).

However, do natural disasters really lead to more migration? General intuition and existing

theoretical research seem to answer yes to this question (Eeckhoudt 1996). Yet, empirical studies

on this topic are still emerging (Paxson 2008, Drabo 2011). Using Indonesia as the case country,

this paper attempts to understand the link between natural disasters and migration by answering

the following two questions: Do natural disasters really make households move out more? Why

natural disasters drive toward such household migration decision?

This study relies on two nationally representative datasets of Indonesia. Using the panel nature

of the datasets, I can conduct a longitudinal study to account for household fixed effects and measure

how time variation of disasters alters household migration. Also, the annual occurrence of disasters

of various kinds in Indonesia provides a natural experiment for the study. Given such environmental

context, I do not treat each type of disaster alike, but separately analyze the impacts of various

types of disasters on different geographical levels of moving. Specifically, the paper studies the three

most common types of disasters, earthquakes, volcanic eruptions and floods, to find out how these

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disasters affect household migration across provinces, districts and subdistricts in Indonesia. After

that, the second part of the study looks at different economic channels through which disasters

operate to shape household moving decision.

Another feature of this paper is to focus on household migration instead of just looking at

individual moving decision. However, household migration can take various forms. The conventional

form is whole household migration which involves relocation of the entire households. In this paper,

I also consider split household migration which is defined as households splitting off and moving to

a new area.

Contrary to the general intuition and results of existing literature, the baseline empirical results

show that natural disasters in fact lead to less migration at both household and individual levels.

Such findings stand for all three types of natural disasters at all three geographical levels of moving.

However, individually looking at (1) split household (SH) migration and (2) whole household (WH)

migration gives different findings. For whole household migration, earthquakes do not have any

significant impact, but eruptions and floods drive down the likelihood for the entire households

to move out. On the other hand, earthquakes lower split migration at all geographical levels and

more eruptions also reduce splits. Yet, floods do not cause significant decrease in splits. Thus, we

can conclude that earthquakes reduce migration primarily by suppressing splits and floods drive

down whole household migration. Meanwhile, eruptions are significant in decreasing both forms of

household moving.

The first part of the main analysis invalidates the claim of more migration after natural dis-

asters. But, why there exists such negative causation? The second part shows that earthquakes

and eruptions operate through different economic channels to shape the two forms of household

migration. Eruptions push up the values of farm business assets which can be due to enrichment of

soil fertility by lava ash. Furthermore, I also find that households with more farm business assets in

fact move out less. Therefore, eruptions suppress migration of the entire households by increasing

farm business assets. On the other hand, earthquakes drive down the household size, total earn-

ings and non-business assets. Because households with smaller size, lower total earnings and less

non-business assets are less likely to split, earthquakes make households split and migrate less by

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slashing household size, total earnings and non-business assets. Meanwhile, the paper finds that

floods do not operate through human and economic assets to reduce whole household migration.

The study discovers that if natural disasters alter the economic status such that households become

less likely to move out, the environmental shock can in fact lower migration as a result.

The economics literature on natural disasters is relatively new. Most prominent studies on

this topic look at some one-off deadly disasters (Noy 2009, Halliday 2007), and link up the cross-

section disaster exposure with household migration pattern. Panel studies (Yang 2003) adopt the

approach of difference in difference to study migration patterns before and after disasters. Existing

research also concentrates on individual migration (Halliday 2007) and treats each kind of disaster

as an homogeneous shock (Naude 2008). To my knowledge, this paper is the first longitudinal

study to consider different disasters as heterogeneous shocks and focus on household migration.

Given the fact that disasters of various types occur in some developing countries regularly, e.g.

Philippines, Bangladesh and Pakistan, a longitudinal study of time variation of natural disasters is

important. Furthermore, natural disasters present an aggregate human and economic risk to the

entire households, which usually trigger migration at both household and individual levels. This

explain why we should also focus on household migration. Yet, household migration can take various

forms and household split is common given the family structure in most developing countries. Also,

different natural disasters have contrasting impacts on household splits and moving of the entire

households. All the above facts point toward the need to separately analyze different forms of

household migration.

The paper is organized as follows. Section 2 presents the background of Indonesia, illustrating

the demographics and disaster occurrence in the country. Section 3 outlines the data used and gives

some descriptive statistics. Section 4 discusses the relationship between disasters and household

migration. Section 5 describes the empirical strategy and section 6 presents the main findings.

Section 7 shows some robustness checks and section 8 gives the extension of the main analysis.

Section 9 concludes.

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2 Background

Indonesia is the most disaster-prone country of the world, according to the UN Office for the

Coordination of Humanitarian Affairs. Most parts of Indonesia are right on the fault line of volcanic

origin, which gives rise to frequent outbreaks of massive earthquakes and volcanic eruptions. The

country is also regularly hit by floods due to its large scale deforestation and soaring global climate

change. In 2009 alone, it experienced 469 earthquakes with a magnitude of 5 or higher. Sumatra,

Java and Papua were especially hard hit. Floods have accounted for about 40 percent of Indonesias

disasters in the past few years, according to government data (BPS Indonesia). Figure 1 shows the

time series pattern of earthquakes and floods and figures 2 to 4 provides geographical snapshots on

where earthquakes, eruptions and floods occur in the country between 1988 and 2000. Java and

Sumatra Islands have always been the black spots of disasters due to its location along the fault

line and the ever worsening problem of deforestation.

However, people do not stay away from disasters but continue to live with the risk of increasing

environmental calamities. Figure 5 depicts the population density of Indonesia in 2000 and most

dwellers are crowded in Java and Sumatra where disasters of different kinds frequently strike there.

E.g. West Javanese people need to face the regular occurrence of floods and earthquakes. The

volcanoes in Yogyakarta pose a constant threat to the inhabitants there, where the eruption in 2010

destroyed numerous villages and killed more than 390 people (New York Times 2010). However,

population density of West Java well surpasses 1000 per km square and Yogyakarta has more than

980 people per same size of area (SEDAC) in 2000. Using simple cross province regressions, the

results show that population density in 1993 is not negatively correlated with disasters within 50

years before 1993. This implies that people are not driven away by disasters, but stay with the

environmental risk instead.

It has always been claimed that communities in Indonesia stay nearby volcanic areas regardless

of the constant threat of eruptions. The regression of rice yield on eruptions at province level within

the last 50 years show that provinces with more eruptions can produce a higher rice yield. This is

plausibly due to the fact that lava ash from the volcanoes enhances the soil fertility which helps

boost the farm yield. As a result, people settle and stay nearby volcanic areas.

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3 Data and descriptive statistics

The paper uses two datasets for the empirical analysis. The first one is a panel dataset from

Indonesian Family Life Survey (IFLS), a nationally representative survey covering both rural and

urban areas. This dataset gives a nation-wide sample of households spreading across 13 provinces

in the first wave of the survey in 1993 (IFLS1) and three more waves were also conducted in 1997

(IFLS2), 2000 (IFLS3) and 2007 (IFLS4).1 One prominent feature of this longitudinal survey is

the very high tracking rate. The survey did not just attempt to re-interview original households

sampled in 1993, but also all the migrant households and those split-off from the original households.

In IFLS4, 93.6% of IFLS1 households were re-contacted and this rate is as high as or even higher

than most longitudinal surveys in the United States and Europe. High re-interview rates contribute

significantly to data quality because this lessens the attenuation bias due to nonrandom attrition,

which is a critical issue of concern for studies of migration and natural disasters.2

A dummy variable indicating whether a household migrates between two successive survey years

is the main outcome of interest in the empirical study. But first, we need a clear definition of house-

hold out-migration. In this paper, I define two forms of household migration: (1) split household

(SH) migration and (2) whole household (WH) migration. In split household migration, one or more

household members, but not including the head of household, leave and establish a new household

at a certain geographical level. On the other hand, if the whole household including the household

head moves to a new area, I call this form of moving as whole household migration.

Apart from a detailed section of household migration history, IFLS also asks several compre-

hensive sets of questions to obtain the economic variables of the sample households. In particular,

I focus on household size, aid received, remittances, total household earnings and level of different

assets to study how natural disasters alter these variables to shape the two forms of household

migration.

The second dataset is Indonesian DesInventar Database (DesInventar) administered by Data

1IFLS2+ was also carried out in 1998 to measure the impact of financial crisis starting from 1997. Yet, only about20% of the households in IFLS2 were re-interviewed in that wave.

2I also test whether there exists non-random attrition in the analysis and the results are not sensitive to thetreatment of households which dropped out from the samples.

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& Informasi Bencana Indonesia. The aim of DesInventar is to record every disaster happening in

Indonesia from early 20th century. The details include geographical location, date, fatalities, finan-

cial losses, infrastructure damage and other relevant information of the disasters. The study looks

at earthquakes, volcanic eruptions and floods which are the three most common forms of natural

disasters occurring in Indonesia. For the empirical analysis, the most important explanatory vari-

able is the average annual number of the three types of disasters happening between two successive

survey years at the province level.

DesInventar adopts a method of counting natural disasters different from the traditional prac-

tice. First, a disaster is defined as the set of effects caused by an event on human lives and

economic infrastructure on a geographical unit of minimum resolution. (DesInventar) It imposes

no thresholds on the amount of damage for an environmental shock to be regarded as a disaster.

Furthermore, instead of treating a single event of environmental shock as one disaster, DesInventar

counts the number of minimal geographical units, which is kecamatan (subdistrict) in the database,

affected in the event. Thus, DesInventar counts an earthquake event of extensive geographical

coverage as multiple earthquake disasters. Thus, this makes statistics kept by DesInventar look

inflated compared with statistics kept under the traditional practice. Yet, such method is desirable

for this study as disaster of extensive coverage should receive more weights. DesInventar defines

earthquakes, eruptions and floods as follows:

Earthquakes - All movements in the earths crust causing any type of damage or negative effect

on communities or properties.

Volcanic eruptions - eruptions with disastrous effects: eruption and emission of gas and ashes,

stone falls (pyroclast), flows of lava, etc.

Floods - Water that overflows river-bed levels (riverine floods) and runs slowly on small areas

or vast regions in usually long duration periods (one or more days).

The study just retains households and their split-offs which exist in all four waves of the survey.

Certainly, I can only keep households with clear migration history between 1993 and 2007. House-

holds without information on some economic variables such as household size, earnings and assets

can only be discarded. This finally leaves the study with 8,217 households.

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Table 2 presents the descriptive statistics of the IFLS sample households. The disaster statistics

records the annual average number of each type of disaster at province level happening between

1988 and 2000. Households on average experience 0.099 earthquakes and 0.20 eruptions annually

between 1988 and 2000. Floods are more prevalent in Indonesia and the sample households are

exposed to more than two floods in every three years. The descriptive statistics also presents

the migration figures between 1993 and 2007. We first look at the annual rate of migration in

general combining both split household migration and whole household moving. On average, 1.43%

of the households move across provinces annually. The corresponding rates across kabupatens

(districts) and kecamatans (subdistricts) are respectively 3.24% and 4.60%, which are considerably

high. Yet, when we consider the two forms of household moving separately, the statistics shows that

most household migration are in the form of splits. More then 3.75% of households have split-off

households located in a new province. On the contrary, whole household migration is much less

frequent. Annually, just less than 0.1% of households move to a new province as a whole on average.

Table 2 also shows that there is a generally even proportion of urban and rural households.

Most of the household heads just finish elementary education and about 15% of the households are

female headed.

Table 3 links household economic well beings in 2000 with household migration between 2000

and 2007. We separate the entire samples into three groups: (1) households which are completely

intact without involving in split or whole household moving, (2) households which split and migrate

across provinces between 2000 and 2007 but do not move out as a whole, (3) households which move

to a new province as a whole. The mean statistics tells us that households which split and move

generally have a bigger size with higher earnings and assets of various kinds. On the other hand,

households which migrate as a whole are smaller and have less non business assets. The median

figures illustrate a much more clear picture. 50% of households which move out as a whole have

non business assets less than 4.7 million rupiah. But, the corresponding figure of for split migrant

households is 16.6 million rupiah. In general, households which migrate as a whole have less farm

business and non business assets compared with the other two groups.

The above descriptive analysis portrays the disparity in asset composition between migrant and

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non-migrant households which illustrates how household asset composition links with migration.

Before moving to the empirical analysis, the paper first explains how disasters may drive down

migration. Since the empirical study emphasizes the disparity between split migration and whole

household migration, the following section also describes how the two forms of migration differ.

4 How natural disasters drive down household migration

It is intuitive to conclude that natural disasters lead to more migration since households want

to stay away from the risk of disasters or they should make a living elsewhere if their livelihoods

are wiped out. However, households exposed to natural disasters can actually move out less. There

exists three possible reasons: (1) increase in marginal product of labor, (2) decrease in financial

resources to pay for migration and (3) strengthened social bonding and mutual insurance, which

can drive to less migration after disasters.

(1) Increase in marginal product of labor (MPL)

Natural disasters can cause recession, higher unemployment and lower wages in general. Yet,

the affected areas with infrastructure and houses destroyed have a high demand for labor to rebuild

villages. The MPL of reconstruction sector can go up as a result, which induces households to stay

after disasters for a better employment. In particular for farming, soil fertility can be enriched by

lava ash in eruptions and alluvial deposits in floods. This drives up the productivity of farming and

hence, households will choose to stay instead.

(2) Decrease in financial resources to pay for migration

With assets destroyed and earnings reduced, households are less capable to afford migration.

Thus, they are not forced to migrate but forced to stay. Also, households find it more difficult to

borrow from others to finance migration as disasters present an aggregate shock and affect most

households living nearby (Yang 2003). Disasters pose liquidity constraints to households and as a

result drive down migration.

(3) Strengthened social bonding and mutual insurance

Disasters can boost family ties and strengthen social bonding, especially in developing countries

since social capital plays a significant role in less developed economies. Households choose to cope

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with disaster shock by accumulating social capital instead of moving out. Thus, they are less likely

to migrate. Also, households with houses damaged by disasters require members to stay nearby

to repair the houses. Furthermore, law and order may break down after disasters and households

should remain to protect property and land rights.

The paper will empirically examine the first two reasons and leave out the third due to data

limitation.

4.1 Split household migration and whole household migration

Split household migration is a rarely studied concept, which involves not just household splits

but the split-off households moving to a new area. A single individual leaving and setting up a

single-member household is also classified as split household migration in this study. Split household

migration differs from individual migration in various aspects: (1) In individual moving, the migrant

individuals may just move out and enter another household in a new area. (2) Individual migration

tends to be temporary and migrants may return after some time. (3) Individual migrants are in

general more attached to the original household. Meanwhile, split-off households are considered

separate from the original household.

Households may also consider split migration as an insurance strategy. Considering household

members, especially the young and educated groups, as human asset, the head of household can

diversify risk by spreading out the asset to various geographical areas. Certainly, the remittances

received from split-off households is an important source of income, which enables the original

household to better mitigate the risk of future economic shocks.

Whole household migration is a completely different concept, which is defined as the moving of

the entire household across a certain geographical level. The insurance factor is much less significant

when the head of household decides to move out as a whole. The decision is rather based on the push

factors of the origin and the pull factors of the destination, taking into account the total migration

cost.

This paper tries to focus on these two forms of household migration separately and understand

how natural disasters shape these two different moving decisions. The rest of the sub-section will

discuss the following economic determinants of split and whole household migration: household size,

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total earnings, external transfer and household assets.

Household size: A bigger household will be more likely to split and migrate as it has more human

asset to allocate to various geographical locations for the purpose of diversifying risk. On the other

hand, household with more members are less likely to move out as a whole because migration cost

goes up with the size of household.

Total earnings: Households with more earnings have higher likelihood to split and migrate

because they have more financial resources to support the splits. Furthermore, considering split

household migration as a risky investment, the risk of the investment decreases with the income of

the households. Thus, higher earnings lower the risk for split-off households to move out and make

an even higher income elsewhere. On the contrary, the total effects of earnings on whole household

migration can be ambiguous. Households with higher earnings are better endowed financially to

pay for migration. Yet, the opportunity cost increases with the current earnings.

External transfer: External transfer such as remittances and government aid, is a positive factor

for split migration. Similar to the theory related to household earnings, household with more

external transfer have more financial resources to pay for split migration. Yet, remittances can have

totally different effects from government aid on whole household migration. Households receiving

more remittances can better afford migration. However, the government aid induces people to stay

for obtaining more aid money, which points toward Samaritans Dilemma.

Household assets: Households with more assets, in particular farm business assets, should be

less likely to split and migrate as they need members to stay and take part in the agricultural

business. They also need more people to protect the property and avoid eviction from their lands.

Yet, households with more assets are in fact better endowed financially to support splits. Also

similar to the theory on total earnings, the risk of split household migration falls with the wealth

of the households. Greater holdings of assets of various kinds lowers the risk of split household

migration. Hence, the likelihood of split migration can go up with household assets. On the other

hand, households with more assets are less likely to migrate as a whole because it is costly for

households to sell and dispose their assets to move out. More assets mean higher cost of whole


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The above discussion suggests how disasters operate through a variety of economic channels to

shape different household migration decisions. The following sections empirically examine all the

above claims.

5 Empirical Strategy

The empirical analysis first starts with equation (1):

Mit = 0 + 1Dct + i + t + it (1)

The LHS variable Mit is the migration dummy indicating whether household i moves out at a

given geographical level between time t and t+ 1. The three different geographical levels are across

provinces, across kabupatens (districts) and across kecamatans (subdistricts). The most important

RHS variable is Dct, which uses the definition given by DesInventar to count the annual average

number of disasters happening in province c, where household i resides in between time t1 and t.

The panel survey spans from 1993 to 2007 with four waves altogether. The regression specification

includes t =1993, 1997 and 2000. I take t 1 =1988 for t =1993 and t + 1 =2007 when t =2000.

Earthquakes, eruptions and floods happen regularly in different provinces across time in Indone-

sia. Such environment acts as a natural experiment, which provides a sufficient degree of dispersion

for the RHS disaster variable, Dct.

Equation (1) also controls for household fixed effect, i and it captures idiosyncratic errors. t

denotes time dummies, which is essential because the panel data set is unevenly spaced.

Yet, I first run regression on equation (1) without including the household fixed effect and

conduct a simple OLS analysis. The OLS results tell us how the cross household variation of

disasters correlates with migration in the following period. Such analysis gives the causal impact

of disasters on household migration only when Dct is uncorrelated with the combined error term,

i + it. This assumption is arguably plausible given the random nature of disasters. However, it

could be possible that people with a high unobserved propensity to migrate tend to live in a disaster

prone province, which will render the coefficients from a simple cross-section regression biased. Thus

the paper takes advantage of the panel nature of the IFLS dataset and includes household fixed

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effects, i, in the equation. By controlling for household fixed effects, the coefficients on the disaster

variable, 1, can measure the causal impacts of disasters on migration in the next period.

Household migration, Mit, consists of split household migration and whole household moving.

Equations (2) and (3) give the regression specification respectively on these two different forms.

Splitit = 0 + 1Dct + i + t + eit (2)

Allit = 0 + 1Dct + i + t + it (3)

Splitit in equation (2) counts how many new households formed between time t and t + 1 by

splitting and moving. In equation (3), Allit is a migration dummy, representing whether the entire

household i migrates to a new location.

The above empirical analysis enables us to measure the total effects of disasters on these two

forms of migration, which makes up the first part of the analysis. The second part goes on to

explain through which channels disasters operate to bring about such effects. To do this, I modify

equations (2) and (3) to include controls for different economic variables, as shown in equations (4)

and (5).

Splitit =

0+

1Dct +

2Yit +

i+

t+ e

it(4)

Allit =

0 +

1Dct +

2Yit +

i +

t +

it (5)

Yit, consists of a list of economic variables of household i at time t. By comparing the coefficients

on disaster variables, Dct, in equations (2) and (4) and also the coefficients on economic variables,

Yit in equation (4), we can tell through which economic channels disasters work on to affect split

migration. We can use the same approach to find out the channels for whole household migration.

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6 Results

Table 3 presents the results of the linear probability model. The dependent variable is the

household migration dummy between time t and t + 1, combining both split household migration

and moving of the entire households. The explanatory variables are the annual average number of

earthquakes, eruptions and floods, happening between time t 1 and t. All specifications allow for

clustering of standard errors at the province-time level.

The first three columns do not control for household fixed effects, which give the analysis of

cross-household variation. Contrary to general intuition, probability for households to move out in

fact goes down with more disasters. Furthermore, floods significantly drive down household moving

across provinces and kabupatens (districts). The effect of eruptions on all three geographical levels

of migration is negatively significant at 0.01 level. When an additional eruption occurs annually,

the probability for households to move to another province falls by 0.0237. Given that the overall

migration rate across provinces is 0.06, eruptions drive down cross-province migration by 39.5%.

By the similar token, one more flood each year lowers cross-province migration by 29.3%. Yet, as

suggested in section 5, simple OLS cannot account for unobserved household migration propensity.

From now on, I control for household fixed effects in all specifications to address this possible

endogeneity.

In columns (4) to (6), the results present an even more negative impact of disasters. Apart from

all the coefficients being negative, the effect of earthquakes is much greater for all geographical levels

of migration. Time variation of all three types of disasters does significantly drive down household

migration. When an additional earthquake strikes annually in a province, households will be 0.024

less likely to migrate to another province in the next period. Translated into percentage terms,

the fall amounts to a whopping 134%. One more eruption and flood each year also push down

cross-province migration by respectively 18% and 24% even though the impacts of eruptions are

not significant.

We now separately consider the two different forms of household moving, split household and

whole household migration. Columns (1) to (3) of table 4 list the results for split household migra-

tion. The dependent variable counts the number of new households formed by splitting and moving

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to a new area. Meanwhile, columns (7) to (9) are about whole household migration. The dependent

variable is a dummy indicating whether the entire household relocates to a new residence. The

main analysis uses count variable for split household migration and dummy for whole household

migration. To enhance comparability, I also include columns (4) to (6) which use split migration

dummy as the dependent variable. The dummy denotes whether the household splits and migrates

to a new area.

Table 4 shows a clear difference between the two forms of migration. For split migration,

earthquakes are significant to drive down all geographical levels of migration. Splits to a new

province goes down by 0.0681, which is 120% in percentage terms. Eruptions also significantly

reduce splits at all geographical levels. Cross-province splits go down by 0.018 when one more

eruption happens annually, which is 31% in percentage terms. However, the effects of floods are not

statistically significant except for splits across provinces.

We now move to whole household migration. As shown in columns (7) to (9), earthquakes do

not significantly drive down whole household migration at any geographical level. These findings

contrast with the results of split migration. Yet, eruptions cause the entire households to move

out less. Cross-district migration goes down by 0.0094, which amounts to 32%. Meanwhile, all

geographical levels of household moving decrease significantly when one more flood occurs each

year. Cross-province migration drop by 0.0068, which is a considerable fall of 64%.

Table 4 shows that floods do not significantly reduce split household migration, but drives down

whole household migration at all geographical levels. On the other hand, earthquakes lower splits

at all levels but have no effects on moving of the entire households. Meanwhile, eruptions cause

both forms of household migration to fall. These contrasting findings tell us that it is crucial to

study each form of migration separately.

The negative impacts of disasters do not just apply to household moving, but also migration

at individual level. From table 5, all types of disasters drive down individual migration at every

geographical level even though the effects of floods are not significant. Earthquakes reduce cross-

province migration by 0.031. This is a large effect (121%), given that the mean of cross-province

migration is just 0.026. The negative impacts of eruptions are also considerable. When an additional

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eruption takes place, cross-province migrations falls by 0.0098, which is a decrease of 15.4%. Thus,

the analysis on migration at both household and individual levels shows that disasters make people

move out less. From now on, the paper will shift the focus back to household migration because

I will explain how disasters operate through economic variables to shape migration. The datasets

just provide economic variables at household level rather than individual level.

The results in tables 4 and 5 clearly invalidate the claim that natural disasters lead to more

migration. However, why is there a negative causation? To answer this question, it is necessary to

first understand how different disasters affect a variety of economic variables. This can be done by

running an auxiliary regression on the following equation. Regression on equation (6) tells us the

impacts of disasters on different economic variables of household i at time t controlling for household

fixed effects, i, and time dummies, t.

Yit = 0 + 1Dct + i + t + eit (6)

Table 6 shows the impacts of disasters on the economic variables. All the variables except house-

hold size are real values in natural logs. The stock variables include household size, non business

assets, farm business assets and nonfarm business assets, which are measured at time t. Non-

business assets are further categorized into land holdings, housing and financial assets. Meanwhile,the flow variables include total household earnings, remittances and financial aid received within

one year before time t. It would be ideal to have the average annual measures of flow variables

between time t 1 and t. But it is not feasible due to data limitations.

Table 6 indicates that earthquakes significantly drive down economic well beings on various

measures. An additional earthquake each year reduces household size by 0.347. Earthquakes also

slash non-business assets substantially by 68.9%. Financial asset, a category of non-business asset,

goes down by 79.4% when one more earthquake takes place annually. This implies that householdsmay drain financial resources to cope with earthquakes. As expected, earthquakes damage housing

assets, driving down the values by 13.7% if one more earthquake happens in every 10 years. House-

holds also suffer from losses in farm and nonfarm business assets but the effects are not significant.

Furthermore, earthquakes also lower total household earnings and the fall amounts to 126%. One

16


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possible explanation is the worsening of macroeconomic conditions or destruction of factories, which

may altogether reduce the employment prospects. On the other hand, remittances and aid received

do not go up significantly with more earthquakes.

While earthquakes give some negative impacts on household economic status, eruptions increase

different measures of economic variables. An additional eruption raises the amount of farm business

assets substantially by 55%. Lava ash in eruptions can highly enrich soil fertility which plausibly

increases the value of farm business assets. Interestingly, eruptions also increase housing assets. This

can be because relief money runs into affected areas for house rebuilding, which consequently helps

boost the housing market. Furthermore, households receive significantly more remittances with the

rise as 48.4%. However, such significant increase is not observed for earthquakes and floods. One

possible explanation is that the impacts of eruptions can be very limited geographically, confined to

the areas nearby volcanoes. Hence, most households in the province are largely unaffected and they

are still financially intact to remit money to affected households. However, the damage of floods and

earthquakes can be much more far reaching, adversely affecting most households in the province.

Earthquakes and floods may constitute aggregate shocks, causing households not to receive more

financial support as non-household members are also financially impaired.

To recap, earthquakes reduce non-business assets and in particular, the values of financial and

housing assets go down as a consequence. Total household earnings and household size also fall

with more earthquakes. Meanwhile, eruptions drive up farm business assets and the amount of

remittances received. Floods in general do not affect any measure of household economic well

beings. Given the results of tables 5 and 6, we can now explore the channels which disasters operate

to affect the two different forms of household migration. I will focus on split household and whole

household migration one by one.

Table 7 presents the findings for split household migration. I put the regression results without

controls and with controls for economic variables side by side. By including controls for economic

variables, the magnitude of coefficients on earthquakes has dropped for all three geographical levels

of moving. From column (1), earthquakes reduce household splits to a new province by 0.068 (120%

in percentage terms), but the magnitude falls to 0.058 (102%) after adding economic variables as

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shown in column (4). The drop in magnitude is even more noticeable for splits to a new kabupaten

(district). Furthermore, the coefficients on split migration to kecamatan (subdistrict) is no longer

significant after adding controls. This suggests that earthquakes operate through some of the

included economic variables to reduce split migration.

Table 7 also shows that household size and total earnings are significant positive factors for

split migration. An additional household member increases cross-province splits by 0.024 or 42.4%

in percentage terms. Also, one percent increase in household earnings raises the number of new

household formed in a new province by 0.00085, which amounts to an elasticity of 1.5%. We have

known from table 6 that earthquakes significantly reduce household size, total earnings. Combining

these findings, we can conclude that earthquakes slash household earnings and household size to

drive down split migration as a result.

However, the findings on non-business assets do not give us a clear conclusion. Table 7 tells us

that non-business assets do not significantly drive up split migration and the coefficient of cross-

subdistrict splits is even negative. Yet, we can also consider farm and nonfarm business assets and

both types of business assets significantly increase split migration. As shown in table 6, earth-

quakes lower the two types of business assets though insignificantly. Thus, the results suggest that

earthquakes decrease split migration through reducing business and non-business assets.

On the other hand, amount of remittance received is not a significant factor for split migration

at all three geographical levels and aid money from the government is just barely significant in

driving up splits across provinces. Hence, we can conclude that the effects of external transfer

are insignificant. Furthermore, earthquakes in fact do not significantly affect remittances and aid

received. We can discard these two variables which earthquakes operate to reduce split migration.

Yet, table 7 presents a completely different story for eruptions. All the negative signs just remain

and the coefficients are even more negative after controlling for economic variables. Households with

more farm business assets split more and farm business assets go up with eruptions. Hence, eruptions

cause more household splits as a result. This explains why the coefficients on eruptions in columns

(4) to (6) of table 7 are even more negative. Since the coefficients are even more negative after

including controls, we can reject all the economic variables listed in table 6 as the channels which

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eruptions work on to suppress household splits.

We now shift our focus to whole household migration. Table 8 shows how disasters and various

economic variables affect moving of the entire households. In particular, we just need to consider

the effects of eruptions and floods because earthquakes are not significant in affecting whole house-

hold migration. After adding economic variables, there is a substantial drop in magnitude for the

coefficients on eruptions. Coefficients for cross-district moving goes down from -0.0094 to -0.0073

and including controls even completely wipes out the significant impacts on migration across sub-

districts. This is because households with more farm business assets are less mobile to move as

a whole. The likelihood of migration to a new district falls 0.0012 when farm business asset goes

up by 1%, with the elasticity as 4.0%. Since eruptions raise the amount of farm business assets,

eruptions drive down whole household migration by increasing farm business assets.

Table 8 shows that the magnitude and significance of coefficients on floods do not change sub-

stantially which implies that the suggested economic variables are not the channels which floods

operate to reduce migration. From table 6, floods in fact do not cause significant impacts on any of

the economic variables, therefore the negative significance remains after including those variables

in the specification. We can conclude that floods do not alter human and economic assets to reduce

whole household moving.

Tables 7 and 8 together tell us some contrasting impacts of economic variables on split migration

and moving of the entire households. The size of household has totally opposite effects on these

two forms of moving. Households with more members split and migrate more , but less likely to

move out as a whole. Similarly, more assets enable households to split and move to a new location,

but drag down migration of the entire households. These results are in line with the discussion in

section 4. Households with more assets have greater ability to support splits. However, most forms

of assets, e.g. land and house are illiquid, accumulating assets in fact makes the entire households

more rooted in its village and less mobile to move out. More assets on the one hand increase

household splits but on the other makes the household less mobile to move out as a whole.

As a summary, when earthquakes, eruptions and floods occur, households move out less in the

following period. But after breaking down the analysis into two different forms of migration, we

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observe that earthquakes only reduce household splits and floods have negative impacts only on

migration of the entire households. Meanwhile, eruptions drive down both forms of migration.

Earthquakes decrease household splits through decreasing household size, household earnings and

non-business assets. On the other hand, eruptions increase farm business assets and consequently

make households move out less as a whole. Floods do not alter human and economic assets to lower

whole household migration.

To quantitatively assess the impacts of disasters on household migration through economic vari-

ables, we can conduct a simple back-of-the-envelope calculation. From tables 6 and 7, an additional

earthquake decreases household size by 0.35 and an additional household member drives up splits

across provinces by 0.024. Thus, earthquakes reduce cross-province splits by 0.0085 (0.35*0.024),

which amounts to 15%. Using the similar method, earthquakes lower earnings to decrease cross-

province splits by 0.0011 or 1.9%. For whole household migration, eruptions increase farm business

assets by 55.4% and consequently drives down moving of the entire households across provinces by

0.00022 (0.554*0.000405), or 2.1%.

We can also tell to what extent the economic variables explain the negative impacts of disasters

on household migration. From table 7, the coefficient on earthquakes for cross-province drop from

0.0681 to 0.0581, which is a 15% drop. Thus, 15% of the negative impacts of earthquakes is

explained by economic variables. Similarly, economic variables explain respectively 25% and 36%

the decrease in cross-district and cross-subdistrict splits. We use the same method to calculate how

much economic variables account for the fall in whole household migration due to eruptions. From

table 8, including economic variables respectively explain 22% and 17% for the cross-district and

cross-subdistrict moving of the entire households.

7 Robustness checks

First, to affirm the negative impacts of disasters on the two forms of migration, the study takes

a placebo test on the migration data. The analysis alters the time interval for the disaster variables.

Instead of using the yearly average number of disasters within the immediate last period, I push the

time period 14 years backward to set up a placebo time frame. E.g., for the regression of migration

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between 1997 and 2000, the time period for disaster variables is from 1983 to 1986. Hence, the

specification uses the number of disasters in the placebo time frame and check whether disasters in

that period have any effects on the two forms of migration.

Table 9 shows that the coefficients on disasters in the placebo time frame are mostly insignificant.

Earthquakes only have barely significant effects on split migration at district level and whole house-

hold moving at province level. Floods are just marginally significant in affecting cross-province

splits. Hence the placebo test affirms the negative relationship between disasters and migration

within the immediate last period.

The surveys are not conducted at a regular time interval and there is a seven-year gap between

the last two waves, IFLS3 (2000) & IFLS4 (2007). Such time period is too long that the effects

of disasters in the previous period (1997-2000) have substantially diminished well before 2007.

Furthermore, a huge tsunami happened in the province of Aceh in 2004, which resulted in massive

fatalities. Though the samples do not include any households from Aceh, tsunami can force Acehnese

households to relocate to neighboring provinces, which may cloud the estimates. To address this

problem, I set a cut-off point at year 2004 and discarded all the sample households which moved

after 2004. Only households moving before 2004 are considered migrants.

Tables 10 and 11 present the results of the revised specification. For split migration, most of

the negative coefficients still remain, but the magnitude and significance drop. Earthquakes still

primarily reduce split migration. The number of cross-province splits decrease by 0.052, or 90.9%.

An additional eruption also causes significantly less splits to district and subdistrict. Furthermore,

the conclusions drawn in section 6 still stand. For household splits, the coefficients on earthquakes

fall in magnitude after adding economic variables. Meanwhile, the size of household and total

earnings are still significant to drive up household splits. Also, household assets have marginally

significant impacts on increasing splits. Thus, earthquakes suppress household splits by reducing

household size, earnings and assets. Such results are similar to the findings in table 7.

For whole household migration, eruption is no longer a significant negative factor at all after

controlling for economic variables. The coefficients either become less negative or even positive.

Following the results that more farm assets lower whole household moving, we can conclude that

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eruptions reduce the likelihood of migration by increasing farm business assets.

Table 10 and 11 show us some contrasting results which we also observe in tables 7 and 8.

The size of household on the one hand increases household splits but on the other hand suppresses

the migration of the entire households. More assets of different kinds enhance the likelihood of

household splits, but at the same time lowers the geographical mobility for the entire households to

move.

The specification regresses migration decision between time t and t + 1 on disaster occurrence

from time t 1 to t. One may be concerned that some earlier disasters have driven out households

with high propensity of moving well before time t. E.g., the disasters in 1988 have prompted

households with high propensity of moving to migrate well before 1993. Thus, the households

left behind in 1993 are more inclined to stay. This may cause the negative relationship between

disasters and household migration. To test whether such bias exists, I shorten the time interval and

count disasters between time t and one year before time t. Shortening time interval eliminates the

possibility that households migrate after disasters and before time t. Table 12 presents the results

for revised specification and the negative impacts of disasters on household migration still remain.

The conclusions drawn from section 6 still stand.

All the above specifications use annual average number of disasters as the explanatory variables.

However, number by itself cannot fully gauge the severity of disasters. A single massive deadly

catastrophe has far much greater effects than a series of small scale disasters of mild intensity.

Hence, I use other disaster variables in the specification, which include number of deaths, injuries,

people missing and houses destroyed. These variables count the average annual number of respective

losses at the province level between time t1 and t. The list also includes the logged value of financial

losses and the tonnes of crop damage due to disasters in the last period.

Table 12 shows some mixed findings. On the front of human losses, earthquakes and eruptions

are just marginally significant to reduce the two forms of household migration. More deaths due to

floods in fact push up split migration and whole household moving, but more injuries from floods

make the entire households less likely to migrate. Number of missing people caused by floods is

another important factor lowering both forms of household migration.

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For economic losses, the effects of disasters on migration are mostly negative. Households

residing in the province with more houses destroyed by earthquakes are significantly less likely to

migrate. Similarly, when floods damage more houses in a province, households are also less likely

to relocate. Financial losses and crop damage from floods also lower the likelihood for households

to split and move.

8 Extension: Heterogeneous effects of disasters on household mi-

gration

The main analysis in section 6 tells us how disasters affect household migration in general. How-

ever, when disasters happen, different households can take different migration decisions, depending

on their economic well beings at time t (Yit). Before examining empirically the heterogeneous im-

pacts, I first give a brief discussion on why different households may respond differently to disasters.

A bigger household is more likely to split and move out after disasters. The environmental shock

acts as a trigger and households with more members have greater propensity to move out in the

following period. On the contrary, whole household migration is less likely for a bigger household

as the migration cost increases with its size.

Households receiving more external transfer and having higher income should tend to move out

less whether in the form of split or whole household moving. They are more financially endowed to

cope with the shock of disasters with less needs to find a living elsewhere.

On the other hand, households with more assets are more capable to support split moving.

Thus, disasters act as a trigger for households to split and migrate. Yet, there should be less whole

household migration since households with more assets need to incur a higher cost to dispose assets

and move out altogether. Also, assets act as a strong buffer against adverse impacts of disasters. In

particular, eruptions increase the value of farmland and households with more farm assets are less

likely to move out as a whole after eruptions.

To empirically analyze the heterogeneous impacts of disasters, I add some interaction terms

between disasters and economic variables to equations (4) and (5).

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Splitit = 0 + 1Dct + 2Yit + 3Yit Dct + i + t + it (7)

Allit = 0 + 1Dct + 2Yit + 3Yit Dct + i + t + it (8)

The coefficients on the interaction term, 3 and 3 denote how disasters in the previous period

interact with economic variables at time t to shape the household migration decision in the next

period. A positive significant coefficient implies that households with higher values of economic

variables are more likely to move out in the following period after disasters. The disaster and

economic variables in the interaction terms are first grand-mean centered such that the results are

comparable to the main results in tables 7 and 8.

Table 13 presents the results on equations (7) and (8). In general the heterogeneous impacts are

minimal and households with different economic status do not have significantly different migration

responses. We consider each of the economic variables individually. For the size of household, a

bigger household will not be more likely to split or move out as a whole given the mean number

of earthquakes in the last period. Floods give some similar findings and the coefficients on the

interaction terms are not significant at all three geographical levels. Eruptions interacting with

household size have negative impacts which are just barely significant. Also the sign flips from

one geographical level of migration to another. Thus, we can conclude that disasters do not have

heterogeneous impacts on households of different sizes.

For receipt of aid, the interaction terms with earthquakes and floods do not give any significant

impacts on household migration. However, eruptions have heterogeneous impacts on households

receiving different amount of aid and the effects are significantly negative. Given the average number

of eruptions, split migration to a new district goes down by 0.0034 with a percentage increase of

aid received. On the other hand, the probability for the entire households to move to a new district

falls by 0.0011.

The rest of the two flow variables, household earnings and remittances received also do not give

rise to any heterogeneous impacts. Households with more earnings or more remittances do not react

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differently in migration in the next period.

For asset variables, the picture is not much different and most of the coefficients on interaction

terms are not statistically significant. However, floods interacting with non-business assets lead to

contrasting impacts on the two forms of migration. Households with more non-business assets will

split and move to a new province and district more. On the contrary, households with more non-

business assets will be less likely to move out as a whole. Households with more non-business assets

are more capable to support splits and floods trigger households to split more. Yet, non-business

assets also act as a buffer against the environmental shock.

However, the above mentioned effects are just barely significant statistically and we can conclude

that disasters do not cause substantially different responses in migration for households with different

levels of economic well beings.

9 Conclusion

Using Indonesia as the case country, the study examines whether natural disasters will lead to

more migration. It discovers that increasing disasters in fact cause households to move out less. The

three most common types of disasters in Indonesia, earthquakes, eruptions and floods, all lead to less

household and individual migration. For household migration, the paper separately considers split

migration and whole household migration. The study shows that disasters have negative impacts

on both. In particular, earthquakes reduce migration primarily through suppressing household

split and floods drive down whole household migration. Meanwhile, eruptions lower both forms of

migration at all geographical levels.

The above analysis enables us to invalidate the claim of more migration after disasters. The paper

then moves on to explain why there exists such negative causation. For split migration, earthquakes

suppress household splits through a variety of economic channels. Earthquakes significantly slash

household size, total earnings and holding of non-business assets. Since smaller households are less

likely to split, so do the households with less earnings and non-business assets, earthquakes cause

less split migration by decreasing the values of those economic variables.

For whole household moving, eruptions increase the values of farm business assets possibly by

25


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enhancing soil fertility through lava ash. Households with more farm assets are less mobile to move

out as a whole. Thus, eruptions lower whole household migration by driving up values of farm

business assets. Finally, floods do not operate through human and economic assets to reduce whole


I also quantitatively assess the explanatory power of the economic variables for the negative

impacts of disasters. For earthquakes, the economic variables explain 15% of cross-province splits.

The economic channels can also account for 25% and 35% for the fall of cross-district and cross-

subdistrict splits repetitively. Meanwhile, for eruptions, economic variables respectively explain

22% and 17% of cross-district and cross-subdistrict moving of the entire households.

This paper shows that the usual claim of more migration after natural disasters is not valid for

Indonesia. The hypothesis in fact ignores two important facts: (1) Disasters can alter household

economic well-beings, which may consequently lower their propensity to migrate as described in

the study. (2) Given the regular occurrence of disasters, households may resort to a variety of

adaptation mechanisms instead of simply moving out of disaster prone areas (IOM 2009). The

adaptation behaviors of households facing increasing natural disasters are not covered in this paper.

The makes a promising avenue of research to further examine the theory of disasters and migration.

Yet, even after adding economic variables in the regression, the negative coefficients still remain

and the significance has not been fully wiped out. For the effects of eruptions on household splits, the

magnitude of the coefficients even goes up. Indonesian communities develop their heritage nearby

volcanic areas which may give rise to positive correlation between eruptions and population density.

But this reason cannot explain the findings on the empirical specification of this paper. Because

the regression controls for household fixed effects, the coefficients measure how the variation of the

eruptions across time alters household migration pattern. Increasing eruptions should not induce

households to stay. Furthermore, none of the suggested economic variables can explain how floods

drive down whole household migration.

Thus the most possible explanation is that the specification has not fully captured some other

variables through which disasters operate to affect migration. Since the regression has controlled

for time invariant household fixed effects, those other possible variables should be time varying

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which may include but not limited to degree of risk aversion, health status of household heads,

accumulation of social capital and other sociological factors as described in section 4. The strong

negative causal relationship of disasters on household migration warrants further research to better

study how households in developing countries determine migration decisions at our time of surging

environmental calamities.

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References

[1] Ahmad, Z. (2011), Impact of Alluvial Deposits on Soil Fertility during the Floods of

2010 in Punjab, Pakistan

[2] Arellano, Manuel; Bond, Stephen (1991), Some Tests of Specification for Panel Data:

Monte Carlo Evidence and an Application to Employment Equations

[3] Attzs, Marlene (2008), Natural Disasters and Remittances: Exploring the Linkages

between Poverty, Gender, and Disaster Vulnerability in Caribbean SIDS

[4] Blaikie, Piers (1994), At Risk: Natural Hazards, Peoples Vulnerability and Disasters

[5] Cameron, Lisa; Shah, Manisha (2010), Risk Taking Behavior in the Wake of Natural

Disasters

[6] Cavallo, Eduardo; Noy, Ilan (2009), The Economics of Natural Disasters: A Survey

[7] Cavallo, Eduardo; Galiani, Sebastian; Noy, Ilan & Pantano, Juan (2010),Catastrophic

Natural Disasters and Economic Growth

[8] Drabo, Alassane (2011), Climate Change, Natural Disasters and Migration: An Em-

pirical Analysis in Developing Countries

[9] Ebeke, Christian; Combes, Jean-Louis (2010), Do remittances dampen the effect of

natural disasters on output growth volatility in developing countries?

[10] Halliday, Timothy J. (2007), Migration, Risk and the Intra-Household Allocation of

Labor in El Salvador

[11] Hsiao, Cheng (1986), Analysis of Panel Data

[12] International Organization of Migration (2009), Migration, Environment and Climate

Change: Assessing the evidence

[13] Naude, Wim. (2008), Conflict, Disasters, and No Jobs: Reasons for International

Migration from Sub-Saharan Africa

[14] New York Times (Nov 02, 2010), http://www.nytimes.com/2010/11/02/world/asia/02indo.ht

[15] Noy, Ilan; Vu, Tam Bang (2010), The economics of natural disasters in a developing

country: The case of Vietnam

[16] O Grada, Cormac (1997), The Great Irish Famine : Winners and Losers

28


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[17] Paxson, Christina; Cecilia Elena Rouse (2008), Returning to New Orleans after Hur-

ricane Katrina

[18] SEDAC - Gridded population of the World, http://sedac.ciesin.columbia.edu/

[19] Skidmore, Mark; Toya, Hideki (2005), Economic Development and the Impacts of

Natural Disasters

[20] UN International Strategy for Disaster Risk Reduction, (2007), World experts unite

to confront growing risks of disasters, Press release UN/ISR 2007/8, Geneva

[21] US Federal Emergency Management Agency (2011),

http://www.columbiamissourian.com/stories/2011/01/01/2010s-world-gone-wild-

quakes-floods-blizzards/

[22] Wooldridge, Jeffrey (2001), Econometric Analysis of Cross Section and Panel Data

[23] Yamamura, Eiji (2011), Institution, economic development, and impact of natural

disasters

[24] Yang, Dean (2008) Risk, Migration, and Rural Financial Markets: Evidence from

Earthquakes in El Salvador

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30

Fig.1:YearlyoccurrenceofearthquakeandfloodinIndonesia

Source:DesInventarDatabase

0

2

4

6

8

10

12

1950 1955 1960 1965 1970 1975 1980 1985 1990 199

no.ofearthquake


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Fig.2:Sp

Fig.3:Sp

Source:De

atialvariati

atialvariati

sInventarDat

onofnumb

onofnumb

base

rofearthq

roferuptio

31

ake,1988

n,198820

000

0


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Fig4:Sp

S

Fig.5Po

tialvariati

urce:DesInve

ulationde

Sourc

nofnumbe

ntarDatabase

sityofIndo

:GriddedPop

rofflood,1

esiain200

ulationofthe

32

9882000

0

World(GPWv )SocioEco omicDataan Application enter


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33

Table1:DescriptiveStatisticsofIFLSandDesInventar

Variable Obs Mean Std.Dev. Median 90 percentile

Earthquake 24651 0.099 0.176 0 0.333

Volcaniceruption 24651 0.195 0.524 0 0.5

Flood

24651

0.680

0.924 0.25 1.75Move_prov 24651 0.0142 0.0609 0 0

Move_kabu 24651 0.0324 0.0889 0 0.143

Move_kec 24651 0.0460 0.103 0 0.25

All_move_prov 24651 0.00218 0.0225 0 0

All_move_kabu 24651 0.00582 0.0362 0 0

All_move_kec 24651 0.00963 0.0457 0 0

Split_move_prov 24651 0.0122 0.0573 0 0

Split_move_kabu 24651 0.0271 0.0835 0 0.143

Split_move_kec 24651 0.0375 0.0966 0 0.143

Notes:All

The

figures

are

annual

statistics.

The

disaster

variables,

earthquake,

eruption

and

flood,

show

the

annual

averagerateofoccurrencebetween1988and2000. Migrationstatisticsshowstheannualrateofmigrationacross

provinces(prov),acrossdistricts(kabu)andacrosssubdistricts(kec)between1993and2007.Move_provisthe

annualaveragemigrationrateacrossprovincescombiningbothwholehousehold(WH)migrationandsplit

household(SH)migration.Allmove_provistheannualWHmigrationrateacrossprovinces.Split_move_provisthe

correspondingstatisticsforSHmigration.

Variable Obs Mean Std.Dev. median 75percentile

HHsize 24651 5.50 2.57 5 7

Aidfromgovt

(000rupiahs)

24651 360 18,000 0 0

Totalearnings

(000rupiahs)24651

2,288

14,600 25 2,070

Remittances

(000rupiahs)

24651 221 1,668 0 0

Farmbizassets

(000rupiahs)

24651 4,843 32,80 0 1,129

Nonfarmbiz

asset

(000rupiahs)

24651 1,887 21,100 0 40

Nonbizassets

(000rupiahs)

24651 18,300 60,900 3,440 12,800

Urban/rural

24651

0.55

0.98 1 1

Educationhead 24651 1.85 1.16 1 2

Femalehead 24651 0.15 0.36 0 0

Agehead 24651 46.42 14.17 45 57

Notes:Allmeasuresareinrealvalues.ForUrban/ruraldummy,thevalue=0standsforhouseholdresidingin

urbanarea,value=1representsresidenceinruralarea.Educationheadgivestheeducationlevelofhousehold

head.Femaleheadshowswhetherthehouseholdisfemaleheaded.


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34

Table2:Comparingthreegroupsofsamples:1.Householdswhichdonotmove,2.Householdssplitand

3.Householdswhichmoveacrossprovincesasawhole,between2000and2007

Variable Nomoveatall Splithousehold migrationacross

provinces

Wholehousehold migrationacross

provinces

Mean

median Mean median T test (1) Mean median T

test (2

Farmbizasset

(000rupiahs)

11,000

(54,100)

0 11,900

(36,100)

0 0.36 3,197

(17,600)

0 1.79**

Nonfarmbizasset

(000rupiahs)

3,800

(31,500)

0 7,186

(46,800)

0 2.36*** 6,019

(55,800)

0 0.84

Nonbizasset

(000rupiahs)

34,400

(83,300)

11,700 58,300

(143,000)

16,600 6.16*** 36,000

(90,100)

4,675 0.24

TotalHHearnings

(000rupiahs)

2,961

(8,306)

400 5,525

(2,400)

900 5.74*** 3,495

(6,644)

850 0.79

HHsize 5.42

(2.65)

5 7.04

(2.74)

7 14.01*** 3.37

(2.34)

3 9.50**

N

7498

565 154

Notes:Standarderrorsinparentheses.Allthemeasuresareinrealvalues. Ttest(1):comparingthemeansbetweenSHmig

moveatall.Ttest(2):comparethemeansbetweenWHmigrationandthosewhichdonotmoveatall.


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35

Table3:BaselineResultsImpactsofdisastersonhouseholdmigrationingeneral(combiningbothsplit

household(WH)migration)

DepVariables Generalhouseholdmigrationacross General householdmigrationac

Province District Subdistrict Province District Sub

(1)

(2) (3) (4) (5) (6)

Earthquake 0.0434 0.0207 0.000149 0.0805*** 0.0821** 0.0

(0.0297) (0.0302) (0.0425) (0.0224) (0.0328) (0.0

Eruption 0.0237*** 0.0217*** 0.0304*** 0.0113 0.0176** 0.0

(0.00815) (0.00475) (0.00813) (0.00845) (0.00656) (0.0

Flood 0.0176*** 0.0137** 0.0127 0.0145** 0.00726 0.0

(0.00591) (0.00607) (0.00868) (0.00669) (0.00624) (0.0

ControlforHHfixed

effect

N N N Y Y Y

Observations 24,651 24,651 24,651 24,651 24,651 24,

Rsquared 0.034 0.061 0.087 0.046 0.093 0.1

Numberofhhid 8,217 8,217 8,2

Notes:Robuststandarderrorsinparentheses,adjustedforclusteringatprovincetimelevel.Allcolumnscontrolfortimedu

controlforHHfixedeffects,columns(4)(6)do.Thedependentvariablesarethemigrationdummiesbetweentandt+1at

areprovince,districtandsubdistrict.Disastervariablesmeasuretheannualaveragenumberofdisasterhappeningatthepr

householdresidesin.t=1993,1997and2000.Fort=1993,t1=1998.Fort=2000,t+1=2007.

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Table4:BaselineResultsImpactsofdisastersonindividualmigration

DepVariables Individualmigrationacross

Province District Subdistrict

(1) (2) (3)

Earthquake 0.0310* 0.0251 0.0146

(0.0174) (0.0217) (0.0327)

Eruption 3.52e05 0.00984** 0.0175**

(0.00278) (0.00396) (0.00784)

Flood 0.00447 0.00477 0.00733

(0.00324) (0.00334) (0.00521)

sex 0.000549 0.00211** 0.00254**

(0.000800) (0.000889) (0.00116)

age 0.000888*** 0.00189*** 0.00261***

(0.000172) (0.000159) (0.000205)

grade

0.000393* 0.000467 0.00110***

(0.000199) (0.000328) (0.000364)

Observations 55,647 55,647 55,647

Rsquared 0.014 0.026 0.039

Numberofhhid 9,990 9,990 9,990


Thedependentvariablesareindividualmigrationdummiesbetweentandt+1atvariousgeographicallevels,,whicharepro

Disastervariablesmeasuretheannualaveragenumberofdisasterhappeningattheprovincebetweent1andtwherehous

2000.Fort=1993,t1=1998.Fort=2000,t+1=2007.

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Table5:Impactsofdisastersonsplithousehold(SH)migrationandwholehousehold(WH)migration

Dependentvariablesascountvariables Dependent variablesasdummyva

DepVariables Splithouseholdmigrationacross Split householdmigrationacross Wholeho

Province District Subdistrict Province District Subdistrict Province

(1)

(2)

(3) (4) (5) (6)

(7)

Earthquake 0.0681*** 0.0823** 0.0813** 0.0613*** 0.0729** 0.0835** 0.0208

(0.0215) (0.0307) (0.0344) (0.0206) (0.0259) (0.0300) (0.0145)

Eruption 0.0177* 0.0199** 0.0307** 0.0120 0.0112* 0.0145 0.000173

(0.00888) (0.00851) (0.0122) (0.00750) (0.00578) (0.00835) (0.00215)

Flood 0.0120* 0.00494 0.00435 0.00791 0.000683 0.000988 0.00684*

(0.00620) (0.00807) (0.0103) (0.00485) (0.00535) (0.00562) (0.00371)

Observations 24,651 24,651 24,651 24,651 24,651 24,651 24,651

Rsquared 0.040 0.080 0.105 0.044 0.093 0.130 0.009

Numberof

hhid

8,217

8,217

8,217 8,217 8,217 8,217

8,217


Thedependentvariablesofcolumns(1)to(3)measurehouseholdsplit,whichcountthenumberofnewhouseholdsformed

householdsandthenmovetoanewprovince,districtandsubdistrictbetweentandt+1.Thedependentvariablesofcolum

whetherthehouseholdhasinvolvedinsplittoanewprovince,districtandsubdistrictbetweentandt+1.Thedependentva

dummiesindicatingwhethertheentirehouseholdhasmovedacrossprovinces,districtsandsubdistrictsbetweentandt+1.

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Table6:Auxiliaryregression Impactsofdisastersonhouseholdeconomicvariables

Dep.

VARIABLES

Household

size

Total

earning

Remittance Aid from

govt

House Land Financial

asset

Nonbiz

asset

(1) (2) (3) (4) (5) (6) (7) (8)

Earthquake 0.347*** 1.262** 0.310 0.0254 1.369** 0.457 0.794*** 0.689**

(0.116) (0.468) (0.382) (0.290) (0.532) (1.470) (0.264) (0.254)

Eruption 0.0214 0.0716 0.484*** 0.105 0.324*** 0.115 0.125 0.0571

(0.0563) (0.0992) (0.101) (0.137) (0.0801) (0.316) (0.134) (0.0332)

Flood 0.0186 0.0608 0.0676 0.0138 0.0984 0.0716 0.0527 0.0354

(0.0312) (0.0789) (0.0699) (0.101) (0.0766) (0.169) (0.127) (0.0308)

Observations 24,651 24,651 24,651 24,651 24,651 24,651 24,651 24,651

Rsquared 0.055 0.055 0.030 0.099 0.045 0.021 0.011 0.279

Numberofhhid 8,217 8,217 8,217 8,217 8,217 8,217 8,217 8,217


The

dependent

variables

are

different

economic

variables

measured

at

time

t.

Total

earnings,

remittances

and

aid

are

the

ayearbeforetimet.Remittancesaretransfersfromnonhouseholdfamilymembers.Aidisthetransferfromgovernment,NG

valuesexcepthouseholdsizearerecordedinlog(1+Y).House,landandfinancialassetaresubcategoriesofnonbizassets.D

numberofdisasterhappeningattheprovincebetweent1andtwherehouseholdresidesin.

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Table7:ImpactsofdisastersonSplitHousehold(SH)migration

Dep.Variables Splithouseholdmigrationacross Split householdmigrationacross

Province District Subdistrict Province District Subdistrict

(1) (2) (3) (4) (5) (6)

Earthquake 0.0681*** 0.0823** 0.0813** 0.0581*** 0.0621** 0.0516

(0.0215) (0.0307) (0.0344) (0.0190) (0.0248) (0.0302)

Eruption 0.0177* 0.0199** 0.0307** 0.0188** 0.0219** 0.0340***

(0.00888) (0.00851) (0.0122) (0.00722) (0.00821) (0.00937)

Flood 0.0120* 0.00494 0.00435 0.0124** 0.00552 0.00468

(0.00620) (0.00807) (0.0103) (0.00547) (0.00654) (0.00850)

HHsize 0.0241*** 0.0411*** 0.0648***

(0.00701) (0.00614) (0.0122)

Totalearnings 0.000847* 0.00270*** 0.00518***

(0.000453) (0.000551)

(0.000957)Remittances 0.000562 0.00134 0.00187

(0.000935) (0.00127) (0.00210)

Aid 0.00180* 0.00154 0.00195

(0.00101) (0.00118) (0.00195)

Farmasset 0.00112** 0.00136 0.00229*

(0.000472) (0.00114) (0.00124)

Nonfarmbizasset 0.000938 0.00112 0.00237*

(0.000672) (0.000808) (0.00113)

Nonbizasset 0.000294 0.00292 0.000158

(0.00157) (0.00184) (0.00196)

Observations 24,651 24,651 24,651 24,651 24,651 24,651

Rsquared 0.040 0.080 0.105 0.071 0.132 0.182

Numberofhhid 8,217 8,217 8,217 8,217 8,217 8,217

Notes:Robuststandarderrorsinparentheses,adjustedforclusteringatprovincetimelevel.Allcolumnscontrol

fortimedummiesandhouseholdfixedeffects.Dependentvariablescountthenumberofhouseholdsformedfrom

splitacrossprovinces,districtsandsubdistricts.Disastervariablesmeasurethenumberofdisasterhappeningat

theprovincebetweent1andtwherehouseholdresidesin.Allotherindependentvariablesaremeasuredattime

t.Totalearnings,remittancesandaidaretheamountsaccumulatedwithinoneyearbeforetimet.Allthevaluesof

economicvariablesexcepthouseholdsizearerecordedinlog(1+Y).Columns(1)(3)donotcontrolforeconomic

variablesandcolumns(4)(6)do.

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Table8:Impactofdisastersonwholehousehold(WH)migration

Dep.Variables Wholehouseholdmigrationacross Wholehouseholdmigrationacross

Province District Subdistrict Province District Subdistrict

(1) (2) (3) (4) (5) (6)

Earthquake 0.0208 0.00898 0.0182 0.0210 0.0112 0.0146

(0.0145) (0.0195) (0.0309) (0.0130) (0.0188) (0.0311)

Eruption 0.000173 0.00937** 0.0151* 0.00103 0.00732* 0.0126

(0.00215) (0.00339) (0.00771) (0.00239) (0.00396) (0.00853)

Flood 0.00684* 0.00677* 0.0114** 0.00711* 0.00708* 0.0114**

(0.00371) (0.00371) (0.00525) (0.00373) (0.00359) (0.00507)

HHsize 0.00480*** 0.0103*** 0.0138***

(0.000947) (0.00222) (0.00286)

Totalearnings 0.000203 2.78e05 3.38e06

(0.000194) (0.000278)

(0.000366)Remittances 2.13e05 0.000203 7.15e05

(0.000124) (0.000204) (0.000381)

Aid 0.000355 0.000128 0.000502

(0.000472) (0.000746) (0.000808)

Farmasset 0.000405** 0.00116*** 0.00204***

(0.000159) (0.000272) (0.000392)

Nonfarmbizasset 0.000457** 0.000974** 0.000541

(0.000203) (0.000337) (0.000483)

Nonbizasset 0.00138** 0.00325*** 0.00468***

(0.000507) (0.000638) (0.000775)

Observations 24,651 24,651 24,651 24,651 24,651 24,651

Rsquared 0.009 0.017 0.033 0.032 0.058 0.080

Numberofhhid 8,217 8,217 8,217 8,217 8,217 8,217


fortimedummiesandhouseholdfixedeffects.Dependentvariablesarewholehouseholdmigrationacross

provinces,districtsandsubdistricts.Disastervariablesmeasurethenumberofdisasterhappeningattheprovince

betweent1andtwherehouseholdresidesin.Allotherindependentvariablesaremeasuredattimet.Total

earnings,remittancesandaidaretheamountsaccumulatedwithinoneyearbeforetimet.Allthevaluesof

economicvariablesexcepthouseholdsizearerecordedinlog(1+Y)..Columns(1)(3)donotcontrolforeconomic

variablesandcolumns(4)(6)do.

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RobustnessChecks

Table9:PlaceboTestimpactsonmigrationofnaturaldisasterswithinplacebotimeinterval

Dep.variables Splithouseholdmigration Wholehouseholdmigration

Province

District

Sub

district Province District Sub

district

(1) (2) (3) (4) (5) (6)

Earthquake 0.0282 0.0538* 0.0580 0.0122** 0.00941 0.0114

(0.0227) (0.0257) (0.0367) (0.00571) (0.0135) (0.0165)

Eruption 0.0247 0.00624 0.0125 0.00972 0.00481 0.00582

(0.0205) (0.0179) (0.0194) (0.00609) (0.00809) (0.0109)

Flood 0.0511* 0.0298 0.00975 0.00134 0.0211 0.0371

(0.0285) (0.0286) (0.0316) (0.00544) (0.0169) (0.0307)

Observations 24,651 24,651 24,651 24,651 24,651 24,651

Rsquared 0.039 0.079 0.104 0.006 0.016 0.031

Numberof

hhid8,217

8,217

8,217 8,217 8,217 8,217


fortimedummiesandhouseholdfixedeffects.Dependentvariablesofcolumns(1)to(3)countthenumberofsplit

offhouseholdsformedacrossprovinces,districtsandsubdistricts.Dependentvariablesofcolumns(4)to(6)are

dummiesofwholehouseholdmigrationacrossprovinces,districtsandsubdistricts.Disastervariablesarethe

numberofdisastersmeasuredintheplacebotimeframe.Theplacebotimeframeforeachtimeinterval:(1)1988

1993,placebotimeframe:19741979;(2)19931997,placebotimeframe:19791983;(3)19972000,placebotime

frame:1983 1986

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natural disasters and migration indonesia

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