Information about Asia and the Pacific Asia y el Pacífico
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Myanmar: Selected Issues

International Monetary Fund. Asia and Pacific Dept
Published Date:
February 2017
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Information about Asia and the Pacific Asia y el Pacífico
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Macro-Fiscal Risks: The Challenge Of Climate Related Disasters1

Climate-related disasters and climate change pose interrelated macro-fiscal challenges. Although the effects of climate change will be global, countries in Developing Asia (DAS) will likely be among the hardest hit.2 Among DAS peer countries, Myanmar appears particularly at risk on account of the interplay between disaster proneness and socio-economic vulnerability. Cross-country evidence for DAS over the past four and a half decades shows that these countries suffered from frequent large-scale climate-related events. While growth generally took a permanent hit, governments refrained from countercyclical fiscal policy. They rather responded through ad-hoc fiscal rebalancing and reprioritization, reflecting policy and structural rigidities, such as overly rigid fiscal policy objectives, perceived absence of fiscal space, low capacity, and budget restrictions. Even relative to that, the policy response of the Myanmar authorities to the severe 2015 floods was more limited in many ways, making the case for structural reforms aimed at enhancing preparedness and response ability to more effectively mitigate the impact of climate-related disasters, predicted to further acerbate with climate change.

A. Myanmar’s Disaster Risk: A Tango of Proneness and Vulnerability

1. Myanmar is exposed to a considerable disaster risk, putting it ahead of other disaster-afflicted peer countries in DAS and the rest of the developing world (RoDW).3 This owes to the interaction of its proneness to physical hazards and its vulnerability of exposed material and human elements, consequently making Myanmar feature prominently in a number of global rankings: for instance, most-at-risk country in Asia and the Pacific according to the UN Risk Model (OCHA, 2012) and second-most affected country worldwide according to the Climate Risk Index (2016). The latter puts it not only at the top between Honduras and Haiti, but also ahead of other DAS peers in the top 10 (Kreft and others, 2016).4

Disaster Proneness

2. Worldwide, Myanmar is among the countries most prone to climate-related hazards.5 During 1970–2015, climate-related hazards have generally been a severe and the predominant hazard type in Myanmar and DAS countries alike,6 entailing large material and human damages: for DAS (including Myanmar), EM-DAT reports more than 3,200 events, causing US$590 billion in material damages, affecting 6 billion people and killing more than 1 million.7 Within DAS, Myanmar stands out because of a disaster record with average-high mortality (Table 1). To start with, DAS has—on average and by several scaled metrics—been hit more severely than the rest of the developing world (RoDW): 3 times more material damages, 1.6 times as many affected people and slightly higher frequency. Myanmar is worse off than the RoDW in terms of mortality and average material damage.

Table 1.Average Climate-Related Disaster Losses(Annual occurrence per thousand square kilometers (tsd. sq. km), material damages in percent of GDP, human damages in percent of population, conditional on an event, 1970–2015)

(w/o Myanmar)
RoDW 1/
Material damage0.56591.29190.4481
People affected1.05084.21322.6235
People dead0.00910.00200.0032
Sources: IMF Staff calculations using CRED EM-DAT; Myanmar PDNA (2015); WB WDI; and IMF WEO.
Sources: IMF Staff calculations using CRED EM-DAT; Myanmar PDNA (2015); WB WDI; and IMF WEO.

3. Climate-related disasters, particularly those from meteorological hazards, dominate the impact of natural disasters on Myanmar (Figure 1). Due to the country’s geographical location and topography, earthquakes, rainfall-induced flooding, droughts, and forest fires are recurring phenomena in Myanmar. Beyond this, the mountain areas face imminent risk of landslide and the coastal line cyclones, tropical storm surges, and tsunamis (OCHA, 2016c). Relative to geophysical disasters, climate-related disasters occurred 9 times more often, caused 12 times more material damages, affected 380 and killed 750 times as many people. Among climate-related disaster types, hydrological hazards are the most frequent and affect the most people. However, while only the second-most frequent, meteorological hazards cause larger material damage and loss of life, much larger than in DAS and RoDW peers.

Figure 1.Disaster Impact Across Hazard Types, 1970–2015 1/

Sources: IMF Staff calculations using CRED EM-DAT; Myanmar PDNA (2015); WB WDI; and IMF WEO.

Note: 1/ Among non-weather-related disaster events, there were only geophysical events.

4. Myanmar tends to suffer from exceptionally severe rather than frequent, small disasters (Figure 2).8 The two most devastating natural disasters in the history of the country hit within a decade: cyclone Nargis in May 2008 and the floods in 2015. They killed some 0.3 percent and 0.0003 percent of population, caused around 12 percent and 2.5 percent of GDP in material damage, and affected 5 percent and 17 percent of population, respectively. Nevertheless, there also appears to be an intensification of frequency, material damage, and human impact over time. In contrast, casualties have been on the decline since the seminal Nargis cyclone in 2008—including from cyclone Giri in 2010 and cyclone Komen in 2015. This partly owes to advances in early warning and disaster preparedness (OCHA, 2016b).

Figure 2.Patterns of Climate-Related Disasters, 1970–2015

Sources: IMF staff calculations using CRED EM-DAT; Myanmar PDNA (2015); World Bank WDI; and IMF WEO.

Disaster Vulnerability

5. Myanmar stands out as one of the most vulnerable countries to disasters. While globally, Myanmar’s susceptibility (i.e. the likelihood of suffering harm) only falls into the second highest quintile (World Risk Index, 2015), its coping capacity (i.e., capacity to reduce negative consequences) and absorptive capacity (i.e. capacity for long-term strategies for societal and spatial change) both fall into the weakest quintile (Figures 34). Also relative to DAS peers, Myanmar’s coping capacity lags behind, reflecting weaknesses especially in the area of governance and government effectiveness; access to healthcare, public safety net, and insurance; physical connectivity and energy security. The same is true for absorptive capacity, owing mainly to a low level of socio-economic development, which typically comes with the confluence of a large share of vulnerable poor people (who tend to live in high-risk areas, rely on fragile infrastructure, and have limited savings and jobs that depend on weather conditions); shallow financial markets; low levels of education, public health, and gender equality; concentration of economic activity; and developing governance and openness. Consequently, Myanmar ranks 11 out of 191 in the Index of Risk Management (INFORM).

Figure 3.Lack of Coping Capacity

(Related to governance, medical care and material security)

Figure 4.Lack of Absorptive Capacities

(Related to future natural events and climate change)

Risk Outlook

6. Although the effects of climate change will be global, DAS countries will likely be among the hardest hit (IPCC, 2014a and GFDRR, 2015). As of today, DAS countries are flashpoints for climate change, owing to a combination of being located in high-risk tropical latitudes and having the special vulnerability of both low levels of development and a rising number of people in urban areas. Projections suggest that temperature increases in DAS may be larger than the global land average (IPCC, 2014b). This would further exacerbate those countries’ susceptibility to climate-related natural disasters by raising the frequency and severity of events (in particular of heat stress, extreme precipitation, inland and coastal flooding, drought and water scarcity, as well as insect infestation and diseases).

7. Thus, climate change poses a growing risk to Myanmar as a result of its proneness and vulnerability to disasters. The country already started to feel the pinch of more extreme climate-related events, such as during the unusually severe El Niño phenomenon in 2015–16: extreme temperatures, unusual rainfall patterns, dry soil, high risk of fires, and acute water shortages (OCHA 2016c). Such events put pressures on climate-sensitive activities that Myanmar is highly dependent on as they are the main source of employment and income for the overwhelming majority of the population: agriculture (especially crops, livestock, forestry, fisheries). They also increase risks to water access, food and energy security, and health, and potentially trigger new poverty traps, population displacements, and conflicts, undermining economic, social, and political stability. As a result, growth could suffer and fiscal pressures mount in order to meet demands for critical public infrastructure and social services.9

B. Disaster Impact: Growth Decline and Fiscal Storm in a Teacup

8. Severe climate-related disasters are typically followed by growth declines, and yet roughly stable headline fiscal numbers. This section examines this paradox by looking at severe disasters that fall in the 90th percentile of annualized and scaled climate-related material damages (pooled across all DAS countries and years with at least one disaster occurrence). This yields a threshold of material damages amounting to 1.12 percent of GDP and 87 such cases (of which four happened in Myanmar in—as exhibited in Figure 2.b—1991, 1992, 2008, and 2015).

Macroeconomic Impact

9. A severe climate-related natural disaster poses macro-critical challenges. In the short term, immediate costs arise from the loss of lives, damage to physical assets, and immediate output contraction.10 Large cash demands strain the fiscal and external position: lower revenues (on account of lower economic activity and disrupted tax collection infrastructure) meet higher expenditure needs (especially for emergency relief and reconstruction where households and the private sector need public support); and lower exports (because of production disruptions) meet higher imports (especially food and reconstruction materials). Over time though, the macro-fiscal effect becomes circumstantial. Scarce cross-country evidence is largely inconclusive, but points to the importance of factors such as: effectiveness of demand smoothing mechanisms (e.g., counter-cyclical fiscal policies, external assistance, remittances or insurance payouts); institutional quality (driving the speed and usefulness of response measures); differences in the degree of crowding-out (especially of productive capital expenditures by reconstruction efforts); and acceleration of a Schumpeterian creative destruction process (boosting productivity by triggering investment in upgraded capital and new technologies).11

Cross-Country Evidence for DAS

10. Empirical evidence for DAS countries suggests that a severe climate-related disaster leads to a permanent growth loss, but triggers a limited countercyclical fiscal response (Figure 5).12 Cumulative impulse reaction functions (IRFs) to a severe disaster in year t0 show an immediate and significant decline of mean growth of 1.2 percentage points. It only fades out slowly over the medium term, leaving a permanent loss of 2.2 percentage points. At the same time, a dynamic fiscal response fails to appear, as evidenced by both the insignificance and small size of the mean reaction of particularly the government primary balance and expenditure-to-GDP ratio. There is, however, large heterogeneity across countries (as shown by the width of the confidence interval at the 10 percent level), likely reflecting differences such as absorptive capacity and policy response.

Figure 5.Cumulative Response to Severe Climate-Related Natural Disasters

(17 DAS countries (unbalanced), material losses as share of GDP in top 10 percentile, 300 bootstraps)

Source: IMF Staff calculations using EM-DAT; WB WDI; IMF WEO; and IMF staff reports.

Note: The impulse reaction functions (IRFs) are derived from a panel vector autoregressive model with exogenous shocks (panel-VARX) for DAS countries. Data constraints require to drop two countries (Afghanistan and Timor-Leste) and to minimize the number of lags (only allowing one, although including longer lags corroborates the robustness of the results). The model includes four endogenous variables (real GDP per capita growth, primary balance, tax revenues and expenditures) and a dummy for the exogenous climate-related natural disaster shocks that hits in t0. Fiscal variables are expressed as a share of GDP and first-differenced to ensure stationarity and intuitive interpretability The disaster dummy takes the value of 1 for annual material losses (as a share of GDP) in the top 10 percentile of the material loss distribution for all years with at least one disaster occurrence.

11. However, event studies suggest that underneath the surface of little affected fiscal aggregates, a range of fiscal policy responses are at play in an effort to contain a fiscal deterioration (Figure 6). Whilst corroborating the growth decline, event study results suggest that fiscal aggregates mask a fiscal policy response through a range of policy actions in DAS countries:13

Figure 6.Event Study for Key Macro-Fiscal Aggregates

(All DAS countries (unbalanced), t0 is a year in the top 10 percentile of costliest climate-related disaster)

Sources: IMF Staff calculations using EM-DAT; WB WDI; IMF WEO; and IMF staff reports.

Note: To derive meaningful and comparable mean paths of variables, the study uses all variables (except for growth and the fiscal balance) in first differences of their ratio to GDP and only includes episodes where data is available for each year. The study does not allow for an overlap of event windows, resulting in dropping the second episode. Results are generally robust if instead the less severe episode gets dropped.

  • Spending restraint in line with available resources. On average, the primary balance only deteriorates very mildly, thanks to expenditure restraints in line with available resources. In the severe disaster year, total spending slightly expands with higher total revenues (driven by nontax revenues). Thereafter, they contract in parallel.
  • Expenditure reallocation. Lower capital expenditure makes room to meet current expenditure pressures from emergency relief measures in the near disaster aftermath, before reversing to make room for reconstruction in the second year after the disaster. In parallel, anecdotal evidence points to the recourse to reprioritization of capital expenditure (especially of the capital expenditure project plan) and rationalization of current expenditure.
  • Revenue and external official support mobilization. Efforts to support failing tax revenues with external grants on average materialize in a pickup in average grants in the second year after the disaster, likely to support the reconstruction phase through. Furthermore, the magnitude and timing of net official development assistance (NODA) and debt relief suggest that donors rather seem to respond through project grants starting in the early reconstruction phase and immediate debt relief (to reduce pressures from debt service mainly coming from the increased scarcity of cash and foreign exchange) rather than budget grants for immediate emergency relief.
  • Public debt dynamics. The aftermath sees a worsening of debt dynamics: at first a slight one reflecting the alleviating impact of debt relief, then an accelerated one reflecting the dominance of the interplay between lower growth and slightly higher overall deficits. Beyond this, however, debt dynamics also exhibit some stock-flow-adjustment in the immediate aftermath, likely on account of exchange rate depreciation and a materialization of contingent liabilities. Concessional borrowing only slowly resumes with reconstruction, with little effect on debt dynamics thanks to typically long grace periods.

12. Meanwhile, the private sector’s disaster response partially substitutes for a public disaster response. Comparing the evolution of the government’s capital expenditure (Figure 6.d) to that of the gross capital formation of the combined public and private sector (Figure 7) suggests that the private sector plays an important role in the disaster response. It even precedes the public response. The private sector response is facilitated by the availability of fast and fresh financing. While financial and insurance markets are still in a development stage in DAS, remittances are a more important source (Figure 6.f). They increase immediately after the disaster and continue to grow, first financing emergency relief and then reconstruction.

Figure 7.Event Study for Gross Capital Formation

(In percent of GDP, first differences)

Note and sources: see Figure 6.

The 2015 Floods in Myanmar

13. Myanmar experienced massive floods in July through September 2015. After making landfall in Bangladesh, cyclone Komen brought strong winds and heavy rains to Myanmar, resulting in severe, widespread flooding and landslides across 12 of the country’s 14 states and regions. According to EM-DAT and OCHA (2016a), they killed 171 people, affected more than 9 million people and temporary displaced 1.7 million people (or 17 percent and 3 percent of the population, respectively). The Myanmar PDNA (2016) reports a total economic impact of US$1.51 billion (or 2.4 percent of GDP). This puts this event in the top 10 percentile of all climate-related disasters in DAS countries over the past four and a half decades along two loss dimensions: material damages and people affected.

14. The disaster aftermath saw a significant drop in growth, but no countercyclical fiscal reaction. Relative to the pre-disaster year, growth turned out lower by 0.7 percentage points in FY 2015/16, and also led the authorities to revise downwards the growth outlook for the near-term outlook. In contrast, both a revised budget in late 2015 and the preliminary FY 2015/16 outcome did not show any substantial disaster response—neither in revenues, nor expenditures.14

15. Looking more closely though, the authorities responded swiftly within their means. Following improvements in their institutional and regulatory framework after cyclone Nargis in 2008,15 the authorities were able to respond much faster and more effectively in the early stages of immediate emergency relief than during past severe disasters. However, their fiscal response fell short of DAS peers’, as it was characterized by:

  • Adherence to the overarching objective of preserving the fiscal stance. Although debt levels provided some fiscal buffer for counter-cyclical policy, the government opted for not expanding spending, pointing to a lack of external budget support and their overarching philosophy of “living within your means.” Unlike in dealing with cyclone Nargis in 2008, however, the authorities made a call for assistance to the international community, but external budget support remained absent—reflecting largely pre-election sensitivities of donors and the governments’ inexperience in dealing with donors.16
  • Predominant reliance on the Reserve Fund—an annually earmarked budgetary provision for natural disaster and emergency projects. Although sizably topped-up since FY 2012/13 to K100 billion (from previously K100 million), the fund was used up very quickly, only covering less than 1 percent of the total estimated damage.
  • Minor reliance on the National Natural Disaster Management Fund (NNDMF)—an off-budget fund created in 2013 (with an annual budget allocation of K20 billion) for immediate relief measures and monitoring of natural disaster risk mitigation projects at the supra-ministerial level. It granted the Ministry of Social Welfare an additional K3 billion for emergency spending.
  • Some reprioritization within spending categories, reflecting severe budget rigidities. Enacted in 1986, current financial rules and regulations only allow for re-appropriations within the same budget line, i.e., line ministry (even project) and sub-federal bodies (i.e, state and regional governments). The authorities did not request approval from cabinet and parliament to implement more substantial re-appropriations. They used the existing budget flexibility to respond to the most immediate spending pressures and submitted revised spending plans for the upcoming budget cycle.

C. Policy Implications: The Importance of Ex-Ante Resilience and Ex-Post Policy Response

16. Going forward Myanmar needs to address weaknesses in ex-ante resilience and ex-post adaptive capacity. While natural disasters cannot be prevented and their impact is unpredictable, Myanmar can reduce its macroeconomic vulnerability by addressing remaining weaknesses in the area of fiscal policy, institution and capacity. 17 Cross-country best practices and its own experience suggest that Myanmar can:

  • Improve fiscal risk management and public financing assistance:18
    • Incorporate disaster risks in fiscal management. There is a need to explicitly identify and adequately integrate climate-related natural disaster risks into the medium-term fiscal framework and debt sustainability analysis. This will help determine how much to spend on mitigating impact and how much to self-insure by creating an adequate fiscal buffer within the budget.19 Unused funds should contribute to bolstering a notional fiscal buffer (i.e. generating public savings in quiet times for use in disaster times).
    • Clarify the role of the National Natural Disaster Management Fund (NNDMF), especially as to its mandate, governance structure, budget linkages, and relations with donors for disaster response and mitigation measures.
  • Regularize budget process flexibility. Fiscal rules and the Disaster Management Law should be integrated to ensure that adequate flexibility remains to respond in a timely and effective way to a natural disaster. This could include an escape clause for (i) determining the extent of a fiscal response in line with a severity classification; (ii) redeploying expenditures across budget chapters; and (iii) a streamlined process for preparing and passing a revised budget.
  • Generate fiscal space to finance climate change mitigation and disaster response programs:
    • Enhance domestic revenue mobilization and tap into newly available international support to secure more resources for the integration of adaption to and mitigation of climate change in development planning in the context of the formulation of the country’s Sustainable Development Goals (SDGs).20 A number of measures can help address macroeconomic vulnerability, including improving social safety nets, facilitating access to finance and insurance, and promoting disaster-resilient infrastructure (especially of physical transport and energy generation, public health capacity, risk-informed spatial planning, building standards, and payment systems).
    • Overhaul energy subsidies and taxation. The current global environment of low fossil fuel prices and the Paris Agreement in 2016 provide an opportune moment to gradually eliminate poorly targeted energy subsidies, adjust artificially low electricity prices, and introduce carbon taxation.21 The redistributive implications of such measures would require flanking measures, such as the introduction of well targeted subsidies for the most vulnerable.
  • Strengthen government effectiveness by improving institutional capacity and quality, which also spills over to private sector disaster response. Capacity and decision making processes need to be strengthened, in particular coordination and communication (not only within the public sector, but also with the civil society, donors, and multilateral organizations), planning, information aggregation and management, policy design and implementation.

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Prepared by Kerstin Gerling and Chanaporn Sereevoravitgul.


Besides Myanmar, the 19 DAS countries are Afghanistan, Bangladesh, Bhutan, Brunei Darussalam, Cambodia, China, India, Indonesia, Laos, Malaysia, Mongolia, Nepal, Pakistan, Philippines, Sri Lanka, Thailand, Timor-Leste, and Vietnam.


RoDW comprises all 188 Fund members except for 35 advanced markets (AMs) as defined by the IMF WEO, 33 small developing states (SDS) as defined in IMF (2013), and 19 DAS countries (as defined in Footnote 1).


There are five such DAS countries, i.e. the Philippines, Bangladesh, Vietnam, Pakistan, and Thailand.


Those include climatological (e.g., heat/cold wave or drought), hydrological (e.g., flood or storm surge), meteorological (e.g., cyclone or snow storm), and biological hazards (e.g., epidemic or locust infestation).


Despite of DAS including earthquake-prone countries located on the intersection of the Indian and Eurasian plate (such as Nepal, India, or Myanmar), climate-related disasters occurred 18 times more often, caused 3 times more material damages, affected 13 times and killed almost as many people over 1970-2015 than geophysical disasters.


A key caveat of available loss statistics is that damages often remain underreported, mainly due to reporting thresholds, weak capacity, and accounting difficulties (see e.g. Kousky, 2012).


For a more detailed discussion, see, e.g., Kreft and others (2016).


The overall economic impact is hard to quantify, as it depends not only on how effectively global climate policy measures can limit global mean temperature increases, but also how effectively countries can adapt to the changing climate environment. Even for the global economy as such, there are only a few, but very varying cost estimates. For instance, Tol (2014) estimates that a global warming of 3°C might cost about 2 percent of GDP, while the World Bank (2013) estimates that 1.5°–2°C warming could lead to 6 percent to 12 percent reduction in rice yields in the Mekong River Delta, whilst other crops may experience decreases ranging from 3 percent to 26 percent by 2050.


The results are fairly robust to the threshold (e.g. the 95 percentile or commonly used 1 percent of GDP). They are also broadly in line with evidence from Acevedo (2014) for 12 Caribbean countries and Cabezon and others (2015) for 12 small Pacific island states.


While not attempting to establish causality, the event study is complementary to the panel study approach: it is less demanding as to the length of time series data and better captures relationships (including non-linear dynamics) before, in, and after an event year.


Since the floods hit mainly rural areas, tax collection was not very much affected.


The Ministry of Social Welfare, Relief and Resettlement (SWRR) was established as the government’s focal point for disaster preparedness and response. In August 2013, a Disaster Management Law was passed, also establishing a National Disaster Management Committee (NDMC) as the highest decision-making body for disaster management (with the Vice President II as a chair and the Minister of SWRR as one of the two Vice-Chairs). Explicit Disaster Management Rules were finalized in April 2015 (OCHA, 2016b).


Elections were held on November 8, 2015. Some donors and UN agencies slightly ramped up funding or redirected funding from other ongoing operations for their own disaster response in support of the government response (OCHA, 2016a). In the end, the government-led response was mainly supplemented by civil society efforts only (especially the Myanmar Red Cross and local NGOs).


See e.g. Laframboise and Loko (2012) or Farid and others (2016) for a general discussion on fiscal policy response.


See OECD (2015) for a cross-country comparison of government disaster compensation and financial assistance arrangements, including examples of peer countries in the region such as India, Malaysia or the Philippines.


International best practices mandate reserving some 1 percent to 3 percent of spending to fiscal risks as such.


To this purpose, advanced countries promised developing countries during the Paris Conference on climate change at end-2015 to mobilizing US$100 billion annually by 2020 to support developing countries.


At this juncture, the objective of carbon taxation would be rather to help guide a cleaner, more sustainable economic development than to raise revenues. In fact, Myanmar’s share of 2012 global greenhouse emissions was 0.99 percent, but mainly on account of ongoing deforestation than old and dirty industries (Admiraal and others, 2015).

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