summarize the main idea of each article, discuss issues being highlighted briefly, give opinion pertaining the coverage of each article and provide recommendations for each issue in the article.

FIRST ARTICLE REVIEW FOR ECONOMIC GROWTH, FOREIGN DIRECT INVESTMENT,

MACROECONOMIC

CONDITIONS AND SUSTAINABILITY IN MALAYSIA

Applied Econometrics and International Development Vol. 14-1 (2014) ECONOMIC GROWTH, FOREIGN DIRECT INVESTMENT, MACROECONOMIC CONDITIONS AND SUSTAINABILITY IN MALAYSIA Jamal OTHMAN1* Yaghoob JAFARI2 Tamat SARMIDI3 Abstract Most studies on examining the links between economic growth and FDI inflows have relied on the conventional GDP measure which has been argued as inadequate to provide clear insights on the macro sustainability of a country. Alternatively, the Genuine Savings (GS) indicator has been proposed as one of the alternative measures to reflect whether an economy is moving on a sustainable path, albeit in the weak sustainability sense. In this paper, we estimated the impact of FDI on conventional GDP and GS growth as well as on the GDP-GS gap for Malaysia from 1974-2009. The potential nonlinearities associated with the impact of FDI are captured using a macroeconomic conditions indicator as a threshold variable. The results demonstrate stronger FDI impacts on Malaysian GDP and GS growth as well as on reducing the GDP-GS gap once the general macroeconomic conditions in the country reaches a particular level. The results may suggest that FDIs will be more impactful in accelerating future economic growth and sustainability if a country is able to maintain a particular state of macroeconomic conditions. Keywords: Malaysian macroeconomic conditions, Sustainable development, Genuine savings, Conventional GDP, GDP- Genuine savings gap, Foreign direct investment JEL Classification: F23, F36, F43, Q56, O16 1. Introduction Many studies have shown positive relationships between FDI inflows and economic growth especially in developing countries. However, most studies have relied on the conventional GDP measure to reflect economic performance, while it has been well established that the conventional GDP indicator fails to measure true economic progress. It has been especially criticized for not appropriately addressing the degradation and depletion of natural capital including defensive expenditures meant for pollution control. In essence the main weakness of the GDP indicator is its inability to provide a sense whether an economy is moving on the sustainability path. One of the popular alternatives to the conventional GDP is the Genuine Saving (GS) measure. The GS is a class of Green GDP systems along with other similar measures, namely Genuine Progress Indicator (GPI) and Index of Sustainable Economic Welfare (ISEW). Essentially, GS defines the sustainability conditions for a resource dependent economy on the ability to maintain a constant stream of consumption into the infinite future. This can be achieved via a rule that ensures the aggregate stock of physical and natural capital remains constant over time. Vincent (2001) attributed the inability of many * Jamal Othman1, Yaghoob Jafari2 and Tamat Sarmidi3 . Authors 1, 2, 3, are respectively, Professor (corresponding author: jortman@ ukm.my, ), Post-Doctoral Associate, and Associate Professor at the School of Economics, Faculty of Economics and Management, National University of Malaysia, Bangi Applied Econometrics and International Development Vol. 14-1 (2014) resource-rich economies to achieve long-term welfare improvements to the failure to offset the depletion of natural resource stocks with sufficient investments in physical capital and human capital; consequently, their total wealth which is the sum of physical, human, and natural capital declines. The World Bank publishes cross-country estimates of GS in the World Development Indicators ever since 1999. The World Bank constructs these estimates by making appropriate adjustments to gross national savings. The major adjustments are to subtract a depreciation allowance for man-made capital and depletion allowances for fossil fuels, minerals, and timber, and to add investment in human capital. A negative GS rate denotes that the overall national capital depletes faster than renewed. A positive GS is desirable, however, it still does not assure sustainable in the strict sense, because the indicator is still based on the weak sustainability paradigm. Hamilton and Clemens (1999) and Bolt et. al (2002) detailed out the methods as used by the World Bank to make these adjustments. While the importance of the relationships between greater foreign direct investment (FDI) and GDP growth has received considerable theoretical and empirical support, in recent years, an important dimension that emerges in the FDI-GDP growth literature has been the role of general economic development in inter-mediating the impact of FDI on economic growth. For instance, Hermes and Lensink (2003) argues that the impact of FDI on economic growth is contingent on the development of financial markets of the host country. They observed that well-functioning financial markets reduce the risks inherent in the investment made by local firms that seek to imitate new technologies and thereby improve the absorptive capacity of a country with respect to FDI inflows. W.N.W. Azman-Saini et al (2011) also asserted that a certain level of financial development is required before host countries can benefit from FDI-generated externalities. Using a threshold regression model, they found that the positive impact of FDI on growth emerges only after financial market development exceeds a certain threshold level. A related observation is from a study by Chan Sok Gee and Mohd Zaini A.K. (2011) who found that FDI inflows from developed countries to selected sectors in Malaysia have greater tendency to create positive impact on the growth of Malaysia's manufacturing sector. The authors asserted that such spillover effects in the economy are possible through the transfer of technologies in relevant sectors only, in this case Malaysia's R&D intensive sectors. Two important observations of the growth-FDI literature are worthy to be further noted here. Firstly, while a number of authors have asserted on the role of specific systems in providing the "channels" towards spillover effects or broader economic development and growth, as deliberated above, we opined that any economic system is not a "means", rather it is a reflection or manifestation of the general macroeconomic conditions that prevail in a country. We define here macroeconomic conditions as the internal socio-economic variables which contribute to the health of macroeconomic system in a country. This encompasses the initiatives of a country to move towards a market oriented economy, good governance and politics, best practices policies, effective institutions, quality infrastructure and resourceful human capital. Hence, an effective financial or R&D system entails the presence of desirable macroeconomic conditions in a country. Such thinking may also help address the issue of endogeneity in the analysis of FDI-growth linkages. Secondly, as aforementioned, many studies which linked FDI and 214 Othman,J.,Jafari,Y.,Sarmidi,T. Economic Growth, FDI, Macroeconomics and Sustainability in Malaysia economic growth have relied on the conventional GDP indicator which is inadequate to provide clear insights on whether an economy is moving on a sustainable path. In this paper, we examined the effect of inward FDI on Malaysia's GDP and GS as well as on the GDP-GS gap from 1974-2009. We have used a regression model based on the concept of threshold effects. The model was specified to examine the relationship between GDP, GS and GDP-GS gap with FDI to be piecewise linear with a macroeconomic conditions indicator acting as a regime-switching trigger. The role of macroeconomic conditions in inter-mediating the impact of FDI on Green GDP (GS) and on the gap between GDP and GS seems to be the missing part of the FDI- growth literature. This paper attempts to narrow down this literature gap. The main motivation of looking into the GDP-GS gap and FDI linkages stems from an increasing body of literature that suggests a growing gap between GDP and green GDP (measured by ISEW or GPI) over time. Most studies have shown that both GDP and green GDP move up together until they reached a certain threshold point, after which green GDP growth declines (Max-Neef, 1995). Such results imply that when the economy expands beyond a certain level, the additional benefits of growth are increasingly offseted by environmental externalities and other welfare costs, and consequently the gap widens (Lawn, 2003). In Section 2 of this paper, we overview the GDP and GS trajectory for Malaysia from 1970 to 2009, followed by an overview of Malaysia's FDI inflows and development of her financial markets, which is presumed in this study to represent Malaysia's macroeconomic conditions. All data were sourced from the World Bank World Development Indicators (2012). The debate on economic growth and FDI is discussed in section 3. The theoretical framework of the study, empirical model,results and implications are presented in subsequent sections. 2. Malaysia's growth, sustainability performance and FDI inflows This section first presents the trend of GS per capita for Malaysia and compares with that of her GDP. As shown in Figure 1, Malaysia's GS has been positive during 1974-2009. This indicates quite well that Malaysia's economy has been operating on the sustainability track. Note that negative GS rates or a marked downtrend are a serious sign denoting unsustainability. Figure 1 seems to suggest that the growth path of the Malaysian GS is lower than her GDP. It further reveals that the gap between GDP and GS per capita has been growing. This signifies to some extent the declining capacity of the Malaysian economy to sustain the levels of overall national capital for future productive activities. Jamal et. al (2012) found that Malaysia's GS to GDP ratio has been falling pronouncedly in the period following the ASIAN financial crisis of 1997/98. It will thus be interesting to know the impact of FDI on GS as well as on the GDP-GS gap. This is an important aim of this study. Figure2 shows the trend of FDI as a percentage of GDP for Malaysia. This figure shows a fluctuated pattern where the ratio reached its peak in the period prior to 1991/92 but declined thereafter. This reflects to some extent the presence of relatively more financial impediments or less opportunities for higher long-run investments returns to prospective foreign investors. Further, the value of domestic credit to private sector as a percentage of GDP, a financial development indicator, which was used to represent 215 Applied Econometrics and International Development Vol. 14-1 (2014) general macroeconomic conditions in this study showed an increasing trend through the outset of the ASEAN financial crisis (1997-1998) - see Figure 3. Thereafter, it declined steadily. Domestic credit to private sector is thought to reflect the general sentiments of businesses and financial agents on the prospect of new investments, economic growth and jobs creation. Thus it may be an appropriate indicator to represent the general macroeconomic conditions in Malaysia. 3. Theoretical framework The neoclassical economic growth and endogenous growth models provide the basis for most of the empirical work on the FDI-growth relationship. Fundamentally, these models emanate from the standard Solow growth model which suggests that GDP is a function of the nation's stocks of capital and labor and other factors which may affect the productivity of these inputs such as financial development. In general notation we have: GDPt = f(Kt, Lt, FDIt) (1) where GDPt is per capita GDP or Green GDP measures of national income at time t. In this study we will use GS to represent the Green GDP indicator. Kt is a measure of the nation's capital stock at time t, Lt is a measure of labor input at time t, and FDIt is an index of financial liberalization at time t. Following Mankiw et al. (1992) and Talberth and Bohara (2006), this relationship is expressed in Cobb-Douglas form production function; (2) The prime motivation of incorporating inward FDI into the production function is to test whether FDI flows play a significant role in economic growth and its sustainability. Note in Talberth and Bohara (2006) the economic openness variable was used in place of FDI as the aim was to identify the links between economic openness and national income. Note also Equation 1 implicitly assumes there is no presence of contemporaneous correlation between the error term and the independent variables. Equation 2 can be represented in log-linear form; (3) where all variables are now logged (for convenience, the log notation is dropped), a is a constant, and ut is the error term. 3.1 A model of GDP- green GDP gap Talberth and Bohara (2006) formulated a model of the GDP- Green GDP gap (hereafter referred to as gap) and examined whether environmental degradation factors and economic openness affect the gap over time. The gap was defined as the difference in logged GDP and green GDP values (GDPgrn) in a given year: GAPt = Ln (GDPt) - Ln(GDPgrnt) (4) In general form, the model can be written; (5) 216 Othman,J.,Jafari,Y.,Sarmidi,T. Economic Growth, FDI, Macroeconomics and Sustainability in Malaysia where E is a vector of environmental quality variables, I is a vector of measures addressing inequality of income, wealth, opportunities and environmental degradation, while Ot represents economic openness. In this study, we used FDI which measures financial openness rather than trade openness as perTalberth and Bohara (2006). Further, Talberth and Bohara (2006) relied on two environmental indicators - a livestock production index and per capita carbon dioxide emissions (CO2). In our study we used oil palm planted area (OPA) in place of livestock production index. Oil palm is Malaysia's most important agro-industrial crop in terms of land use and value adding activities while livestock (except for poultry) is a negligible sector in the Malaysian economy. Since the GS do not directly capture CO2 emmited from land use changes and deforestation, we presumed that the OPA is sufficiently independent econometrically. However, increases in OPA may lead to long-run environmental costs in the form of land use changes, biodiversity loss, soil erosion, degradation of water catchment function and losses in timber rents. On the other hand, increases in palm oil-based value adding activities may also result in increases in fossil fuel combustion and consequently CO2 emission. Note, of all these impacts, only losses in timber rents and increases in CO2 from fuel combustions are captured by the World Bank GS indicator. We also removed the CO2 emission indicator from the gap model of Talberth and Bohara (2006). Inclusion of CO2 would lead to collinearity problem since CO2 is a cost component in the GS calculation. The inclusion of CO2 emissionin the original construct of Talberth and Bohara (2006) is acceptable as they used GPI and ISEW rather than GS to represent Green GDP. Both the ISEW and GPI do not directly deduct CO2 emissions costs and there should be no collinearity problem. Taking into consideration the above arguments and the existence of non-stationary process in the data series (as per Talberth and Bohara, 2006) our gap model is thus written in the growth rate form; GAP_Grwt = 1 + 2OPA_Grwt + 3FDIt+ ut (6) 4. Empirical model Following W.N.W. Azman-Saini et. al (2011) we posited that there is a threshold state of macro-economic conditions in a country where FDI inflows would affect national income in a more pronounced manner. This is written; FDI e i , ME GROWTH i X i 1 2 FDI e i , ME (7) Where GROWTH is the growth rates of traditional GDP, GS and GDP-GS gap over the period1974-2009, FDI is foreign direct investment, and Xi is a vector of variables in growth rates form which are thought to affect the dependent variables. When the dependent variable is traditional GDP and GS, X includes the nation's capital stock and labor input; and when GAP is the dependent variable, Xi refers to oil palm hectareage (OPA). Note ME (i.e., state of macro-economic conditions) is the threshold variable used to split the sample into regimes and is the unknown threshold parameter. This type of modelling approach allows the role of FDI to differ depending on whether macroeconomic conditions are below or above some unknown level of . In this study, as mentioned earlier, a financial indicator (ratio of domestic credit to private sector to GDP) 217 Applied Econometrics and International Development Vol. 14-1 (2014) was used to represent the state of macro-economic conditions and acts as sample-splitting (or threshold) variables. The impact of FDI on GDP and GS per capita and the gap will be 1 and 2 given a poor (low) or high macro-economic conditions, respectively. It is obvious that under the hypothesis 1= 2the model becomes linear and reduces to: GROWTHi =Xi + iFDIt (8) Equation 8 relies on updated GDP and GS time series data from the World Bank World Development Indicators (2012). Physical capital is represented by the ratio of gross fixed capital formation to GDP as, for example, used by Moudatsou (2003) and Yaghoob et.al (2012). The labor input is represented by the number of employed persons. Time series data for gross fixed capital formation was also taken from the latest World Development Indicators (2012), while, the number of employed persons was taken from Hand Book of Statistics public (1973-2010) published yearly by the Department of Statistics, Malaysia. Our measure of financial openness is the ratio of the value of FDI to GDP, as commonly used in the literature. The ratio of domestic private sector credit to GDP, representing macroeconomic conditions, was also obtained from the same data source. Further, data for OPA was sourced from the Malaysian oil palm 2010 statistics (MPOB, 2011). The first step of our estimation is to test the null hypothesis of linearity H 0 : 1 2 against the threshold model in Equation (2). We follow Hansen (1996, 2000) who suggested a heteroskedasticity consistent Lagrange Multiplier (LM) bootstrap procedure to test the null hypothesis of a linear specification against a threshold regression alternative. Since the threshold parameter is not identified under the null hypothesis of the no-threshold effect, the p values are computed by a fixed bootstrap method. Hansen (2000) shows that this procedure yields asymptotically correct p values. If the hypothesis of 1 2 is rejected and a threshold level is identified, we should test again the threshold regression model against a linear formulation after dividing the original sample according to the identified threshold. This procedure is carried out until the null of 1 2 can no longer be rejected. 5. Results and discussions The impact of FDI on GDP growth (Model A: Traditional GDP), GS (Model B: GS), and the GDP-GS gap (Model C: GAP), respectively, was estimated using Equation 7. As noted previously, we employed Hansen (1996 and 2000) splitting sample threshold method for model A, B and C to investigate the threshold effect of macroeconomic conditions as measured by the ratio of domestic private sector credit to GDP. The results of each model are presented in Table 1. The findings reveal several interesting observations. First, it shows that the p-value of the hypothesis of no threshold effect as computed by the bootstrap method with 1,000 replications and 10% trimming percentage are rejected at a very high significant level for all of the models. The finding clearly indicates that the relationship between FDI on GDP and GS growth on one hand and on the GAP on the other hand is non-linear. Second, the presence of threshold level also indicates that the sample can be split into two different groups depending on the state of macroeconomic conditions in the country. The country is said to have low macroeconomic conditions if within a period of time the state of macroeconomic conditions is below the threshold level, vice versa. The behaviour of 218 Othman,J.,Jafari,Y.,Sarmidi,T. Economic Growth, FDI, Macroeconomics and Sustainability in Malaysia the relationships between FDI and GDP, GS and GAP is markedly different for low and high macroeconomic conditions. Table 1: Threshold estimates using share of domestic credit to GDP (to proxy macroeconomic conditions) Model A GDP as dependent variable Coeff. S.E. t.test Model B GS as dependent variable Coeff. S.E. t.test Model C GAP as dependent variable Coeff. S.E. t.test Constant 12.0805 0.6369 18.96 22.6001 2.1806 10.36 13.2217 2.1997 6.01 Labor 0.0937 0.0635 1.47 1.6526 0.3575 4.62 - - - Capital 0.5518 0.0470 11.73 0.0866 0.1994 0.43 - - - OPA - - - - - - 0.0962 0.0273 3.52 Linear 0.0297 0.0083 3.57 0.0591 0.0252 2.33 -0.0887 0.0255 3.47 Low ME 4.60 -0.0644 0.0053 11.93 -0.1003 0.0287 -3.49 0.0257 0.0234 1.10 High ME>4.60 0.0191 0.0046 4.16 0.0569 0.0144 3.92 -0.0394 0.0182 2.16 Threshold Estimate 4.6085 4.4951 4.6223 Boot (pvalue) 0.0000 0.0000 0.0000 FDI 17.3729 18.2934 LM test for 18.6591 no threshold Notes: The standard errors are reported in parentheses (White corrected for heteroskedasticity). Results correspond to trimming percentage of 10%. Table 1 depicts that the hypotheses of FDI-led Growth, FDI-led GS, and FDI-led GAP reduction are rejected at lower level of macroeconomic conditions for all the models - A, B and C. In Model A, the coefficients for FDI variable for the low and high level of macroeconomic conditions are -0.064 and 0.019, respectively, and significant at the one percent level, implying that contributions of general macro-economic health are evident 219 Applied Econometrics and International Development Vol. 14-1 (2014) on the impact of FDI on GDP growth. For Model B, at lower level of macroeconomic conditions ( 4.6) FDI has a negative impact on economic sustainability where the coefficient of FDI is -0.10 while at higher level (>0.46) of macroeconomic conditions the results noticeably change to 0.06. The regression results of Equation 7 also provide insight to the understanding of the role of FDIs on the absorptive capacity of the country. As evidenced by the results of Model C, the threshold regression coefficient for FDI is negative which reinforces the important role of FDI in reducing the sustainability gap after a certain state of macroeconomic conditions. The change in sign of the FDI coefficient from positive to negative denotes the sustainability path of Malaysia's economic growth improves as macroeconomic development effects in the country become more widespread. Further, in this model the coefficient for OPA is positive; implying that as the area allocated to oil palm plantation expanded in the 1974-2009 period, the GDP-GS gap likewise increased, i.e., the absorptive capacity of the economy somewhat decreases as long term environmental repercussions work its way out in the economy. Support for such finding is provided by Talberth and Bohara(2006) using livestock index to represent the pressures exerted on environmental resources. 6. Conclusion and policy implications It is an interesting policy issue about whether FDI enhances growth, sustainability and the absorptive capacity of the economy in the host countries. In this study, we have attempted to address the issue by employing a threshold model for the period 1974-2009 for Malaysia. In order to capture the nonlinearities associated with the impact of FDI on the variables of interest we used an indicator of macro-economic conditions, represented by the ratio of private banking credit to GDP as a threshold variable. An important contribution of the paper is the adoption of the regression model based on such threshold effects to capture the relationship between FDI, macroeconomic conditions, national output growth and the absorptive capacity of the Malaysian economy, separately. There are several major findings of this paper. First, a priori monotonic restriction on the analysis of FDI on economic growth and sustainability may lead to misleading conclusion. For instance, the study by Jarita (2007) using a linear model and TodaYamamoto causality test found that there was no causality between FDI and economic growth for Malaysia. In this paper, we have provided evidence on the role of macroeconomic conditions in "channeling" the impact of inward FDIs on Malaysia's economic growth. Most importantly, we presented new evidence on the role of macroeconomic conditions in inter-mediating the impact of FDI on GS as a measure of macro sustainability and further on the gap between GDP and GS. In this study, we fail to reject the presence of threshold effect in the estimation irrespective of the models. We observe robust results showing that inward FDI has contributed markedly to Malaysia's GDP growth, sustainability, and absorptive capacity especially after her macroeconomic conditions exceeds a particular threshold state. The results suggest that attracting FDIs may be an appropriate strategy to generate future economic growth and enhances the productive capacity of the country to remain on the path of sustainability, so long as the country is able to maintain her threshold level in terms of macroeconomic conditions. This further implies that continous improvements of 220 Othman,J.,Jafari,Y.,Sarmidi,T. Economic Growth, FDI, Macroeconomics and Sustainability in Malaysia the local macroeconomic conditions including socio-political stability is imperative to the country. The findings further underline the importance of national policies to focus on local diffusion of knowledge presumably embodied in FDIs to ensure greater productive capacity of the economy and hence sustaining the welfare impacts of FDIs. In short, FDI policies need to be part of a comprehensive development strategy aimed at new knowledge diffusion while promoting the overall macroeconomic conditions including her financial markets and long-run sustainability. It is important to note that thus far there has been no general agreement globally on the choice of specific alternative measure to the conventional GDP indicator. The GS rule although denotes explicitly whether the national capital in aggregate is depleting faster than renewed, still does not assure sustainability in the strict sense of the term, due to its reliance on the weak sustainability paradigm. Future studies may consider addressing similar issues using other alternative Green GDP measures such as ISEW and GPI. These indicators may provide more insights on the linkages between FDI, sustainability and the future productive capacity of an economy. Despite the limitations of the GS indicator and the single country application in this study, we believe this study contributes to narrowing some of the literature gap. As rightly pointed byTalberth and Bohara (2006), the use of ISEW and GPI to reflect Green GDP in their study on economic growth-openness linkages will set the stage for more rigorous future studies once standardized green GDP systems such as the United Nations System of Environmental and Economic Accounting (SEEA) are more widely implemented. Finally, it will be worthy to note that all the models were estimated in reduced forms. Hence, the problems of endogeneity, causality, and omitted exogenous variable bias may arise in such modelling context. Some of the macroeconomic variables particularly FDI and oil palm area (OPA) could be endogenously determined and therefore should be modelled simultaneously with growth. However, in our growth models, we assumed that our regressors were simply exogenous to avoid somewhat complicated econometric issues. Also, the employment of OPA in our gap model as well as financial development indicator may better be seen as illustrative, representing respectively, Malaysia's environmental degradation and general macroeconomic conditions in a very general manner. They are not meant to be indicative of the respective variables in contributing to the GDP, GS and GDP-GS gap. References Alfaro, L., Chanda, A., Kalemli-Ozcan, S. and S. Sayek, 2004. FDI and economic growth: the role of local financial markets, Journal of International Economics 64, 89-112. Chan Sok GEE & Mohd Zaini Abd KARIM, 2011. FDI's country of origin and output growth: The case of Malaysia's manufacturing sector, 1991-2006. Applied Econometrics and International Development, Euro-American Association of Economic Development, vol. 11(1). Department of Statistics (1973-201), Handbook of Statistics, Dep. of Statistics, Malaysia. Gorg, H. and D. Greenaway, 2004. Much ado about nothing? 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Vincent, Jeffrey R., 2001. Genuine Savings in Latin America:Estimates for 1973-97. Center for International Development, Harvard University. W.N.W. Azman-Saini, Siong Hook Law, Abd Halim Ahmad, 2011. FDI and economic growth: New evidence on the role of financial markets. Economics Letters107 (2), 211-213 World Development Indicators, 2012. World Bank. Online, http://data.worldbank.org/ news/world-development-indicators-2012-now-available Yaghoob Jafari, Jamal Othman and Abu Hassan Shaari Mohd Nor, 2012. Energy consumption, economic growth and environmental pollutants in Indonesia. Journal of Policy Modeling,http://dx.doi.org/10.1016/j.jpolmod.2012.05.020. Annex on line at the journal Website: http://www.usc.es/economet/eaat.htm 222 Othman,J.,Jafari,Y.,Sarmidi,T. Economic Growth, FDI, Macroeconomics and Sustainability in Malaysia Figure 1: Genuine Savings and Gross Domestic Products of Malaysia from 1974 to 2009. SOURCE: World Bank, World Development Indicators (2012) Figure 2: The plot of FDI to GDP ratio (1974-2009). SOURCE: World Bank, World Development Indicators (2012) Figure 3: Domestic credit to private sector as a percentage of GDP (proxy for macroeconomic conditions). SOURCE: World Bank, World Development Indicators (2012) 223 The Economic Impact of the COVID-19 Outbreak on Developing Asia ADB BRIEFS NO. 128 6 March 2020 Key Messages The ongoing COVID-19 outbreak affects the PRC and other developing Asian economies through numerous channels, including sharp declines in domestic demand, lower tourism and business travel, trade and production linkages, supply disruptions, and health effects. The magnitude of the economic impact will depend on how the outbreak evolves, which remains highly uncertain. Rather than focusing on a single estimate, it is important to explore a range of scenarios, assess the impact conditional on these scenarios materializing, and to update the scenarios as needed. The Economic Impact of the COVID-19 Outbreak on Developing Asia1 What is COVID-19? A new coronavirus disease, now known as COVID-19, was first identified in Wuhan, People's Republic of China (PRC), in early January 2020. From the information known at this point, several facts are pertinent. First, it belongs to the same family of coronaviruses that caused the Severe Acute Respiratory Syndrome (SARS) outbreak in 2003 and the Middle East Respiratory Syndrome (MERS) outbreak in 2012. Second, the mortality rate (number of deaths relative to number of cases), which is as yet imprecisely estimated, is probably in the range of 1%-3.4%significantly lower than 10% for SARS and 34% for MERS (Table 1, first column), but substantially higher than the mortality rate for seasonal flu, which is less than 0.1%.2 Third, even though it emerged from animal hosts, it now spreads through human-to-human contact. The infection rate of COVID-19 appears to be higher than that for the seasonal flu and MERS, with the range of possible estimates encompassing the infection rates of SARS and Ebola (Table 1, second column). The range of scenarios explored in this brief suggest a global impact of $77 billion to $347 billion or 0.1% to 0.4% of global GDP, with a moderate case estimate of $156 billion or 0.2% of global GDP. Two-thirds of the impact falls on the PRC, where the outbreak has been concentrated so far. The estimated impact on individual developing Asian economiesand on sectors within these economiesis provided in this brief, including a hypothetical worst-case scenario for a given economy that experiences a significant outbreak of its own. Table 1. Fatality Rates and Infection Rates of COVID-19 and Other Epidemics Fatality rate (deaths/cases) Ebola 50% 1.5-2.5 MERS 34.30% 0.42-0.92 SARS 10% 3 COVID-19 1%-3.4% 1.5-3.5 Seasonal flu 0.05% 1.3 MERS = Middle East Respiratory Syndrome, SARS = Severe Acute Respiratory Syndrome. Sources: World Health Organization; Centers for Disease Control and Prevention; Althus, C. 2014. Estimating the Reproduction Number of Ebola Virus (EBOV) During the 2014 Outbreak in West Africa. https://doi.org/10.1371/currents.outbreaks.91afb5e0f279e7f29e7056095255b288; Choi, S., E. Jung, B.Y. Choi, Y.J. Hur, and M. Ki. 2018. High Reproduction Number of Middle East Respiratory Syndrome Coronavirus in Nosocomial Outbreaks: Mathematical Modelling in Saudi Arabia and South Korea. Journal of Hospital Infection. 99. pp.162-168; Heymann, D. L. and N. Shindo. 2020. COVID-19: What is Next for Public Health?. Lancet. https://doi.org/10.1016/S0140-6736(20)30374-3; and Wu, Z. and J. McGoogan. 2020. Characteristics of and Important Lessons from the Coronavirus Disease 2019 (COVID-19) Outbreak in China: Summary of a Report of 72,314 Cases from the Chinese Center for Disease Control and Prevention. JAMA. https://10.1001/jama.2020.2648. The authors of this brief are Abdul Abiad, Mia Arao, Suzette Dagli, Benno Ferrarini, Ilan Noy, Patrick Osewe, Jesson Pagaduan, Donghyun Park, and Reizle Platitas. The work has benefited from comments received from numerous colleagues across the Asian Development Bank (ADB). 2 An analysis published in JAMA (https://jamanetwork.com/journals/jama/fullarticle/2762130) of 72,314 cases in the PRC found an overall case fatality rate of 2.3%, with much higher fatality rates for those aged 70-79 (8.0%) and those aged 80 and above (14.8%). 1 ISBN 978-92-9262-063-9 (print) ISBN 978-92-9262-064-6 (electronic) ISSN 2071-7202 (print) ISSN 2218-2675 (electronic) Publication Stock No. BRF200096 DOI: http://dx.doi.org/10.22617/BRF200096 Infection rate (per infected person) ADB BRIEFS NO. 128 Figure 1. Total COVID-19 Cases, 20 Jan-29 Feb 2020 100,000 80,000 60,000 40,000 20,000 0 20/01/2020 30/01/2020 09/02/2020 People's Republic of China 19/02/2020 29/02/2020 Rest of the World Note: The discrete jump in the series in mid-February is due to the change in the diagnostic criterion applied to identify infections. Sources: CEIC Data Company; and World Health Organization. 2020. Coronavirus disease (COVID-19) situation reports. https://www.who.int/emergencies/ diseasesovel-coronavirus-2019/situation-reports/ (accessed 2 March 2020). The number of confirmed COVID-19 cases has risen rapidly, first in the PRC and more recently worldwide, quickly surpassing the totals from SARS. As of end-February 2020, COVID-19 had infected 85,403 people in 55 economies, with a global death toll of 2,924. The PRC still accounts for the vast majority97% of total fatalities and 93% of total cases (Figure 1). As of early March, however, the number of confirmed cases outside the PRC has been rising, particularly in the Republic of Korea (3,150), Italy (888), and Iran (388). Despite having a similar infection rate yet lower fatality rate than SARS, total cases and fatalities from COVID-19 have already far surpassed the totals for the 2003 SARS outbreak (Figure 2). This brief summarizes ADB analysis of the global, regional, and economy- and sector-specific economic impact of the COVID-19 outbreak. It lays out the various channels through which economies will be affected and quantifies the likely magnitudes of the effects under a range of scenarios. It is explicit about the scenario assumptions, and the methods used to calculate the impact. Importantly, the brief provides estimates not only of the global and regional impacts, but also granular details on how individual economiesand sectors within economieswill be affected, including under an illustrative worst-case scenario for an economy that experiences a significant outbreak. The brief concludes by summarizing the actions ADB and its developing member countries (DMCs) are taking to respond to the COVID-19 outbreak. 2 Economic activity will be affected in many ways There are several channels through which the COVID-19 outbreak will affect economic activity in the PRC, the rest of developing Asia, and the world. These include a sharp but temporary decline in domestic consumption in the PRC and other outbreak-affected economies, and possibly investment if the outbreak affects views on future business activity; declines in tourism and business travel; spillovers of weaker demand to other sectors and economies through trade and production linkages; supply-side disruptions to production and trade (which are distinct from demand-side shocks spilling over through trade and production linkages); and effects on health such as increased disease and mortality as well as shifts in health care spending. Each of these are taken in turn. Consumption in the PRC will experience a sharp, temporary drop, as occurred during the 2003 SARS outbreak. Perhaps the most important channel through which economic activity is affected is through a sharp but temporary decline in domestic consumption in the PRC resulting from behavioral and/or policy changespeople staying home as a precaution, or because they are told to. This occurred during the SARS outbreak in 2003; retail sales growth in the PRC declined by almost 3 percentage points (pp) during the second quarter of 2003 (Figure 3). The size of the consumption shock in the current outbreak could be The Economic Impact of the COVID-19 Outbreak on Developing Asia Figure 2. SARS and COVID-19 Infections and Fatalities 90,000 80,000 70,000 60,000 50,000 40,000 30,000 20,000 10,000 0 1 Mar 3 5 2003 7 9 11 13 15 17 19 21 232527293126 3335373941 17 Apr 2003 4345474951 5355575961 6365676971 7375 20 Jan 2020 29 Feb 2020 7 Aug 2003 COVID-19 infections COVID-19 deaths SARS infections SARS deaths SARS = Severe Acute Respiratory Syndrome. Source: Asian Development Bank calculations using data from CEIC Data Company and World Health Organization. 2020. Coronavirus disease (COVID-19) situation reports. https://www.who.int/emergencies/diseasesovel-coronavirus-2019/situation-reports/ (accessed 2 March 2020). Figure 3. Retail Sales and Personal Consumption Expenditures during SARS Episode 12 SARS Outbreak (Feb-June 2003) PRC: Retail sales, 2002-2003 (y/y % change) 11 10 9 8 7 6 Dec-03 Nov-03 Oct-03 Sep-03 Aug-03 Jul-03 Jun-03 May-03 Apr-03 Mar-03 Feb-03 Jan-03 Dec-02 Nov-02 Oct-02 Sep-02 Aug-02 Jul-02 Jun-02 May-02 Apr-02 Mar-02 Feb-02 4 Jan-02 5 SARS = Severe Acute Respiratory Syndrome. Sources: Haver Analytics; CEIC Data Company; WHO; and ADB. bigger than that experienced in 2003, depending on the length and severity of the outbreak and the policy responses taken. In a scenario where the outbreak is more protracted, expands its geographic reach, and/or becomes a recurring phenomenon that affects future business activity materially, a decline in investment is also possible.3 The precise assumptions about the size and duration of the consumption and/or investment declines under various scenarios are spelled out in the next section, particularly in Table 2. 3 3 ADB BRIEFS NO. 128 Another important channel though which economies will be affected is tourism and business travel, in the PRC and other economies. Tourism is an important source of revenue for many economies in developing Asiainternational tourism receipts account for more than 40% of the gross domestic product (GDP) in economies like Palau and Maldives, for example (Figure 4), and total travel and tourism (including domestic tourism) exceeds 10% of GDP in almost half of ADB's members.4 Importantly, Chinese visitors now comprise a significant share of tourists in many of these economies, as the number of outbound tourists from the PRC has increased eight-fold from less than 11 million in 2003 to close to 87 million by 2018. In 2018, tourists from the PRC accounted for more than a quarter of total tourist arrivals in Myanmar; Thailand; Mongolia; the Republic of Korea; Viet Nam; Cambodia; Palau; and Hong Kong, China (Figure 5). Tourism arrivals and receipts in many developing Asian economies are expected to decline sharply, as a result of numerous travel bans as well as precautionary behavior. One of the most significant travel bans is the one imposed by the PRC itself. On 24 January 2020, the Government of the PRC imposed a travel ban on all outbound tourism by tour groups.5 This ban, which remains in effect, affects 55% of the PRC's total outbound tourism.6 In addition, at least 47 economies have imposed bans on travel to and from the PRC, including Australia, the United States, and the Russian Federation.7 Many airlines have suspended or sharply curtailed flights to the PRC as well. It is likely that the PRC will see a decline in tourist arrivals by at least as large as the 7.7% year-on-year decline it experienced in 2003 during the SARS outbreak (Figure 6). As for the rest of developing Asia, even without explicit bans on travel to other Asian economies, non-Chinese tourist arrivals are likely to decline as tourists avoid traveling in the region. During the 2003 SARS outbreak, for example, Southeast and East Asian economies such as Indonesia, Thailand, and the Republic of Korea all saw declines in arrivals from economies outside Asia in 2003, even though they had very few SARS cases (Figure 7).8 These demand shocks can spill over to other sectors and economies via trade and production linkages. The PRC is now the world's second-largest economy, and accounts for onethird of global growth. It is a major export market for many ADB DMCs, with exports to the PRC being a substantial fraction of GDP (Figure 8). Thus, a drop in demand for goods and services from the PRC is likely to be felt widely. ADB's 2018 Multiregional Input-Output Table (MRIOT) was used to incorporate spillovers of demand shocks via trade and production linkages. It measures all inter-sector and inter-economy linkages for 62 economies (accounting for 95% of global GDP), with each economy disaggregated into 35 sectors covering both goods and services. Shocks to final demandin this case, tourism demand and domestic consumptionare transmitted across sectors and borders via trade and production linkages, and one can trace their knock-on effects via the MRIOT.9 There are other important channels, including supply-side disruptions and economic effects through health and health care. There have been substantial production disruptions as a result of forced business closures and the inability of workers to get to work, as well as disruptions to trade and business as a result of border closures, travel bans, and other restrictions on the movement of goods, people, and capital. High-frequency indicators suggest that production in the PRC as a whole fell to 50%-60% of normal levels but is now normalizing. The PRC is a global and regional hub for manufacturing and value chains many economies export a significant amount of intermediate goods to the PRC, and other economies use inputs from the PRC in their production (Figure 9). As a result, these temporary disruptions can affect production and trade in other economies, although the overall impact may be mitigated by the fact that in some sectors (particularly in manufacturing) production can be ramped up in later periods to make up for lower production in the past. Lastly, there may also be important long-term economic effects through COVID-19's health impacts on mortality and morbidity, and through changes in (and diversion of) health care World Travel and Tourism Council. 2019. Top 20 Countries - Largest Contribution of Travel and Tourism GDP. https://www.wttc.org/economic-impact/countryanalysis/league-table-summaries/ (accessed 6 February 2020). 5 China Daily. 2020. Group tours, travel packages suspended across China. China Daily, 25 Jan. https://www.chinadaily.com.cn/a/202001/25/ WS5e2c486ea3101282172733a9.html (accessed 6 February 2020). 6 World Tourism Organization. 2019. Guidelines for the Success in the Chinese Outbound Tourism Market. Madrid: UNWTO. https://doi. org/10.18111/9789284421138. 7 SCMP Graphics. 2020. Coronavirus: Places and Airlines Restricting China transit. 18 Feb. https://multimedia.scmp.com/infographicsews/world/ article/3051149/coronavirus-travel-restrictions-on-china/index.html (accessed 3 Mar 2020). 8 To calculate the impact of travel bans and precautionary behavior on tourism receipts, the authors used 2018 bilateral tourism arrivals data from the World Tourism Organization. The authors assume travel bans and precautionary travel behavior will last for two months in the best-case scenario; three months in the moderate scenario; and six months in the worse-case scenario (see the discussion that follows, and Table 2). The authors also used declines in tourism observed during the 2003 SARS episode to estimate the decline in inbound tourism to the PRC and other East and Southeast Asian DMCs. The resulting declines in tourism arrivals in each economy are then translated into a decline in tourism receipts, where average spending per tourist is estimated by dividing international tourism receipts in each economy (available up to 2017) with the total number of arrivals. 9 The MRIOT allows the calculation of a technical coefficients matrix A that specifies how much inputs are needed from every sector in every economy, to produce one unit of output in sector i in economy j. Given the vectors of gross outputs x and final demand f (covering all economy-sectors), one can show that x = Ax + f and x = (I - A)-1 f , or x = (I - A)-1 f. That is, for a given (exogenous) change in final demand one can calculate the impact on gross output and on value-added or GDP, using the matrix (I - A)-1, also known as the Leontief inverse. More sophisticated general equilibrium models are richer as they allow for substitution, prices adjustments, and policy responses, but results tend to be of the same order as this simpler analysis. Exogenous shocks to supply can also be examined using the MRIOT, but this is not done in this brief. 4 4 The Economic Impact of the COVID-19 Outbreak on Developing Asia Figure 4. International Tourism Receipts by Percentage of the Gross Domestic Product, 2017 Maldives Palau Vanuatu Fiji Samoa Georgia Cambodia Marshall Islands Thailand Tuvalu Tonga Hong Kong, China Armenia Federated States of Micronesia Azerbaijan Kyrgyz Republic Solomon Islands Singapore Sri Lanka Malaysia Lao People's Democratic Republic Taipei,China Bhutan Mongolia Viet Nam Myanmar Timor-Leste Nepal Philippines Tajikistan Kiribati Brunei Darussalam Indonesia Kazakhstan Republic of Korea India Uzbekistan People's Republic of China Bangladesh Papua New Guinea 0 Source: World Bank. 10 20 30 40 50 60 % of GDP 5 ADB BRIEFS NO. 128 Figure 5. Tourist Arrivals from the People's Republic of China as a Share of Total Arrivals, 2018 68 Hong Kong, China 39 Palau 33 Cambodia 32 Viet Nam 31 Republic of Korea 31 Mongolia 28 Thailand 27 Myanmar 24 Taipei,China 23 Singapore 23 Brunei Darussalam 21 Lao People's Democratic Republic 19 Maldives 18 Philippines 16 Indonesia 14 Federated States of Micronesia 13 Nepal Malaysia 11 Sri Lanka 11 Papua New Guinea 9 Timor-Leste 9 7 Kyrgyz Republic 6 Fiji Solomon Islands 5 Bangladesh 4 3 Vanuatu Tonga 3 Bhutan 3 Tuvalu 2 Samoa 2 Marshall Islands 2 India 2 Tajikistan 1 Georgia 1 Uzbekistan 1 Azerbaijan 1 Kazakhstan 1 Armenia 1 Cook Islands 0 0% Source: World Tourism Organization. 6 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% The Economic Impact of the COVID-19 Outbreak on Developing Asia Figure 6. Inbound Tourist Arrivals in the People's Republic of China , 1995-2018 140,000 Figure 7. Tourist Arrivals from Outside Asia to Selected Developing Member Countries, 2002-2004 1,600 5,000 4,500 1,400 120,000 4,000 1,200 3,500 Thousands 100,000 1,000 80,000 SARS outbreak: -7.7% decrease in tourist arrivals 60,000 3,000 2,500 800 2,000 600 1,500 400 40,000 1,000 200 500 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 20,000 SARS = Severe Acute Respiratory Syndrome. Source: World Tourism Organization. expenditures. ADB will be publishing updated estimates in the April 2020 edition of the Asian Development Outlook, and will analyze the longer-term impacts on health, education, and other outcomes in subsequent reports. Given the very large uncertainties, several scenarios are explored The evolution of the COVID-19 outbreak has beenand continues to bevery unpredictable, requiring the use of multiple scenarios. The outbreak originated in the PRC right in the middle of Chunyunthe 40-day festival from 10 January 2020 to 18 February 2020 centered around the Chinese Lunar New Yearwhich is the biggest annual migration of people on the planet. Authorities in the PRC estimated that during this period, 79 million domestic and international flights were taken by Chinese, alongside 2.4 billion trips by automobile, 440 million by rail, and 45 million by sea.10 This, combined with large celebratory gatherings (including one in Wuhan on 18 January 2020) and a COVID-19 incubation period that the World Health Organization (WHO) estimates at between 1 day and 14 days, has played a large role in the extent of COVID-19's spread within and 0 0 2000 2001 Republic of Korea 2002 2003 Indonesia 2004 2005 Thailand (RHS) Source: World Tourism Organization. outside the PRC. As a result, the analysis explores a number of scenarios described as follows, with detailed assumptions spelled out in Table 2: Best-case scenario: The PRC outbreak is contained relatively quickly, with travel bans and precautionary behavior abating after 2 months (measured from late January, when the outbreak intensified and quarantines as well as travel and other restrictions were imposed); there is a moderate and relatively short-lived decline in the PRC's consumption growth of 2.75pp in one quarter only, or 0.7pp for the year relative to a no-outbreak scenario (the size of the retail sales growth decline during the quarter of the SARS episode, relative to previous quarters). Moderate scenario: The PRC outbreak is more widespread and lasts longer, with travel bans and precautionary behavior abating only after 3 months; there is a larger decline in the PRC's consumption growth of 2pp for the year, relative to a no-outbreak scenario. Worse-case scenario: The PRC outbreak is even more protracted, with precautionary behavior and restrictive policies remaining in place for 6 months; there is a large decline in both consumption and investment growth in the PRC, with both down by 2pp relative to a no-outbreak scenario. The State Council of the People's Republic of China. 2020. Press Conference on 2020 Spring Festival Transport Situation. 9 January. http://www.gov.cn/ xinwen/2020-01/09/content_5467778.htm. 10 7 ADB BRIEFS NO. 128 Table 2. Full Set of Scenario Assumptions CHANNELS Duration of Tourism and travel bans Decline in PRC consumption relative to no-outbreak scenario Decline in PRC investment relative to no-outbreak scenario Decline in [selected DMC] domestic consumption SCENARIOS travel bans and sharp decline in domestic demand Best case 2 months - Outbound PRC tourism drops by 50% for 2 months - For economies imposing travel bans, no tourism receipts from the PRC for 2 months - Inbound PRC tourism and receipts fall by as much as during the SARS outbreak - Tourism from outside Asia to non-PRC East and Southeast Asian economies falls by as much as during the SARS outbreak (assume peak decline lasts 2 months) 0.7% (based on 2.75pp decline in retail sales growth in 2003 Q3 vs. prior 9 quarters) none none Moderate case 3 months - Outbound PRC tourism drops by 50% for 3 months - For economies imposing travel bans, no tourism receipts from the PRC for 3 months - Inbound PRC tourism and receipts falls by an additional 10% relative to the base case - Tourism from outside Asia to non-PRC East and Southeast Asian economies falls by an additional 10% relative to the best-case scenario (i.e., 1 additional month) 2% (based on 2pp decline in PCE growth in 2003 vs. 2000-2002 average) none none Worse case 6 months - Outbound PRC tourism drops by 50% for 6 months - For economies imposing travel bans, no tourism receipts from the PRC for 6 months - Inbound PRC tourism and receipts falls by an additional 30% relative to the base case - Tourism from outside Asia to non-PRC East and Southeast Asian economies falls by an additional by an additional 40% relative to the best-case scenario (i.e., 4 additional months) 2% (based on 2pp decline in PCE growth in 2003 vs. 2000-2002 average) 2% (protracted outbreak worsens business sentiment) none Hypothetical worst case (specific to each economy) 6 months; plus outbreak in other DMCs lasting 3 months - Outbound PRC tourism drops by 50% for 6 months - For economies imposing travel bans, no tourism receipts from the PRC for 6 months - Inbound PRC tourism and receipts falls by an additional 30% relative to the base case - Tourism from outside Asia to non-PRC East and Southeast Asian economies falls by an additional 40% relative to the best-case scenario (i.e., 4 additional months). 2% (based on 2pp decline in PCE growth in 2003 vs. 2000-2002 average) 2% (protracted outbreak worsens business sentiment) 2% (selected economy only) DMC = developing member country, PCE = personal consumption expenditures, pp = percentage point, PRC = People's Republic of China, SARS = Severe Acute Respiratory Syndrome. Source: Asian Development Bank staff estimates. Hypothetical worst-case scenarios for other economies, describing the economic impact if a significant outbreak occurs there are also explored. These should not be interpreted as predictions that an outbreak will occur in the economies. Rather, they are meant to guide policy makers in determining how costly an outbreak could be, so they can properly evaluate the benefits and costs of prevention and early response. These worst-case scenarios are specific to each economy. 8 They assume that if an outbreak occurs in a given economy, that economy will experience a large but temporary decline in consumption growth of 2pp, due to precautionary behaviors and policies. This assumed magnitude of the domestic demand shock may be on the low end, particularly for economies with weak health systems. In those economies, containment and response will be more difficult, and a more protracted outbreak could materialize, with more sizable effects. In addition, The Economic Impact of the COVID-19 Outbreak on Developing Asia the long-term costs through other channels such as health and education could also be significant, and those costs are not captured here. These scenarios will be updated, especially if the COVID-19 outbreak expands significantly into a global pandemic. They reflect the fact that the outbreak is still mainly concentrated in the PRC, which still accounts for 97% of fatalities and 93% of total global cases. While outbreaks have now occurred in the Republic of Korea, Italy, and Iran, none is anywhere near the scale of the PRC outbreak at this point. But with the possibility of intensification and of similar outbreaks occurring in additional economies including in developing Asia, ADB will update its assessments as the situation warrants, with the next update coming in the April 2020 edition of the Asian Development Outlook. The global impact ranges from $77 billion to $347 billion, with the PRC accounting for two-thirds of the total The scenarios explored here suggests a global impact ranging from $77 billion to $347 billion or 0.1% to 0.4% of global GDP, with a moderate-case estimate of $156 billion or 0.2% of global GDP (Table 3). Across all three scenarios, the PRC accounts for roughly two-thirds of the global impact; in the moderate scenario the loss to the PRC relative to a no-outbreak scenario is $103 billion, or close to 0.8% of the PRC's GDP. The rest of the impact on the global economy is split roughly equally between the impact on the rest of developing Asia, and on the rest of the world. The rest of developing Asia would experience a loss of $22 billion or 0.24% of its GDP under the moderate-case scenario. The main channel through which many ADB DMCs will be affected will be through a substantial drop in tourism demand. For an economy like Palau, where international tourism receipts are close to 50% of GDP and over a third of international tourists are from the PRC, the decline in tourism receipts will be substantial, anywhere between 3% of GDP in the best-case scenario to 9% of GDP under the worse-case scenario (Table 4).11 Other economies for which tourism is important such as Cambodia, Maldives, and Thailand are also likely to see a significant decline in tourism revenues. There is already anecdotal evidence that tourism arrivals in many developing Asian economies have dropped by 50%-90% in February 2020 relative to the previous year. Overall, the authors' estimates suggest a loss of $15 billion-$35 billion in tourism receipts for the PRC and $19 billion-$45 billion in tourism receipts for the rest of developing Asia. Other ADB DMCs that will be significantly affected are those with strong trade and production linkages with the PRC. In addition to the aforementioned tourism-dependent economies, other developing Asian economies such as Hong Kong, China; Mongolia; the Philippines; Singapore; Taipei,China; and Viet Nam will be materially affected by the COVID-19 outbreak (Figure 10). Many of these economies see a significant share of tourists from the PRC and are affected through that channel as well. But as can be seen in Figures 8 and 9, the PRC is also a major destination for these economies' final as well as intermediate goods and services. The impact under different scenarios on various developing Asian economies, and on sectors within those economies, can be found on the ADB website (https://www.adb.org/covid-19). Developing Asian economies and ADB are responding to the COVID-19 outbreak Most developing Asian economies are already responding to the COVID-19 outbreak in various ways. Many governments have mobilized inter-agency task forces and other coordinating mechanisms to ensure a harmonized response. To help protect their citizens, a number of ADB DMCs have implemented various forms of travel restrictions or advisories, strengthened screening Table 3. Estimated Global and Regional Impact of COVID-19, under Different Scenarios Best case as % of GDP Moderate case losses in $ millions as % of GDP losses in $ millions Worse case as % of GDP losses in $ millions World -0.089 $76,693 -0.182 $155,948 -0.404 $346,975 People's Republic of China -0.323 $43,890 -0.757 $103,056 -1.740 $236,793 Developing Asia excluding the People's Republic of China -0.171 $15,658 -0.244 $22,284 -0.463 $42,243 Rest of the World -0.011 $17,145 -0.020 $30,608 -0.044 $67,938 Source: Asian Development Bank staff estimates. We assume that the decline in tourism receipts in the hypothetical worst case are the same as the ones in the worse case. The use of 2018 bilateral tourism arrivals may lead to an overstatement of the impact on Palau as the PRC's share of overseas tourists declined from 39% in 2018 to 33% in 2019, and because in that economy, at least, there is evidence the PRC's tourist spending is below the average for other tourists, many of whom come for extended dive trips. The estimates can be adjusted downward, but it will still amount to a significant decline in international tourism receipts. 11 9 ADB BRIEFS NO. 128 Figure 8. Exports to the People's Republic of China by Percentage of the Gross Domestic Product, 2016-2018 average Hong Kong, China Mongolia Taipei,China Solomon Islands Turkmenistan Viet Nam Singapore Republic of Korea Malaysia Lao People's Democratic Republic Myanmar Thailand Papua New Guinea Cambodia Kazakhstan Federated States of Micronesia Uzbekistan Tajikistan Philippines Indonesia Brunei Darussalam Marshall Islands Georgia Kyrgyz Republic Fiji Armenia Azerbaijan Pakistan India Bangladesh Sri Lanka Vanuatu Nauru Afghanistan Nepal Timor-Leste Samoa Tuvalu Bhutan Tonga Maldives Palau Kiribati 0 10 20 30 40 50 % of GDP Source: CEIC Data Company (accessed 10 February 2020). 10 60 70 80 90 The Economic Impact of the COVID-19 Outbreak on Developing Asia Figure 9. Global Value Chain Exposure to the People's Republic of China, Selected Economies, 2018 Viet Nam Mongolia Cambodia Kyrgyz Republic Taipei,China Lao People's Democratic Republic procedures and quarantine policies, and undertaken repatriation of their nationals from outbreak-affected economies. Economies are also strengthening their health systems by implementing contact tracing when needed, ensuring adequate supplies of personal protective equipment, strengthening laboratory capacities, and ensuring adequate communication of risks. Importantly, in light of the findings in this brief, many economies are already undertaking supportive macroeconomic policies. Many DMCs have cut interest rates, continuing a cycle of easing that began in 2019, and others are also putting in place supportive fiscal measures. ADB is also supporting its members in responding to the COVID-19 outbreak through finance, knowledge, and partnerships. ADB support on the financing side includes an approved $2 million technical assistance (TA) grant to support the PRC and the Greater Mekong Subregion to prevent, detect, and respond to the ongoing COVID-19 outbreak and future communicable disease outbreaks, and a $2 million regional TA grant for all DMCs to support response activities in the region. Private sector engagement is being supported through an $18.6 million short-term loan facility to a private Chinese pharmaceutical distributor in Wuhan that is responsible for centralized procurement and distribution of medical supplies in Hubei Province, the epicenter of the outbreak. A reallocation of existing resources is also taking place, as ADB has several health projects in the region totaling $469 million and some of this can be reallocated in response to the outbreak. ADB stands ready to provide additional support to DMCs via countercyclical support programs, emergency assistance loans, and other instruments, if needed. On the knowledge side, this initial economic impact assessment is but one part of ADB's work, and further analysis of the COVID-19 outbreak and its effects will continue as earlier noted in this brief. Furthermore, ADB has been convening partnerships, including various experts' m