Do natural disasters change savings and employment choices? Evidence from Bangladesh and Pakistan

Eskander, Shaikh M. S. U.; Fankhauser, Samuel; Jha, Shikha Working Paper Do natural disasters change savings and employment choices? Evidence from Bangladesh and Pakistan ADB Economics Working Paper Series, No. 505 Provided in Cooperation with: Asian Development Bank (ADB), Manila Suggested Citation: Eskander, Shaikh M. S. U.; Fankhauser, Samuel; Jha, Shikha (2016) : Do natural disasters change savings and employment choices? Evidence from Bangladesh and Pakistan, ADB Economics Working Paper Series, No. 505, Asian Development Bank (ADB), Manila This Version is available at: https://hdl.handle.net/10419/169336 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may be saved and copied for your personal and scholarly purposes. You are not to copy documents for public or commercial purposes, to exhibit the documents publicly, to make them publicly available on the internet, or to distribute or otherwise use the documents in public. If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. http://creativecommons.org/licenses/by/3.0/igo/ ASIAN DEVELOPMENT BANK AsiAn Development BAnk 6 ADB Avenue, Mandaluyong City 1550 Metro Manila, Philippines www.adb.org Do Natural Disasters Change Savings and Employment Choices? Evidence from Bangladesh and Pakistan Bangladesh and Pakistan are among the countries most vulnerable to livelihood risks arising from frequent exposure to large-scale natural disasters. We study household responses to floods and storms in terms of short-term changes in their dependence on agriculture. Results show that rural households temporarily move away from agriculture in response to disaster then come back after a short period of time. They therefore remain vulnerable to climatic extremes. Development of nonfarm employment opportunities in rural areas can therefore be a useful public policy to lower their dependence on agriculture and reduce their income and livelihood vulnerabilities. About the Asian Development Bank ADB’s vision is an Asia and Pacific region free of poverty. Its mission is to help its developing member countries reduce poverty and improve the quality of life of their people. Despite the region’s many successes, it remains home to a large share of the world’s poor. ADB is committed to reducing poverty through inclusive economic growth, environmentally sustainable growth, and regional integration. Based in Manila, ADB is owned by 67 members, including 48 from the region. Its main instruments for helping its developing member countries are policy dialogue, loans, equity investments, guarantees, grants, and technical assistance. adb economics working paper series NO. 505 december 2016 DO NAturAl DiSAStErS ChANgE SAviNgS AND EmplOymENt ChOiCES? EviDENCE frOm BANglADESh AND pAkiStAN Shaikh M.S.U. Eskander, Samuel Fankhauser, and Shikha Jha ADB Economics Working Paper Series Do Natural Disasters Change Savings and Employment Choices? Evidence from Bangladesh and Pakistan Shaikh M.S.U. Eskander, Samuel Fankhauser, and Shikha Jha No. 505 | December 2016 Shaikh M.S.U. Eskander ([email protected]) is a postdoctoral research officer and Samuel Fankhauser ([email protected]) is a co-director of the Grantham Research Institute on Climate Change and the Environment and Centre for Climate Change Economics and Policy, London School of Economics. Shikha Jha ([email protected]) is principal economist at the Economic Research and Regional Cooperation Department of the Asian Development Bank. The authors thank, without implicating, Giles Atkinson, Ed Barbier, Matthew Kahn, and Akiko Terada-Hagiwara for useful feedback and suggestions. The work was also supported by the International Development Research Centre project on Pathways for Resilience in Semi-Arid Economies. Creative Commons Attribution 3.0 IGO license (CC BY 3.0 IGO) © 2016 Asian Development Bank 6 ADB Avenue, Mandaluyong City, 1550 Metro Manila, Philippines Tel +63 2 632 4444; Fax +63 2 636 2444 www.adb.org Some rights reserved. Published in 2016. Printed in the Philippines. ISSN 2313-6537 (Print), 2313-6545 (e-ISSN) Publication Stock No. WPS168588-2 Cataloging-In-Publication Data Asian Development Bank. Do natural disasters change savings and employment choices? Evidence from Bangladesh and Pakistan. Mandaluyong City, Philippines: Asian Development Bank, 2016. 1. Bangladesh. 2. Income. 3. Natural disasters. 4. Pakistan. 5. Savings. I. Asian Development Bank. The views expressed in this publication are those of the authors and do not necessarily reflect the views and policies of the Asian Development Bank (ADB), the Department for International Development (DfID), the International Development Research Centre (IDRC), or their Board of Governors or the governments they represent. ADB does not guarantee the accuracy of the data included in this publication and accepts no responsibility for any consequence of their use. 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Attribution—You should always acknowledge ADB as the source using the following format: [Author]. [Year of publication]. [Title of the work in italics]. [City of publication]: [Publisher]. © ADB. [URL or DOI] [license]. Translations—Any translations you create should carry the following disclaimer: Originally published by ADB in English under the title [title in italics]. © ADB. [URL or DOI] [license]. The quality ofthe translation and its coherence with the original text is the sole responsibility of the translator. The English original of this work is the only official version. Adaptations—Any adaptations you create should carry the following disclaimer: This is an adaptation of an original work titled [title in italics]. © ADB. [URL or DOI][license]. The views expressed here are those of the authors and do not necessarily reflect the views and policies of ADB or its Board ofGovernors orthe governments they represent. ADB does not endorse this work or guarantee the accuracy of the data included inthis publication and accepts no responsibility for any consequence of their use. Please contact [email protected] if you have questions or comments with respect to content, or if you wish toobtain copyright permission for your intended use that does not fall within these terms, or for permission to use theADB logo. Notes: 1. In this publication, “$” refers to US dollars. 2. Corrigenda to ADB publications may be found at http://www.adb.org/publications/corrigenda CONTENTS TABLES AND FIGURES iv ABSTRACT v I. INTRODUCTION 1 II. BACKGROUND AND EMPIRICAL STRATEGY 3 III. DATA AND VARIABLES 6 A. Bangladesh Climate Change Adaptation Survey 6 B. Pakistan Rural Household Panel Survey 7 IV. EMPIRICAL SPECIFICATION 8 V. RESULTS AND DISCUSSIONS 11 A. Bangladesh 11 B. Pakistan 14 C. Additional Results 15 VI. SUMMARY AND CONCLUSION 15 APPENDIXES 17 REFERENCES 21 TABLES AND FIGURES TABLES 1 Baseline Summary Statistics, Bangladesh 6 2 Baseline Summary Statistics, Pakistan 8 3 Exposure to Disaster and Change in the Dependence on Agriculture 12 4 Exposure to Disaster and Savings Change 13 A1.1 List of Natural Disasters in Bangladesh, 2010–2013 17 A1.2 List of Natural Disasters in Pakistan, 2010–2013 17 A2.1 Changes from Disaster Exposure, Seemingly Unrelated Regression Results 18 A2.2 Changes from Disaster Exposure, Results Excluding the Control Variables 19 FIGURES 1 Disaster and Dependence on Agriculture 9 2 Disaster and Savings Change 10 ABSTRACT We investigate the economic response of households to natural disasters in Bangladesh and Pakistan. In particular, we explore to what extent households adjust their income and employment strategies and savings in response to exposure to floods and storms. Using two unique panel datasets, we find evidence of autonomous adjustments in both countries. In Bangladesh, farmers move away from farm to nonfarm employment as a coping strategy to tackle immediate reductions in their total household income from exposure to disasters, whereas nonfarmers increase their off-farm labor supply. Such adjustments in employment strategies are stronger among the storm-affected households than the flood-affected households. On the other hand, although farmers in Pakistan move away from agriculture as an immediate response to disasters, they eventually come back to agriculture within a year of disaster exposure. We also identify that such adjustments in employment and income strategies help farmers to overcome the immediate losses from disaster exposure as the disasteraffected households from both Bangladesh and Pakistan exhibit at least no decrease in their savings behavior. We discuss policy implications in terms of developing nonfarm employment opportunities to reduce the future harms of disaster and financing economic migration to reduce income vulnerability. Keywords: Bangladesh, income, natural disasters, Pakistan, savings JEL codes: D13, D64, Q15, Q24, Q54 I. INTRODUCTION Natural disasters such as floods and storms particularly harm the rural poor, who mostly depend on agriculture for employment and income. As the rural nonfarm sector is usually tied to agricultural production, rural nonfarm employment and income are also vulnerable to exposure to such climatic events. Climate-induced natural disasters have both short- and long-term harmful impacts on affected households (Maccini and Yang 2009), who may lose their livelihoods, life savings, and creditworthiness. By destroying productive assets acquired through years of foregone consumption, natural disasters can push the poor into deep poverty, making it hard to recover their predisaster consumption levels and rebuild assets (Barnett and Mahul 2007). For example, analyzing 25 years of data from the Philippines, Anttila-Hughes and Hsiang (2013) observed that typhoons destroyed durable assets and depressed incomes, and led in turn to broad reductions in household expenditures. Motivated by immediate survival as well as profit maximization, disaster-affected households may change their employment and income strategies, which may result in changes in their dependence on agriculture. Although the existing literature addresses the welfare effects of increases in agricultural and nonagricultural labor supply (e.g., Mueller and Quisumbing 2011), the possibility of change in dependence on agriculture in the aftermath of a disaster is not yet addressed. Against this backdrop, this paper contributes by investigating the extent of household-level adjustment in income and employment strategies and savings behavior in response to exposure to floods and storms for the case of Bangladesh and Pakistan. It may be argued that if disaster shocks are anticipated, households would adapt to them. In fact, households who are aware of the potential impacts of common weather shocks try to sustain consumption by adopting low-risk, low-return investment strategies and mitigation measures such as levies to prevent flooding, supplementary irrigation to offset lack of rainfall, and seasonal migration to avoid chronic poverty (Barnett and Mahul 2007). However, when such shocks cannot be anticipated, they may cause a lot of damage. While farmers usually show considerable experience of coping and risk management strategies by taking into account seasonal risks and uncertainties in agricultural practice, with climate change the magnitude and frequency of stresses and shocks are changing (Davies et al. 2009).1 Arguably, such responses to disasters may result in farmers either moving away from farm to nonfarm employment, which will reduce their future vulnerability to disaster, or intensifying agricultural activities in order to compensate for the lost income. Typically, natural disasters force rural households and farmers to adopt coping and adaptation strategies such as crop switching, increased labor supply and land transactions (sell land or rent for use)—within the same area—or sale of productive assets and temporary migration—to another area (e.g., Duflo 2003; Jensen 2000; Moniruzzaman 2015; Penning‒Rowsell, Sultana, and Thompson 2013; Banerjee 2007; Mueller and Quisumbing 2011; Eskander and Barbier 2016; Bryan, Chowdhury, and Mobarak 2014). While disaster-affected people may decide to migrate to less disaster-prone regions (e.g., Boustan, Kahn, and Rhode 2012; Hornbeck 2012), migration to urban areas is conditional on a household’s ability to find alternative employment while facing liquidity constraints. For example, Bryan, Chowdhury, and Mobarak (2014) identified that rural households in Bangladesh respond to incentives that relax their liquidity constraint when making seasonal migration decision during the lean 1 Indeed, the rising global temperature is leading to more frequent extreme weather events such as droughts, floods, storms, and heat waves. In particular, the numbers of such intense calamities are not only higher in developing Asia than in any other region, but they are also increasing (Thomas et al. 2013). Bangladesh, Cambodia, India, Pakistan, the Philippines, and Viet Nam, are among the countries that are heavily exposed to such events. By changing the pattern of occurrence of disasters, such trends have made it more difficult to anticipate disasters. 2 | ADB Economics Working Paper Series No. 505 period.2 However, Bohra-Mishra, Oppenheimer, and Hsiang (2014) analyzed province-to-province movement of more than 7,000 households in Indonesia over 15 years to find that while there can be a nonlinear permanent migration response to climatic variations, the evidence of permanent migration is minimal among the disaster-affected households. In the case of Bangladesh, Penning-Rowsell, Sultana, and Thompson (2013) found that permanent migration is an unlikely response of rural people who are less likely to migrate even in the face of extreme disasters, although they may temporarily migrate to safer places;3 whereas Mueller, Gray, and Kosec (2014) found that floods have modest to insignificant impacts on long-term migration in Pakistan. Consistent with this argument, Eskander and Barbier (2016) found that disaster-affected rural households instead intensify agricultural activities by increasing their operational farm size through increased transactions in the land rental market. Rural farmers may change their savings behavior either in response to disaster exposure or in preparation for combating the harms of future disasters. Although forward-looking agents usually save in order to smooth their consumption during disasters, the high frequency of natural disasters in both Bangladesh and Pakistan often adversely affects the accumulation of cash savings in the period between two disasters. Especially for the poor farming households, the recovery may take longer and they might focus on investing in productive assets such as bullocks for crop cultivation rather than cash savings. In fact, the poor farmers often set their primary focus on meeting immediate subsistence needs while experiencing frequent events of disaster and, therefore, may not be able to save to combat any future risk of disasters. Harmful effects of disaster are further heightened in the case of low-income countries such as Bangladesh and Pakistan (Field et al. 2012). Insurance programs, which are often scarce in rural areas of low-income countries, to protect life, property, and agricultural crop fail when large numbers of clients are simultaneously affected by a disaster. In particular, farming households from low-income countries are often left with fewer means to invest in protection to reduce risks of natural disasters and in insurance to reduce possible losses from such disasters.4, 5 Moreover, exposure to disasters and adaptation practices leave longer-lasting impacts on income and savings of disaster-exposed households. It is for such reasons that Sustainable Development Goal 13 (Climate Action) emphasizes the need to strengthen resilience and adaptive capacity to climate-related hazards and natural disasters. Against this backdrop, this paper investigates the impact of natural disasters on economic behavior. It explores two questions in particular: (i) Do disaster-affected farmers move away from agriculture for employment and income in comparison to unaffected households? And (ii) Do disaster-affected farmers have a lower increase in their savings than the unaffected households? Our empirical analysis identifies that disaster-affected farmers and nonfarmers in Bangladesh increase respectively their nonfarm and farm labor supply, whereas such adjustments are stronger among the storm-affected households than the flood-affected households. On the other hand, although farmers 2 Consistent with this result, Cattaneo and Peri (2016) found that in low-income countries a temperature increase decreases migration and traps people into poverty. 3 This tendency is historically true for Bangladesh. For example, even the people affected by the great 1970 Bhola cyclone did not migrate permanently (Sommer and Mosley 1972). 4 The losses from natural disasters in low-income countries amounted to 0.3% of gross domestic product (GDP) during 2001–2006. However, such loss values are lower-bound estimates due to difficulties in monetizing many subjective losses (Field et al. 2012, 7). 5 In 2012, 47% of the population of low-income countries (2014 gross national income per capita $1,045 or less) still lived on less than $1.9 (2011 purchasing power parity) a day per capita, and 74% lived on less than $3.1 (2011 purchasing power parity) a day per capita (World Bank 2015). Do Natural Disasters Change Savings and Employment Choices? Evidence from Bangladesh and Pakistan | 9 where    ∀ denotes the structural change, with  and  denoting the dependence on agriculture in years 1 and 2, respectively, and   and   implying increased and decreased dependence on agriculture as a result of disaster exposure. More precisely, we define structural change in terms of employment for the case of Bangladesh as          , where   and   denote the shares of farm and nonfarm employment of Bangladeshi households in 2010 () and 2012 ( ). Panels A and B in Figure 1 show that disaster-affected households from Bangladesh have a higher change in their dependence on agriculture than the unaffected households. On the other hand, we define structural change in terms of income for the case of Pakistan as          , where   and   denote the shares of farm and nonfarm incomes of Pakistani households in 2011 () and 2013 (). Similar to Bangladesh, panels C and D in Figure 1 show that disaster-affected households from Pakistan have a higher change in their dependence on agriculture than the unaffected households. In both cases, we only consider the household members aged 15 years or older when calculating these outcome variables. Figure 1: Disaster and Dependence on Agriculture Sources: Data from the Bangladesh Climate Change Adaptation Survey (BCCAS) I and II for Bangladesh and the Pakistan Rural Household Panel Survey (PRHPS) I and II for Pakistan. Next, we evaluate whether variations in disaster exposure predict the magnitude of the changes in savings by Bangladeshi and Pakistani households according to: Δ  󰇛,,,󰇜, (4) 0 2 4 6 Density −1 −.5 0 .5 1 kernel = epanechnikov, bandwidth = 0.0768 (a) Bangladesh − Floods 0 2 4 6 Density −1 −.5 0 .5 1 kernel = epanechnikov, bandwidth = 0.0546 (b) Bangladesh − Storms 0 0.5 1.0 1.5 2.0 Density −1 −.5 0 .5 1 kernel = epanechnikov, bandwidth = 0.1680 (c) Pakistan − 2012 Flood 0 0.5 1.0 1.5 2.0 Density −1 −.5 0 .5 1 kernel = epanechnikov, bandwidth = 0.1724 (d) Pakistan − 2011 Flood Affected Household Unaffected Household 10 | ADB Economics Working Paper Series No. 505 where    ∀ is the change in savings, with  and  denoting logged (one plus) annual savings in years 1 and 2, respectively, and   and   implying increased and decreased savings as a result of disaster exposure. Panels A–D in Figure 2 show that disaster-affected households from Bangladesh and Pakistan have similar changes in their savings behavior compared to the unaffected households. Figure 2: Disaster and Savings Change Sources: Data from the Bangladesh Climate Change Adaptation Survey (BCCAS) I and II for Bangladesh and the Pakistan Rural Household Panel Survey (PRHPS) I and II for Pakistan. Our empirical approaches to estimating equations (3) and (4) involve specifying the components of the vectors  and . Vector  includes our variables of interest defining the disaster exposure of a household between the survey years and will be specified for Bangladesh and Pakistan separately in the following sections. In addition, vector  includes the base year household- and farmlevel characteristics affecting farm and nonfarm employment, income, and savings opportunities. A household is defined to include the number of people that dine-in together from the same pot. Household characteristics include the age and squared age of the household head, size and squared size of the household, average years of schooling of all the household members, number of workingage males and females in the household (defined as the number of males and females aged between 15 and 65 years), credit constraint (defined as logged one plus unpaid loans of the household), and access to facilities defining their relative entitlement such as access to electricity (defined as 1 if the household has access to electricity connection and 0 if not). On the other hand, farm-level characteristics include ownership of a tractor (1 if the household owns a tractor or a plow–yoke, 0 if not), an irrigation pump (1 if the household owns an irrigation pump, 0 if not), and other agricultural assets (1 if the household owns other agricultural assets, 0 if not), as well as operational farm size (natural log of one plus acres of owned–operated and rented–operated land). Finally,  represents the 0 .05 .10 .15 .20 .25 Density −10 −5 0 5 10 kernel = epanechnikov, bandwidth = 0.8070 (a) Bangladesh − Floods 0 .1 .2 .3 Density −5 0 5 10 kernel = epanechnikov, bandwidth = 0.5797 (b) Bangladesh − Storms 0 .1 .2 .3 .4 Density −10 −5 0 5 10 kernel = epanechnikov, bandwidth = 0.7297 (c) Pakistan − 2012 Flood 0 .1 .2 .3 .4 .5 Density −10 −5 0 5 10 kernel = epanechnikov, bandwidth = 0.6109 (d) Pakistan − 2011 Flood Affected Households Unaffected Households Do Natural Disasters Change Savings and Employment Choices? Evidence from Bangladesh and Pakistan | 11 location vector. Within a cohort, future effects of disaster exposure should be common to all households and individuals born in the same locality (e.g., Almond, Edlund, and Palme 2009; Maccini and Yang 2009). Therefore, variations in employment, income, and savings resulting from the variations in location of residence should be absorbed by , which controls for persistent effects of disaster exposure on the regions and households. V. RESULTS AND DISCUSSIONS A. Bangladesh In BCCAS II, surveyed households report whether they were exposed to natural disasters such as floods and storms during 2011. Based on this reporting, we consider two separate measures of exposure by the type of disaster: (i) exposure to floods only and (ii) exposure to storms only. In both cases, a household’s self-reported disaster exposure is defined as 1 if exposed to a disaster and 0 if not. In addition, since farmers and nonfarmers may experience different degrees of severity in the aftermath of a disaster, we interact these measures of disaster exposure with their primary occupation, which we define as 1 if the household head is primarily a farmer (i.e., a primarily farming household) and 0 if otherwise (i.e., a household who does farming but not primarily a farming household). Therefore, for Bangladesh, we evaluate whether variations in disaster exposure and primary occupation predict the magnitude of the changes in employment and savings behaviors by Bangladeshi households. Columns 1 and 2 in Table 3 report the regression results based on equation (3) for flood and storm exposure in Bangladesh, with corresponding  values of 0.107 and 0.1, respectively. We do not report the thana (location) dummies in any of the regression tables; however, they are available upon request. In addition, although we report the parameter estimates of control variables, we confine our discussion of results only to the parameters of interest. As shown in Appendix 2, Table A2.2, estimates of our parameters of interest are similar without the control variables, therefore supporting our claim that the change in Bangladeshi households’ employment strategy and savings behavior comes from disaster exposure. In general, disaster exposure increases dependence on agriculture. We find that flood-affected households have an 18.2% higher proportion of farm employment than flood-unaffected households. Further, storm-affected households have a 29.3% higher proportion of farm employment than stormunaffected households. However, disaster-affected farmers lower their dependence on agriculture more than unaffected nonfarmers. Our results show that flood- and storm-affected farmers have 18.1% and 32.9% lower proportions of farm employment than their corresponding unaffected counterparts. Together, these results indicate a structural change from nonfarm to farm employment in general, and farm to nonfarm for the farming households. These results are consistent with the existing literature on the comparison between agricultural and nonagricultural labor supply in the aftermath of a disaster. While natural disasters such as flooding increase households’ vulnerability to poverty (Khandker 2007), flood-affected households who increase their nonfarm labor supply cope better due to higher receipts of wages from nonfarm than farm employment (e.g., Banerjee 2007; Mueller and Quisumbing 2011). Therefore, given that rational households are motivated by income maximization when making labor supply decisions, our results indicate that disaster-affected farmers move away from farm to nonfarm employment as a coping strategy to tackle short-term reductions in their total household income. 12 | ADB Economics Working Paper Series No. 505 Table 3: Exposure to Disaster and Change in the Dependence on Agriculture Bangladesh Pakistan Variable Floods Storms Floods Flood 0.182*** (0.047) Farmer x Flood –0.181*** (0.048) Storm 0.293*** (0.089) Farmer x Storm –0.329*** (0.120) Flood 2012 –0.419*** (0.036) Flood 2011 0.451*** (0.092) Age 0.009** 0.008** –0.007 (0.004) (0.004) (0.008) (Age)2 –0.000* –0.000* 0.000 (0.000) (0.000) (0.000) Household size 0.018 0.015 0.034 (0.036) (0.037) (0.052) (Household size)2 –0.002 –0.002 –0.002 (0.003) (0.003) (0.004) Education 0.007 0.008 –0.010 (0.005) (0.005) (0.006) Males 0.008 0.010 –0.009 (0.015) (0.014) (0.022) Females 0.015 0.013 0.005 (0.014) (0.015) (0.021) Tractor –0.005 –0.003 –0.009 (0.025) (0.025) (0.047) Irrigation pump –0.026 –0.027 0.014 (0.031) (0.030) (0.050) Other agricultural assets 0.040 0.027 –0.009 (0.045) (0.044) (0.056) Electricity connection –0.010 –0.017 0.116 (0.022) (0.021) (0.072) Ln ( Farm size ) 0.187 0.124 –0.175 (0.342) (0.330) (0.284) Credit constraint 0.002 0.002 –0.004 (0.005) (0.005) (0.006) Observations 703 703 842 R2 0.107 0.100 0.170 Notes: Standard errors clustered at the union level are shown in parentheses. ***, **, and * represent statistical significance at the 1%, 5%, and 10% levels, respectively. Ordinary least squares regression coefficients are obtained from using equation (3). We do not report the thana or tehsil (location) dummies; however, they are available upon request. Regressions excluding the control variables yield similar coefficient estimates for our parameters of interest (Appendix 2, Table A2.3). Therefore, they justify that the change in the proportion of farm income comes from exposure to disaster. Sources: All household data come from the Bangladesh Climate Change Adaptation Survey (BCCAS) I and II for Bangladesh and the Pakistan Rural Household Panel Survey (PRHPS) I and II for Pakistan. Moreover, we further can predict that the compensating effects of such changes in employment strategies may be reflected in a household’s savings behavior. Columns 1 and 2 in Table 4 report the regression results based on equation (4) for flood and storm exposure in Bangladesh, with corresponding  values of 0.145 and 0.144, respectively. We define change in savings as     ∀ in terms of livestock purchase, so that  and  denote logged (one plus) annual spending on the purchase of livestock by Bangladeshi households in 2010 () and 2012 (). We find that flood-affected households have 50.7% lower savings than flood-unaffected households, whereas Do Natural Disasters Change Savings and Employment Choices? Evidence from Bangladesh and Pakistan | 13 storm-affected households have 67.1% lower savings than storm-unaffected households. In addition, flood- and storm-affected farmers have 39% and 42.2% higher savings than their corresponding unaffected counterparts. Together, although our parameters of interest are statistically insignificant, we find some evidence suggesting that disaster-affected household may be able to increase their savings as a consequence of successfully coping with the immediate harms of disaster. Table 4: Exposure to Disaster and Savings Change Bangladesh Pakistan Floods Storms Variable Ln(Livestock) Ln(Livestock) Ln(Savings) Ln(Livestock) Flood –0.507 (0.602) Farmer x Flood 0.390 (0.659) Storm –0.671 (0.576) Farmer x Storm 0.422 (0.478) Flood 2012 0.819*** 0.309*** (0.156) (0.107) Flood 2011 0.731* –0.089 (0.435) (0.686) Age 0.079 0.080 0.032 –0.008 (0.048) (0.048) (0.049) (0.026) (Age)2 –0.001* –0.001* –0.000 0.000 (0.000) (0.000) (0.001) (0.000) Household size –0.258 –0.253 –0.654*** –0.131 (0.301) (0.310) (0.240) (0.170) (Household size)2 0.015 0.014 0.049** 0.010 (0.025) (0.026) (0.019) (0.013) Education 0.014 0.013 0.014 0.003 (0.055) (0.057) (0.027) (0.015) Males 0.164 0.157 –0.047 –0.030 (0.133) (0.129) (0.109) (0.053) Females 0.045 0.053 –0.170 0.094 (0.183) (0.178) (0.110) (0.095) Tractor –0.087 –0.094 0.033 –0.018 (0.331) (0.333) (0.336) (0.246) Irrigation pump 0.270 0.277 –0.583** –0.151 (0.510) (0.505) (0.248) (0.149) Other agricultural assets 0.158 0.189 0.331 –0.169 (0.381) (0.387) (0.221) (0.215) Electricity connection –0.287 –0.285 –0.422 0.323* (0.256) (0.258) (0.293) (0.192) Ln ( Farm size ) 2.016 2.104 –0.387 –1.911** (3.067) (3.296) (2.576) (0.905) Credit constraint 0.025 0.027 –0.039 0.037* (0.035) (0.035) (0.031) (0.021) Observations 703 703 842 842 R2 0.145 0.144 0.314 0.100 Notes: Standard errors clustered at the union level are shown in parentheses. ***, **, and * represent statistical significance at the 1%, 5%, and 10% levels, respectively. Ordinary least squares regression coefficients are obtained from using equation (3). We do not report the thana or tehsil dummies; however, they are available upon request. Regressions excluding the control variables yield similar coefficient estimates for our parameters of interest (Appendix 2, Table A2.3). Therefore, they justify that the changes in savings come from exposure to disaster. Sources: All household data come from the Bangladesh Climate Change Adaptation Survey (BCCAS) I and II for Bangladesh and the Pakistan Rural Household Panel Survey (PRHPS) I and II for Pakistan. 14 | ADB Economics Working Paper Series No. 505 We also identify that the effects of exposure vary by the type of disaster. Clearly, the stormaffected households undergo a greater structural change than the flood-affected households, whereas storm-affected farmers have a greater reduction in their agriculture dependence than flood-affected farmers when they are compared to the corresponding unaffected farmers. Consistent with these employment effects of exposure, we also identify that the storm-affected households have a greater decrease in savings than the flood-affected households, whereas storm-affected farmers have a greater increase in their savings than flood-affected farmers when compared to corresponding unaffected farmers. B. Pakistan According to EM-DAT (2016), the 2010 Pakistan flood affected all the districts surveyed in the PRHPS. However, the floods of 2011 and 2012 that took place between PRHPS I and II affected four and three districts, respectively, out of 19 surveyed districts. Therefore, for the case of Pakistan, we include two dummy variables defining disaster exposure in the vector : (i) Flood 2011 defined as 1 for the districts affected by the flood of 2011 (Dadu, Jacobabad, Hyderabad, and Sanghar districts in Sindh province) and 0 otherwise, and (ii) Flood 2012 defined as 1 for the districts affected by the flood of 2012 (DG Khan in Punjab province, and Dadu and Jacobabad in Sindh province) and 0 otherwise. Table 3 reports that 23% and 19% of surveyed Pakistani households were affected by 2011 and 2012 floods, respectively. Column 3 in Table 3 reports the regression results based on equation (3) for flood exposure in Pakistan, with a corresponding  value of 0.17. We do not report the tehsil (location) dummies in any of the regression tables; however, they are available upon request. In addition, although we report the parameter estimates of control variables, we confine our discussion of results only to the parameters of interest. As shown in Appendix 2, Table A2.2, estimates of our parameters of interest are similar without the control variables, therefore supporting our claim that the change in Pakistani households’ income strategy and savings behavior comes from disaster exposure. Results from Table 3 indicate that although Pakistani households change their income strategy in response to flood exposure, such changes are short-lived and do not necessarily imply a structural change. We find that the change in the dependence on agriculture differs by flood year. In particular, 2012 flood-affected farmers have a 41.9% decrease in their dependence on agriculture; however, the situation is exactly opposite for farmers affected by the 2011 flood who have a 45.1% increase in their dependence on agriculture. The short-lived nature of the decrease in the dependence on agriculture is consistent with the existing literature on migration response to climatic change and climatic extremes in Pakistan showing that although rising temperatures increase rural–urban migration, and thereby lower the dependence on agriculture, floods do not significantly influence long-term migration in Pakistan (Mueller, Gray, and Kosec 2014). Although liquidity constraints may be responsible for their reluctance or inability to migrate permanently (e.g., Bryan, Chowdhury, and Mobarak 2014; Cattaneo and Peri 2016), guaranteed availability of humanitarian aid in response to climatic extremes such as floods and storms may also be responsible for slowing down the migration response to floods (e.g., Looney 2012; Strömberg 2007) and also for facilitating farmers’ return to their ancestral location. On the contrary, but almost equal, the estimated magnitude of the effects of the 2011 and 2012 floods may also imply that such a return to ancestry happens within a year of flood exposure. Do Natural Disasters Change Savings and Employment Choices? Evidence from Bangladesh and Pakistan | 15 However, similar to the case of Bangladesh, such a return to ancestry may also imply Pakistani farmers’ successful coping with the harms of flood exposure through temporary movement away from agriculture. Regressions investigating the savings effects of flood exposure in fact confirm this alternative implication of our estimated income effects of exposure to the 2011 and 2012 floods (see columns 3 and 4 in Table 4). Here, we use two different definitions of savings: logged one plus cash savings and logged one plus spending on the purchase of livestock, with  and  denoting savings by Pakistani households in 2011 (i) and 2013 (). Both the 2011 and 2012 flood-affected farmers in Pakistan have a significantly higher increase in their savings than their corresponding unaffected counterparts, with this effect being stronger for the latter flood. In particular, we find that the 2011 flood results in a 73.1% increase in savings, whereas the 2012 flood increases savings by 81.9%. C. Additional Results Although absolute changes in farm and nonfarm employment and incomes may not always reflect a household’s movement between sectors—since in the case of Bangladesh the number of working-age members may also change between the survey years and all the surveyed Pakistani households are farmers with zero nonfarm income in 2011—we employ a seemingly unrelated regression framework to provide additional results supporting our main results reported in Table 3. Simultaneously determined outcome variables are farm and nonfarm employments for Bangladesh and farm and nonfarm incomes for Pakistan. Our results, as reported in Appendix 2, Table A2.1, reassure us that floods and storms cause households to increase farming and decrease nonfarming employment, therefore increasing the dependence on agriculture in general. On the other hand, flood- and storm-affected farmers have lower farming and higher nonfarming employment than unaffected nonfarmers, suggesting a decreased dependence on agriculture of the farming households in Bangladesh in response to disaster exposure. In addition, consistent with our main results in Table 3, we identify that the effects are stronger for storms than floods. Consistent with Table 3, we also find that the 2012 and 2011 floods have quite different effects on Pakistani households in 2013: while the 2012 flood decreases agricultural income, households affected by the 2011 flood have a higher agricultural income than their unaffected counterparts. Together, these results support our main result implying that the changes in the composition of income due to disaster exposure may be temporary and do not necessarily imply a structural change. VI. SUMMARY AND CONCLUSION This paper identifies to what extent households in Bangladesh and Pakistan adjust their income and employment strategies and savings behavior in response to exposure to floods and storms. Farmers in Bangladesh move away from farm to nonfarm employment as a coping strategy to tackle immediate reductions in their total household income from exposure to disasters. On the other hand, although farmers in Pakistan move away from agriculture as an immediate response to disasters, they eventually come back to agriculture within a year of disaster exposure. Therefore, such changes in employment and income strategies may not necessarily imply a structural change. However, they do imply a household’s success in coping with the harms of disaster: Disaster-affected households from both Bangladesh and Pakistan exhibit at least a nondecrease in their savings behavior. 16 | ADB Economics Working Paper Series No. 505 Our empirical results carry important implications for developing countries with frequent exposure to natural disasters. The reduced dependence on farm income as a consequence of disaster exposure has two alternative policy implications. First, it might imply the persistence of income vulnerability of the farming households in case they experience lower farm and nonfarm incomes due to disaster exposure. Since rural households may respond to incentives to migrate (Bryan, Chowdhury, and Mobarak 2014), public policies should aim at financing the economic migration of disasteraffected rural households in order to reduce their income vulnerability. Second, the short-lived nature of such movement between farm and nonfarm sectors may imply the status quo is motivated by reverse incentive in the form of guaranteed access to humanitarian aid in the aftermath of a climatic extreme such as a flood or storm. Since the number of farmers and rural households who do not permanently migrate in response to natural disasters may not accelerate (Penning-Rowsell, Sultana, and Thompson 2013; Mueller, Gray, and Kosec 2014) and farmers usually intensify their agricultural activities to compensate for their lost income (e.g., Eskander and Barbier 2016), a sustainable structural change in order to facilitate economic growth requires the development of nonfarm employment opportunities in rural areas. Reduced savings in the aftermath of a disaster is a rational response to the destruction of assets and a changed economic outlook, which affects the potential economic growth of an economy (e.g., Fankhauser and Tol 2005). However, disaster-affected farmers from both countries were successful in overcoming their losses. We find that disaster-affected households in Pakistan actually have a higher increase in their savings than unaffected households, whereas disaster-affected households in Bangladesh maintain similar savings. However, since bullocks are commonly used for cultivation in both countries, such increases in livestock purchases may actually imply that farmers invest in the accumulation of productive assets in order to revive their postdisaster agricultural activities. In addition to public financing of the postdisaster reconstruction, this is an example of farmers’ private financing of the reconstruction process. APPENDIX 1: LIST OF DISASTERS Table A1.1: List of Natural Disasters in Bangladesh, 2010–2013 Disaster No. Disaster Type Date Started Totals Deaths Total Affected Affected Regions (Districts) 2010-0171 Storm 13 Apr 2010 8 247,110 Rangpur, Dinajpur, Nilphamari, Lalmonirhat, Kurigram, Gaibandha, Sirajganj, Bogra 2010-0205 Storm 17 Apr 2010 3 10,000 Lalmonirhat 2010-0269 Flood 24 Jun 2010 75,000 Sylhet, Moulvibazar, Sunamganj, Habiganj, Netrokona, Kurigram, Gaibandha, Lalmonirhat 2010-0676 Flood 1 Oct 2010 15 500,000 — 2010-0686 Storm 1 May 2010 15 50 Mymensingh 2011-0262 Flood 21 Jul 2011 10 1,570,559 Chittagong, Cox’s Bazar, Satkhira, Jessore, Narail, Bagerhat, Chuadanga, Kustia, Bogra, Sirajganj, Pabna, Lalmonirhat, Thakurgaon, Kurigram, Sherpur, Netrokona, Bandarban, Rajbari, Manikganj, Gaibandha, Naogaon 2011-0591 Storm 4 Apr 2011 13 121 Sherpur, Mymensingh, Rangpur, Thakurgaon, Jamalpur, Netrokona, Gaibandha, Pabna 2012-0082 Storm 6 Apr 2012 25 55,121 Satkhira, Jessore, Chuadanga, Panchagarh, Noakhali, Comilla, Narsingdi, Jamalpur, Rajshahi, Sylhet, Faridpur, Rangpur, Bhola, Shariatpur, Khulna, Nilphamari 2012-0175 Flood 24 Jun 2012 139 5,148,475 Chittagong, Cox’s Bazar, Bandarban, Sylhet 2012-0382 Flood 21 Sep 2012 250,000 Barisal, Bhola, Patuakhali, Dhaka, Faridpur, Jamalpur, Madaripur, Manikganj, Rajbari, Shariatpur, Tangail, Bogra, Pabna, Sirajganj, Gaibandha, Kurigram 2012-0385 Storm 10 Oct 2012 108 129,558 Noakhali, Bhola, Chittagong 2013-0085 Storm 22 Mar 2013 31 8,543 Brahminbaria 2013-0090 Storm 29 Mar 2013 2 25,020 Nature, Naogaon 2013-0138 Storm 16 May 2013 17 1,498,644 Patuakhali, Bhola, Barguna Source: All data come from the EM-DAT database (http://www.emdat.be/database), an emergency events database collected by the Centre for Research on the Epidemiology of Disasters (CRED). Table A1.2: List of Natural Disasters in Pakistan, 2010–2013 Disaster No. Disaster Type Date Started Totals Deaths Total Affected Affected Regions (Districts) 2010-0053 Flood 8 Feb 2010 22 Batagram, Kohistan, Shangla, Swat (North- West Frontier [NWF] province) 2010-0210 Storm 6 Jun 2010 23 4,000 Karachi, Hyderabad (Sindh), Balochistan 2010-0282 Flood 22 Jun 2010 46 Chitral (NWF province) 2010-0293 Flood 21 Jul 2010 60 4,000 Barkhan (Balochistan) 2010-0341 Flood 28 Jul 2010 1,985 20,359,496 Entire Pakistan 2011-0347 Flood 12 Aug 2011 509 5,400,755 Sindh province 2012-0325 Flood 4 Sep 2012 12 Other province 2012-0363 Flood Aug 2012 480 5,049,364 Jaffarabad, Jhal Magsi, Nasirabad (Balochistan), DG Khan, Rajanpur (Punjab), Dadu, Ghotki, Jacobabad, Larkana (Sindh) 2012-0475 Flood 23 Aug 2012 26 1,200 NWF province 2013-0068 Flood 3 Feb 2013 34 57 Punjab, other, NWF provinces 2013-0276 Flood 7 Aug 2013 234 1,497,725 Entire Pakistan Source: All data come from the EM-DAT database (http://www.emdat.be/database), an emergency events database collected by the Centre for Research on the Epidemiology of Disasters (CRED). 18 | Appendixes APPENDIX 2. SUPPLEMENTARY RESULTS Table A2.1: Changes from Disaster Exposure, Seemingly Unrelated Regression Results Bangladesh Pakistan (1) (2) (3) (4) (5) (6) Floods Storms Flood Variable Farm Nonfarm Farm Nonfarm Farm Nonfarm Flood 0.444*** –0.489*** (0.137) (0.131) Farmer x Flood –0.420*** 0.489*** (0.140) (0.134) Storm 0.748*** –0.519** (0.268) (0.258) Farmer x Storm –0.723** 0.642** (0.306) (0.294) Flood 2012 –2.662** 0.479 (1.268) (1.052) Flood 2011 2.498* 0.448 (1.302) (1.080) Age 0.037*** 0.011 0.036*** 0.013 –0.078 –0.115*** (0.013) (0.013) (0.013) (0.013) (0.052) (0.043) (Age)2 –0.000*** –0.000 –0.000*** –0.000 0.001* 0.001*** (0.000) (0.000) (0.000) (0.000) (0.001) (0.000) Household size 0.117 –0.095 0.110 –0.089 0.334 –0.011 (0.100) (0.096) (0.100) (0.097) (0.373) (0.309) (Household size)2 –0.015* 0.010 –0.015* 0.010 –0.023 0.009 (0.009) (0.008) (0.009) (0.008) (0.029) (0.024) Education 0.024 –0.022 0.026* –0.024* –0.027 0.066*** (0.014) (0.014) (0.014) (0.014) (0.031) (0.025) Males –0.055 –0.151*** –0.051 –0.158*** 0.176 0.535*** (0.040) (0.038) (0.040) (0.038) (0.134) (0.111) Females 0.070 –0.033 0.063 –0.031 0.160 –0.001 (0.048) (0.046) (0.048) (0.046) (0.149) (0.123) Tractor –0.041 0.049 –0.037 0.048 –0.108 –0.385 (0.073) (0.070) (0.073) (0.070) (0.390) (0.323) Irrigation pump –0.071 0.134 –0.075 0.140 –0.022 –0.241 (0.108) (0.103) (0.108) (0.104) (0.322) (0.267) Other agricultural assets 0.171* –0.017 0.138 0.013 –0.022 0.227 (0.097) (0.092) (0.097) (0.093) (0.304) (0.253) Electricity connection –0.080 –0.038 –0.093 –0.024 0.598 –0.651 (0.068) (0.065) (0.069) (0.066) (0.505) (0.419) Ln ( Farm size) 0.708 –0.203 0.575 –0.002 –7.914*** –6.600*** (1.181) (1.130) (1.179) (1.134) (2.189) (1.817) Credit constraint 0.014 0.012 0.014 0.011 –0.026 –0.001 (0.011) (0.010) (0.011) (0.010) (0.042) (0.035) Constant –1.450*** –0.002 –1.287*** –0.167 –1.084 1.950 (0.438) (0.420) (0.438) (0.421) (1.732) (1.437) Observations 703 703 703 703 842 842 R2 0.143 0.139 0.139 0.127 0.193 0.278 Notes: Standard errors in parentheses. ***, **, and * represent statistical significance at the 1%, 5%, and 10% levels, respectively. We do not report the thana or tehsil (location) dummies; however, they are available upon request. Sources: All household data come from the Bangladesh Climate Change Adaptation Survey (BCCAS) I and II for Bangladesh and the Pakistan Rural Household Panel Survey (PRHPS) I and II for Pakistan.