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1 econtor Make Your Publication Viible. A Service of Wirtchaft Centre zbwleibniz-informationzentrum Economic Fujii, Tomoki Working Paper Climate change and vulnerability to poverty: An empirical invetigation in rural Indoneia ADBI Working Paper Serie, No. 622 Provided in Cooperation with: Aian Development Bank Intitute (ADBI), Tokyo Suggeted Citation: Fujii, Tomoki (2016) : Climate change and vulnerability to poverty: An empirical invetigation in rural Indoneia, ADBI Working Paper Serie, No. 622 Thi Verion i available at: Standard-Nutzungbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wienchaftlichen Zwecken und zum Privatgebrauch gepeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich autellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. Sofern die Verfaer die Dokumente unter Open-Content-Lizenzen (inbeondere CC-Lizenzen) zur Verfügung getellt haben ollten, gelten abweichend von dieen Nutzungbedingungen die in der dort genannten Lizenz gewährten Nutzungrechte. Term of ue: Document in EconStor may be aved and copied for your peronal and cholarly purpoe. You are not to copy document for public or commercial purpoe, to exhibit the document publicly, to make them publicly available on the internet, or to ditribute or otherwie ue the document in public. If the document have been made available under an Open Content Licence (epecially Creative Common Licence), you may exercie further uage right a pecified in the indicated licence.

2 ADBI Working Paper Serie CLIMATE CHANGE AND VULNERABILITY TO POVERTY: AN EMPIRICAL INVESTIGATION IN RURAL INDONESIA Tomoki Fujii No. 622 December 2016 Aian Development Bank Intitute

3 Tomoki Fujii i an aociate profeor of economic at Singapore Management Univerity. The view expreed in thi paper are the view of the author and do not necearily reflect the view or policie of ADBI, ADB, it Board of Director, or the government they repreent. ADBI doe not guarantee the accuracy of the data included in thi paper and accept no reponibility for any conequence of their ue. Terminology ued may not necearily be conitent with ADB official term. Working paper are ubject to formal reviion and correction before they are finalized and conidered publihed. The Working Paper erie i a continuation of the formerly named Dicuion Paper erie; the numbering of the paper continued without interruption or change. ADBI working paper reflect initial idea on a topic and are poted online for dicuion. ADBI encourage reader to pot their comment on the main page for each working paper (given in the citation below). Some working paper may develop into other form of publication. Suggeted citation: Fujii, T Climate Change and Vulnerability to Poverty: An Empirical Invetigation in Rural Indoneia. ADBI Working Paper 622. Tokyo: Aian Development Bank Intitute. Available: Pleae contact the author for information about thi paper. tfujii@mu.edu.g Aian Development Bank Intitute Kaumigaeki Building, 8th Floor Kaumigaeki, Chiyoda-ku Tokyo , Japan Tel: Fax: URL: info@adbi.org 2016 Aian Development Bank Intitute

4 Abtract Scientit etimate that anthropogenic climate change lead to increaed urface temperature, ea-level rie, more frequent and ignificant extreme weather and climate event, among other. In thi tudy, we invetigate how climate change can potentially change the vulnerability to poverty uing a panel data et in Indoneia. We focu on the effect of drought and flood, two of the commonly oberved diater there. Our imulation reult indicate that vulnerability to poverty may increae ubtantially a a reult of climate change in Indoneia. JEL Claification: I32, O10

5 Content 1. INTRODUCTION CLIMATE CHANGE AND DISASTERS IN INDONESIA... 2 Drought... 3 Flood DATA AND SUMMARY STATISTICS METHODOLOGY... 7 Meaure of Vulnerability... 7 Future Climate Scenario and Simulation EMPIRICAL RESULTS Baeline Reult Scenario 1(a): Doubling Incidence of Flood and Drought from Scenario 1(b): Special Treatment of Major ENSO Event Scenario 2: Uing Linearly Extrapolated Standard Deviation of Daily Rainfall DISCUSSION REFERENCES APPENDIX: ADDITIONAL TABLES... 22

6 1. INTRODUCTION The impact of climate change are multifariou and heterogeneou acro the globe. Scientit now widely agree that climate change i likely to affect not only the average temperature of the earth urface but alo variou other dimenion, including agriculture, water reource, ecoytem, and prevalence of dieae. Climate change i alo expected to affect frequency and magnitude of extreme weather and climate event, which, in turn, may alter the pattern of diater uch a flood and drought. The way people are affected by thee diater may be different, even within relatively mall area, becaue ome people are more reilient or adaptive. Thoe who are not reilient or adaptive may fall into poverty a a reult of the negative hock that diater bring about. It i, therefore, important to undertand who are vulnerable to extreme weather and climate event o that appropriate meaure can be taken to minimize the negative hock that thee event bring about. However, depite the potential importance of thee event, there i a dearth of reearch on climate-driven vulnerability to poverty. There are a few reaon for thi. Firt, although there are ome indication that the pattern of ome extreme event ha changed a a reult of anthropogenic influence, including increae in atmopheric concentration of greenhoue gae, there i a lack of clear cientific evidence that quality and quantity of extreme event have changed on regional and global cale for certain pecific event. For example, the available intrumental record of flood at gauge tation are limited in pace and time for a complete aement of the climate-driven oberved change in the magnitude and frequency of flood at regional cale (IPCC 2012). Thi i alo an important iue in Indoneia. Although the National Diater Management Agency (Badan Naional Penanggulangan Bencana) collect and maintain diater information in Indoneia, the data are not directly comparable over time. For example, the number of recorded flood event i le than 15 each year between 1985 and However, the number of event after 2002 i over 100 every year between 2003 and Thi maive increae in the number of recorded flood event may be partly due to the actual increae in flood event, but it i mot likely due to the better data collection in recent year. Second, the phyical impact of extreme event may tranlate into different economic hock to different houehold, even within the ame town or village. Variou factor, including the occupation of the houehold head, the houehold aet, the acce to credit and inurance, and the local infratructure development, are all likely to matter. However, ocioeconomic urvey, from which poverty tatitic are uually derived, typically contain no or very limited information about diater and extreme event. Therefore, it i difficult to directly link poverty with extreme event. Depite thee difficultie, given the oberved increae in extreme event acro the world, the topic i more relevant than ever before. The timeline and increaed importance of the climate-driven vulnerability to poverty can alo be een from the fact that the Fifth Aement Report by the Intergovernmental Panel on Climate Change (IPCC) Working Group II, which traditionally focue on adaptation and vulnerability, ha a new chapter on Livelihood and Poverty (IPCC 2014). 1 See alo, 1

7 Becaue of the data availability and relevance, we focu on two common type of diater in Indoneia, flood and drought. We evaluate how thee two type of diater affect the vulnerability of houehold to poverty and imulate the impact of climate change on vulnerability to poverty under ome plauible cenario. Thi paper i organized a follow. In Section 2, we briefly preent an overview of the ituation of flood and drought in Indoneia. In Section 3, we decribe the data ued, followed by a dicuion of the method in Section 4. Section 5 preent the reult and Section 6 offer ome dicuion. 2. CLIMATE CHANGE AND DISASTERS IN INDONESIA In Indoneia, variou impact of climate change have already been oberved and are expected to take place. For example, modet temperature increae ha already occurred and it i expected to continue. The rainy eaon i expected to horten with more intene rainfall during the rainy eaon which, in turn, lead to a ignificant increae in the rik of flooding. 2 Sea-level rie will inundate productive coatal zone and the warming of ocean water will affect the marine biodiverity. Climate change will alo intenify water- and vector-borne dieae and threaten food ecurity (PEACE 2007). Indoneia i among the firt countrie to experience the climate departure, which i the moment when the average temperature become o impacted by climate change that the old climate i left behind. It can be conidered a tipping point uch that the average temperature of the coolet year from then on i projected to be warmer than the average temperature of the hottet year between 1960 and Mora et al. (2013) etimate that Manokwari, Indoneia, i going to experience climate departure a early a Jakarta i etimated to have climate departure in Thee are ubtantially earlier than the world average of 2047 reported in the ame tudy. The climate departure potentially will have a ignificant impact on the live of people in Indoneia, the poor in particular, becaue there remain a izable fraction of people who are either till under the poverty line or only lightly above the poverty line. For thee people, the threat of poverty i far from over. If they are hit by a negative hock due to climate change, they may fall (further) below the poverty line. Therefore, Indoneia i a particularly important country to tudy in the context of climate-driven vulnerability to poverty. A mentioned earlier, we chooe to focu on flood and drought. We make thi choice for two reaon. Firt, they are two of the mot important impact of climate change in Indoneia. Future climate change i likely to increae their frequency and everity in Indoneia. Second, flood and drought are among the mot commonly oberved diater. and, therefore, we have an accumulation of data on thee type of diater. Hence, we can arguably better predict whether climate change alter their frequency or incidence. In contrat, it i generally much more difficult to predict the impact of event that have never happened before. Jut for the ake of comparion, conider coatal eroion induced by climate change. A ubtantial fraction of the population live cloe to the coat in Indoneia and they are ure to be negatively affected by ea-level rie; their live a well a home, land, and other aet may become more vulnerable a a 2 The increaing trend in the tandard deviation of daily rainfall preented later in Figure 1 i alo indicative of the heightened rik of drought and flood in the future. 2

8 reult of climate change. However, thi impact i difficult to predict becaue we have little information on how people would cope with coatal eroion. Drought Drought are common diater in Indoneia, affecting ome part of Indoneia every year. Drought negatively affect agricultural output and water upply. They are alo aociated with an increaed incidence of foret fire. The incidence and magnitude of drought tend to be particularly higher during the phae of El Niño Southern Ocillation (ENSO), which refer to the variation in the urface temperature of the tropical eatern Pacific Ocean and in air urface preure in the tropical wetern Pacific. Thee variation happen becaue the trade wind, which carry wet and warm air from the wet, tend to be weaker and, thu, dry and cold air tend to blow from the eat during the El Niño year in Indoneia. Thi, in turn, tend to puh back the onet of the rainy eaon a much a two month. A a reult, ENSO tend to lead to drought at the end of dry eaon. ENSO alo tend to lead to flood during the rainy eaon becaue the rain tend to intenify during the rainy eaon. 3 Uing a model linking ENSO-baed climate variability to Indoneian cereal production, Naylor et al. (2002) find, among other, that Indoneia paddy production varie, on average, by 1.4 million tonne for every 1 C change in ea-urface temperature anomalie the deviation in temperature from a long-term monthly mean ea-urface temperature for Augut. Drought affect agricultural output becaue water i a key input for mot agricultural output including rice, the main food crop grown in Indoneia. During El Niño year, widepread drought affected 1-3 million hectare under paddy cultivation. Even during La Niña year, in which rainfall tend to be higher than average, localized drought affect 30,000 to 80,000 hectare. On average, 280,000 hectare under paddy cultivation, which i much more than two percent of the total paddy area, are affected annually by drought to varying degree. Thi mean that nearly 160,000 farm houehold are vulnerable to thee periodic drought (Kihore et al. 2000). Drought affect thoe farmer whoe live are dependent on their farmland. Baed on regreion analyi with cro-ectional data, Skoufia, Katayama and Eama-Nah (2012) report a negative welfare impact of a ignificant hortfall in rain for farm houehold. Korkeala, Newhoue and Duarte (2009) find that a delayed onet of the monoon eaon i aociated with a 13 percent decline in per capita conumption for poor houehold but the delayed onet two year ago wa poitively correlated with conumption. Thi mean that poor houehold experience greater volatility, but no lating reduction in conumption, following delayed onet of the monoon eaon. The finding of thee tudie indicate that drought mitigation meaure may be ueful. For example, Pattanayak and Kramer (2001a) meaure the willingne to pay for drought mitigation from waterhed protection in Ruteng Park in Indoneia by the Contingent Valuation Method. They find that farmer are willing to pay up to $2 3, which i about 10 percent of annual agricultural cot, 75 percent of the annual irrigation fee, and 3 percent of annual food expenditure. Pattanayak and Kramer (2001b) alo report a izable benefit of drought mitigation baed on a eparate houehold model. 3 See, for example, Garrion (2010) for a general introductory dicuion on ENSO event. 3

9 Flood Flood are alo common in Indoneia. For example, Jakarta ha a long hitory of flood becaue of it geomorphology and intene eaonal rainfall. Thi problem ha been exacerbated by rapid population growth, land-ue change, waterway being clogged with houehold wate and ediment from uptream. In recent year, maive flood were recorded in January 2002 and February There were, repectively, 57 and 70 death and 365,000 and 150,000 evacuee in thee event. 4 In January 2014, 17.4 percent of Jakarta acro 89 ditrict had been affected by a flood with 23 death and over 65,000 evacuee, according to the Jakarta Province Regionl Diater Mitigation Agency (Badan Penanggulangan Bencana Daerah Provini DKI Jakarta). Flood alo affect agricultural output. The order of magnitude of the impact of flood i comparable with that of drought. For example, Hadi et al. (2000), cited by Paaribu (2010), etimate that the ize of paddy harvet failure due to flood and drought are, repectively, 0.21 and 0.50 percent of the planted area during According to the etimate by the Directorate General of Crop Protection, Minitry of Agriculture cited by Paaribu (2010), the actual rice area affected by flood and drought are 333,000 and 319,000 hectare in DATA AND SUMMARY STATISTICS The main data ource for thi tudy i the Indoneian Family Life Survey (), an on-going panel urvey in Indoneia. The original ample frame covered 13 of the 27 province in Indoneia in Within each of thee 13 province, enumeration area were randomly drawn from a nationally repreentative ample frame ued in the 1993 National Socio-Economic Survey (SUSENAS) deigned by the Indoneian Central Bureau of Statitic (BPS). The ample wa repreentative of about 83 percent of the Indoneian population in The firt round of the ( 1) wa conducted in 1993/94 by the RAND Corporation, in collaboration with Lembaga Demografi, Univerity of Indoneia. 2 wa conducted in 1997, by the RAND Corporation, in collaboration with the Univerity of California at Lo Angele and Lembaga Demografi, Univerity of Indoneia. 5 3 wa completed in 2000 and conducted by the RAND Corporation, in collaboration with the Population Reearch Center, Univerity of Gadjah Mada. The 4 took place in 2007/08 and it wa conducted by the RAND Corporation, the Center for Population and Policy Studie of the Univerity of Gadjah Mada, and Survey METRE. In 1, a total of 7,224 houehold were interviewed and detailed individual-level data were collected from over 22,000 individual. In 2, 94 percent of the 1 houehold and 91 percent of the 1 target individual were re-interviewed. In 3, 95.3 percent of 1 houehold were re-contacted. In 4, the recontact rate wa 93.6 percent. Among 1 dynaty houehold (any part of the original 1 houehold, 90.3 percent were either interviewed in all four wave or died, and 87.6 percent were actually interviewed in all four wave). Thee recontact rate are a high a or higher than mot panel urvey in the United State and Europe. High reinterview rate were obtained, in part, becaue the data collection team wa 4 5 Your letter: Flooding in Jakarta the fact, Jakarta Pot, January 28, Additionally, 2+ wa conducted in 1998, which covered a 25 percent ub-ample of the houehold. 2+ i not ued in thi tudy. 4

10 committed to tracking and interviewing individual who had moved or plit off from the origin 1 houehold. High reinterview rate contribute ignificantly to improve the data quality in a longitudinal urvey becaue they leen the rik of bia due to nonrandom attrition. 6 In each round of the, there wa alo an aociated community-level urvey, in which quetion about the characteritic of the community were aked. We ue the climate component of thee data. Becaue the urvey format ha changed over round and becaue a complete hitory of extreme event that houehold have experienced i not available, we only ue the indicator variable for whether the community ha experienced each of flood and drought over the lat five year for our main analyi. In thi tudy, we chooe to ue only thoe rural houehold that appear in all round of the urvey and did not move acro village. 7 Removing the record with miing value in key variable, we are left with a total of 4,680 obervation acro four round, or 1,170 houehold, to be ued for our main analyi. The difference between our ample and the whole ample will be brifly dicued later. Table 1 provide ome ummary tatitic for our ample. All the reported tatitic in the table are weighted by the ample weight that take into account attrition. Table 1: Sample Mean of Key Variable by the Round Decription Head age houehold ize Toilet in premie (%) Single-level ingle unit (%) Roof i tile (%) Roof i foliage/leave (%) Wall i maonry (%) Flood in lat 5 year (%) Drought in lat 5 year (%) The firt row in the table how that the average age of the houehold head increae a expected. However, even though we track the ame et of houehold, the average age of the houehold head doe not increae exactly by the number of year between the urvey becaue the original head may die or diappear from the houehold for other reaon. Similarly, houehold ize tend to get maller over time. Table 1 alo how that the houing condition ha generally improved over time. For example, the proportion of houehold that have a toilet within their premie ha increaed from 12.9 percent to 50.7 percent over the four round. The lat and firt row from the lat, repectively, how the proportion of houehold that have experienced drought and flood within the lat five year before the urvey. A the table how, there are ubtantial fluctuation in the incidence of drought and flood acro round. Table 2 how the ditribution of houehold that experienced flood and drought over the four round of the urvey. Due to the limitation of the data dicued earlier, we ue the indicator that the community ha experienced flood and/or drought over the lat five year. Therefore, a caution mut be exercied when interpreting Table 2. The table how, for example, that 18 houehold in our ample 6 7 See the following webite for further detail: We retain a mall number of houehold that moved within the village. 5

11 experienced at leat one drought within a period of five year before an urvey for three round but no flood within a period of five year within any round of the urvey. Note that thee houehold may have experienced drought more than three time in our tudy period, becaue, for example, they may have experienced multiple drought within five year before a particular round of the. Table 2: The Number of Houehold that have Experienced Flood and Drought in the Round Drought Flood Total Total ,170 Becaue the flood and drought are reported by the urvey repondent and the way flood and drought are reported acro communitie may not be trictly comparable, it i deirable to have an alternative meaure of climate variation. To thi end, we have compiled daily rainfall data at the provincial level. 8 We then computed for each houehold the tandard deviation in daily rainfall in the province the houehold belong to over the pat 365 day from the firt interview for the houehold conumption module. We took thi meaure a a convenient meaure of climate variability. Thi meaure alo ha an advantage that the reference period i horter than the flood and drought indicator taken from the data. However, becaue the rainfall are available only from 1997, the tandard deviation over the lat 365 day can be computed only from In Figure 1, we plot the tandard deviation of provincial-level daily rainfall averaged over all the province for each year between 1997 and The dahed line repreent the linear trend in the tandard deviation of daily rainfall. We can ee from thi figure, that there i an increaing trend in the tandard deviation of daily rainfall over the year involved. In thi paper, we follow the tandard conumption-baed definition of poverty. To thi end, we firt define poverty line. We conider the following three alternative et of poverty line: i) the official poverty line, which are defined at the level of urban and rural area annually; 9 ii) the US$1.25-a-day international poverty line; and iii) the US$2-a-day international poverty line. For ii) and iii), we ue the purchaing power parity converion factor for private conumption in 2005 publihed in the World Development Indicator (USD 1=INR ) and adjut for the patial price difference and inflation uing the Conumer Price Index alo available from the BPS webite. 10 Becaue the CPI data are only available for major citie, we ue the CPI for the capital of the province in which the houehold wa located. 8 We firt obtain the provincial-level geographical coordinate from MyGeoPoition ( and ue thee coordinate to obtain daily rainfall data from the agroclimatology data webite by the Prediction of World Energy Reource, the National Aeronautic and Space Adminitration ( 9 They are available from the following webite: tabel =1 daftar=1 id_ubyek=23 notab=7. 10 Obtained from Becaue the bae year for the CPI changed over time, we link them by the CPI for the two contiguou month and the inflation rate reported in thi webite to cover our tudy period. 6

12 Figure 1: Standard Deviation of Daily Rainfall between 1997 and 2013 To meaure poverty at the houehold level, we compare the total monthly conumption expenditure per capita, or the total monthly houehold expenditure divided by the houehold ize, with the poverty line. If the conumption per capita of the houehold that the individual belonged to fell below the poverty line, the individual i deemed poor. 4. METHODOLOGY Meaure of Vulnerability A with mot other tudie in the literature, we define vulnerability to poverty V a expected poverty. We denote the conumption per capita by c, the poverty line by z, their ratio by q c/z. Further, we denote the cenored ratio by q = min(1, q ). We conider the following four vulnerability meaure: V = E[Ind(q < 1)] V 1 = E[1 q] V 1/2 = E[1 q 1/2 ] V 0 = E[lnq], where Ind( ) i an indicator function that i equal to one when the argument i true and zero otherwie. The firt meaure i imply the expected headcount index and the mot widely ued meaure in the literature including Chaudhuri, Jyotna, and Suryahadi (2002). The econd meaure i the expected poverty-gap index. The third meaure i the expected Chakravarty index with parameter 1/2. The fourth meaure i the expected Watt meaure. All thee meaure are an uncaled verion of the meaure propoed by Calvo and Dercon (2013). 11 Although their parameter retriction would exclude V and 11 Their meaure i V r CD = E[(1 q r )/r] for r < 1 and r 0 and V 0 CD = E[lnq]. 7

13 V 1, we include them in thi tudy becaue they have intuitive interpretation (they are repectively expected poverty rate and expected poverty gap). The former i alo cloely related to other vulnerability meaure (See alo Klaen and Povel (2013) and Fujii (2015a) for review of variou vulnerability meaure). To operationalize the expectation given above, we aume the following model of the ratio for each individual i at time t: lnq it = X it T β + ε it, (1) where X it i a column vector of value of covariate for lnq it ; and the idioyncratic error term ε it i aumed to be normally ditributed with a zero mean but may be correlated acro time or individual. The error term ε it i allowed to be heterokedatic and it tandard deviation i given by it Var[ε it ] = exp(z it T θ), where it i a column vector of covariate for the variance of the idioyncratic term. Although we et Z it = X it in our empirical application a with variou other empirical tudie in the literature, Z it and X it can be different, in general, and we maintain thi difference in thi ection. Note that there are 1,170 individual and 4 time period. Hereafter, we focu on a particular individual in a particular period and drop ubcript i and t for mot of the remainder of thi ection to keep the preentation imple. Given thee aumption, the vulnerability meaure can be rewritten with the probability denity function and the cumulative ditribution function of normal ditribution a the following propoition how: Propoition 1 Given the aumption above, V, V 1, V 1/2, and V 0 can be written a follow: V = Φ XT β (2) V 1 = Φ XT β exp X T β + 2 Φ β 2 XT (3) V 1/2 = Φ XT β exp XT β + 2 Φ β 2 8 XT (4) 2 V 0 = X T βφ XT β + φ XT β (5) Proof It i convenient to define the normalized error term by v ε/. Then, eq. (2) (5) follow from below: V = Pr(ε < X T β) = Pr v < XT β V 1 = E[max(0,1 exp(x T β + v))] = Φ XT β exp(x T β) X T β exp(v)vφ(v)dv 8

14 = Φ XT β exp X T β X T β V 1/2 = E[max(0,1 exp(x T β + v))] = Φ XT β exp XT β 2 X T β = Φ XT β exp XT β V 0 = E[max(0, (X T β + v))] = X T βφ XT β X T β exp (v )2 2 2π exp v φ(v)dv 2 X T β vφ(v)dv, where we ue φ (v) = vφ(v) to obtain eq. (5). exp dv (v /2)2 2 2π dv A it can be een from Propoition 1, both V 1 and V 1/2 have a very imilar form. Their firt term are the ame and repreent the expected change in the extenive margin (i.e., whether the individual i below the poverty line). The difference in the econd term eentially come from the way the two meaure treat the left tail in the conumption ditribution. To etimate thee meaure, we firt obtain an etimate β of the coefficient β by ordinary leat quare (OLS) regreion. We then compute a logarithmic quared reidual u ln((lnq X T β ) 2 ). By an OLS regreion of u on Z, we obtain an etimate θ. Then, we obtain an etimate it of it for each combination of (i, t) a follow: it = exp Z it T θ Replacing β and by β and in eq. (2) (5), we can etimate the vulnerability meaure for each individual and each time period. In our empirical application, we aume that the vulnerability i the ame for every member in the houehold. Therefore, we will aggregate houehold-level vulnerability by taking the average acro houehold weighted by the population expanion factor, or the product of the houehold ize and the houehold weight. It hould be noted here that we run a linear regreion of the logarithmic houehold conumption per capita over the poverty line on it covariate. Thi point i different from variou other method including that of Chaudhuri, Jyotna, and Suryahadi (2002), which often involve etimating a binary regreion of poverty tatu on it covariate. We choe a linear model becaue we can analytically derive variou vulnerability meaure in a coherent manner. Thi, in turn, ha an added advantage that we are able to verify how our reult are (in)enitive to the choice of vulnerability meaure. 9

15 Future Climate Scenario and Simulation Uing the meaure introduced above, we imulate the impact of climate change on vulnerability to poverty by changing the value of covariate. To operationalize thi idea, we need ome future climate cenario. The main challenge here i that we do not yet know exactly how climate change would affect the live of people through the channel of flood and drought. In particular, cientit do not yet have enough evidence to etablih a clear caual relationhip between climate change and flood, even though they generally agree that anthropogenic climate change ha increaed and i likely to continue to increae the incidence of drought and change the frequency and pattern of ENSO event. Therefore, we chooe to adopt a few imple cenario to preent the poible order of magnitude of the impact that future climate change may bring about. Our firt cenario i the doubling incidence of flood and/or drought from the 2007 ( 4) level. Thi cenario i motivated by Cai et al. (2014), who predict that the frequency of major El Niño event may double in thi century. Becaue El Niño event are related to flood and drought, the doubling incidence of flood and drought would not be completely unrealitic. However, becaue doubling incidence may appear extreme and the time horizon involved i very long, we alo conider the cae where the incidence of flood and/or drought increae by 50 percent. A dicued in detail in the next ection, we conider two cae under thi cenario. In the firt cae (Scenario 1(a)), we treat all the flood and drought oberved in the data equally. In the econd cae (Scenario 1(b)), we aume that the drought and flood in 1997 were different, becaue the ENSO event in 1997 i conidered one of the larget in the obervation hitory. A we hall how, there i ome evidence that the ENSO event in 1997 wa indeed different. Our econd cenario (Scenario 2) i that the tandard deviation of daily rainfall in a year at the provincial level change linearly over time. In thi exercie, we are, eentially, uing the linear trend line imilar to the one drawn in Figure 1 to predict the future tandard deviation except that the trend line i defined for each province. Uing a linear extrapolation to year 2030 for each province, we obtain the predicted tandard deviation of daily rainfall. We then ue thi predicted value to compute the vulnerability to poverty under climate change. Although thee cenario are admittedly naïve, the reult we preent in the next ection provide a plauible order of magnitude of the impact of climate change on vulnerability to poverty. 5. EMPIRICAL RESULTS Baeline Reult To compute the vulnerability meaure, we firt run regreion to etimate β and θ. Becaue the dependent variable in eq. (1) i lnq, which i the logarithm of conumption per capita normalized by the poverty line, the etimate depend not only on the conumption per capita but alo on the poverty line. In Table 3, we report the baeline regreion reult when international poverty line are ued. In thee regreion, we include the houehold-level fixed-effect term to capture the unoberved heterogeneity acro houehold. We alo include -round-pecific fixed-effect to aborb the aggregate hock to rural Indoneia in each round of the urvey o that the changing macroeconomic environment i appropriately controlled for. In addition, we control for demographic characteritic of 10

16 houehold a well a our main variable of interet, indicator variable for flood and drought experienced over the lat five year in the community of reidence. Note that the reult preented in Table 3 are independent of whether we ue $1.25 poverty line or $2 poverty line, becaue the contant term will aborb the difference. However, when the national poverty line are ued, the regreion reult are lightly different. Thi i becaue the national poverty line are uniform within the rural area each year wherea we adjut for the patial price difference for the international poverty line. In thi ection, we preent the regreion reult baed on international poverty line only. The correponding regreion reult baed on national poverty line are reported in Table A.1 in the Appendix. A Table 3 how, the flood variable ha a negative β-coefficient, indicating that a flood tend to decreae the expected logarithmic conumption, though thi coefficient i not ignificant. The θ-coefficient on flood and drought are both poitive, uggeting that they tend to increae the variance of conumption, though the coefficient for flood i the only one that i ignificant. Table 3: Regreion Etimate of β and θ for Scenario 1(a) β θ Variable Et. (.e.) Et. (.e.) Head age 0.018*** (0.0046) (0.022) Head age quared/ *** (0.0043) (0.021) Houehold ize 0.15*** (0.0066) (0.031) Flood lat five year (0.024) 0.25** (0.12) Drought lat five year (0.026) 0.14 (0.12) R N 4,680 4,680 Note: Houehold-pecific and -round-pecific fixed-effect term are included in the model. International poverty line are ued for the calculation of q. *, **, and ***, repectively, repreent tatitical ignificance at 10, 5, and 1 percent level. Table 4 preent variou poverty and vulnerability meaure for each round of the urvey. All the reult are weighted by the population expanion factor. In the firt three row, we report the Foter-Greer-Thorbecke (FGT) poverty meaure (Foter, Greer and Thorbecke 1984) with parameter α = 0, α = 1, and α = 2 for each round of urvey, where the FGT meaure with parameter α i defined a follow: FGT α = 1 N Ind(q i < 1)(1 q i ) α. i FGT 0 i imply the proportion of people who are under the poverty line and i often called the poverty rate or headcount index. Therefore, Table 4 how, for example, that 53.8 percent of people in the 4 ample wa living in a houehold whoe conumption per capita wa below the $2-a-day international poverty line. FGT 1 i alo called the poverty gap, which meaure the average hortfall from the poverty line. FGT 2 i called the poverty everity or the quared poverty gap and put higher weight on the pooret of the poor. In the fourth and fifth row, we repectively report the Watt poverty meaure (Watt 1968) and the Chakravarty poverty meaure (Chakravarty 1983) with parameter ω = 1/2, which are defined a follow: 11

17 W = 1 N lnq i, C ω = 1 N (1 q ω i ). i The Watt meaure i the average logarithmic hortfall from the poverty line. A with FGT 2, both the Watt and the Chakravarty meaure put higher weight on the pooret of the poor. In all thee meaure, poverty ha generally dropped over the four round of urvey, except that the poverty rate under the national poverty line ha lightly increaed between 2 and 3. Regardle of the poverty meaure ued, there i a ubtantial drop in poverty between 3 and 4, during which Indoneia achieved a healthy economic growth of around 4 percent per year in per capita income. Table 4: Poverty Meaure and Vulnerability Meaure baed on the Regreion Reported in Table 3 and A.1. Population Expanion Factor i Applied. Poverty Line National Poverty Line International Poverty Line $1.25 International Poverty Line $2 Round ILFS 4 FGT FGT FGT W C 1/ V V V 1/ V The fifth to ninth row are our vulnerability meaure. To compute thee, we plug the parameter value reported in Table 3 or Table A.1 in the Appendix a well a the etimate of V into eq. (2) (6). Becaue we have V = E[FGT 0 ], V 1 = E[FGT 1 ], V 1/2 = E[C 1/2 ], and V 0 = E[W] by definition, we expect to have V FGT 0, V 1 FGT 1, V 1/2 C 1/2, and V 0 W, which indeed hold a hown in Table 4. A expected, the change in our vulnerability meaure have been imilar to thoe of poverty meaure. Scenario 1(a): Doubling Incidence of Flood and Drought from 4 We now imulate how the vulnerability meaure change a a reult of future climate change. A dicued in Section 4, our firt cenario i where the incidence of flood and drought double from the 2007 level oberved in 4. More preciely, 17.7 percent and 14.5 percent of the ample houehold experienced flood and drought no more than five year from the 4 urvey, repectively. We conider the effect of doubling thee proportion. Becaue doubling may appear extreme and involve a long time horizon, we alo conider 50 percent increae a a plauible change in the middle run. 12

18 A problem in thi exercie i which houehold hould bear the impact of flood and drought in the future. Although it would not be impoible to etimate the flood and drought rik for each houehold, we chooe to aign flood and drought randomly with an equal probability. We do thi repeatedly under the aumption of independence between flood and drought. 12 That i, in each round of imulation, we randomly pick a predetermined number of houehold that are affected by flood or drought. For thee houehold, we change the value of X and Z correponding to flood or drought in the computation of vulnerability while keeping all the other covariate and fixed-effect term contant at the baeline level in We repeat thi 1,000 time and take an average over all the round of imulation. The random aignment carried out in thi way i not without problem. For the ake of argument, conider a ituation in which only thoe houehold that are well above the poverty line are affected by flood and drought. In thi cae, flood and drought would not increae the vulnerability meaure much, becaue the houehold that are hit by the diater are likely to remain well above the poverty line. If we randomly aign flood and drought without taking thi pattern into conideration, the vulnerability would unambiguouly increae, becaue the vulnerability meaure for thoe houehold that are cloe or below the poverty line auming that we have uch houehold would woren. In other word, the random aignment would increae the vulnerability meaure. Hence, random aignment i not an innocuou exercie in general. It turn out that the pure effect of the random aignment i mall in our data. The econd column ( 4 ) of Table 5 refer to the vulnerability meaure for the 4 urvey (they are the ame a thoe reported in Table 4), which erve a our baeline meaurement. In the third column ( Randomize ), we compute vulnerability meaure by randomly and independently aigning flood and drought without changing the total number of houehold that are affected by each of thee diater. Since there i little difference in thee two column, the random aignment ha only negligible effect on the reulting vulnerability meaure. The fourth column (1.5x Fl) of Table 5 how the effect of increaing the incidence of flood by 50 percent. Compared with the third column, the vulnerability meaure increae by around 2 3 percent (e.g., ( )/ % for V ) when the national poverty line i ued. The increae i even maller when an international poverty line, epecially the $2-a-day poverty line, i adopted. The fifth column (1.5x Dr) give the effect of increaing the incidence of drought by fifty percent. The change in vulnerability i generally maller than thoe found for flood. The ixth column (1.5x Fl&Dr) give the combined effect of the increae of incidence of both flood and drought by 50 percent. The eventh, eighth, and ninth column give the vulnerability meaure when the incidence of flood, drought and both flood and drought double, repectively. A can be een from the table, the impact of doubling the incidence i alo mall. The bigget relative change i een in V 0 under the national poverty line, but even in thi cae, the increae i only around 6 percent. Therefore, Table 5 how that the combined impact of increaed incidence of flood and drought i relatively mall. The impact imulated here hould be conidered a long-run average and not a one-off impact a the flood and drought indicator ued in thi tudy are baed on the incidence over the lat five year. 12 It i alo poible to aign flood and drought jointly. However, we choe to maintain the independence aumption becaue the correlation between the flood and drought incidence i very mall in our ample. 13

19 Table 5: Simulated Effect of Increaing the Incidence of Flood and Drought by 50 percent (1.5x) and 100 percent (2x) for Variou Poverty Line under Scenario 1(a). Population Expanion Factor i Applied. Scenario 4 Randomize 1.5x Fl 1.5x Dr 1.5x Fl&Dr 2x Fl 2x Dr 2x Fl&Dr National Poverty Line V V V 1/ V International Poverty Line $1.25 V V V 1/ V International Poverty Line $2 V V V 1/ V Scenario 1(b): Special Treatment of Major ENSO Event Although an up to 7 percent increae in vulnerability (expected poverty) i not negligible, it may give a mileading impreion about the importance of the impact of flood and drought a the hort-run effect may be everer. Hence, to imulate the poible magnitude of the hort-run effect of major ENSO event, we utilize the fact that there wa a major ENSO event right before the data collection of the 2 urvey Becaue thi event wa clearly a major one, it i reaonable to treat flood and drought eparately from thoe in other year. Table 6 report the regreion reult under international poverty line 13 when the flood and drought effect are aumed to be different between 2 and other round of urvey. The table clearly how that the order of magnitude of the effect of flood and drought are different between 2 and other round. Unlike Table 3, the β-coefficient are tatitically ignificant for both flood and drought for 2, but not for other round of. Furthermore, we find that the major drought ignificantly increaed the variance of conumption. It hould be noted here that the vulnerability meaure are generally model dependent. Therefore, the vulnerability meaure reported in Table 4 are generally different from thoe calculated from the regreion reult reported in Table The regreion reult under the national poverty line are reported in Table A.2 in the Appendix. A with Table 3, the regreion reult for $2 and $1.25 international poverty line are identical except for the contant term. 14

20 Table 6: Regreion Etimate of β and θ for Scenario 1(b) β Variable Et. (.e.) Et. (.e.) Head age 0.019*** (0.005) (0.022) Head age quared/ *** (0.004) (0.021) Houehold ize 0.15*** (0.007) (0.031) Flood lat five year (non-2) (0.026) 0.32** (0.13) Drought lat five year (non-2) (0.030) 0.14 (0.14) Flood lat five year (2) 0.21*** (0.071) 0.46 (0.34) Drought lat five year (2) 0.073* (0.044) 0.36* (0.21) R N 4,680 5,584 Note: Houehold-pecific and -round-pecific fixed-effect term are included in the model. International poverty line are ued for the calculation of q. *, **, and *** repectively repreent tatitical ignificance at 10, 5, and 1 percent level. It hould be noted here that the vulnerability meaure are generally model dependent. Therefore, the vulnerability meaure reported in Table 4 are generally different from thoe calculated from the regreion reult reported in Table 6. However, becaue the model are imilar, the vulnerability meaure are generally very cloe. 14 A with Table 5, we report, in Table 7, the imulated effect of increaed incidence of flood and drought from the 4 level by 50 or 100 percent. However, unlike Scenario 1(a), the impact of flood and drought conidered in Scenario 1(b) are thoe aociated with a major ENSO event. Hence, we firt replace the effect of flood and drought in 4 with thoe effect for 1997 ( 2) without changing the flood or drought tatu in the 4 record. However, becaue the model are imilar, the vulnerability meaure are generally very cloe. 15 A with Table 5, we report, in Table 7, the imulated effect of increaed incidence of flood and drought from the 4 level by 50 or 100 percent. However, unlike Scenario 1(a), the impact of flood and drought conidered in Scenario 1(b) are thoe aociated with a major ENSO event. Hence, we firt replace the effect of flood and drought in 4 with thoe effect for 1997 ( 2) without changing the flood or drought tatu in the 4 record. By comparing the baeline vulnerability in the econd column ( 4) with the third column (1997-effect), it can be een that imply replacing the effect of flood and drought in 2007 (or non- 2) with thoe in 1997 (or 2) have a ubtantial impact on vulnerability. When the national poverty line are ued, there i about 40 percent increae in vulnerability, wherea the increae i around 20 and 10 percent when $1.25-a-day and $2-a-day poverty line are ued, repectively. The fourth column (Randomize) report vulnerability meaure when the aignment of flood and drought are randomized. A with Table 5, the randomization ha very little impact on the reulting vulnerability meaure. The fifth column (1.5x Fl) report the imulated vulnerability meaure when the incidence of flood increae by 50 percent, where the impact of flood i equivalent to that oberved in 2. Compared with the baeline vulnerability, the vulnerability ha increaed by well more than 50 percent in thi cae under the national poverty line. θ 14 Round-by-round vulnerability meaure for Scenario 1(b) are reported in Table A.4 in the Appendix. 15

21 Under international poverty line, the relative change i about percent, depending on the poverty line and vulnerability meaure ued. The impact of drought i le ubtantial than flood a hown in the ixth column (1.5x Dr) but the impact i till izable. The combined effect i even more ubtantial a hown in the eventh column (1.5x Fl&Dr). Obviouly, the impact i even larger when the incidence increae by 100 percent intead of 50 percent. The eighth to tenth column report the vulnerability meaure under the doubling incidence cenario. The combined effect of doubling the incidence of both flood and drought i particularly large with the increae in vulnerability from the -4 baeline reaching a high a 91 percent. Scenario 2: Uing Linearly Extrapolated Standard Deviation of Daily Rainfall In our econd cenario, intead of the flood and drought over the lat five year, we ue the tandard deviation of daily provincial-level rainfall over the pat 365 day counting from the firt interview for the conumption component of the urvey for each houehold. Table 8 report the regreion reult with international poverty line. 16 Note that the number of obervation in thi table i maller becaue we can compute the tandard deviation of daily rainfall only for 3 and 4 record. Table 7: Simulated Effect of Increaing the Incidence of Flood and Drought by 50 percent (1.5x) and 100 percent (2x) for Variou Poverty Line under Scenario 1(b). Population Expanion Factor i Applied. Scenario effect Randomize 1.5x Fl 1.5x Dr 1.5x Fl&Dr 2x Fl 2x Dr 2x Fl&Dr National Poverty Line V V V 1/ V International Poverty Line $1.25 V V V 1/ V International Poverty Line $2 V V V 1/ V Table 8 how that the β-coefficient on the tandard deviation of daily rainfall over the pat 365 day i negative and ignificant. The θ-coefficient i alo negative but it i not ignificant. To imulate the impact of climate change, we extrapolate the linear trend of provinciallevel tandard deviation in the annual rainfall to year To predict the future vulnerability in 2030, we replace the current tandard deviation for 4 record with thoe extrapolated tandard deviation. The reult obtained in thi way are provided in Table 9. For each et of poverty line, we report the baeline vulnerability at 4 16 The regreion reult under national poverty line are reported in Table A.3 in the Appendix. 16