The impact of the Indian Ocean tsunami on Aceh’s long-term economic growth
Introduction
On December 26, 2004, an earthquake of magnitude 9.1 on the Richter scale off the west coast of Sumatra triggered a series of devastating tsunamis hitting 14 countries across the Indian Ocean, particularly Indonesia, Sri Lanka and India. The death toll in Indonesia was estimated to be up to 130,000 people (Frankenberg et al., 2011) with an immediate economic cost of up to USD 5 billion (World Bank, 2005; Masyrafah and McKeon, 2008). Within Indonesia, the only affected large island was Sumatra. Of this easternmost island, only two of its nine provinces were affected and of the two, the province of Nanggroe Aceh Darussalam, or ‘Aceh’ for short, was most severely hit, accounting for approximately 90 percent of damages (BRR, 2006).
In the aftermath of the disaster, Aceh became the location of the single largest reconstruction effort in the developing world. The aid allocated to the region (USD 7.7 billion) by far surpassed the direct monetary damages. On average, for large disasters about 10 percent of the damage costs are compensated for by relief and reconstruction aid (Freeman et al., 2002). In Aceh, the compensation was 150 percent. Aside from the sheer size of the reconstruction effort, also its quality was to a very high standard, albeit far from perfect (Fengler et al., 2008). To avoid coordination failures, Indonesia and Aceh set up an agency tasked exclusively with coordinating the relief and recovery efforts, named the Reconstruction and Rehabilitation Agency (BRR). There was very little corruption and waste recorded; USD 7.0 billion out of the committed USD 7.7 billion were actually disbursed and spent. The agency applied sound fiduciary principles (Fengler et al., 2008), there was very little aid fragmentation and aid volatility, normally a huge problem, was kept under control (Masyrafah and McKeon, 2008). Aceh not only benefitted from aid, but also from increased transfers from central government and remittances. In addition, a historic peace deal was struck in 2006, successfully ending a nearly 30-years-long secessionist war against the central government that had cost approximately 15,000 lives over the course of the conflict.
Did the reconstruction effort succeed in these very favourable conditions? Can human effort turn the impact of a natural disaster that results in devastating human loss hitting a poor region of this world into positive economic consequences after the economy has recovered from the initial shock, lifting it onto a higher output trajectory than it otherwise would have been on? Employing sub-national gross domestic product (GDP) and night-lights data we demonstrate that the districts and sub-districts of the Indonesian province of Aceh that have been hit by the Indian Ocean tsunami of December 2004 experience sustained higher economic growth and night luminosity from 2006 onwards than counterfactual districts and sub-districts that have not been hit. In Authors (2018), which exclusively focuses on the economic legacy effects of armed conflict, we show that the tsunami-triggered growth effects cannot be the consequence of the end of violent conflict since we find that the spatial distribution of the conflict is uncorrelated with the spatial distribution of where the waves made landfall and that districts that benefited more from peace (where conflict had been more intense) grew relatively less than those that were less affected by the violence.
Economic studies overwhelmingly find natural disasters to either have no noticeable impact or negative consequences on the economies of affected countries (e.g. Cavallo et al., 2013). Only few studies find sustained positive economic effects in the aftermath of disasters and typically only under certain circumstances such as high development level of the country, insured losses and, most importantly, only moderately destructive disasters (Loayza et al., 2012; Cavallo and Noy, 2011). What explains this pessimistic outlook for developing countries in particular?
Natural disasters destroy productive capital and lead to expenditure reductions and therefore also reduce the consumption component of aggregate GDP (see e.g. Baez et al., 2010; Carter et al., 2007; and Anttila-Hughes and Hsiang, 2011). Most studies find short-term negative effects after which economies rebound and enter onto a “recovery to trend” trajectory: the disaster results in an immediate substantial contraction in output that remains below the non-disaster counterfactual potentially for a few years, but eventually re-bounds and converges to the pre-disaster trend (see e.g. Strobl, 2011). By contrast, other studies suggest affected countries are permanently relegated to a lower growth path (e.g. Hsiang and Jina, 2014). In this perspective, the destruction of capital is so severe and long-lasting that affected economies never fully recover. An economy might eventually start to grow again without, however, ever reaching its pre-disaster trajectory (see e.g. Burke et al., 2009; Hsiang et al., 2011; and Hsiang and Jina, 2014). Developing countries, in particular, are expected to suffer economically from natural disasters (Kahn, 2005; Heger et al., 2009; Loayza et al., 2012; and Fomby et al., 2009) though Gignoux and Menéndez (2016) provide some counter-evidence from long-run effects of earthquakes in rural Indonesia, not covering either Aceh or the 2004 tsunami, however. Developing countries typically lack safety nets (Linnerooth-Bayer and Mechler, 2007), insurance schemes (Linnerooth-Bayer et al., 2011; Hochrainer-Stigler et al., 2012), fully functioning credit markets (Noy, 2009; Noy and Nualsri, 2008), and sufficient savings (Paxson, 1992; and Mechler, 2009) that would cushion the economic blow.
Against this pessimistic outlook, some have argued for positive economic effects via concerted and well-executed reconstruction efforts. Replacing lost capital increases the demand for goods and services, boosting aggregate demand in the wake of the natural disaster, often buttressed by significant foreign aid inflows (Horwich, 2000; Kunreuther et al., 2004). Affected economies might bounce back after initial losses, following a trajectory which could best be described as “recovery to counterfactual trend”. In another scenario, a so-called “recovery beyond counterfactual trend” takes place. This might be temporary, in which case the economy would go back down to the initial trend after some time period, or might be sustainable, in which case the aid-fuelled reconstruction effort propels the economy not only above the counterfactual trend or growth trajectory but there is no relative contraction back down to the counterfactual trend when the aid and reconstruction efforts cease. Note that in either version of “recovery beyond counterfactual trend”, the positive economic shock that follows the negative economic shock of the natural disaster does not change the long-term growth fundamentals and therefore the long-term growth trajectory of the affected economy.
Some argue that an even better post-disaster economic performance is possible, namely a permanently higher long-term growth trajectory. From this perspective, natural disasters can result in ‘creative destruction’. Specifically, if the capital destroyed was old and unproductive a switch to a higher productivity equilibrium is possible if the new capital stock is more productive (Benson and Clay, 1998, 2004; Skidmore and Toya, 2002; Hallegatte and Dumas, 2009; and Cuaresma et al., 2008).
Fig. 1 graphically displays the five principal stylized trajectories for an economy hit by natural disaster relative to the counterfactual that would have occurred in the absence of the disaster. The first one is the most pessimistic one, in which the disaster results in permanently lower economic output. The second trajectory, “recovery to counterfactual trend”, results in a net economic loss, albeit a temporary one, with the counterfactual growth trajectory reached again after some time. The severity of the initial contraction in economic activity and the length of the time until the old trend line is reached again together determine the size of the temporary economic loss. Whether the third trajectory, “temporary recovery beyond counterfactual trend”, results in a temporary net economic gain or temporary net economic loss depends on the size of the initial contraction, the length of time until recovery propelled the economy beyond trend, the size of the expansion beyond trend and the length of time it remains above trend. By contrast, the fourth trajectory, “sustainable recovery beyond counterfactual trend”, provides a definite economic gain since after the initial contraction the economy is not contracting back to trend but grows at trend value but from the higher starting point above the initial trend line. Finally, the fifth trajectory, “creative destruction”, provides a productivity gain and would predict not only that there is a definite economic gain but that the economy was permanently set on a higher growth trajectory. As we will see below, Aceh experienced the fourth scenario, one of a sustainable recovery beyond the counterfactual trend.
There are three methodological issues with many of the existing empirical studies that are potentially problematic. First, the vast majority of existing studies have to resort to employing national GDP data.1 However, “… much of the interesting variation in economic growth takes place within, rather than between, countries” (Henderson et al., 2012, p. 995). This is particularly true for assessing the growth consequences of natural disasters which typically hit only a sub-set of a nation-state. Using spatially coarse indicators such as national GDP as impact measures to evaluate the effect of localised events such as natural disasters masks spatially heterogeneous effects.2 Second, with some exceptions (Hsiang and Jina, 2014; Strobl, 2011; Bertinelli and Strobl, 2013; Felbermayr and Groeschl, 2014; duPont et al., 2015; and Lynham et al., 2017), researchers have not had access to exogenous measures of the intensity of natural disaster impact, using endogenous measures such as estimates of direct human or economic loss instead. Third, relevant studies that employ causal identification strategies have only recently started to emerge (e.g., Kousky et al., 2013; Hsiang and Jina, 2014; Cavallo et al., 2013; and Lynham et al., 2017).
As section 2 explains, we overcome these problems by employing GDP and night-lights data at the district and sub-district level, by computing exogenous measures of disaster intensity and by employing difference-in-differences as well as synthetic control causal identification techniques that exploit the quasi-natural experiment character of the tsunami. Section 3 reports results from our main estimations, while section 4 explores the robustness of results to various specification issues and discusses five potential threats to their inferential validity. Section 5 goes beyond looking at aggregate growth in economic activity and analyses the causal effect on structural economic change in Aceh. Section 6 concludes.
Section snippets
The Indian Ocean tsunami: a quasi-natural experiment
Our empirical strategy of identifying causal economic impacts of the 2004 Indian Ocean tsunami and subsequent reconstruction effort is based on observing differences in economic activity between flooded districts/sub-districts and non-flooded districts/sub-districts, which serve as a plausible counter-factual comparator group to the treated (flooded) units. We exploit the unexpected and unprepared-for nature of the 2004 tsunami. Geographic happenstance dictated why some stretches on Sumatra
Results
Fig. 2 displays the mean GDP dynamics of the treated group with the mean dynamics of two control groups, namely non-flooded districts once within Aceh and once from the rest of Sumatra. The figure is consistent with the parallel historic paths assumption, which allows the inference that the treatment group is indeed comparable to the counterfactuals, as they perform historically similarly in the years leading up to the disaster. Following the natural disaster the parallel historic paths are
Robustness tests and threats to inferential validity
In this section, we submit our findings to a number of robustness checks. First, we also included the districts of North Sumatra (see map 4). For this analysis there are now 12 (blue) districts flooded by the tsunami in the treatment group and 37 (red) districts in the first control group of non-flooded districts within Aceh and North Sumatra. A second control group is composed of 76 non-affected (yellow) districts consisting of districts from the remainder of unaffected Sumatra districts,
The tsunami’s effect on structural economic change
In this section, we briefly explore the economic transformation and structural change resulting from the tsunami and the ensuing aid and reconstruction effort. Economic development is typically associated with structural change out of agriculture and possibly manufacturing toward services. Hence, one would expect to find evidence of this kind of structural change in Aceh. Using the synthetic control method introduced in section 4 above and employing the same INDO-DAPOER dataset as before, but
Conclusion
Can a concerted and very large scale aid and reconstruction effort ever turn a devastating natural disaster into an opportunity such that economic output is higher in its aftermath, as compared to a scenario where the disaster did not happen? If there is one case where the odds for this to have happened are high, it is the Indonesian province of Aceh, which experienced the single largest reconstruction effort to ever benefit a region in the developing world hit by natural disaster. The sudden
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