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date: 30 April 2017

Economic and Business Recovery

Summary and Keywords

The economics of disasters is a relatively new and emerging branch of economics. Advances made in analysis, including modeling the spatial economic impacts of disasters, is increasing our ability to project disaster outcomes and explore how to reduce their negative impacts. This work is supported by a growing body of case studies on the organizational and economic impacts of disasters, such as Chang’s in-depth analysis of the Port of Kobe’s decline following the 1995 Great Hanshin earthquake, and the evolving studies of the workforce trends during the ongoing recovery of Christchurch, New Zealand, following a series of earthquakes in 2010 and 2011.

The typical view of post-disaster economies depicts a pattern of destruction, renewal, and improvement. Evidence shows, however, that this pattern does not occur in all cases. The degree of economic disruption and the time it takes for different economies to recover varies significantly depending on characteristics such as literacy rates, institutional competency, per capita income, and government spending.

If the impacts are large relative to the national economy, a disaster can negatively affect the country or sub-national region’s fiscal position. Similarly, disasters may have significant implications for the national trade balance. If, for example, productive capacity is reduced by disaster damage, exports decrease, the trade balance may weaken, and localized inflation may increase.

Studies of individual, household, industry, and business responses to disasters (i.e., microeconomic analyses) cover a broad range of topics relevant to the choices actors make and their interactions with markets. Both household consumption and labor markets face expansion and contraction in areas affected by disasters, with increased consumption and employment often happening in reconstruction related industries.

Additionally, the ability of businesses to absorb, respond, and recover in the face of disasters varies widely. Characteristics such as size, number of locations, and pre-disaster financial health are positively correlated with successful business recovery. Businesses can minimize productivity disruptions and recapture lost productivity by conserving scarce inputs, utilizing inventories, and rescheduling production.

Assessing the progress of economic recovery and predicting future outcomes are important and complex challenges. Researchers use various methodologies to evaluate the effects of natural disasters at different scales of the economy. Surveys, microeconomic models, econometric models, input-output models, and computable general equilibrium models each offer different insights into the effect of disasters on economies.

The study of disaster economics still faces issues with consistency, comprehensiveness, and comparability. Yet, as the science continues to advance there is a growing cross-disciplinary accumulation of knowledge with real implications for policy and the private sector.

Keywords: economics of disasters, business recovery, resilience, industry sectors, public-private partnerships

Disasters and Economics: An Emerging Field

In this article, we start with a discussion of the macroeconomic effects of disasters, including vulnerability at the national level, the effect of disasters on national fiscal policy, and the trade effects of disasters. We then examine the economic recovery of economies at the sub-national level and the microeconomics of disasters. We conclude with a brief overview of methods employed in the assessment of economic recovery from disasters and an exploration of the future of the field of disaster economics.

The “economics of disasters” is a relatively new branch of study emerging within economics. Although, there have long been records kept of the impacts of catastrophic events, such as centuries of climate records recording drought losses in China (Zhang & Liang, 2010), the sub-field of disaster economics did not begin in any systematic way until the mid-20th century.

Historically, natural disasters were associated with very high death tolls, such as the All Saints’ Day earthquake near Lisbon, Portugal in 1755, which killed approximately 30,000 people (Trevino, 2011). The economic costs of disasters were also significant but generally were expected to be born by private individuals. As a result there was very little demand for comprehensive economic assessments of disasters (Clower, 2013).

Using public monies for disaster aid only became common practice in the mid-1900s. In the United States, public policy sanctioned federal disaster grants and loans as of 1915, but it was many more years before the public at large found the receipt of such aid socially acceptable (Clower, 2013). Similarly, in Europe, widespread public relief efforts began in the early 1900s, following disasters such as the 1939 earthquake and floods that struck Erzincan, Turkey, but systematic implementation of public funding became more common later in the 20th century (Trevino, 2011). Throughout this time, disasters came to be seen as economic phenomena associated with social and political factors, rather than exogenous natural physical events or “acts of God” (World Bank, 2010).

At the same time, in high-income countries, warning systems and other preparedness and mitigation measures improved life safety, while development trends, such as building in coastal areas and flood plains, increased the exposure of property to hazards. This gradual shift coincided with increased interest in quantifying the cost of disasters and understanding their short- and long-term influence on economies (Dacy & Kunreuther, 1969).

A foundational work in the development of disaster economics as a branch of study was Dacy and Kunreuther’s (1969) book The Economics of Natural Disasters. The authors introduce some of the first frameworks for the economic analysis of disasters and promote a comprehensive system of disaster insurance in the United States.

Disaster losses are broadly categorized into two categories: direct and higher-order (indirect). Direct losses are the actual disaster-related physical damage and include stock losses (i.e., damage to property or assets), mortality, and morbidity. Direct losses can also refer to flow losses resulting from disaster-induced damage (i.e., business interruption or reduced productivity). Higher-order losses cover all flow losses that are not directly caused by damage, including downstream and supply-side disruptions.1 Higher-order losses, for example, include decreased customer numbers or increased costs of labor or supplies (ECLAC, 2014).

Moving beyond loss assessment, the empirical work in economics focusing on recovery has been pursued in at least four strands: (a) a macro-econometric approach based on historical data from case studies and comparative work (e.g., Leiter, Oberhofer, & Raschky, 2009; Noy, 2009; Strobl, 2012); (b) modelling the spatial economic impacts of disasters, using input-output analysis, computable general equilibrium models, or some combination of these (e.g., Henriet, Hallegatte, & Tabourier, 2012; Okuyama & Chang, 2004; Rose & Liao, 2005); (c) a micro-econometric approach, using data from firms, from households, or other standard micro-economic data (e.g., Arouri, Nguyen, & Youssef, 2015; Cole, Elliott, Okubo, & Strobl, 2014); and (d) a variety of different inter-disciplinary approaches, often based on data from expert surveys, observations, and interviews (e.g., Aldrich, 2012; Tierney, 2006; Useem, Kunreuther, & Michel-Kerjan, 2015). This latter strand is linked to a growing body of case studies on the organizational impacts of disasters, such as Chang’s (2000) in-depth analysis of the Port of Kobe’s decline following the 1995 earthquake or the study of Chang-Richards, Seville, and Wilkinson (2013) of the workforce trends during Christchurch’s recovery following a series of earthquakes in 2010 and 2011.

Economic and Business RecoveryClick to view larger

Figure 1. Damaged block of shops in Christchurch’s central business district. (G. Tobin, personal communication, July 20, 2013).

The domain of disaster economics encompasses every stage of the disaster risk reduction cycle: preparedness, mitigation, response, and recovery; as well as every aspect of economic study from behavioral change to fiscal policy implications. While the fundamental assumptions that underpin economics do not change for analyses of disasters, this subfield addresses the disruption of pre-existing trends and the implications of shocks and, often, sudden reorganization within economies.

This article offers a brief survey of some of the main issues being investigated in the dispersed field of disaster economics today. Beginning with an explanation of the various ways spatial and temporal scales are used in this discussion, the article explores major issues in the macroeconomics of disasters, including vulnerability in national economies, the fiscal effects of disasters, the effects of disasters on trade, and a brief overview of the way disasters effect regional and local economies. This is followed by a short overview of two key aspects of the microeconomics of disasters: shifts in consumer behavior and labor markets, as well as an extended overview of the ways businesses are affected by disasters. Finally, we review various methods for assessing economic recovery and estimating the economic impacts of disasters, and we conclude by summarizing some of the major questions remaining in the field. The article touches on a broad range of topics, offering examples from published research and suggestions for further reading, as a starting point for those interested in the economics of disasters.

Notes on Scale

This article is organized around a hierarchical categorization of the economy, starting with macroeconomic issues at the national level including fiscal policy and the impacts on trade, and at the regional and local levels. This is followed by a discussion of the economy at the “micro” scale (e.g., sectors, behavior of firms, organizations, and households). It is important to acknowledge, however, that such clear scalar distinctions are artificial and may obscure some of the important interactions that occur across various levels of the economy. For example, in the years following Hurricane Katrina, the convergence of the global financial crisis and the U.S. housing market crash (2008–2010) significantly influenced the real estate market and access to finance along the Gulf Coast. Over five years after the disaster, housing numbers and values had still not recovered to their pre-disaster levels (Cheng, Ganapati, & Ganapati, 2015).

Economic and Business RecoveryClick to view larger

Figure 2. Patchwork of residential development following Hurricane Katrina. Empty sections next to a new housing development, Mississippi Gulf Coast 2007.

(Photo: Hazards and Vulnerability Research Institute, 2007).

The scale of the disaster itself matters as well. Some disasters are much more local (earthquakes are typically very local), but the damage from tsunamis, for example, can be spread out over very long coastal zones. Indeed, the 2004 South East Asian tsunami led to high mortality in multiple countries, and even to mortality as far west as the eastern coast of Africa. Volcanic eruptions are another type of event that can exert influence on large areas; the most notorious example of that is the “year without a summer”: 1816, in which the eruption of Mt. Tambora in Indonesia had catastrophic impact on weather conditions in Europe and North America.

In this article, we also discuss how disaster impacts and recovery unfold on different timescales. The dynamics identified throughout this article reflect various points in time throughout the post-disaster phase. It is important to note that the economic dynamics of impact and recovery will unfold differently depending on the state of the economy before the event, the characteristics of the disaster (e.g., whether there was warning, the duration and magnitude of the event), and the responses of the affected populations and decision makers.

Often economists address time through discounting, calculating the rate at which society is willing to trade-off present for future benefits (Gollier, 2015; Murray, McDonald, & Cronin, 2015). Discounting, however, does not adequately incorporate the timescales at which decision makers need to think in order to address the risk of low-probability, high-impact events, such as a major volcanic eruption, or long-term post-disaster planning for a sustainable and equitable recovery. Therefore, time needs to be understood as a complex variable in the analysis of disasters.

Macroeconomics of Disasters

Although few would deny that the loss of life and trauma associated with disasters is a negative outcome for society, there are unresolved questions about the economic consequences associated with disasters at the national, regional, and local levels. A central question is whether disasters have lasting macroeconomic impacts, and if so, do they stimulate growth, lead to decline, or have other long-term effects?

The typical view of post-disaster economies depicts a pattern of destruction, renewal, and improvement. The initial impact causes a downturn in economic activity as productive capacity is reduced, local consumption decreases, infrastructure is destroyed, and supply networks are interrupted. In this typical view, the disruptive period is followed by enhanced productivity; increased investment or injections of aid, resources, and new funds from government, insurance, and private sources; and associated spending on the reconstruction of infrastructure and on commercial and residential property. Increased spending stimulates additional economic activity as resources cycle through household and business consumption and investment. The reconstruction stimulus then transitions into a new normal, assumed to resemble the economic conditions that would have prevailed without a disaster; a thorough non-quantitative treatment of these issues can be found in Davis and Alexander (2016). This pattern of destruction, renewal, and restoration to some approximation of pre-disaster trends or better does not occur in all cases.

Ultimately, few generalizations about the macroeconomic impact of disasters are applicable across all economies. Indeed, Albala-Bertrand’s (1993) influential paper presenting a macroeconomic model for sudden disaster impacts provided evidence of real gross domestic product (GDP) growth, but unfavorable outcomes for trade following disasters in several Latin American countries. Conversely, Heger, Julca, and Paddison’s (2008) analysis of small island nations in the Caribbean showed that disasters had negative effects on GDP per capita. Additionally, the time it takes for different economies to recover from disasters varies significantly depending on characteristics such as literacy rates, institutional competency, per capita income, and government spending (Vu & Noy, 2015).

Vulnerability and National Economies

It is generally true that, in the short term, disasters have a destabilizing effect on the economy with an associated slowdown in economic activity (OECD, 2004). The degree of the slowdown is partially a function of the nature of the disaster (e.g., earthquake vs. flood), its spatial extent, its magnitude, and its timing. It is also a function of the economic vulnerability of the affected area. Economies with a higher degree of vulnerability, often in developing countries, may lack the resources and the political and social stability to reconstruct their buildings and infrastructure and retain their human capital in a way that facilitates economic recovery (Klomp & Valckx, 2014; Lazzaroni & van Bergeijk, 2014). For example, the 2010 earthquake in Haiti—one of the most catastrophic disasters in the modern era—has adversely affected the long-term functioning of that country’s economy. Estimates show that by 2020, Haiti will have a per capita income of approximately $1,060, while it could have been about $1,410 if the earthquake had not occurred (Cavallo, Galiani, Noy, & Pantano, 2013). Conversely, existing estimates show no impact of catastrophic events on economic developments of high-income countries, even when they experience very costly events relative to their size (e.g., Doyle & Noy, 2015).

Analyzing the short-run dynamics of the macro-economy following disasters, Noy (2009) found that when disaster losses are standardized by GDP, disasters are more costly for developing nations than for developed ones. The results also indicate that disasters in Asia are more costly than those in the Middle East and Latin America, and disasters in island nations are by far the most costly. The means, medians, and number of observations of direct damage, number of people killed, and number of people affected for each disaster in 109 countries between 1970 and 2003 are reported in Table 1, grouped by region.

In a separate analysis, Heger, Julca, and Paddison (2008) suggest that many small island nations are especially vulnerable to the short-term macroeconomic impacts of disasters as their economies are often dominated by a few large sectors (usually agriculture and tourism) that are particularly exposed to disruptions in the natural environment.

Table 1. Mean/Medians for Disaster Variables by Region (Observations)

Direct Damage

Deaths

Affected People

Developed countries

0.000/0.000 (51)

0.000/0.000 (51)

1.43/0.05 (50)

Developing countries

0.029/0.001 (377)

0.094/0.001 (456)

5.92/0.90 (416)

Latin America

0.002/0.000 (83)

0.003/0.000 (86)

3.77/0.62 (85)

East Asia

0.086/0.006 (81)

0.379/0.006 (101)

9.87/0.80 (69)

South- & South-East Asia

0.005/0.001 (42)

0.014/0.001 (48)

6.25/1.28 (48)

Middle-East & North-Africa

0.028/0.000 (77)

0.011/0.001 (91)

2.13/0.28 (90)

Africa

0.015/0.003 (71)

0.014/0.001 (74)

9.02/1.50 (70)

Islands

0.127/0.018 (54)

0.560/0.053 (68)

16.35/1.21 (36)

Source: Noy (2009, p. 223).

In high-income economies, even large disasters have had very minimal macroeconomic aggregate effect in the medium to long run (Cavallo et al., 2013). This lack of any longer term effect is, first, because the disasters in high-income countries are typically small, relative to the size of the economy. Even for the Tohoku tsunami, the costliest disaster in history, the immediate damages did not amount to more than 5 percent of Japan’s GDP. Second, high-income countries have the resources needed to finance the reconstruction. This is in stark contrast to low-income countries. In Haiti, after the 2010 Port-au-Prince earthquake, recovery was much slower and more difficult as there were strict limits on the availability of funding, and maybe more importantly, limits on the ability of the government to effectively mobilize the funding it could access (Katz, 2013). A national economy that retains most of its human capital and productivity after a large-scale disaster will generally find that renewal and restoration are achievable within a few years of the disaster, as long as the funds necessary to pay for reconstruction are available (Cavallo & Noy, 2011; Noy, 2009).

The reconstruction phase following a disaster can both stimulate and disrupt the economy depending on the pre-disaster economic conditions. If, for example, a disaster occurs in a depressed economy, reconstruction may activate unused resources and spare capacity and can dampen the effects of production disruption. Conversely, if the economy is in a period of high growth, disasters can be more disruptive to the economy due to the potential for wage inflation and productivity losses (Hallegate, 2014). These dynamics will not only depend on the state of the business cycle, but also on the extent of pre-disaster planning at both the micro- and macro-levels.

Fiscal Effects

Disasters can affect the fiscal position of a national or sub-national economy. This often occurs when the tax base shrinks in the affected area and spending needs rise. When the disaster impact is small relative to the national economy, increasing tax rates can raise adequate revenues. Generally, decline in aggregate economic activity is temporary, and thus decline in tax revenue is usually minimal unless tax rates differ across economic activities or the disaster leads to significant shifts in tax policy. These dynamics may be different for lower-income countries, whose tax base is much lower, whose ability to recover is much more limited, and whose need to finance recovery through debt is even greater (Lis & Nickel, 2010; Noy & Nualsri, 2011).

If the disaster’s impacts are large relative to the national economy, even a government’s increased tax revenues may not be adequate to cover the increased public spending. As a result governments may need to increase borrowing, sell public assets, or monetize their shortfall (e.g., by printing more money or issuing new bonds) (Freeman, Keen, & Mani, 2003).

Trade Effects

Disasters also have at least three significant implications for trade at the national level:

  • As productive capacity falls exports will decrease. Reconstruction needs will lead to increased imports, and thus a worsening of the trade balance. On the other hand, reduced domestic income may lead to decreases in imports, and depreciating exchange rates can equally increase exports and thus an improvement of the trade balance. The evidence suggests, however, that trade balances typically worsen in the aftermath of disasters (e.g. Gassebner, Keck, & Teh, 2010). There is evidence that in both high and low-income countries consumer spending in poorer households tends to decrease following disaster (Karim & Noy, 2014). Spending on luxury goods also tends to decrease (Pelling, Özerdem, & Barakat, 2002) resulting in decreased imports of luxury goods. In some cases productive capacity, and therefore, the ability to produce adequate exports falls. This can also include exports of services, such as tourism, which are almost always harmed by disasters.

  • As the trade balance weakens and foreign investor confidence goes down, there may be downward pressure on the exchange rate. This can reduce buying power just as government spending on reconstruction ramps up (Gassebner et al., 2010).

  • As the exchange rate depreciates and borrowing becomes more expensive, governments may choose to monetize deficits, and inflation may increase (Freeman, Keen, & Mani, 2003). Large disasters in small or vulnerable economies have caused such effects. For example, following a 1974 earthquake in Honduras, tax revenue fell 15 percent, expenditure increased 65 percent, and as a result fiscal deficit grew 79 percent (Freeman, Keen, & Mani, 2003). Even in less extreme conditions, public spending on reconstruction can last for more than a decade resulting in increased budget deficits and borrowing.

Although it is unclear whether a disaster is always detrimental to a national economy in the long run, it would be fair to say that disasters are not social or economic goods. Only in rare cases is a government’s fiscal position sustained following a major disaster, such as when most of the damage is privately insured or re-insured internationally (Doyle & Noy, 2015). Additionally, while there is evidence that increased demand and limited supply do tend to stimulate wage and price increases in reconstruction related sectors, these increases do not always translate to other parts of the economy unless there are specific supply constraints. Stimulus benefits for a depressed economy could be achieved in the absence of a disaster through standard stimulus policy without the negative effects on human welfare (Hallegate, 2014). Even when post-disaster stimulus causes GDP to rise, this may be diverting spending from more productive public investment (e.g., providing basic needs instead of financing infrastructure investment), and it may also have a lasting negative effect on the government’s balance sheet (Noy, 2009).

Disasters and Regional and Local Economies

There is relatively consistent evidence that disasters can have persistent negative effects on sub-national economies (Noy & Vu, 2015). Adverse outcomes for regional and local economies are often associated with disruptive events leading to populations and economic activity relocating away from the affected area. For example, Hornbeck (2012) examined significant population departures from the drought stricken areas during the American Dust Bowl of the 1930s; Hornbeck and Naidu (2014) identify out-migrations after the 1929 Mississippi floods, while Coffman and Noy (2012) describe long-term population loss in the Hawaiian island of Kauai after it was hit by a destructive hurricane in 1992. In the latter case, the long-term population loss was about 15 percent of the previous population that permanently left the island. The figures for the earlier events are less precise, but in both cases these out-migrations led to long-term and very persistent changes in the economic structure of the affected areas.2

The nature of the impact and progression of recovery for regional and local economies depend on the economic structure of the region. Economies that depend heavily on a single industry or on industries that are vulnerable to the impacts of the disaster tend to fare worse than more diverse economies. In the Phang Nga province of Southern Thailand, two of the most important industries, tourism and fisheries, were among the sectors that were hardest hit by the 2004 Indian Ocean tsunami. The tsunami severely damaged over 80 percent of hotels in this area and almost completely destroyed the primary harbor and fishing village Ban Nam Khem (Neef, Panyakotkaew, & Elstner, 2015).

Economic and Business RecoveryClick to view larger

Figure 3. Damaged fishing boats following the 2004 Indian Ocean tsunami.

(Photo: E. Seville, 2004).

The loss of key industries made recovery in this province especially difficult; the province experienced significant depopulation, high rates of unemployment, and greatly increased household debt in the years following the tsunami. The tsunami led to significant structural shifts in the economy, with some new industries emerging, but it also exacerbated pre-existing economic disparities leading to poor long-term outcomes for segments of the community (Neef, Panyakotkaew, & Elstner, 2015).

Microeconomics of Disasters

Microeconomics analyses related to disasters cover a broad range of topics relevant to the choices individuals make and their interactions with markets. In this section we focus on consumption shifts and labor markets as two important and related microeconomic functions in the aftermath of disaster.

Consumer Behavior

Large disasters can influence the consumption behavior of individuals and households, with repercussions throughout the economy. Those directly impacted by the event may experience on-going costs associated with health care, food, and accommodation at significantly higher market rates.

The consumption behavior of households in the aftermath of an event has been linked to factors such as: the magnitude of the negative shock of the disaster on the household (Sawada & Shimizutani, 2008), household wealth prior to the disaster, the ability of households to borrow or access credit markets to finance recovery (Kurosaki, 2015; Sawada & Shimizutani, 2008), and the pervasive presence of risk (Heltberg, Oviedo, & Talukdar, 2015).

Shifts in consumer behavior can shape business survival, creating “winners” and “losers” (Xiao & Nilawar, 2013). In damaged areas, consumer demand may shift toward construction related industries, at least temporarily, creating opportunities for growth and expansion in these businesses (Xiao & Nilawar, 2013; Zhang, Lindell, & Prater, 2009). Conversely, demand may shift away from sectors that are considered non-essentials, such as specialty shops and fashion retail (Alesch, Holly, Mittler, & Nagy, 2001). In low-income countries people affected by disasters commonly respond by consuming fewer expendable items and less food. In these cases, reduced consumption leads to much poorer outcomes for disaster-affected populations (Heltberg, Oviedo, & Talukdar, 2015; Skoufias, 2003).

Labor Markets and the Workforce

A number of factors can shape the labor market in disaster-affected areas. Disasters often result in reduced employment in the affected area in the short to medium term. Belasen and Polachek (2008) found that, in Florida counties affected by hurricanes, employment fell between 1.5 and 5 percent, depending on hurricane strength, but these effects dissipated over time. Similarly, Di Pietro and Mora (2015) found that the probability of labor participation decreased for a period of 9 months in the area affected by the L’Aquila earthquake.

Longer-term adverse effects on employment rates or economic productivity may occur for several reasons. Organizations and individuals may choose not to invest in areas they perceive as facing on-going risk. The pool of available labor may decrease as populations migrate away from the area, temporarily or permanently. Following Hurricane Katrina, for example, a majority of residents were temporarily evacuated; in 2013 (eight years after the disaster) the population of New Orleans was still less than 80 percent of its pre-storm level (Fussell, 2015). Additionally, disasters may have an adverse impact on human capital accumulation (Di Pietro & Mora, 2015). In Ethiopia and Malawi, populations exposed to frequent droughts tended to reduce schooling investments. This economic decision reduced household stocks of human capital, creating additional employment vulnerability in the future (Yamauchi, Yohannes, & Quisumbing, 2009). Similar patterns of reduced human capital investment were also observed in China following the 1976 Tangshan earthquake (Xu, 2011) and Latin American countries affected by a range of shocks in the 1990s (Skoufias, 2003).

On the other hand, in areas where households and the government are able to invest adequate resources in reconstruction, and where limited resources and accumulated debt do not hinder human capital investment, disasters may bring some economic benefits. Some research has indicated that the type of disaster may influence the impact on longer-term employment and productivity. Acute shocks that damage mainly capital stocks may actually stimulate investment in newer means of production and generate new employment opportunities. Hazards like droughts and repeated flooding hinder the inputs in production (e.g., destroying the means of agricultural production) and deter investment (Loayza, Olaberria, Rigolini, & Christiaensen, 2009).

Minimizing negative impacts of disasters on the workforce and finding ways to adapt to changing conditions through the recovery requires a multifaceted approach. A study conducted as part of the Asia-Pacific Economic Cooperation (APEC) compared a number of disasters in China, Japan, New Zealand, Australia, and Canada. The study highlighted best practice approaches that allow decision makers to integrate post-disaster labor market response into regional disaster recovery and forward development (Chang-Richards et al., 2013). Best practice requires an approach that unfolds throughout the response and recovery period:

  • Immediate and short term: Stabilize economic conditions and minimize labor market disruptions.

  • Medium term: Restore confidence and community spirit, retain employment, and promote economic opportunities.

  • Long-term expansion and development: Develop a skilled workforce for the rebuild and regional development (Chang-Richards et al., 2013).

The study also highlights policies employed in APEC economies post-disaster, as shown in Table 2. The table includes a range of re-employment assistance offered through public and private providers. These programs are targeted toward reducing the mismatch between available jobs and the local skill base, particularly in rural areas in Indonesia and China. Others, such as Hello Work in Japan are targeted to help those displaced from employment in hard hit industries (in Japan: agriculture, fishery, forestry, and the self-employed) find new jobs. The Jobs and Skills Package in Australia was part of a wider Rural Resilience Package that provided industry grants and expert support to help organizations better prepare for future disasters in addition to responding to their immediate economic needs (Chang-Richards et al., 2013).

Reports from these programs indicate relatively positive outcomes (although systematic evaluations of the efficacy of such programs are rare). For example, the Queensland program served a projected 10,000 disaster-affected people by helping them pursue training and find job placements to aid the reconstruction effort between 2010 and 2012 (Venn, 2012).

Table 2. Labor Market Assistance Programs in APEC Countries

Economy

Program

Provider

Targeted population

Associated disaster event (year)

United States

One Stop Career Centre

Public sector

Job seekers and employers

Hurricane Katrina (2005)

New Zealand

Skills and Employment Hub

Public sector

Job seekers and employers

Canterbury earthquakes (2010, 2011)

Australia

Jobs and Skills Package

Public sector

Job seekers, employers, industry, and community

Queensland floods (2010, 2011)

Japan

Hello Work

Public sector

Job seekers

Great East Japan Earthquake and Tsunami (2011)

Indonesia

Education and Skills Training

International Labour Office

Youth

Boxing Day Tsunami (2004)

China

Vocational training and micro loans

International Federation of Red Cross Red Crescent

Vulnerable people: women, disabled people

Wenchuan Earthquake (2008)

Source: Adapted from Chang-Richards, Seville, Wilkinson, and Walker (2013).

In combination with another larger program, Japan as One, the Hello Work measure was estimated to have created or supported around 580,000 jobs (Venn, 2012). As of late 2015, the Skills and Employment Hub continued to operate in Canterbury, New Zealand and reported that, since its inception in 2012 over 3,500 Canterbury employers and 13,000 jobseekers had registered at the Hub (MBIE, 2015). Best practice programs addressed the workforce challenges presented by the disaster and attempted to enhance economic resilience and improve long-term employment opportunities for disadvantaged populations (Chang-Richards et al., 2013).

Disasters and Businesses

Businesses and organizations are the individual building blocks of an economy. Private investment in disaster-affected areas is an important engine of economic recovery. The ability of businesses to absorb, respond, and recover in the face of disasters, however, varies widely.

There are a number of characteristics that have been shown to influence a business’ vulnerability to disasters. Small to medium-size enterprises (SMEs) tend to have less access to capital and beneficial networks than larger businesses. As a result, SMEs tend to experience higher permanent closure rates than large businesses, although this is not the case following every disaster (Corey & Deitch, 2011).

Very large firms, such as multinational corporations are more likely to be well diversified in their supply chains and revenue sources. As such, localized disasters will be unlikely to affect the bottom line of the organization. It is now common for major retailers, such as Wal-Mart and Home Depot in the United States, to have in-house departments to facilitate their response and recovery from disasters and other disruptions (Horwitz, 2009). During and after Hurricane Katrina, the Wal-Mart Corporation had to temporarily close 126 stores, 15 of which remained closed for extended periods due to major damage. This had no lasting effect on the corporation’s income, and Wal-Mart took the opportunity provided by the disaster to partake in very public responses to the hurricane, which were considered invaluable to its public image in the years following (Horwitz, 2009).

Businesses with multiple locations or that are part of a corporate structure, and those that reach markets via the internet, are better able to diffuse the localized impacts of a disaster. Small and medium-sized businesses, on the other hand, with locally oriented markets are far more exposed to short and longer-term neighborhood effects, reduced foot traffic, increased competitive pressure from unaffected markets, and population dislocation (Kroll, Landis, Shen, & Stryker, 1991; Pearson, Hickman, & Lawrence, 2010).

Businesses in badly damaged neighborhoods may suffer even if their own building and operability is not damaged at all. In Seattle following the 2001 Nisqually earthquake, neighborhood-related problems, including on-going repairs, loss of street parking, the district’s image (e.g. perceived safety), and whether there were enough businesses to sustain foot traffic, had notable negative effects on businesses in the area (Chang & Falit-Biamonte, 2002).

A significant indicator of the ability of a business to perform after disasters is its pre-disaster financial health. Businesses with greater access to assets and capital often have more flexibility and control over key decisions. Businesses that own their buildings, for example, may have more control over access and decision making about repairs and the use of insurance payouts. Renters will often find that they have limited input on these decisions (Brown, Stevenson, Seville, & Vargo, 2015). Businesses performing poorly before a disaster tend to have fewer resources and capacity to cope with disruption.

There are many approaches businesses can take to minimize productivity disruptions and recapture lost productivity. Options include: conserving scarce inputs or maintaining production with fewer inputs, utilizing inventories and any excess capacity (e.g., idle plant and equipment), finding substitutions for production process inputs, and production rescheduling (e.g., working overtime or extra shifts to recoup lost production or making up production at a later date) (Rose & Krausmann, 2013).

Business continuity planning is a rapidly growing industry and is increasingly being utilized by organizations of all sizes, although it is far more common among larger businesses (Xiao & Peacock, 2014). There is inconsistent evidence about whether mitigation and preparedness activities are effective at reducing physical damage and business interruption losses for smaller businesses. There were no significant relationships found between preparedness and disaster losses incurred by businesses in studies following the 1989 Loma Prieta earthquake, 1992 Hurricane Andrew (Webb, Tierney, & Dahlhamer, 2002), the 1994 Northridge earthquake (Dahlhamer & Reshaur, 1996), or the 2001 Nisqually earthquake (Chang & Falit-Baiamonte, 2002). In a study specifically targeted at evaluating planning efficacy, however, Xiao and Peacock (2014) found that having an emergency/disaster plan was significantly associated with reduced levels of physical damage (extrapolated to indicate greater business continuity) in an event with a significant warning lead time, specifically the 2008 Hurricane Ike on the U.S. Gulf Coast.

Some businesses are also able to implement ex post-facto adaptations in ways that allow them to recapture production or recoup lost income and to position them better for the future. The Canterbury earthquakes acted as a catalyst to re-evaluate underperforming segments of their business, restructure, develop new markets, and adopt new technologies. For example, a building supply wholesaler introduced a GPS system that allowed plant managers to track truck movements and review driver performance, which created a more efficient delivery process. New technologies not only boosted productivity, but with the integration of things such as social media, video conferencing, and cloud storage, it also meant that organizations were more adaptable in future disruptions (Stevenson et al., 2014).

Box 1. Business Recovery Following the 2010/2011 Canterbury Earthquakes

Brown, Stevenson, Giovinazzi, Seville, and Vargo (2015) conducted a study using empirical evidence collected from 541 organizations following the Canterbury earthquakes to evaluate a number of hypotheses derived from the hazards literature. The authors found that in Canterbury:

  • Organizations reported that “customers issues” were more disruptive to their business than physical or environmental impacts of the earthquakes.

  • As the level of impact experienced by an organization increased, the degree of their recovery two to three years following the earthquake decreased.

  • Neither smaller nor younger organizations in the study suffered significantly worse impacts or recovery outcomes compared to larger or older organizations in this dataset.

  • Organizations in different industry sectors were differentially impacted and recovered to differing degrees. For example, the best performing organizations two to three years following the 2011 earthquake were accommodation and food services, construction, and financial and insurance services.

  • Organizations that rented their premises recovered better than those that owned their premises.

This study showed that, although it can be useful to understand generalized trends of business impact and recovery following disasters, it is important to consider the unique contexts in which these events occur. Each business and each disaster is different.

Financing Business Recovery

Private insurance is an important source of recovery funding for businesses following disasters. Insurance can both facilitate and delay business recovery. Quick settlements or interim payments from insurers can allow businesses to pursue repairs; replace damaged stock, equipment, and contents; and shore up cash flow. Runyan (2006) noted that for firms in the U.S. context, insurance is the key to recovery. Often, however, businesses are covered inadequately or not covered at all. For example, surveys of Sri Lankan businesses following the 2004 Indian Ocean tsunami showed that, even among larger enterprises, only 13 percent had insurance coverage for asset losses, and those with insurance reported that on average less than 8 percent of their losses were covered (De Mel, McKenzie, & Woodruff, 2011). A comparative study of earthquake affected businesses in Santa Cruz, California and hurricane affected businesses in South Dade, Florida found that coverage for property damage was 14.4 percent and 87 percent, respectively. Only 20 percent of businesses in Santa Cruz and 35 percent of businesses in South Dade, however, had business interruption insurance (Wasileski, Rodriguez, & Diaz, 2011).

Businesses can also finance recovery costs using cash flow, savings, loans, and credit. Post-disaster aid can have positive effects on employment, may reduce the risk of population losses, and promote consumption in ways that speed economic recovery. Post-disaster grants also have mixed degrees of efficacy, although are often preferred as they do not cause businesses to incur additional debt. In Sri Lanka following the Indian Ocean tsunami, De Mel, McKenzie, and Woodruff (2011) used random allocation of cash to local enterprises to assess the efficacy of post-disaster grants. They found that additional capital provided to microenterprises did increase recovery speed. Results varied greatly by sector, with retail enterprises benefiting significantly from the grants, while manufacturing and service sector recovery was hindered more by supply chain disruptions and other issues not directly assisted by infusions of capital. The authors concluded that aid should be targeted appropriately to maximize its value (De Mel, McKenzie, & Woodruff, 2011).

Assistance to businesses more often comes in the form of private loans or government-subsidized loans that must be repaid by businesses. Post-disaster loans have often been characterized as risky options for businesses in uncertain environments, as businesses can accumulate debt that they will ultimately be unable to repay (Dahlhamer & Tierney, 1998; Runyan, 2006). Because of this, many small business owners will rely on personal savings to finance recovery rather than incur additional indebtedness (e.g., Webb, Tierney, & Dahlhamer, 2002).

Methods for Assessing Economic Recovery

Those interested in the economics of disasters tend to focus on the costs of disasters, estimating the consequences on social welfare, and the effects of response and recovery on economic growth, labor participation, and equity. Assessing the progress of economic recovery and predicting future outcomes are important and complex challenges.

Recovery is usually evaluated using various indicators of performance. An indicator is an observable and measurable item or entity that represents or “stands in” for a characteristic of a system (e.g., the economy) that is less readily measureable. Indicators of economic recovery can include a number of measurable items that give an idea of economic performance (such as productivity), growth, stability, welfare, or factors that may influence economic trends. Population, for example, is not a direct measure of economic performance, but population recovery in a disaster-affected area may reflect the availability of housing, employment, utilities, and amenities (Seville, Vargo, & Noy, 2014). Other indicators of economic recovery are summarized in Table 3.

Table 3. Indicators and Measures of Economic Recovery

Indicator

Measures

Residential population

  • Population counts

  • Number of household residences

  • Occupancy rates

  • Immigration/ emigration rates

Community performance

  • Gross regional product

  • Tax revenues

  • School rolls

Employment & income

  • Employment/unemployment rates

  • Job creation

  • Average household income

Infrastructure capacity

  • Total kilometers of horizontal infrastructure (e.g., pipes, roads) repaired

  • Percentage of repairs and reconstruction completed (kilometers or dollars)

  • Utility usage rates (e.g., kilowatt hours per household)

Building stock

  • Percentage of demolitions, repairs, and reconstruction completed (dollars, total counts, square meters of floor space)

  • Vacancy rates in buildings able to be occupied

Business performance

  • Business deaths and births

  • Business numbers

  • Total sales or revenue for retail/wholesale sectors

  • Profitability

  • Commercial building occupancy rates

  • Industry growth rates

Different combinations of these indicators will provide slightly different perspectives of economic recovery in the aftermath of a disaster. No one indicator can provide a complete view of how an economy is performing, and depending on the resolution of the data, the indicators will reflect or mask variations across space and time. For example, business numbers in a disaster-affected region may reach pre-disaster levels or better, but those businesses may have relocated away from heavily damaged areas and formed new agglomerations, leaving some areas to stagnate while others surge ahead.

Similarly, GDP can reflect economic activity in terms of the output of goods and services produced. It does not, however, adequately capture the losses caused by the disaster. For example, the measurement of GDP cannot distinguish between new beneficial spending and spending on replacement of destroyed assets. The classic description of this is Bastiat’s (1850) parable of the Broken Window. Economic magnitudes also do not capture the impact on human welfare (for example, morbidity) that does not directly affect these magnitudes; for example, when morbidity does not lead to disability and reduced work (see discussion in Noy, 2015). Often, these also do not account for the distribution of incomes, as segments of the population or economic sectors struggle to recover and find themselves worse off. Therefore, it is important when evaluating economic recovery to examine a range of indicators and not only the conventional yardsticks of economic activity.

Researchers mobilize various methodologies to evaluate the effects of natural disasters at various scales of the economy. As summarized by Hallegatte and Przyluski (2010), higher-order (indirect) losses tend to be assessed: at the firm- or household level using surveys (e.g., Kroll et al., 1991; Tierney, 1997) or microeconomic models (e.g., Dercon, 2004); at the local and national level using econometrics models (e.g., Albala-Bertrand, 1993; Strobl, 2008); and at the regional and national level using input-output models (e.g., Okuyama & Chang, 2004) and computable general equilibrium models (e.g., Rose & Liao, 2005).

Each of these approaches offers different insights into the effect of disasters on economies. Data collection using surveys from past events can offer depth of understanding of microeconomic trends and decisions related to individual events. Econometric approaches utilize means across a series of events or locations. For example, Vu and Noy (2015) used Blundell-Bond General Method of Moments to estimate the impact of disasters on the macro-economy in Vietnam, finding that lethal disasters result in lower output growth, while disasters that destroy more capital can create short-run boosts to the economy. Finally, model-based approaches can be used to assess dynamics in the economy (e.g., estimating the cost of disruptions to a transportation system) and to assess disruption scenarios that have not yet occurred (Hallegatte & Przyluski, 2010).

The Future of the Field

The economic impact of disasters can be significant at the local and regional level, causing population dislocation, altering economic structures, and influencing workforce and consumption trends. Economics can also be devastating at the national level for small and low-income countries. For example, Hurricane Gilbert and Hurricane Ivan caused damages that exceeded 200 percent of GDP in St. Lucia and Grenada, respectively (Cummins & Mahul, 2009). The effects tend to be less pronounced and shorter-lived at the national level in larger mid- to high-income countries, but national governments often must invest significant time and resources in response and recovery.

The capacity of economies, businesses, and households to recover following a disaster is often contingent on pre-existing characteristics and trends. Vulnerable economies are more likely to enter unsustainable fiscal positions, and disasters have the potential to exacerbate inequalities at the regional, local, industry sector, and household level. Recovery outcomes, however, may be improved by adaptive responses and appropriate interventions in the aftermath.

There is limited empirical research and longitudinal case studies of economic and business recovery following disasters. There are a number of important gaps remaining that the field must strive to fill. These include answering questions such as:

  • What types of policy and decisions are most effective for aiding household and business recovery, and under what circumstances? What are the potential costs and benefits?

  • How should financial aid and resources be distributed to ensure a speedy and equitable recovery?

  • What is the most effective and equitable mix of private (for-profit and non-profit) and public sector engagement, management, and funding of disaster preparation, mitigation, response, and recovery?

  • Where are the most effective places for governments, households, and businesses to invest prior to a disaster to improve resilience?

  • How can the insurance sector better facilitate disaster risk reduction and recovery? Can, for example, a “community-based” insurance scheme, where people have a vested interest not only in their property, but also in their surrounding property, versus the simple homeowner-based insurance schemes enhance post-disaster outcomes?

The field would also benefit from improved modeling of a range of disaster scenarios, which can inform policy makers about how recovery might progress under different conditions and policy environments (Chang & Rose, 2012). As these developments are made, they should be accompanied by an increased focus on using economic analysis and policy to understand and enhance social wellbeing and equity as a means of improving resilience to a range of crises and disasters.

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Notes:

(1.) Rose and Liao (2005) notes that in input-output modeling, “indirect” tends to refer to business-to-business interactions. Since indirect impacts of a disaster include consumers and the workforce, we refer to “higher-order” effects, reflecting the sum of at least two effects (i.e., disruption to downstream and supply-side actors caused by disruption from a disaster).

(2.) Disasters are not always associated with long-term localized population decline. Schultz and Elliott (2013) provide a more general overview of population movements in the United States following disasters, arguing that, in some cases, population can also increase in affected areas; especially if the initial shock was not large enough, or if the reconstruction efforts increased employment opportunities.