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Do Natural Disasters Lead to More Migration? Evidence from Indonesia∗ Chun-Wing Tse† November 2012 Abstract Using panel datasets of Indonesia, I examine how natural disasters affect household migration. Specifically, the study analyzes the effects of earthquakes, volcanic eruptions and floods on household tendency to migrate. Contrary to conventional wisdom, I find that all three kinds of disasters significantly reduce migration rates. Nevertheless, the channels of impact are quite different. Earthquakes reduce earnings and non-business assets, each of which tends to reduce migration rates. Volcanic eruptions on the other hand raise the value of farmland, which, in turn, induce households to stay. Floods have no significant impacts on household assets or earnings, which can be the results from attenuation bias due to measurement errors . Keywords: Indonesia, natural disasters, migration JEL codes: O15, Q54 ∗ I would like to thank Dilip Mookherjee for all his guidance and support. I also wish to thank Daniele Paserman and Michael Manove for their advice and comments. I am also grateful to Ye Li, Jie Hou, Julian Chan, Hyo-Youn Chu, Saori Chiba and seminar participants at Boston University. All errors are my own. † CHLR, Central University of Finance and Economics, Beijing, China ([email protected]) 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 kinds killed at least a quarter million people, which exceeds the number of people killed in terrorist attacks in the past 40 years combined (U.S. Federal Emergency Management Agency). The study of environmental shocks is even more important in development economics given the fact that poor households have limited resources to deal with natural disasters, which are highly unpredictable and aggregate in nature (Cavallo and Noy 2009; Noy and Bang 2010; Ebeke and Combes 2010; Cavallo et al. 2010). Based on a descriptive study of cross-country analysis, the International Organization for Migration (IOM) in 2009, suggests that rising natural disasters can lead to more migration because poor households in developing countries resort to migration to stay away from disaster-prone areas. Soaring climate change exacerbates the problem of water shortages and agricultural failures. Seismic activities destroy industrial establishments and deter prospective investors from investing in quake zones. Having lost the livelihoods after natural disasters, households have to make a living elsewhere and hence, move out (IOM 2009). Natural disasters, however, can lower migration. Disasters destroy assets and consequently reduce household financial resources to support migration. Disasters also present an aggregate shock that hurts most households in affected villages, and therefore, it becomes more difficult to borrow from others to pay migration costs (Yang 2008). They are not forced to migrate but forced to stay. On the other hand, disasters can attract households to stay. Eruptions and floods enrich soil fertility by lava ash and alluvial deposits (Ahmad 2011) respectively, which induces households to stay for increased farming productivity. Furthermore, the marginal product of labor of some sectors can rise after disasters and consequently lower migration. E.g., wages in the construction sector increase after earthquakes because of a higher labor demand for village rebuilding. Regarding eruptions and floods, both make farmlands more fertile and hence raise agricultural wages. Finally, some other social factors such as stronger family ties and enhanced community bonding after disasters can also draw households to stay. 2 By studying Indonesia, this paper attempts to understand the link between natural disasters and migration. The study relies on two panel datasets, which are nationally representative. I conduct a longitudinal study to account for household fixed effects and measure how time variation of disasters alters household tendency to migrate. In particular, this paper highlights the effects of disasters on two different forms of household migration: (1) split-household migration, in which part of the household splits and migrates; and (2) whole-household migration, in which the entire household relocates. Furthermore, disasters of various kinds occur in Indonesia frequently, and I investigate each disaster individually. Specifically, this paper explores the effects of three most common kinds of disasters in Indonesia: earthquakes, volcanic eruptions and floods. The baseline empirical results show that all three kinds of disasters reduce household tendency to move and the negative effects hold for both forms of migration. Nevertheless, the channels of impact for each kind of disaster are quite different. Eruptions raise the value of farmland which can be due to enrichment of soil fertility by lava ash. Households with more farm assets move out less. Hence, eruptions lower household migration by increasing farm assets. On the other hand, evidence shows that migration is less likely to occur if a household has lower earnings or nonbusiness assets. Earthquakes decrease earnings and non-business assets, and hence, reduce household financial capability of supporting migration. Floods do not lead to reductions in household assets or earnings and the negative impacts of floods remain unexplained. Yet the lack of significant effects on households assets can be the results from attenuation bias since the data on floods are subject to bigger measurement errors. The economics literature on the link between natural disasters and migration is relatively new. Naude (2008) uses panel analysis at the country level and finds that environmental shocks raise migration because of increased conflicts. The cross-household studies by Halliday (2007), Ó Gráda (1997) and Attzs (2008) analyze specific one-off deadly disasters and relate cross-section disaster exposure with migration at the individual level. They find that individual migration increases after massive disasters. This study is most related to Yang’s (2008) paper, which is a panel study on El Salvador, examining how the massive earthquakes in 2001 affect household migration. Specifically, he highlights 3 how the earthquakes affect household access to credit to finance migration, and discovers that the earthquakes present a large aggregate shock across households in quake-affected villages. It becomes more difficult to borrow from other households to pay fixed migration costs because most households in the villages are financially impaired. Hence, household tendency to migrate decreases. This finding is similar to the overall result of my paper. In addition, I investigate a wide range of natural disasters and a broad range of channels of impact, such as household size, total earnings, value of farmland and non-business assets. The same outcome is obtained despite heterogeneous impacts of different kinds of disasters on different types of assets. Similar to Yang’s study, this paper relies on a longitudinal analysis and focuses on household migration. However, I treat different disasters as heterogeneous shocks. Different disasters cause differential changes in household asset composition and marginal product of labor in various sectors, which can lead to very different migration decisions. Furthermore, I separately consider splithousehold and whole-household migration, because the determinants of these two forms of migration can be very different. In split migration, the household aims to reduce risk by sending members to other locations to diversify sources of income in anticipation of receiving remittances in the future. However, whole-household migration is a risk-taking strategy which involves relocation of the entire household to a new location. All the above facts point toward the need to treat different kinds of disasters as heterogeneous shocks and to separately analyze various forms of household migration. Finally, I do not consider the effects of some specific one-off massive disaster shocks but, instead, investigate how time variation of disasters shapes the pattern of household migration. Given that disasters of various kinds occur in some less-developed countries regularly, such as the Philippines, Bangladesh and Pakistan, a longitudinal study of time variation of natural disasters is important. The paper is organized as follows. Section 2 provides background information on Indonesia, illustrating the demographics and disaster occurrence in the country. Section 3 outlines the data used and give some descriptive statistics. Section 4 discusses the relationship between disasters and household migration. Section 5 describes the empirical strategy, and Section 6 presents the main findings. Section 7 checks for robustness and Section 8 concludes. 4 2 Background According to the UN Office for the Coordination of Humanitarian Affairs, Indonesia is the most disaster-prone country of the world. Most parts of Indonesia are on the fault line of volcanic origin, which gives rise to frequent outbreaks of massive earthquakes and volcanic eruptions. The country is also regularly hit by floods due to the deforestation of massive scale. In 2009 alone, it experienced 469 earthquakes with a magnitude of 5 or higher. Sumatra, Java and Papua were especially hard hit. According to the government data (BPS Indonesia), floods have accounted for about 40 percent of Indonesia’s disasters in the past few years. Figure 1 shows the time-series patterns of earthquakes and floods, and Figures 2 to 4 provide geographic snapshots on where earthquakes, eruptions and floods occurred in the country between 1988 and 2000. Java and Sumatra Islands have always been the black spots of disasters. However, people do not stay away from disasters but continue to live with the environmental risk. Figure 5 depicts the population density of Indonesia in 2000, with most dwellers crowded in Java and Sumatra where disasters of different kinds occur frequently. People in the West Java need to face the regular occurrence of floods and earthquakes. The volcanoes in Yogyakarta pose a constant threat to the inhabitants there, where the eruption in 2010 destroyed numerous villages and killed more than 390 people (New York Times 2010). However, population density of West Java well surpasses 1000 per km square and Yogyakarta has more than 980 people per same size of area (SEDAC) in 2000. Using simple cross-province regressions, the results show that population density in 1993 is not negatively correlated with disaster occurrence within 50 years before 1993. People are not driven away by disasters, but stay with the environmental risk instead. It has always been claimed that communities in Indonesia stay near volcanic areas regardless of the constant threat of eruptions. The regression of rice yield on eruptions at the province level within the last 50 years shows that provinces with more eruptions can produce higher rice yield. Lava ash from volcanoes enhances soil fertility to boost the farm yield, which may explain why people settle near volcanic areas. 5 3 Data and descriptive statistics Data This paper uses two datasets for the empirical analysis. The first one is a panel dataset from the Indonesian Family Life Survey (IFLS), a nationally representative household survey covering both rural and urban areas. This dataset gives a nation-wide sample of households spreading across 13 provinces in the first wave of the survey in 1993 (IFLS1) with three more waves conducted in 1997 (IFLS2), 2000 (IFLS3) and 2007 (IFLS4).1 One prominent feature of this longitudinal survey is the very high tracking rate. The survey did not just attempt to re-interview original households sampled in 1993, but also all the migrant households and those split off from the original households. In IFLS4, 94% of IFLS1 households were re-contacted and this rate is as high as, or even higher than, most longitudinal surveys in the United States and Europe. High re-interview rates contribute significantly to data quality because this lessens the attenuation bias due to nonrandom attrition, which is a critical issue of concern for studies of migration and natural disasters.2 A dummy variable indicating whether a household migrates between two successive survey years is the main outcome of interest in the empirical study. But first, we need a clear definition of household migration. In this paper, I define two forms of household migration: (1) split-household migration and (2) whole-household migration. For split-household migration, one or more household members, but not including the head of household, leave and establish a new household in a new location. On the other hand, if the entire household including the household head moves to a new place, I call this whole-household migration. Apart from a detailed section of household migration history, IFLS also asks several comprehensive sets of questions to obtain the economic variables of the sample households. Specifically, I focus on household size, government aid received, remittances, total household earnings and amounts of different types of assets, to study how natural disasters alter these economic variables to shape the 1 IFLS2+ was also carried out in 1998 to measure the impact of financial crisis. Yet only about 20% of the households from IFLS2 were re-interviewed in IFLS2+. 2 I also test whether there exists non-random attrition and the findings are not sensitive to the treatment of households, who dropped out from the samples. 6 two forms of household migration. The second dataset is the Indonesian DesInventar Database (DesInventar) administered by Data & Informasi Bencana Indonesia. DesInventar aims to record every disaster happening in Indonesia from the early 20th century. The details include location, date, fatalities, financial losses, damage of infrastructure and other relevant information of the disasters. This paper looks at earthquakes, volcanic eruptions and floods, which are the three most common kinds of natural disasters in Indonesia. DesInventar adopts a method of counting natural disasters, different from the traditional practice. First, a disaster is defined as “the set of effects caused by an event on human lives and economic infrastructure on a geographic unit of minimum resolution” (DesInventar). It imposes no thresholds on the amount of damage for an environmental shock to be regarded as a disaster. Furthermore, instead of treating a single event of environmental shock as one disaster, DesInventar counts the number of minimal geographic units, referred to as kecamatan (subdistrict), affected in the event. Thus, DesInventar counts an earthquake event of extensive geographic coverage as multiple earthquake disasters. Thus, this makes statistics recorded by DesInventar look inflated compared with statistics kept under the traditional practice. Yet such method is desirable for this study, as disaster of extensive coverage should receive more weight. DesInventar defines earthquakes, eruptions and floods as follows: Earthquakes - All movements in the earth’s crust causing any type of damage or negative effect on communities or properties. Volcanic eruptions - eruptions with disastrous effects: eruption and emission of gas and ashes, stone falls (pyroclast), flows of lava, etc. Floods - Water that overflows river-bed levels (“riverine floods”) and runs slowly on small areas or vast regions in usually long duration periods (one or more days). This study retains households and their split-offs which exist in all four waves of the survey. In addition, I only keep households with clear migration history between 1993 and 2007. Households without information of some economic variables such as household size, earnings and assets are discarded. This finally leaves the study with 8,217 households. 7 Descriptive Statistics Table 1 presents the descriptive statistics of the sample households. The disaster statistics record the annual average number of each kind of disaster at the province level occurring between 1988 and 2000. Households, on average, experience 0.1 earthquakes annually between 1988 and 2000. In other words, earthquake occurs in 10% of Indonesian provinces every year. Eruptions and floods are also prevalent. The sample households are exposed to one eruption in every five years and more than two floods in every three years. The mean annual exposure to any one of the three kinds of disasters is 0.98. Table 1 also shows the migration statistics between 1993 and 2007. The overall rate of migration combines both split-household migration and whole-household moving, and 1.4% of the households in Indonesia move across provinces every year. Moving rates across kabupatens (districts) and kecamatans (subdistricts) are respectively 3.2% and 4.6%, which are considerably high. Yet most household migration is in the form of splits. More than 3.8% of households split and relocate to a new subdistrict annually. Meanwhile, just less than 1% of households, as a whole, move to a new subdistrict. Split-migration accounts for 83% of overall household moving. Even though Indonesia is the most disaster-prone country, disaster is still a rare event, with small mean numbers and relatively large standard deviation as shown in Table 1. Hence, I also include the median and 90-percentile statistics. More than 50% of the households do not experience any earthquake and eruption between 1988 and 2000. Only around 10% of the households are affected by more than one earthquake in every three years. The occurrence is low for an individual kind of disaster. However, more than 68% of households experienced one or more of three kinds of disasters between 1993 and 2000, and more than half of the households are affected by more than 0.4 disasters every year. 3 Migration rate is also low in Indonesia. More than half of the households have never moved beyond their sub-districts between 1993 and 2007, and more than 90% of the households have never moved across provinces. 4 Table 1 also shows some other household statistics. There is an even proportion of urban and 3 I also restricted the sample to households, who have experienced at least one kind of disaster between 1993 and 2000 and the empirical findings do not change much. 4 Empirical study on rare events warrants the need of conditional analysis. Hence, I also use conditional logit model to check the robustness and the findings do not change much. The results can be available upon request. 8 rural samples. Most of the household heads have only finished elementary education and about 15% of the households are headed by females. Extended family is a common practice in Indonesia, 18% of the households have extended family living in a single household. Table 2 links household economic status in 2000, with migration decisions between 2000 and 2007. I separate the sample into three groups: (1) households which do not migrate between 2000 and 2007, (2) households which split and migrate across provinces but do not move out as a whole, (3) households which move to a new province, as a whole. For statistics on assets, households which migrate as a whole have significantly less farm assets than nonmigrant households. Whole-household migrants have 3.2 million rupiahs of farm assets but the nonmigrants have twice more. On the other hand, split-household migrants have more assets of all types, both business and non-business, than the other two groups, and the differences in asset holdings are significant compared with nonmigrant households. This suggests that the same effects on asset composition due to a disaster can lead to very different impacts on split-household and whole-household migration. Considering total earnings, split-household migrants also earn significantly more in year 2000. The amount of annual earnings is more than 5.5 million rupiahs compared with less than 3 million rupiahs for nonmigrants. Household size in year 2000 is also correlated with migration decisions. Households who split and migrate have, on average, 7 people, significantly bigger than nonmigrant households and households who move as a whole. The above descriptive analysis illustrates how household size, asset composition and earnings link with migration. Before presenting the empirical analysis, the paper first explains how disasters affect migration. Since the empirical study emphasizes the differences between split migration and whole-household migration, the following section also describes how the two forms of migration differ. 4 How natural disasters drive down household migration Migration can increase with natural disasters because households want to stay away from the risk or they need to make a living elsewhere if their livelihoods are destroyed. Yet households exposed to natural disasters may move out less. There exist three possible reasons: (1) increase in marginal 9 product of labor, (2) decrease in financial resources to pay for migration, and (3) strengthened social bonding and mutual insurance. (1) Increase in marginal product of labor (MPL) Disasters destroy infrastructure and houses, which increases demand for labor to rebuild villages afterward. Hence, the MPL of the reconstruction sector rises, which induces households to stay for better employment opportunities. Regarding the agricultural sector, soil fertility can be enriched by lava ash after eruptions and alluvial deposits after floods, which causes the farming productivity to rise. Therefore, households may choose to stay. (2) Decrease in financial resources to pay for migration With assets destroyed and earnings reduced after disasters, households are less capable of affording migration. Thus, they are not forced to migrate but forced to stay. Moreover, households find it more difficult to borrow from others to finance migration as disasters present an aggregate shock and hurt most households living in the affected villages (Yang 2008). Disasters pose liquidity constraints and, as a result, lower migration. (3) Strengthened social bonding and mutual insurance Disasters can boost family ties and strengthen social bonding, especially in developing countries since social capital plays a significant role in less developed economies. Households may choose to cope with disaster shock by accumulating social capital instead of moving out, which reduces the tendency to migrate. Furthermore, households with houses destroyed by disasters need members to stay to rebuild houses. Law and order may also break down after disasters and households should remain to protect property and land rights. This paper will empirically examine the first two reasons and leave out the third because the relevant data are not available. 10 4.1 Split-household migration and whole-household migration Split-household migration is a rarely studied concept, which involves not just household splits but the split-off households moving to a new location. In this study, the migration of a single individual to set up a single-member household is also classified as split-household migration. Split-household migration differs from individual migration in several ways: (1) in individual moving, the migrants may just move out and enter another household in the new location; (2) individual migration tends to be temporary and migrants may return after some time; and (3) individual migrants are, in general, more attached to the original household. Yet split-off households are considered separate from the original household. Households may also regard split migration as an insurance strategy. Considering household members, especially the young and educated groups, as human asset, the head of household can diversify risk by spreading the human asset to various locations. The remittances received from split-off households is also an important source of income, which enables the original household to better mitigate the risk of future economic shocks. Whole-household migration is a completely different concept, which is defined as the moving of the entire household to a new location. The insurance factor is much less significant, and the decision is primarily based on the “push factors” of the origin and the “pull factors” of the destination, taking into account of the total migration cost. The above facts point toward the need to separately analyze these two forms of household migration. Yet it is first necessary to discuss the determinants of split and whole-household migration, including household size, total earnings, external transfer and household assets. Household size: A bigger household will be more likely to split and migrate, as it has more human asset to allocate to various locations for the purpose of risk diversification. On the other hand, a household with more members is less likely to move out as a whole because migration cost increases with the size of household. Total earnings: Households with more earnings have a higher likelihood to split and migrate because they have more financial resources to support the splits. Furthermore, split-household migration can be a risky investment for the split-off households, and higher earnings lower the risk 11 for split-off households to relocate and make a higher income elsewhere. On the contrary, higher earnings at the place of origin can reduce whole-household migration because the opportunity cost of moving increases with current earnings. External transfer: External transfer such as remittances and government aid, is a positive factor for split migration. Similar to the theory related to household earnings, households with more external transfer have more financial resources to pay for split migration. Yet remittances can have totally different effects from government aid on whole-household migration. Households receiving more remittances can better afford migration. On the other hand, government aid received at the place of origin, however, induces people to stay in order to obtain more aid money, which points toward the issue of moral hazard. Household assets: Households with more assets are better endowed financially to support splits. Similar to the theory on total earnings, the risk of split-household migration is lower for households with more assets. Hence, greater assets of various types increase split migration. On the other hand, households with more assets are less likely to migrate as a whole because it is costly for households to dispose of their assets and move out. This theory is particularly important for farm assets. Households owning more farmland have a much higher cost to sell off their farm assets and hence they have a lower tendency to move out as a whole. 5 Empirical Strategy The empirical analysis starts with equation (1): Mit = α0 + α1 Dct + θi + ρt + it (1) The LHS variable Mit is a migration dummy indicating whether household i moves out at a given geographic level between time t and t + 1. The three different geographic levels are across provinces, across kabupatens (districts) and across kecamatans (subdistricts). The most important RHS variable is Dct , which uses the definition given by DesInventar to count the annual average number of disasters happening in province c, where household i resides in between time t − 1 and t. 12 Hence, equation (1) studies how disaster occurrence in the last period affects household migration in the following period. The panel survey spans from 1993 to 2007, with a total of four waves. The specification includes t =1993, 1997 and 2000. I take t − 1 =1988 for t =1993 and t + 1 =2007 when t =2000. Equation (1) also controls for household fixed effect, θi . it captures idiosyncratic errors and ρt denotes time dummies, which is essential because the panel dataset is unevenly spaced. The specification is a linear probability model with controls for households fixed effects.5 There are two issues of concerns here: (1) number of disasters by itself cannot fully capture the severity of disasters. (2) Disaster occurrence at the province level is too coarse for the analysis at the household level. To address the first issue, I also use some other disaster measures, such as deaths, casualties, and economic loss, as a robustness check. For the second issue, the province level data are much more reliable with lower measurement errors. Furthermore, using disaster variable at province level can also capture the spillover effect on households, who are not directly hit by disasters. E.g., an earthquake occurring in a district can cause general equilibrium effects and adversely affects the labor and property markets, which consequently reduce wages and housing prices of the entire province. DesInventar also has disaster record at the kabupaten (district) level. However, the data are less clean with missing values. The database can sometimes only identify the province but not the district, where disasters occur. This issue renders the variable suffering measurement errors, which may reduce the significance of the results. I also conducted the analysis using district level data. The significance of the results is greatly reduced with some inconsistent findings. Hence, I use the disaster variable at the province level instead.6 However, I first run a regression on equation (1) without including the household fixed effect and conduct a simple OLS analysis. The OLS results shows how cross-household variation of disasters in the last period correlates with migration in the following period. Such correlation gives the causal impact only when Dct is uncorrelated with the combined error term, θi + it . This assumption is arguably plausible given the random nature of disasters. However, it could be possible that people with a high unobserved propensity to migrate tend to live in a disaster prone province, which 5 The migration variable is a dummy, which allows for logit or probit analysis. I conducted regressions using fixed effect logit model and the findings are robust. Results can be available upon request. 6 The results can be available upon request. 13 will render the coefficients from a simple cross-section regression biased. Hence equation (1) also accounts for household fixed effects, θi . Household migration, Mit , consists of two different forms: (1) split-household migration and (2) whole-household moving. Splitit = β0 + β1 Dct + δi + ηt + eit (2) Allit = γ0 + γ1 Dct + µi + πt + εit (3) Splitit in equation (2) counts how many new households are formed between time t and t + 1 in splitting and moving. In equation (3), Allit is a migration dummy, indicating whether the entire household i migrates to a new location between t and t + 1. The above empirical specifications measure the total effects of disasters on these two forms of migration, making up the first part of the analysis. The second part explains which channels disasters operate, to bring about such effects. To do this, I modify equations (2) and (3) to control for different economic variables, as shown in equations (4) and (5). 0 0 0 0 0 0 Splitit = β0 + β1 Dct + β2 Yit + δi + ηt + eit 0 0 0 0 0 0 Allit = γ0 + γ1 Dct + γ2 Yit + µi + πt + εit (4) (5) Yit , consists of a list of economic variables, which includes household size, total earnings, external transfer and household assets, of household i at time t. By comparing the coefficients on disaster variables, Dct , in equations (2) and (4), and also the coefficients on economic variables, Yit in equation (4), we can tell which economic channels disasters operate to shape split migration. I also use the same approach to discover the channels for whole-household migration. 14 6 Results In table 3, the dependent variable is the household-migration dummy between time t and t + 1, combining both split-household migration and moving of an entire household. The explanatory variables are the annual average number of earthquakes, eruptions and floods, happening between time t − 1 and t. All standard errors are clustered at the province-time level. 7 The first three columns show the pooled-household analysis without controlling for household fixed effects. Contrary to conventional wisdom, the probability for households to move out decreases with disasters. Floods have significantly negative effects on household moving across provinces and districts. The effects of eruptions are significantly negative at the 0.01 level for all three geographic levels of migration. An additional eruption each year reduces the probability of moving to another province by 0.024. Given the overall cross-province migration rate as 0.06, eruption reduces household moving by 40%. Using the same computation, one more flood each year lowers cross-province migration by 29%. Yet as suggested in Section 5, simple OLS may not account for unobserved migration propensity. From now on, I control for household fixed effects in all specifications to resolve this possible endogeneity. The regression coefficients now estimate how time-variation of disaster occurrence affects the change in migration decisions. According to columns (4) to (6), controlling for household fixed effects affirms the negative impacts of disasters. Besides all the coefficients being negative, the effect of earthquakes is much greater for all geographic levels of migration. Time variations of all three kinds of disasters do significantly reduce household migration. Earthquakes decrease cross-province migration by 0.081, which amounts to 13.4% for one more earthquake in every 10 years. An annual additional eruption and flood also lower cross-province migration by 18% and 24% respectively. This paper highlights the difference between split-household migration and whole-household migration and I separately shows the results for the two forms of migration in Table 4. In columns (1) to (3), the dependent variable counts the number of new households formed by splitting and moving to a new location. In columns (4) to (6), the dependent variable is a dummy indicating whether 7 Given the clustering at the province-time level, there are 44 groups of cluster, which is greater than the benchmark of 30 (Cameron et al. 2008). Hence, we can be less concerned about over-rejection of null hypothesis due to small number of clusters and just rely on the standard technique of error clustering. 15 the entire household moves to a new location. Earthquakes significantly reduce split-migration at all geographic levels. Splits to a new province fall by 0.068, which is 120% in percentage terms. Eruptions also decrease cross-province splits by 31%. However, the effects of floods are just barely significant on splits across provinces. Regarding whole-household migration, earthquakes have just barely significantly negative effect on migration across provinces. The effect on moving across subdistricts is even positive. These findings show a clear contrast with the effects on split migration. Yet the differences are not quite remarkable for eruptions and floods. Eruptions significantly reduce the tendency for an entire household to move at the district and subdistrict levels. Migration across districts falls by 32% with one more eruption annually. The effects of flood are significantly negative at all three geographic levels. Cross-province migration drops by 64% with one more flood every year. In percentage terms, the negative effect of disasters increases with the distance of migration. E.g., an additional earthquake in every 10 years reduces cross-province splits by 12%, but the decreases in cross-district and cross-subdistrict splits are 6.3% and 4.4% respectively. Similar patterns are observed for eruptions and floods, with greater effects on migration of longer distance. Previous results show the reduced-forms effects of disasters. To explain why disasters lower migration, I first examine the impacts of disasters on different economic variables, as the possible channels which disasters operate. Equation (6) regresses various household economic variables on disaster occurrence controlling for household fixed effects, ϑi , and time dummies, νt . Yit = λ0 + λ1 Dct + ϑi + νt + eit (6) In Table 5, all the economic variables are measured in logs of real values except household size. The stock variables include household size, non-business assets, farm assets and nonfarm-business assets, recorded at time t. I highlight the effects on housing asset, as a category of non-business assets. The table also shows how disasters affect the values of farmland, as a category of farm assets. The flow variables include total household earnings, remittances and financial aid received within one year before time t. It would be ideal to have the average annual measures of flow variables between time t − 1 and t, yet such data are not available. 16 Table 5 shows that earthquakes lower all economic variables except remittances received. An additional earthquake each year significantly reduces household size by 0.35. The decrease is also significant for non-business assets, which amounts to 69%. Housing assets significantly fall by 14% for an additional earthquake in every 10 years. Households also suffer from losses in farm and nonfarm-business assets even though the effects are not significant. Total household earnings drop by 13% for one more earthquake in every decade. While earthquakes reduce household economic status, eruptions significantly increase various economic variables. An additional eruption raises the amount of farm assets by 55%. Lava ash in eruptions can highly enrich soil fertility which plausibly increases soil fertility, as shown by an increase in the value of farmland by 25%. Eruptions also significantly raise housing assets, which can be due to the fact that relief money runs into affected areas for house rebuilding and consequently helps boost the housing market. Furthermore, remittances received increase by 48% after eruptions. Significant increase, however, is not observed for earthquakes. One possible explanation is that the impacts of eruptions may only be geographically confined to the areas near volcanoes. Hence, most households in the province are largely unaffected and they are still financially intact and able to remit money to affected households. However, the damage of earthquakes can be much more far reaching, adversely affecting most households in the province. Earthquakes constitute aggregate shocks and do not lead to more financial support received, as non-household members are also financially impaired. Unlike earthquakes, eruptions do not significant decrease household size and total earnings. There exists no negative effect of eruption on household assets of any types. Furthermore, earthquakes and eruptions do not cause households to receive more government aid. Finally, Table 5 shows that floods significantly increase the value of farmland, plausibly due to the better soil fertility by alluvial deposits. However, floods do not significantly affect household well-being on any other measures. This may be due to bigger measurement errors of flood data, which attenuate the effects on the economic variables. Given the results of Tables 4 and 5, we can now explore the channels which disasters operate to affect household migration. Tables 6 and 7 investigate the channels for split-household and whole-household migration respectively. 17 In Table 6, I put the regression results without controls and with controls for economic variables, side by side for ease of comparison. By adding economic variables, the magnitude of coefficients on earthquakes has dropped for all three geographic levels of moving. From column (1), earthquakes reduce split migration to a new province by 0.068 (120% in percentage terms), but the magnitude falls to 0.058 (102%) after adding economic variables as shown in column (4). The drops in magnitude are even more noticeable for splits to a new district and subdistrict. This suggests that earthquakes operate through some of the economic variables to reduce split migration. From Table 6, households with bigger size and higher earnings split and migrate more, and the positive effects are significant at all three geographic levels of moving. An additional household member raises cross-province splits by 42%. One percentage rise in household earnings also increases the number of new household formed in a new province by 0.00088, which amounts to an elasticity of 1.5%. As shown in Table 5, earthquakes significantly reduce household size and total earnings. Hence, earthquakes reduce split migration by decreasing household size and earnings. Non-business assets just significantly increase cross-province splits. Yet the effects of business assets of farm and nonfarm are significantly positive and the positive sign holds for migration at all three geographic levels. From Table 5, earthquakes lower the two types of business assets, although not significantly. Thus, the results suggest that earthquakes decrease split migration through reducing both business and non-business assets. Table 6 shows different findings for eruptions. All the negative signs for eruption remain and the coefficients are even more negative after controlling for economic variables. Since households with more farm assets split more and eruption raise farm assets, hence household splits increase with eruptions and, hence, the coefficients on eruptions in columns (4) to (6) are even more negative. The results invalidate the economic variables listed in Table 6, as the channels which eruptions operate to reduce household splits. The estimated effects of economic variables on split migration in Table 6 suffer the problem of endogeneity. Hence, the results just provide suggestive evidence on the channels which disasters operate. Yet the effects of the economic variables are mostly consistent across different geographic levels of migration. Specifically, for household assets, the positive sign nearly always remains even I 18 separately consider assets of various types. Such robust results provide strong evidence that those variables are plausible channels. Table 7 presents the results for whole-household migration and we, in particular, focus on the effects of eruptions and floods because earthquakes do not significantly affect whole-household migration. After adding economic variables, magnitude for the coefficients on eruptions drops substantially and the level of significance also falls. Coefficients for cross-district moving falls from -0.0094 to -0.0073, and the effects on cross-subdistrict migration reduces from -0.015 to -0.012. Table 7 also shows that households with more farm assets move out less, as a whole, at all geographic levels. The tendency of moving to a new district falls by 4% when farm asset increases by 1%. Eruptions raise the amount of farm assets, as shown in Table 5, which explains why eruptions reduce whole-household migration. Table 7 shows that the magnitude and significance of coefficients on floods do not change substantially. From Table 5, the estimated effects of floods on any of the economic variables are not significant. Hence, including controls for the suggested variables will not reduce the magnitude of the coefficients on floods. However, we should beware of higher measurement errors in counting floods, which may attenuate the effects. Tables 6 and 7 show some contrasting impacts of economic variables on split migration and whole-household moving. Household size has significant but totally opposite effects on these two forms of moving. Households with more members split and migrate more, but move out less as a whole. Household asset also presents a contrasting picture. More assets of any type increases split-migration to a new location, but reduces whole-household moving, which match the findings of descriptive statistics in Table 2. These results suggest that households with more assets have greater ability to support splits. However, most forms of assets, such as land and house are illiquid, and accumulating assets makes the entire household more rooted in its village and less mobile to move out. The contrasting effects also exist for earnings but the impact on whole-household migration is not significant. Finally, remittances and government aid received have no significant effects on both forms of migration. I conduct a back-of-the-envelope calculation to measure the effects of disasters through eco- 19 nomic variables as different channels. From Tables 5 and 6, earthquakes decrease household size by 0.35, and an additional household member drives up splits across provinces by 0.023. Thus, earthquakes reduce cross-province splits by 0.0081 (0.35*0.023), which accounts for 15% of the decrease. Using the same computation method, earthquakes lower earnings to decrease cross-province splits by 0.0011, or 1.9%. For whole-household migration, eruptions increase farm assets by 55% and consequently lower migration across provinces by 0.00020 (0.55*0.00036), or 2.1%. We can also tell to what extent the suggested economic variables explain the negative impacts of disasters. From Table 6, the coefficient on earthquakes for cross-province migration drops from 0.068 to 0.058, which amounts to a reduction of 15%. Thus, 15% of the negative impacts of earthquakes is explained by economic variables. Similarly, economic variables explain 25% and 36% of the decline in cross-district and cross-subdistrict splits, respectively. We use the same method for whole-household migration. From Table 8, including economic variables explain 22% and 17% of the cross-district and cross-subdistrict migration, respectively. 7 Robustness checks The surveys are not conducted at a regular time interval and there is a seven-year gap between the last two waves, IFLS3 (2000) and IFLS4 (2007). This time period is so long that the effects of disasters in the previous period (1997-2000) may have substantially diminished well before 2007. Furthermore, a huge tsunami happened in the province of Aceh in 2004, and resulted in massive fatalities. Although the samples do not include any households from Aceh, the tsunami could have forced Acehnese households to relocate to neighboring provinces, which may cloud the estimates. To address this problem, I set a cut-off point in year 2004, and discarded all the households which moved after 2004. Only households moving before 2004 are considered migrants, and the samples are reduced to 7,843 households. Table 8 shows the effects on split migration including the controls for economic variables. Most of the negative coefficients still remain, but the magnitude drops. Earthquakes still reduce migration rates, and the number of cross-province splits decreases by 9.1% for one more earthquake in every 10 years. More eruption also causes significantly less splits to districts and subdistricts. Furthermore, 20 the conclusions drawn in Section 6 still stand. The effects of earthquakes decrease after adding economic variables. Also, the size of household and total earnings are still significant to raise household splits, and the positive effects of household assets on splits are barely significant. Thus, earthquakes lower household splits by reducing household size, earnings and assets, which is similar to the findings in Table 6. Table 9 shows the results for whole-household migration. The negative effects of eruption become weaker and they are no longer significant, after controlling for economic variables. The coefficients either become less negative or even positive. The previous reasoning still applies: more farm assets lower whole-household moving, and eruptions increase farm assets, which leads to lower migration. We can also observe, from Tables 8 and 9, some contrasting results with the revised samples, similar to the findings from Tables 6 and 7. The size of household, on the one hand, increases household splits, but on the other hand, lowers the migration of an entire household. More assets of all types enhance the likelihood of household splits, but at the same time reduces the tendency for an entire household to move. All the above specifications use annual average number of disasters as the explanatory variables. However, number by itself cannot fully capture the severity of disasters. A single massive deadly catastrophe has far greater effects than a series of small-scale disasters of mild intensity. Hence, I use alternative disaster measures in the specification, which include number of deaths, injuries, people missing, financial loss and crop damage. Yet we should beware that those disaster measures are subject to greater measurement errors, which can bias the estimated effects of disaster variables. This suggests that number of disasters can be a better measure. The above alternative disaster variables count the annual average of respective losses at the province level between time t − 1 and t. To simplify the exposition, I sum the losses from earthquakes, eruptions and floods as the explanatory variables. 8 Table 10 shows consistent findings. Most of the significant effects are negative. Hence, greater the severity of disasters, lower the tendency for household splits and migration of the entire households. More deaths from disasters significantly reduce split migration across all geographic levels, even 8 Findings are quite robust when I separately consider the three kinds of disasters and the results are available upon request. 21 though the effects on whole-household migration is not significant. The effects of financial losses are more clear. Greater financial loss reduces both split and whole household migration, and the negative effects of financial loss on split migration are significant for all three geographic levels of moving. Regarding crop damage, the dataset just records the total farm loss due to floods. Table 10 shows the contrasting results on splits and whole household migration. Greater crop damage reduces household ability to finance migration, but on the other hand, may push household members to leave the family farmland and migrate to cities for non-agricultural employments, and consequently raise split migration. The results using alternative disaster measure further support the finding that disaster can reduce household migration through posing financial constraints or increasing farming productivity. 7.1 Extension: Heterogeneous effects of disasters on household migration The main analysis in Section 6 presents how disasters affect household migration on average. Yet disasters can have heterogeneous impacts on household migration, depending on the household economic variables at time t (Yit ). To measure the heterogeneous effects of disasters, I add some interaction terms between disasters and economic variables to equations (4) and (5). Splitit = κ0 + κ1 Dct + κ2 Yit + κ3 Yit × Dct + ψi + νt + ωit (7) Allit = ϕ0 + ϕ1 Dct + ϕ2 Yit + ϕ3 Yit × Dct + χi + $t + ξit (8) The coefficients on the interaction term, κ3 and ϕ3 denote how disasters in the previous period interact with economic variables at time t to affect household-migration decision in the next period. A positive coefficient suggests that after disasters, households with higher values of economic variables are more likely to move out in the following period. To simplify the exposition, I sum the number of earthquakes, eruptions and floods as the disaster measure to interact with various household economic variables. I also consider total assets, adding up farm farm assets, nonfarm 22 business assets and non-business assets. According to Table 11, there exist heterogeneous impacts of disaster for households with different household sizes, amounts of government aid received, remittances and levels of total assets. For household size, the effects on split across districts and subdistricts are significant, which suggests that a bigger household is more likely to split and move in the following period after disasters. However, the heterogeneous effects of aid received from government are totally different. Households, having received more aid from the government, are less likely to move out in the following period. The effects are significantly negative for both split and whole-household migration. These findings implies that humanitarian aid, on the one hand, lessens the needs to find livelihoods elsewhere, but at the same time, attracts households to stay in a disaster prone area, leading to an issue of moral hazard. Similarly, more remittances received after disasters causes household to split and migrate less, which implies more remittances lower household need for migration after disasters. However, such negative effect is not observed for whole-household migration. Finally, for the heterogeneous effects of earnings and total assets, the results are contrasting for split and whole-household migration. Households with more earnings and assets split and move out more for all geographic levels of migration even though the effects of assets are not significant. On the other hand, the heterogeneous effects are negative on whole-household moving at all geographic levels. Households with more earnings can better support splits but they have greater opportunity cost of moving out as a whole. Households with more assets have higher costs of selling them off, which can also discourage wholehousehold migration. 8 Conclusion This study examines whether natural disasters lead to more migration in Indonesia. It discovers that more disasters result in less migration. The three most common kinds of disasters in Indonesia, earthquakes, volcanic eruptions and floods, all lower household moving. I concentrate on household migration, and separately consider split migration and whole-household migration. The results show that disasters have negative impacts on both forms of migration. In particular, earthquakes cause less household splits, and floods reduce whole-household migration. Eruptions decrease both forms 23 of migration. The channels of impact for each kind of disaster are quite different. For split migration, earthquakes significantly reduce household size, total earnings and non-business assets. Smaller households are less likely to split, as are the households with less earnings and non-business assets, which explains why earthquakes cause less split migration. For whole-household migration, eruptions increase the values of farm assets possibly because lava ash can raise farmland productivity. Households with more farm assets are less likely to move out as a whole, which explains why eruptions lower whole-household migration. Finally, the reductions of whole-household migration due to floods cannot be traced to household assets or earnings. I also quantitatively assess the explanatory power of the economic variables for the negative impacts of disasters. For earthquakes, the economic variables explain 15% of the fall in cross-province splits, and account for 25% and 36% of the decline of cross-district and cross-subdistrict splits, respectively. In the case of eruptions, economic variables explain 22% and 17% of the reduction in cross-district and cross-subdistrict migration of an entire household, respectively. This paper shows that the claim of more migration after natural disasters is not valid in Indonesia. Two important facts are ignored: (1) disasters can alter household economic well-being, which may consequently lower household propensity to migrate as described in the study; and (2) given the regular occurrence of disasters, households may resort to a variety of adaptation mechanisms instead of simply moving out of disaster-prone areas (IOM 2009). However, after adding economic variables to the regression, the negative coefficients still remain, and the significance has not been fully eliminated. In terms of the negative effects of eruptions on household splits, the magnitude of the coefficients even rises. Furthermore, none of the suggested economic variables can explain how floods drive down whole-household migration. Thus, the most plausible explanation is that the specification has not captured some other variables which disasters also operate to affect migration. Since the regression has controlled for time-invariant household fixed effects, those other possible variables should be time varying which may include degree of risk aversion, health status of household heads, accumulation of social capital and other social factors as described in Section 4. The negative causal relationship between disasters 24 and household migration warrants further research to better study how households in developing countries determine migration decisions in the time of surging environmental calamities. 25 References [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, Colin, Jonah Gelbach and Douglas Miller (2008), “Bootstrap-based Improvements for Inference with Clustered Errors,” The Review of Economics and Statistics, Vol. 90. [4] Cameron, Lisa and Manisha Shah (2010), “Risk Taking Behavior in the Wake of Natural Disasters,” Working Paper. [5] Cavallo, Eduardo and Ilan Noy (2009), “The Economics of Natural Disasters: A Survey,” IDB Working Paper Series, No. IDB-WP-124. [6] Cavallo, Eduardo, Sebastian Galiani, Ilan Noy and Juan Pantano (2010), “Catastrophic Natural Disasters and Economic Growth,” IDB Working Paper Series, No. IDB-WP-183. [7] DesInventar, http://dibi.bnpb.go.id/DesInventar/main.jsp?countrycode=id&lang=EN [8] Ebeke, Christian and Jean-Louis Combes (2010), “Do remittances dampen the effect of natural disasters on output growth volatility in developing countries?,” Working Paper Series, No.: 201031, CERDI [9] Halliday, Timothy J. (2007), “Migration, Risk and the Intra-Household Allocation of Labor in El Salvador,” Working Paper. [10] Hsiao, Cheng (1986), “Analysis of Panel Data,” Econometric Society Monographs, Chapter 4. [11] International Organization of Migration (2009), “Migration, Environment and Climate Change: Assessing the evidence,” IOM publication. 26 [12] Naude, Wim (2008), “Conflict, Disasters, and No Jobs: Reasons for International Migration from Sub-Saharan Africa,” Research Paper No. 2008/85, UNU-Wider. [13] New York Times (Nov 02, 2010), http://www.nytimes.com/2010/11/02/world/asia/02indo.html [14] Noy, Ilan and Tam-Bang Vu (2010), “The economics of natural disasters in a developing country: The case of Vietnam,” Working Paper series 200903, University of Hawaii at Manoa, Department of Economics. [15] Ó Gráda, Cormac (1997), “The Great Irish Famine : Winners and Losers,” No 97-23, Discussion Papers from University of Copenhagen, Department of Economics. [16] SEDAC - Gridded population of the World, http://sedac.ciesin.columbia.edu/ [17] UN International Strategy for Disaster Risk Reduction, (2007), “World experts unite to confront growing risks of disasters,” Press release UN/ISR 2007/8, Geneva. [18] US Federal Emergency Management Agency (2011), http://www.columbiamissourian.com/ stories / 2011 /01 /01 / 2010s-world-gone-wild-quakes-floods-blizzards/ [19] Wooldridge, Jeffrey (2001), “Econometric Analysis of Cross Section and Panel Data,” MIT press. [20] Yang, Dean (2008) “Risk, Migration, and Rural Financial Markets: Evidence from Earthquakes in El Salvador”, Social Research Vol 75 : No 3. 27 Figure 1: Yearly occurrence of earthquakes and floods in Indonesia number of floods number of earthquakes 12 50 45 10 40 35 8 30 Eathquake 6 25 20 4 15 10 2 5 0 0 1950 1955 1960 1965 1970 1975 1980 Source: DesInventar Database 28 1985 1990 1995 2000 Flood Figure 2: Spatial variation of number of earthquakes, 1988-2000 Figure 3: Spatial variation of number of eruptions, 1988-2000 Source: DesInventar Database 29 Figure 4: Spatial variation of number of floods, 1988-2000 Source: DesInventar Database Figure 5: Population density of Indonesia in 2000 Source: Gridded Population of the World (GPWv3) – Socio-Economic Data and Application Center 30 Table 1: Descriptive Statistics of IFLS and DesInventar Variable Observation Mean Std. Dev. Median 90-percentile Earthquake 24651 0.099 0.18 0 0.33 Volcanic eruption 24651 0.20 0.52 0 0.5 Flood 24651 0.68 0.92 0.25 1.75 Disaster 24651 0.98 1.26 0.4 2.33 Migrate: province 24651 0.014 0.061 0 0 Migrate: district 24651 0.032 0.089 0 0.14 Migrate: subdistrict 24651 0.046 0.10 0 0.25 Whole move: province 24651 0.0022 0.023 0 0 Whole move: district 24651 0.0058 0.036 0 0 Whole move: subdistrict 24651 0.0096 0.046 0 0 Split move: province 24651 0.012 0.057 0 0 Split move: district 24651 0.027 0.084 0 0.14 Split move: subdistrict 24651 0.038 0.097 0 0.14 Notes: All The figures are annual statistics. The disaster variables, earthquake, eruption and flood, show the annual average occurrence between 1988 and 2000. Migration statistics shows the annual rate of migration across provinces, across districts and across sub-districts between 1993 and 2007. “Migrate: province” is the migration rate across provinces combining both whole household (WH) migration and split household (SH) migration. “Whole move: province” is the annual WH migration rate across provinces. “Split move: province” is the corresponding statistics for SH migration. 31 Table 1 (Continued): Descriptive Statistics of IFLS and DesInventar Variable Observation Mean Std. Dev. Household size 24651 5.50 2.57 Aid from government(‘000 rupiahs) 24651 360 18,000 Total earnings(‘000 rupiahs) 24651 2,288 14,600 Remittances(‘000 rupiahs) 24651 221 1,668 Farm asset(‘000 rupiahs) 24651 4,843 32,800 Nonfarm business asset(‘000 rupiahs) 24651 1,887 21,100 Non-business assets(‘000 rupiahs) 24651 18,300 60,900 Urban/Rural 24651 0.55 0.98 Education of household head 24651 1.85 1.16 Female headed 24651 0.15 0.36 Age of household head 24651 46.42 14.17 Extended family 24651 0.18 0.39 Notes: All measures are in real values. For Urban/Rural dummy, the value = 0 stands for urban household, value=1 represents residence in rural area. Education head gives the education level of household head. Female head shows whether the household is headed by a female. 32 Table 2: Comparing three sub-samples: 1. Households which do not move 2. Households split and migrate across provinces, and, 3. Households which move across provinces as a whole between 2000 and 2007 Variable Farm asset(‘000 rupiahs) Nonfarm business asset(‘000 rupiahs) Non-business asset(‘000 rupiahs) Total household earnings(‘000 rupiahs) household size Number of households No move at all Mean Split household migration across provinces Mean T-test (1) Whole household migration across provinces Mean T-test (2) 11,000 (54,100) 3,800 (31,500) 34,400 (83,300) 3,000 (8,306) 5.42 (2.65) 7498 11,900 (36,100) 7,200 (46,800) 58,300 (143,000) 5,500 (2,400) 7.04 (2.74) 565 3,200 (17,600) 6,000 (55,800) 36,000 (90,100) 3,500 (6,644) 3.37 (2.34) 154 0.36 2.36*** 6.16*** 5.74*** 14.01*** -1.79** 0.84 0.24 0.79 -9.50*** Notes: Standard errors in parentheses. All the measures are in real values. T-test (1): comparing the means between split-household migration and those which do not move at all. T-test (2): compares the means between whole household migration and those which do not move at all. 33 Table 3: Baseline Results – Impacts of disasters on household migration in general (combining both split household (SH) and whole household (WH) migration) Dep. Variables General household migration across General household migration across Province District Sub-district Province District Sub-district (1) (2) (3) (4) (5) (6) -0.0434* -0.0207 0.000149 -0.0805*** -0.0821** -0.0693* (0.0245) (0.0242) (0.0382) (0.0296) (0.0312) (0.0384) -0.0237*** -0.0217*** -0.0304*** -0.0113 -0.0176** -0.0242** (0.00875) (0.00537) (0.00909) (0.0101) (0.00793) (0.0104) -0.0176*** -0.0137* -0.0127 -0.0145** -0.00726 -0.00807 (0.00612) (0.00696) (0.00990) (0.00595) (0.00647) (0.00750) Control for household fixed effect Observations N N N Y Y Y 24,651 24,651 24,651 24,651 24,651 24,651 R-squared 0.034 0.061 0.087 0.046 0.093 0.128 8,217 8,217 8,217 Earthquake Eruption Flood Number of households Notes: Robust standard errors in parentheses, adjusted for clustering at province-time level. All columns control for time dummies. Columns (1) – (3) do not control for household fixed effects, columns (4) – (6) do. The dependent variables are the migration dummies between t and t+1 at various geographic levels, which are province, district and subdistrict. Disaster variables measure the annual average number of disaster occurring at the province between t-1 and t where household resides in. t=1993, 1997 and 2000. For t = 1993, t-1 =1998. For t =2000, t+1=2007. (*** p<0.01, ** p<0.05, * p<0.1) 34 Table 4: Impacts of disasters on split-household (SH) migration and whole-household (WH) migration Dep. Variables Earthquake Eruption Flood Observations R-squared Number of households Split household migration across Whole household migration across Province (1) District (2) Sub-district (3) Province (4) District (5) Sub-district (6) -0.0681** (0.0277) -0.0177 (0.0122) -0.0120* (0.00612) -0.0823** (0.0305) -0.0199* (0.0110) -0.00494 (0.00834) -0.0813** (0.0330) -0.0307** (0.0126) -0.00435 (0.00995) -0.0208* (0.0107) 0.000173 (0.00303) -0.00684** (0.00269) -0.00898 (0.0148) -0.00937** (0.00359) -0.00677** (0.00276) 0.0182 (0.0243) -0.0151** (0.00631) -0.0114*** (0.00393) 24,651 0.040 8,217 24,651 0.080 8,217 24,651 0.105 8,217 24,651 0.009 8,217 24,651 0.017 8,217 24,651 0.033 8,217 Notes: Robust standard errors in parentheses, adjusted for clustering at province-time level. All columns control for time dummies and household fixed effects. The dependent variables of columns (1) to (3) measure household split, which count the number of new households formed by splitting from the original households and then move to a new province, district and subdistrict between t and t+1. The dependent variables of columns (4) to (6) are dummies indicating whether the entire household moves across provinces, districts and subdistricts between t and t+1. (*** p<0.01, ** p<0.05, * p<0.1) 35 Table 5: Auxiliary regression - Impacts of disasters on household economic variables Dep. VARIABLES Earthquake Eruption Flood Household size Total earning Remittances Aid money House Farmland Farm asset (7) Nonbusiness asset (8) (9) Nonfarm business asset (10) (1) (2) (3) (4) (5) -0.347*** (0.107) 0.0214 (0.0423) 0.0186 (0.0253) -1.262*** (0.386) -0.0716 (0.0774) -0.0608 (0.0621) 0.310 (0.380) 0.484*** (0.162) -0.0676 (0.109) -0.0254 (0.257) 0.105 (0.106) -0.0138 (0.0731) -1.369*** (0.454) 0.324** (0.126) 0.0984 (0.0747) -0.517 (0.388) 0.246** (0.117) 0.185* (0.0935) -0.689*** (0.223) 0.0571 (0.0508) 0.0354 (0.0274) -0.169 (0.372) 0.554*** (0.120) 0.182 (0.120) -0.430 (0.334) 0.149 (0.143) -0.0753 (0.0751) Observations 24,651 24,651 24,651 24,651 24,651 24,651 24,651 24,651 24,651 R-squared 0.055 0.055 0.030 0.099 0.045 0.024 0.279 0.035 0.043 Number of 8,217 8,217 8,217 8,217 8,217 8,217 8,217 8,217 8,217 households Notes: Robust standard errors in parentheses, adjusted for clustering at province-time level. All columns control for time dummies and household fixed effects. The dependent variables are different economic variables measured at time t. Total earnings, remittances and aid are the amounts accumulated within one year before time t. Remittances are transfers from non-household family members. Aid money is the transfer received from government, NGOs or some other parties. All the values except household size are transformed to log(1+Y). House is a category of non-business assets, and farmland is a category of farm asset. Disaster variables measure the annual average number of disaster occurring at the province between t-1 and t, where household resides in. (*** p<0.01, ** p<0.05, * p<0.1) 36 Table 6: Impacts of disasters on Split Household (SH) migration with controls for economic variables Dep. Variables Earthquake Eruption Flood Split household migration across Split household migration across Province District Sub-district Province District Sub-district (1) (2) (3) (4) (5) (6) -0.0681** -0.0823** -0.0813** -0.0581** -0.0618** -0.0513* (0.0277) (0.0305) (0.0330) (0.0255) (0.0250) (0.0279) -0.0177 -0.0199* -0.0307** -0.0188 -0.0222** -0.0343*** (0.0122) (0.0110) (0.0126) (0.0114) (0.0102) (0.0109) -0.0120* -0.00494 -0.00435 -0.0123** -0.00538 -0.00449 (0.00612) (0.00834) (0.00995) (0.00574) (0.00776) (0.00928) 0.0233*** 0.0383*** 0.0611*** (0.00607) (0.00624) (0.0116) 0.000883** 0.00282*** 0.00533*** (0.000407) (0.000524) (0.000770) -0.000654 0.00104 0.00147 (0.000831) (0.00113) (0.00155) 0.00179* 0.00150 0.00190 (0.000900) (0.00120) (0.00170) 0.000221 0.00268* -0.000477 (0.00118) (0.00146) (0.00148) 0.00102*** 0.00101 0.00183** (0.000347) (0.000708) (0.000819) 0.000942* 0.00113* 0.00239** Household size Total earnings Remittances Aid money Non-business asset Farm asset Nonfarm business asset (0.000527) (0.000650) (0.000892) Observations 24,651 24,651 24,651 24,651 24,651 24,651 R-squared 0.040 0.080 0.105 0.071 0.132 0.182 Number of 8,217 8,217 8,217 8,217 8,217 8,217 households Notes: Robust standard errors in parentheses, adjusted for clustering at province-time level. All columns control for time dummies and household fixed effects. Dependent variables count the number of households formed from split across provinces, districts and sub-districts. Disaster variables measure the annual average number of disaster occurring at the province between t-1 and t where household resides in. All other independent variables are measured at time t. Total earnings, remittances and aid money are the amounts accumulated within one year before time t. All the values of economic variables except household size are recorded in log(1+Y). Columns (1) – (3) do not control for economic variables and columns (4) – (6) do. (*** p<0.01, ** p<0.05, * p<0.1) 37 Table 7: Impact of disasters on whole household (WH) migration with controls for economic variables Dep. Variable Earthquake Eruption Flood Whole household migration across Whole household migration across Province District Sub-district Province District Sub-district (1) (2) (3) (4) (5) (6) -0.0208* -0.00898 0.0182 -0.0210** -0.0113 0.0143 (0.0107) (0.0148) (0.0243) (0.0104) (0.0153) (0.0252) 0.000173 -0.00937** -0.0151** 0.00106 -0.00726* -0.0124* (0.00303) (0.00359) (0.00631) (0.00298) (0.00364) (0.00659) -0.00684** -0.00677** -0.0114*** -0.00713*** -0.00712*** -0.0114*** (0.00269) (0.00276) (0.00393) (0.00262) (0.00261) (0.00376) -0.00446*** -0.00952*** -0.0130*** (0.000704) (0.00140) (0.00187) 0.000189 -5.94e-05 -3.69e-05 (0.000159) (0.000218) (0.000269) 1.56e-05 -0.000123 0.000157 (0.000137) (0.000261) (0.000346) 0.000359 -0.000118 -0.000493 (0.000373) (0.000579) (0.000620) -0.00135*** -0.00318*** -0.00461*** (0.000423) (0.000634) (0.000846) -0.000362** -0.00107*** -0.00194*** (0.000141) (0.000223) (0.000378) -0.000458** -0.00098*** -0.000546 Household size Total earnings Remittances Aid money Nonbusiness asset Farm asset Nonfarm business asset Observations 24,651 24,651 24,651 (0.000209) 24,651 (0.000269) 24,651 (0.000327) 24,651 R-squared 0.009 0.017 0.033 0.032 0.058 0.080 Number of 8,217 8,217 8,217 8,217 8,217 8,217 households Notes: Robust standard errors in parentheses, adjusted for clustering at province-time level. All columns control for time dummies and household fixed effects. Dependent variables are whole household migration across provinces, districts and sub-districts. Disaster variables measure the annual average number of disaster occurring at the province between t-1 and t where household resides in. All other independent variables are measured at time t. Total earnings, remittances and aid are the amounts accumulated within one year before time t. All the values of economic variables except household size are recorded in log(1+Y). Columns (1) – (3) do not control for economic variables and columns (4) – (6) do. (*** p<0.01, ** p<0.05, * p<0.1) 38 Robustness Checks Table 8: Use 2004 as cut-off year – Impacts on split household migration Dep. Variables Earthquake Eruption Flood Split household migration across Split household migration across Province District Province District Sub-district (1) (2) Subdistrict (3) (4) (5) (6) -0.052** -0.054** -0.056** -0.0443** -0.0409** -0.0339 (0.0183) (0.0190) (0.0253) (0.0167) (0.0159) (0.0250) -0.011 -0.017** -0.027* -0.0115* -0.0181*** -0.0284*** (0.00715) (0.00664) (0.0134) (0.00559) (0.00489) (0.00965) -0.0043 0.0024 0.0029 -0.00503 0.00156 0.00223 (0.00417) (0.00486) (0.0078) (0.00384) (0.00431) (0.00727) 0.0213*** 0.0334*** 0.0576*** (0.00729) (0.00640) (0.0130) 0.000549 0.00225*** 0.00473*** (0.000354) (0.000494) (0.000948) -0.000871 0.000956 0.00140 (0.000969) (0.00120) (0.00209) 0.00141 0.00136 0.00169 (0.000858) (0.000894) (0.00180) -0.000542 0.00207 -0.00124 (0.00160) (0.00166) (0.00188) 0.000964* 0.000614 0.00150 (0.000470) (0.00105) (0.00109) 0.000736 0.000711 0.00218* (0.000670) (0.000838) (0.00115) Household size Total earnings Remittances Government aid Non business asset Farm asset Nonfarm business asset Observations 23,529 23,529 23,529 23,529 23,529 23,529 R-squared 0.036 0.081 0.104 0.066 0.124 0.175 Number of 7,843 7,843 7,843 7,843 7,843 7,843 households Notes: Robust standard errors in parentheses, adjusted for clustering at province-time level. All columns control for time dummies and household fixed effects. Households migrated after 2004 are discarded from the samples. Dependent variables count the number of households formed from split. Disaster variables measure the annual average number of disaster occurring at the province between t-1 and t where household resides in. All other independent variables are measured at time t. Total earnings, remittances and aid are the amounts accumulated within one year before time t. All the values of economic variables except household size are recorded in log(1+Y). Columns (1) – (3) do not control for economic variables and columns (4) – (6) do. (*** p<0.01, ** p<0.05, * p<0.1) 39 Table 9: Use 2004 as cut off year – Impacts on whole household migration Dep. Variables Earthquake Eruption Flood whole household migration across whole household migration across Province District Province District Sub-district (1) (2) Subdistrict (3) (4) (5) (6) -0.019 -0.012 0.026 -0.0187 -0.0116 0.0231 (0.0133) (0.0168) (0.0314) (0.0117) (0.0160) (0.0315) 0.0013 -0.0042 -0.015* 0.00193 -0.00257 -0.0131 (0.00200) (0.00321) (0.00755) (0.00219) (0.00371) (0.00799) -0.0058 -0.0064* -0.0091* -0.00618* -0.00705** -0.00844 (0.00347) (0.00334) (0.00514) (0.00348) (0.00322) (0.00497) -0.00407*** -0.00827*** -0.00968*** (0.000907) (0.00203) (0.00233) 0.000202 1.36e-05 -0.000139 (0.000206) (0.000261) (0.000265) -6.38e-05 -0.000259 -0.000346 (9.82e-05) (0.000220) (0.000380) 0.000316 -8.88e-05 -0.00110 (0.000385) (0.000641) (0.000678) -0.00144*** -0.00308*** -0.00415*** (0.000470) (0.000651) (0.000900) -0.000182 -0.000786*** -0.00193*** (0.000124) (0.000217) (0.000372) -0.000547*** -0.000985*** -0.000479 (0.000177) (0.000274) (0.000300) Household size Total earnings Remittances Government aid Non business asset Farm asset Nonfarm business asset Observations 23,529 23,529 23,529 23,529 23,529 23,529 R-squared 0.006 0.009 0.053 0.027 0.046 0.093 Number of 7,843 7,843 7,843 7,843 7,843 7,843 households Notes: Robust standard errors in parentheses, adjusted for clustering at province-time level. All columns control for time dummies and household fixed effects. Household migrated after 2004 are discarded from the samples. Dependent variables are whole-household migration. Disaster variables measure the annual average number of disaster occurring at the province between t-1 and t where household resides in. All other independent variables are measured at time t. Total earnings, remittances and aid are the amounts accumulated within one year before time t. All the values of economic variables except household size are recorded in log(1+Y). Columns (1) – (3) do not control for economic variables and columns (4) – (6) do. (*** p<0.01, ** p<0.05, * p<0.1) 40 Table 10: Impacts of using alternative disaster measures on split migration and moving of the entire household Dep. Variables Deaths Injured Missing Financial loss Crop damage Observations R-squared Number of household Split household migration across whole household migration across Province (1) -0.000739** (0.000278) 6.40e-05* (3.54e-05) -8.59e-05 (0.000628) -0.0156*** (0.00318) 1.41e-06 (4.85e-06) District (2) -0.000410* (0.000233) 4.28e-05 (2.98e-05) 0.000200 (0.000541) -0.0175*** (0.00393) 1.70e-05*** (6.07e-06) Sub-district (3) -0.000466** (0.000193) 9.91e-06 (2.62e-05) -7.25e-05 (0.000607) -0.0212*** (0.00560) 1.92e-05*** (6.49e-06) Province (4) -0.000106 (6.74e-05) 1.09e-05 (7.05e-06) 8.30e-05 (0.000127) -0.00247 (0.00175) -3.97e-06 (2.98e-06) District (5) 0.000129 (0.000114) 3.88e-06 (1.85e-05) -0.000541** (0.000236) -0.00582*** (0.00212) -4.99e-06* (2.85e-06) Sub-district (6) 0.000184 (0.000174) 1.14e-05 (2.80e-05) -0.000604 (0.000369) -0.00799* (0.00403) -7.38e-06** (3.06e-06) 24,651 0.041 8,217 24,651 0.081 8,217 24,651 0.106 8,217 24,651 0.007 8,217 24,651 0.018 8,217 24,651 0.033 8,217 Notes: Robust standard errors in parentheses, adjusted for clustering at province-time level. All columns control for time dummies and household fixed effects. Columns (1) – (3) are about split household migration. Columns (4) – (6) are about whole household migration. Different disaster variables measure various total human and economic losses due to earthquakes, eruption and floods at the province level between time t-1 and t. Financial loss is recorded in log(1+Y). (*** p<0.01, ** p<0.05, * p<0.1) 41 Table 11: Heterogeneous impacts of disasters on split migration and moving of the entire household Dep. Variables Disaster Household size Earnings Remittances Aid money Total asset Disaster*hhsize Disaster*aid Disaster*earn Disaster*remit Disaster*asset Observations R-squared Number of household Split household migration across whole household migration across Province (1) -0.0378** (0.0160) 0.0241*** (0.00622) 0.000787* (0.000407) -0.000762 (0.000820) -0.000118 (0.000975) -0.000279 (0.00176) -0.000912 (0.00181) -0.00318** (0.00138) 0.000232 (0.000231) -0.000351 (0.000407) 0.00119 (0.000836) District (2) -0.0508** (0.0213) 0.0383*** (0.00676) 0.00261*** (0.000554) 0.000709 (0.00108) -0.000748 (0.00130) 0.00221 (0.00208) 0.00670*** (0.00211) -0.00323* (0.00177) 0.000463 (0.000278) -0.0021*** (0.000659) 0.00216 (0.00136) Sub-district (3) -0.0543** (0.0235) 0.0617*** (0.0124) 0.00492*** (0.000814) 0.00112 (0.00152) -0.000340 (0.00197) -0.000936 (0.00241) 0.00803** (0.00370) -0.00313 (0.00264) 0.000237 (0.000391) -0.00214** (0.000889) 0.00230 (0.00139) Province (4) -0.00292 (0.00489) -0.00461*** (0.000752) 0.000251 (0.000167) 1.61e-05 (0.000134) -0.000122 (0.000429) -0.00165** (0.000651) 0.000292* (0.000171) -0.000872* (0.000491) -0.000205** (9.50e-05) 0.000143** (6.59e-05) -0.000204 (0.000229) District (5) 0.0169** (0.00649) -0.00973*** (0.00143) 7.03e-05 (0.000209) -0.000133 (0.000257) -0.000373 (0.000604) -0.00346*** (0.000786) 0.000540 (0.000548) -0.000468 (0.000759) -0.000273** (0.000107) 0.000127 (0.000132) -0.00149*** (0.000376) Sub-district (6) 0.0314** (0.0120) -0.0130*** (0.00186) 7.31e-05 (0.000270) 0.000179 (0.000346) -0.000780 (0.000792) -0.00456*** (0.00119) 8.47e-06 (0.000642) -0.000521 (0.00104) -0.000123 (0.000111) 0.000172 (0.000221) -0.00258*** (0.000718) 24,651 0.072 8,217 24,651 0.138 8,217 24,651 0.188 8,217 24,651 0.032 8,217 24,651 0.059 8,217 24,651 0.080 8,217 Notes: Robust standard errors in parentheses, adjusted for clustering at province-time level. All columns control for time dummies and household fixed effects. Columns (1) – (3) are about split household migration. Columns (4) – (6) are about whole household migration. All the economic variables except household size are recorded in log(1+Y).Disaster variable is the sum of earthquakes, eruptions and floods between time t-1 and t. The interaction term interacts number of disasters with different economic variables at time t. (*** p<0.01, ** p<0.05, * p<0.1) 42