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Macroeconomic impacts of the 2010 earthquake in Haiti

Abstract

In this paper, we use the synthetic control method to estimate the macroeconomic losses from the 2010 earthquake in Haiti, one of the most severe natural disasters in the modern era. The macroeconomic effects of the earthquake were equal to an average loss of up to 12% of gross domestic product over the period 2010–2015. While surges in imports and foreign aid supported a temporary increase in aggregate consumption, aggregate investment and services sector output experienced large contractions. The road transport sector was severely affected. Impacts on electricity use have been less pronounced. The data suggest that macroeconomic losses may be permanent. The earthquake is thus a case of an extreme natural disaster contributing to divergence in development outcomes.

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Fig. 1

Sources: WDI, author calculations

Fig. 2

Sources: UN, author calculations

Fig. 3

Sources: UN, author calculations

Fig. 4

Sources: WDI, author calculations

Fig. 5

Sources: UN, author calculations

Fig. 6

Sources: UN, author calculations

Fig. 7

Sources: UN, author calculations

Fig. 8

Sources: WDI, author calculations

Fig. 9

Sources: WDI, author calculations

Fig. 10

Sources: IMF, WDI, author calculations

Fig. 11

Sources: IMF, WDI, author calculations

Fig. 12

Sources: WDI, author calculations

Fig. 13

Sources: WDI, author calculations

Fig. 14

Sources: WDI, author calculations

Fig. 15

Sources: WDI, author calculations

Fig. 16

Sources: WDI, author calculations

Fig. 17

Sources: WDI, author calculations

Fig. 18

Sources: IEA, WDI, author calculations

Fig. 19

Sources: IEA, WDI, author calculations

Fig. 20

Sources: WDI, author calculations

Fig. 21

Sources: WDI, author calculations

Notes

  1. These damages relate to fixed assets and capital (including inventories), raw material and extractable natural resources, and mortality and morbidity that are a direct consequence of the earthquake.

  2. Not controlling for political changes.

  3. The outcome variables in the years 2005, 2007, and 2009 are used as predictor variables. Four values are used for the robustness test in Fig. 21. Inflation is also a predictor variable when there are enough data to form a relatively large donor pool with World Bank (2016) data.

  4. Larger values of the State Fragility Index are for more fragile states.

  5. The null hypothesis of Haiti’s GDP having a unit root is not rejected at the 10% level based on an augmented Dickey–Fuller test with trend term.

  6. The optimization process is in respect of all of the predictor variables, so it is possible that a small number of synthetic control predictor variables could be further from the actual outcome in Haiti than the equally weighted donor pool.

  7. Appendix Fig.  23 shows a synthetic control for consumption as a % of GDP. This produces a more stable synthetic control than Fig. 8 with consumption increasing above counterfactual and then returning towards the counterfactual, but the variable in Fig. 23 includes impacts of both consumption increases and GDP losses.

  8. Government consumption expenditure was less than 10% of aggregate consumption for Haiti in each of the 11 years to 2014 (UN 2016a).

  9. While the actual and synthetic control are very similar prior to the earthquake, there was an unusually large increase in exports of over 150% in Burkina Faso from 2009 to 2012 compared to an average of 44% for the donor pool. Burkina Faso makes up over 45% of the synthetic control for exports.

  10. The World Bank defines external balance on goods and services as exports of goods and services minus imports of goods and services.

  11. Using 2010 constant US dollars rather than 2009 constant US dollars gives GDP values that are approximately 6% higher; the loss estimations would be similar using either currency base year when rounding to the nearest billion.

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Acknowledgements

We are grateful for comments from Terence Wood, David Stern, Yusaku Horiuchi, Sadia Afrin, Ryan Edwards, Huy Nguyen, anonymous referees, and participants in the Arndt-Corden Department of Economics Seminars. This research was also supported by an Australian Government Research Training Program (RTP) Scholarship.

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Correspondence to Rohan Best.

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Data and computer code availability

The datasets of this paper (1. data, 2. code including instructions) are collected in the electronic supplementary material of this article.

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Appendix

Appendix

Fig. 22
figure 22

Sources: UN, author calculations

Ratio of MSPE in the post-earthquake period to MSPE in the pre-earthquake period in logs for wholesale, retail, restaurants, hotels.

Fig. 23
figure 23

Sources: WDI, author calculations

Consumption share of GDP for Haiti and Synthetic Haiti.

Fig. 24
figure 24

Sources: WDI, author calculations

Ratio of MSPE in the post-earthquake period to MSPE in the pre-earthquake period in logs for Net ODA and official aid received.

Fig. 25
figure 25

Sources: IEA, WDI, author calculations

Total primary energy supply (thousand tonnes of oil equivalent) for Haiti and Synthetic Haiti.

Fig. 26
figure 26

Sources: UN, author calculations, WDI

GDP per capita (constant 2005 US dollars) for Haiti versus Synthetic Haiti, donor pool of five countries.

Table 3 Variables from the World Bank (2016) World Development Indicators
Table 4 Variables from UN (2016a)
Table 5 Variables from IEA (2016)
Table 6 Variables from IMF (2016)
Table 7 Weights of countries in the donor pool for figures using WDI data
Table 8 Weights of countries in the donor pool for figures using UN data
Table 9 Weights of countries in the donor pool for figures using IEA and WDI data
Table 10 Weights of countries in the donor pool for Fig. 21 using the longer series of WDI data
Table 11 Weights of countries in the donor pool for Fig. 26 using the smaller donor pool of five countries

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Best, R., Burke, P.J. Macroeconomic impacts of the 2010 earthquake in Haiti. Empir Econ 56, 1647–1681 (2019). https://doi.org/10.1007/s00181-017-1405-4

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Keywords

  • Macroeconomic impact
  • Haiti
  • Earthquake
  • Synthetic control method

JEL Classification

  • E21
  • E22
  • E23
  • O11
  • O54