Build back better: What is it, and what should it be?

Noy, Ilan; Ferrarini, Benno; Park, Donghyun Working Paper Build back better: What is it, and what should it be? ADB Economics Working Paper Series, No. 600 Provided in Cooperation with: Asian Development Bank (ADB), Manila Suggested Citation: Noy, Ilan; Ferrarini, Benno; Park, Donghyun (2019) : Build back better: What is it, and what should it be?, ADB Economics Working Paper Series, No. 600, Asian Development Bank (ADB), Manila, https://doi.org/10.22617/WPS190583-2 This Version is available at: https://hdl.handle.net/10419/230354 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. 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Ilan Noy, Benno Ferrarini, and Donghyun Park ASIAN DEVELOPMENT BANK Ilan Noy, Benno Ferrarini, and Donghyun Park No. 600 | December 2019 Ilan Noy ([email protected]) is Chair in the Economics of Disasters and Professor of Economics at the Victoria University of Wellington. Benno Ferrarini (bferrarini@adb. org) and Donghyun Park ([email protected]g) are Principal Economists in the Economic Research and Regional Cooperation Department of the Asian Development Bank. This paper was prepared as background material for the Asian Development Outlook 2019 theme chapter on “Strengthening Disaster Resilience.” We thank Diana De Alwis for her excellent assistance in collecting and collating the data used in this paper. ADB Economics Working Paper Series Build Back Better: What Is It, and What Should It Be? Creative Commons Attribution 3.0 IGO license (CC BY 3.0 IGO) © 2019 Asian Development Bank 6 ADB Avenue, Mandaluyong City, 1550 Metro Manila, Philippines Tel +63 2 8632 4444; Fax +63 2 8636 2444 www.adb.org Some rights reserved. Published in 2019. ISSN 2313-6537 (print), 2313-6545 (electronic) Publication Stock No. WPS190583-2 DOI: http://dx.doi.org/10.22617/WPS190583-2 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) or its 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. The mention of specific companies or products of manufacturers does not imply that they are endorsed or recommended by ADB in preference to others of a similar nature that are not mentioned. 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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. Corrigenda to ADB publications may be found at http://www.adb.org/publications/corrigenda. Notes: In this publication, “$” refers to United States dollars. ADB recognizes “China” as the People’s Republic of China. The ADB Economics Working Paper Series presents data, information, and/or findings from ongoing research and studies to encourage exchange of ideas and to elicit comment and feedback about development issues in Asia and the Pacific. Since papers in this series are intended for quick and easy dissemination, the content may or may not be fully edited and may later be modified for final publication. CONTENTS TABLES AND FIGURES iv ABSTRACT v I. INTRODUCTION 1 II. THE TYPOLOGY OF POSTDISASTER RECOVERY 2 III. LONG-TERM MACROECONOMIC BUILD BACK BETTER 4 IV. BUILD BACK BETTER AT THE LOCAL LEVEL 5 A. Incomes, Asset Prices, Productivity, and Sectoral Employment 5 B. Demography and Human Capital (Health and Education) 6 C. Social Capital and Institutions 8 V. THE CASE OF SRI LANKA AFTER THE 2004 TSUNAMI 9 A. What Can We Learn from the Macroeconomic Data? 10 B. What Do the Household Surveys Tell Us? 17 C. What Can We Conclude about Build Back Better in Sri Lanka after 2004? 21 VI. THE 2008 WENCHUAN EARTHQUAKE 22 A. What Can We Learn from the Macroeconomic Provincial Data? 23 B. What Do the Household Surveys Tell Us? 28 VII. POLICY CONCLUSIONS ABOUT BUILD BACK BETTER 29 A. Build Back Safe 29 B. Build Back Fast 29 C. Build Back Fair 30 D. Build Back Potential 30 REFERENCES 31 TABLES AND FIGURES TABLES 1 Tsunami Damage in Sri Lanka 10 2 Percentage Share of Gross Domestic Product by Main Service Sector Categories 13 3 Impact of Tsunami on Household Income and Consumption 20 4 Impact of Tsunami on Household Consumption by Type of Consumption 20 5 Income Variation Depending on Wealth and Damage Intensity 20 6 Consumption Variation Depending on Wealth and Damage Intensity 21 7 Affected Areas and Damage 22 8 Sum of Direct Damage and Losses 23 FIGURES 1 Typology of Disaster Impacts 2 2 Districts Affected by the Tsunami 9 3 Growth of Per Capita Gross Domestic Product at Constant Prices 11 4 Agriculture and Industry Sector Contribution to Gross Domestic Product 11 5 Percentage Share of Gross Domestic Product by Fishery Types at Constant 2002 Prices 12 6 Percentage Share of Gross Domestic Product by Main Industry Sector Categories 13 at Constant 2002 Prices 7 Inflation and Unemployment 14 8 Total Government Expenditure 14 9 Current Account Balance 15 10 Personal Remittance Growth Rate 16 11 Foreign Aid 16 12 Damage at District Level 18 13 Income across Tsunami (Treatment) and Nonaffected (Control) Households 18 14 Consumption across Tsunami (Treatment) and Nonaffected (Control) Households 19 15 Per Capita Gross Regional Product in Sichuan 24 16 Value Added by Sectors in Sichuan 24 17 Total Value of Commercial Buildings Sold in Sichuan 25 18 Investments in Residential Buildings in Sichuan 25 19 Building Construction by Floor Space in Sichuan 26 20 Consumer Price Index by Commodity Type 27 21 Sichuan Government Budgetary Expenditure 27 22 Resident Population in Sichuan 28 ABSTRACT The long-term economic consequences of catastrophic disasters are poorly understood. This lacuna is surprising since the long-term effects may be much more important than the short-term emergency phase. In contrast, the policy literature is full of aspirational plans to “build back better” (BBB)—a recovery that leads to improvements above and beyond the predisaster status quo. BBB is clearly multidimensional, but the focus here is the assessment of economic BBB. We first delve into two wellknown BBB cases—Sri Lanka after the 2004 Indian Ocean tsunami and Sichuan province in the People’s Republic of China after its 2008 earthquake. Following that analysis, the central objective of our paper is to propose a more precise and concrete definition of economic BBB. To do so, we propose four criteria against which one should evaluate BBB policies: safety, speed, fairness (inclusiveness), and socioeconomic potential. We conclude by describing each of the four criteria in greater detail. Keywords: build back better, disaster, economic impact, long run, recovery JEL codes: H54, Q54 “Would you tell me, please, which way I ought to go from here?” “That depends a good deal on where you want to get to,” said the Cat. “I don’t much care where–” said Alice. “Then it doesn’t matter which way you go,” said the Cat. “–so long as I get SOMEWHERE,” Alice added as an explanation. “Oh, you’re sure to do that,” said the Cat, “if you only walk long enough.” (Alice in Wonderland by Lewis Carroll, Chapter 6) I.  INTRODUCTION Almost all the measurement, empirical estimation, and theoretical modeling of disaster risk focuses on the immediate economic impact of disasters triggered by natural hazards such as tropical storms, earthquakes, or droughts. Even more effort is expanded outside economics, for example on the science of natural hazards, to improve our understanding of their causes and likely occurrences, our ability to predict them, and our knowledge of the ways they affect us. In comparison, relatively little research attention has been directed toward the longer-term consequences of these events (see section IV). This is true for economic long-term trajectories, but it is equally true for cultural and social effects, public health effects, and even geospatial effects such as where people choose to live and work. This lack of attention to long-term effects is puzzling since these effects, which can last decades, are likely more important than the short-term postdisaster emergency phase. In contrast to these lacunae, the policy literature is full of largely aspirational plans to “build back better” (BBB) and facilitate a recovery from a disaster that is not only complete but that leads to improvements above and beyond the predisaster status quo, maybe even improvements that would have been difficult to achieve in the absence of disasters. Although BBB is multidimensional, this paper focuses on economic dimensions rather than other dimensions that may be relevant for a fuller assessment of long-term effects. For example, here we are not evaluating the long-term environmental impacts—for example, ecological diversity—or, at the other end of the consequence spectrum, the long-term psychological impact of catastrophes on emotional well-being of victims. While environmental and psychosocial BBB are important, noneconomic BBB is outside the scope of our paper. Furthermore, the scope of our paper is limited to economic trajectories that have been measured or can be measured. We recognize that this limits our discussion, and that some equally important but unmeasured and/or unmeasurable effects are researched in the qualitative literature in disciplines such as geography and sociology. In sections II-IV, we examine the existing body of knowledge about economic BBB. In the following two sections, we take an in-depth look at two Asian case studies—Sri Lanka after the 2004 Indian Ocean tsunami (section V) and the province of Sichuan, in the People’s Republic of China (PRC), after its 2008 earthquake (section VI). For reasons we describe below, we consider both as potential poster children for economic BBB. However, in section VII we argue that BBB is often so vaguely specified that policy makers and analysts can often declare their aspiration to BBB as fulfilled, even if the long-term outcome is less than an unalloyed success. BBB is akin to the observation in Lewis Carroll’s Alice in Wonderland, made 2 | ADB Economics Working Paper Series No. 600 by the Cheshire Cat to Alice, that she is sure to arrive at her destination as long as she does not have one. Maybe appropriately for our analogy with BBB, the Cheshire Cat observes that Alice will “get SOMEWHERE” if she walks “long enough.” In trying to define economic BBB more precisely and concretely, we propose a new set of criteria for economic BBB: ybuild back safe ybuild back fast ybuild back fair ybuild back potential We briefly describe each criterion and conclude with some final thoughts for future research.  II. THE TYPOLOGY OF POSTDISASTER RECOVERY A disaster occurs when a hazard interacts with an exposed and vulnerable population, causing harm to people and/or damaging physical assets such as property or infrastructure. Hazards include natural hazards (such as hurricanes and earthquakes); humanmade hazards (such as industrial failures and nuclear meltdowns); or some combination of the two (e.g., the severe acute respiratory syndrome, or SARS, epidemic). Some disasters take place instantly and abruptly while others take place over a longer span of time.It is important to note that in and of themselves hazards are not disasters. Rather, it is a society’s failure to cope with a hazard that turns a hazard into a disaster. Here, we focus only on disasters that are triggered by natural hazards although we refrain from using the term natural disaster. A standard framework classifies disaster impact into direct impact versus indirect impact. Direct costs encompass damage to physical and natural assets, such as factories and farmland, and of course, loss of human life (i.e., mortality) and injury and illness (i.e., morbidity). Indirect losses denote the adverse effect of the disaster on economic activity (see Figure 1). There is also a distinction between short-run losses and long-run losses. Figure 1: Typology of Disaster Impacts Source: Ilan Noy. 2016. “Tropical Storms: The Socio-Economics of Cyclones.” Nature Climate Change 6: 343–45. Build Back Better: What Is It, and What Should It Be? | 9 V. THE CASE OF SRI LANKA AFTER THE 2004 TSUNAMI The 2004 Indian Ocean earthquake lifted the ocean floor by more than 3 meters, triggering a catastrophic tsunami which took 226,000 lives and displaced at least 2 million people in a dozen countries (De Alwis and Noy 2019). In Sri Lanka, where the tsunami was completely unexpected, it hit 13 out of the country’s 14 coastal districts (Figure 2). The death toll approached 35,500, and at least 1 million people lost their homes (Table 1). The tsunami inflicted serious damage on the infrastructure of Sri Lanka, which suffered total direct economic losses of $1.5 billion or around 5% of the GDP (Government of Sri Lanka 2005). Figure 2: Districts Affected by the Tsunami  Source: Government of Sri Lanka. 2005. Report on Impact of Tsunami 2004 on Sri Lanka. Department of Census and Statistics. http://www.statistics.gov.lk/tsunami/census/Summarynew.pdf 10 | ADB Economics Working Paper Series No. 600 Table 1: Tsunami Damage in Sri Lanka District Deaths Displaced Population Population that Became Homeless Public Infrastructure Damage (SLRs million) Jaffna 2,640 39,607 20,734 1,716.4 Mullaitivu 3,000 22,657 22,831 2,166.1 Trincomalee 1,078 81,643 36,326 3,446 Batticaloa 2,840 61,912 70,282 3,208.4 Ampara 10,436 75,172 67,707 3,959.2 Hambantota 4,500 17,723 8,955 1,296.5 Matara 1,342 13,206 28,860 2,216.9 Galle 4,214 128,077 53,440 4,289.9 Kalutara 256 27,713 24,855 1,009.4 Colombo 79 31,239 24,457 235.1 Gampaha 6 1,449 4,401 348.1 Puttalam 4 66 228 16.9 Kilinochchi 0 1,603 1,186 232.3 Mannar 0 0 0 11 Source: Government of Sri Lanka. 2005. Report on Impact of Tsunami 2004 on Sri Lanka. Department of Census and Statistics. http://www.statistics.gov.lk/tsunami/census/Summarynew.pdf.  A. What Can We Learn from the Macroeconomic Data? In Figure 3, we compare the growth rate of real per capita GDP in Sri Lanka to the average per capita GDP growth of middle-income countries, South Asia, and the world. Sri Lanka suffered a recession in 2001, but since 2002, including the period following the 2004 tsunami catastrophe, the economic growth of Sri Lanka follows a very similar pattern of growth to the comparison groups. When examining these aggregate figures, it is important to note that Sri Lanka received an unusually large amount of external assistance for rebuilding from 2005 to 2008. As is true elsewhere, the economy of Sri Lanka then suffered a slowdown in 2009 after the global financial crisis hit the entire world economy. Sri Lanka’s slowdown, however, was, if anything, milder than elsewhere. In addition to the global financial crisis, the fighting between army and Liberation Tigers of Tamil Eelam, a well-armed separatist ethnic rebel group, intensified in 2009, and eventually the conflict ended when the army managed to defeat the Liberation Tigers of Tamil Eelam. The double negative shock of the global financial crisis and intensified conflict during 2008–2009 could have severely dented Sri Lanka’s growth. In the postcrisis, postconflict period, growth converged to the average observed across other groupings and the global economy.  Build Back Better: What Is It, and What Should It Be? | 11 Figure 3: Growth of Per Capita Gross Domestic Product at Constant Prices  Source: World Bank. 1960–2018. World Development Indicators. Washington, DC. wdi.worldbank.org/ (accessed 12 October 2018).  In Figure 4, we observe that the share of agriculture in the Sri Lankan economy had declined in the early 2000s, but the decline started in 2002, before the tsunami. So, it is difficult to argue there is any evidence that the agriculture sector was disproportionally affected by the tsunami. When the agricultural data is further segmented into its constituents, it is again apparent that the years 2004– 2005 did not bring about substantial change in the agriculture sector. Fishing, which obviously suffered from Figure 4: Agriculture and Industry Sector Contribution to Gross Domestic Product  GDP = gross domestic product. Source: World Bank. 1960–2018. World Development Indicators. Washington, DC. wdi.worldbank.org/ (accessed 12 October 2018). 12 | ADB Economics Working Paper Series No. 600 tsunami damage to fisheries and to the fishing fleet, did experience a short lived and dramatic decline during 2005–2006. However, the long-term consequences are tangible but not that large, and more associated with faster increases in other parts of the economy (Figure 5).  Figure 5: Percentage Share of Gross Domestic Product by Fishery Types at Constant 2002 Prices  Source: Government of Sri Lanka. 2015. National Accounts. Department of Census and Statistics. http://www.statistics.gov.lk/page. asp?page=National%20Accounts (accessed 12 October 2018). Figure 6 and Table 2 provide more data about the sectoral breakdown of GDP over time in Sri Lanka. The main message from the data is that 2004 does not present any structural break in the sectoral composition of the Sri Lankan economy. In light of that, we should not expect to observe much aggregate long-term adverse impact of the tsunami on the aggregate statistics of the Sri Lankan economy. We do observe an uptick in inflation, most likely due to the increase in demand associated with post-tsunami reconstruction. Eventually, however, inflation did decline about a decade after the tsunami (see Figure 7). Build Back Better: What Is It, and What Should It Be? | 13 Figure 6: Percentage Share of Gross Domestic Product by Main Industry Sector Categories at Constant 2002 Prices  Source: Government of Sri Lanka. 2015. National Accounts. Department of Census and Statistics. http://www.statistics.gov.lk/page. asp?page=National%20Accounts (accessed 12 October 2018.). Table 2: Percentage Share of Gross Domestic Product by Main Service Sector Categories 2002 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2014 Services 57.7 59.3 59.4 59.5 59.6 59.5 59.3 59.3 59.5 58.6 58.1 57.6 58.0 Wholesale and retail 23.8 24.7 24.7 24.6 24.5 24.2 23.3 23.2 23.6 23.0 22.7 22.8 23.3 Hotels and restaurants 0.2 0.6 0.5 0.5 0.4 0.4 0.4 0.5 0.6 0.7 0.8 0.8 0.8 Transport 10.6 11.5 11.9 12.4 12.8 13.1 13.5 13.9 14.3 14.3 14.6 14.6 14.5 Post and telecommunications 0.6 0.8 1.1 1.2 1.4 1.6 1.7 1.8 1.9 1.9 2.0 2.0 1.9 Banking, insurance, and real estate 8.0 8.4 8.4 8.5 8.7 8.7 8.9 8.9 8.8 8.9 8.7 8.7 8.7 Government services 8.5 8.0 7.9 7.7 7.7 7.7 7.8 7.6 7.1 6.8 6.5 6.2 6.2 Private services 2.4 2.3 2.3 2.3 2.4 2.4 2.4 2.4 2.3 2.3 2.3 2.3 2.3 Source: Government of Sri Lanka. 2015. National Accounts. Department of Census and Statistics. http://www.statistics.gov.lk/page.asp?page =National%20Accounts (accessed 12 October 2018).   Puzzlingly, however, the fiscal data do not indicate any significant jump in expenditures in the immediate post-tsunami period (Figure 8). This is because much of the funding for reconstruction did not come from general government expenditures, but from foreign aid that was not channeled through the government accounts. 14 | ADB Economics Working Paper Series No. 600 Figure 7: Inflation and Unemployment  GDP = gross domestic product. Source: International Monetary Fund. 1948–2018. International Financial Statistics. https://data.imf.org/IFS (accessed 12 October 2018).   Figure 8: Total Government Expenditure  SLRs = Sri Lanka rupees. Source: Government of Sri Lanka. 1974–2017. National Summary Data Page. Central Bank of Sri Lanka. https://www.cbsl.gov.lk/en/ statistics/data/special-data-dissemination-standard (accessed 10 December 2018). When compared to the averages for advanced and middle-income countries, Sri Lanka has higher current account deficit as a percentage of GDP (Figure 9). After the recession of 2001, the deficit starts to worsen from 2003 until 2008. It improves dramatically from 2008 to 2009 but Build Back Better: What Is It, and What Should It Be? | 15 worsens again in 2011. The increase in deficit coincides with the post-tsunami reconstruction and the resolution of the internal conflict. However, it is also affected by the global economic crisis and subsequent recovery, which at first slowed demand for Sri Lanka’s exports and later sent import bills soaring, mainly on high oil prices. In Figure 10, we observe what happened to remittances in the aftermath of the tsunami. As we suggested above, the large inflow of international capital allowed the government to spend heavily on recovery effort without any tangible impact on public finances. Figure 10 shows the large increase in personal remittances to the country in the post-tsunami era. In particular, they peaked in 2005 and 2010, immediately after the tsunami and the end of the conflict, respectively. One can observe an equally dramatic increase in capital inflows in the form of foreign aid. Aid peaked in 2005 and remained elevated in subsequent years (Figure 11).  Figure 9: Current Account Balance  GDP = gross domestic product. Source: International Monetary Fund. 1948–2018. International Financial Statistics. https://data.imf.org/IFS (accessed 12 October 2018). 16 | ADB Economics Working Paper Series No. 600 Figure 10: Personal Remittance Growth Rate  Source: World Bank. 1960–2018. World Development Indicators. Washington, DC. wdi.worldbank.org/ (accessed 12 October 2018). Figure 11: Foreign Aid  ODA = official development assistance, OOF = other official flows. Source: Organisation for Economic Co-operation and Development. Statistics. https://stats.oecd.org/ (accessed 12 October 2018). Build Back Better: What Is It, and What Should It Be? | 17 B. What Do the Household Surveys Tell Us? In the aftermath of the catastrophe, the Government of Sri Lanka limited reconstruction in a buffer zone along the coast. However, strong opposition from the general public forced the government to abandon this policy. New houses were provided to those who could prove ownership of destroyed houses and through a donor-driven program for those unable to document land ownership. For houses that were completely destroyed, the Government of Sri Lanka provided land and cash grants for rebuilding houses. Sri Lanka drew heavily on foreign financing for its tsunami reconstruction efforts. An extended search for tsunami aid inflow data did not yield reliable data on aid distribution. The Government of Sri Lanka initially estimated that $2 billion was needed for reconstruction, including an ambitious BBB reconstruction program (Government of Sri Lanka 2005). Sri Lanka’s reconstruction spending approached $1.4 billion by the end of 2006 (Jayasuriya and McCawley 2010). A review of the Reconstruction and Development Agency, the designated authority for tsunami reconstruction coordination in Sri Lanka, reported that $2.8 billion in aid was pledged and $2.3 billion was committed, but only $1.2 billion was disbursed and $0.8 billion was expended by midyear 2006. We have not identified any later information sources tabulating these data, and there is no available data that provide any spatial detail. After 2006, the government’s standard development program assumed the responsibility for managing tsunami reconstruction. De Alwis and Noy (2019) examine the household survey data available from the post-tsunami decade, to identify the causal effect of the 2004 tsunami. Household surveys were conducted in 1995, 2002, 2006, 2009, and 2012. The details available in the surveys make them suited to investigate the tsunami’s long-term potential for BBB policies when measured by income and consumption at the household level. Measured household consumption expenditures include food, nonfood, durables, as well as insurance and savings. Household income is broken down into paid employment income; net income from agricultural and other work; cash receipts from pension, property rent, dividends and other sources; and both overseas and domestic remittances. A confounding factor in any analysis of the post-tsunami recovery is the civil conflict that ended in 2009. Cavallo et al. (2013) already postulated that in some cases, disasters lead to institutional and politico-structural changes that can have either adverse or favorable long-term implication for development trajectories. Whether the end of the conflict is at all related to the tsunami is debated—for example, Kikuta (2018). In the other most tsunami-affected area, Aceh province in Indonesia, the end of the civil conflict was directly tied to the tsunami damage and the need to establish access to reconstruction funding. Figure 12 describes the damage data for tsunamiaffected nonconflict districts as a share of population. The findings described below are based on a quantitative analysis of 84,393 complete household records in the years before and after the tsunami; see De Alwis and Noy (2019) for more detail. 18 | ADB Economics Working Paper Series No. 600 Figure 12: Damage at District Level  DSDs = divisional secretariats in district. Sources: Government of Sri Lanka. 2015. National Accounts. Department of Census and Statistics. http://www.statistics.gov.lk/page. asp?page=National%20Accounts (accessed 12 October 2018); Government of Sri Lanka. 2002. Census of Population. Department of Census and Statistics.http://www.statistics.gov.lk/ (accessed 12 October 2018). Figure 13: Income across Tsunami (Treatment) and Nonaffected (Control) Households  Source: De Alwis, Diana, and Ilan Noy. 2019. “Sri Lankan Households a Decade after the Indian Ocean Tsunami.” Review of Development Economics 23 (2): 1000–26. https://onlinelibrary.wiley.com/doi/abs/10.1111/rode.12586. Build Back Better: What Is It, and What Should It Be? | 25 Figure 17: Total Value of Commercial Buildings Sold in Sichuan  CNY = yuan. Source: Government of the People’s Republic of China. 1999–2018. National Bureau of Statistics of China. http://www.stats.gov.cn/english/Statisticaldata/AnnualData/ (accessed 12 October 2018). Figure 18: Investments in Residential Buildings in Sichuan  CNY = yuan. Source: Government of the People’s Republic of China. 1999–2018. National Bureau of Statistics of China. http://www.stats.gov.cn/english/Statisticaldata/AnnualData/ (accessed 12 October 2018). 26 | ADB Economics Working Paper Series No. 600 Figure 19: Building Construction by Floor Space in Sichuan  m 2 = square meter. Source: Government of the People’s Republic of China. 1999–2018. National Bureau of Statistics of China. http://www.stats.gov.cn/english/Statisticaldata/AnnualData/ (accessed 12 October 2018). Figure 20 shows the inflation rate of Sichuan province. Somewhat counterintuitively, there is no observable impact on inflation in any of the sectors for which we have inflation (consumer price index) data. Trends in government expenditures (Figure 21) indicate that the Sichuan provincial government is consistently spending more, but money earmarked for reconstruction and recovery was only available in the immediate postearthquake period (2008–2011). Another sign of a strong recovery is the rebound in provincial population since 2010 (Figure 22). Build Back Better: What Is It, and What Should It Be? | 27 Figure 20: Consumer Price Index by Commodity Type  Source: Government of the People’s Republic of China. 1999–2018. National Bureau of Statistics of China. http://www.stats.gov.cn/english/Statisticaldata/AnnualData/ (accessed 12 October 2018). Figure 21: Sichuan Government Budgetary Expenditure  CNY = yuan. Source: Government of the People’s Republic of China. 1999–2018. National Bureau of Statistics of China. http://www.stats.gov.cn/english/Statisticaldata/AnnualData/ (accessed 12 October 2018). 28 | ADB Economics Working Paper Series No. 600 Figure 22: Resident Population in Sichuan   Note: At year-end, 10,000 people. Source: Government of the People’s Republic of China. 1999–2018. National Bureau of Statistics of China. http://www.stats.gov.cn/english/Statisticaldata/AnnualData/ (accessed 12 October 2018). B. What Do the Household Surveys Tell Us? In their analysis, Park and Wang (2017) use data from a unique survey conducted 10 months after the earthquake. The survey sampled 3,000 rural households living in 100 poor villages in 10 counties in disaster-affected areas. They find that asset and income losses for surveyed households were substantial, especially in the hardest-hit areas. They describe “an overwhelming government response to the disaster.” Subsidies provided to households in 2008 were so large that mean income per capita was 17.5% higher in 2008 than in 2007, and the poverty rate actually plummeted from 34% to 19%. The survey asked retrospective questions about the household’s economic conditions before and after the earthquake, including detailed information on income from various sources. The survey also asked direct questions about the value of damage inflicted by the earthquake. The extent of government support for victims of the Wenchuan earthquake was not only impressive but unprecedented in scope and scale (Park and Wang 2017). Indeed, households on average were better off during the year of the earthquake. Luo and Kinugasa (2018) use aggregate provincial data for the period 1995–2015 for their analysis. Utilizing a synthetic control approach, they assess the disaster’s impact on household saving. Maybe not surprisingly, they find that the savings rate of rural households declined drastically following the earthquake but recovered quickly in the subsequent year. It appears that while there was a significant short-term effect on household saving, the event did not influence people’s long-term behavior with regards to savings. This may be partly due to the generous disaster relief that the victims received. Build Back Better: What Is It, and What Should It Be? | 29 VII. POLICY CONCLUSIONS ABOUT BUILD BACK BETTER Kennedy et al. (2008) observed that “building back safer” might be a preferable tagline to “building back better.” “Better” has multiple interpretations, many of which can cause further problems and the accumulation of risk, whereas “safer” provides a clearer goal for anchoring postdisaster settlement and shelter. Following that logic, the World Bank has suggested three separate BBB components: stronger, faster, and more inclusive (Hallegatte, Rentschler, and Walsh 2018). Here, we further develop these criteria to propose four components: safe, fast, fair, and with future potential. We describe each of these below. A. Build Back Safe Reducing the likelihood of mortality and morbidity in future events is undoubtedly an uncontroversial goal of any recovery and reconstruction in the aftermath of an event. Ceteris Paribus, it is likely to always be one of the more important goals guiding government policy. It seems indisputable that safety should be prioritized, especially because unsafe or less safe reconstruction affects individual households in the disaster zone for a very long time. Hallegatte, Rentschler, and Walsh (2018), use the term “stronger” instead of “safer.” This implies reconstructing facilities—that is, housing, public buildings, and transportation infrastructure—so that they are stronger and better able to withstand an extreme hazard event. However, safety can also be achieved by softer defenses such as the wellknown example of mangrove forests protecting against sea surges, or even the soft defense of retreating from an exposed location (Hino, Field, and Mach 2017). It is possible to build safer communities by other policies that do not involve “strong” hard defenses. For example, strengthening social ties within communities can make them safer, as can establishing more efficient ways to evacuate when an early warning system alarm goes off—for example, by widening roads. B. Build Back Fast Rebuilding faster is another fairly obvious and uncontroversial goal for public policy. The problem, of course, is that the quest for speed is often in conflict with some of the other aims of BBB. In both Sri Lanka and Sichuan, the respective governments made a concerted effort to speed up the recovery process. In both cases, one notable uniqueness of the recovery process was the exceptionally ample resources to fund reconstruction. In Sri Lanka, the government received substantial financial support from the international community. The determinants of emergency financial support in the international context are established. These determinants include need, but also include other aspects such as geostrategic interests of donor countries and multilaterals. There were probably two additional reasons why Sri Lanka was relatively well supported after this catastrophe. First, Sri Lanka was more accessible than Aceh, the most heavily affected region in the island of Sumatra, Indonesia. Second, the country was a familiar destination for tourists from some of the main donor countries (again, unlike Aceh). Indeed, compared to other events—for example, the 2008 tropical cyclone Nargis in Myanmar, or even reconstruction in high-income Japan and New Zealand after their 2011 earthquakes—the recovery process in Sri Lanka was much faster. The recovery processes from these other events were much slower and are, in fact, still incomplete. But beyond the need for ample funds, the desire for speedy reconstruction is clearly in conflict with the desire to consult and seek participation from the affected local communities. In addition, the 30 | ADB Economics Working Paper Series No. 600 quest for speed is typically in conflict with the careful consideration of all possible development, planning, and reconstruction paths. Many of these alternative paths require significant planning effort. Most often, and most challenging, is the need to reallocate property rights for certain assets, the most difficult of which is land. These are challenging endeavors, and that is clearly part of the reason why speed is a lower priority in many reconstruction projects. Hence, the trade-off between speed and carefully planned reconstruction seems undeniable, but it is still worthwhile simply to note that, ceteris paribus, speed should be prioritized. A slow recovery makes it more difficult to achieve a BBB recovery. C. Build Back Fair A recovery that is fair and inclusive—that is, one that benefits all segments of the affected population—is yet another apparent and obvious objective. In this connection, a plethora of research studies find that recoveries frequently exclude the most vulnerable, disadvantaged, and poorest population segments (for a survey, see Karim and Noy 2016). Given the large amount of evidence of noninclusive recoveries, public planning for BBB needs to explicitly and systematically incorporate ways to mitigate this risk by including the weakest segments of society in the postdisaster reconstruction process. This aim needs a conscious and sustained effort in that direction, since reaching the most disadvantaged and vulnerable is not necessarily “speedy.” Without a well targeted focus, the hectic and often chaotic process of reconstruction is liable to leave the poor behind. D. Build Back Potential Beyond fair, fast, and safe, postdisaster recovery should also aim to generate future growth opportunities. Without economic opportunities, the quality of life and well-being deteriorates (e.g., Sen 2000, Friedman 2006). A fair, fast, and safe recovery does not necessarily translate into greater economic potential and opportunities for the reconstructed city or community. A cautionary tale comes from the city of Kobe, which experienced fast, safe, and most likely inclusive reconstruction but suffered a decline in economic opportunities and thus its long-term economic fortunes (duPont et al. 2015). Policy makers at all levels should be thinking of reconstruction that promotes economic opportunities and economic dynamism. Without renewed economic potential, a BBB recovery will not be sustainable, and is unlikely to be safe nor fair. To conclude, the four basic criteria for assessing the effectiveness of any BBB effort should be safety, speed, inclusiveness, and long-term economic potential. One of these criteria may sometimes come into conflict with another criterion. Nevertheless, the four objectives do provide a rough concrete checklist for planning BBB. 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