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Institute of Medicine (US) Forum on Microbial Threats; Knobler S, Mahmoud A, Lemon S, et al., editors. Learning from SARS: Preparing for the Next Disease Outbreak: Workshop Summary. Washington (DC): National Academies Press (US); 2004.

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Learning from SARS: Preparing for the Next Disease Outbreak: Workshop Summary.

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ESTIMATING THE GLOBAL ECONOMIC COSTS OF SARS*

and .

Author Information

While the number of patients affected by the SARS coronavirus and its broader impact on the global public health community have been surveyed in considerable detail, the consequences of the disease in other areas are less well calibrated. The purpose of this paper is to provide an assessment of the global economic costs of SARS. Our empirical estimates of the economic effects of the SARS epidemic are based on a global model called the G-Cubed (Asia-Pacific) model. Most previous studies on the economic effects of epidemics focus on the economic costs deriving from disease-associated medical costs or forgone incomes as a result of the disease-related morbidity and mortality. However, the direct consequences of the SARS epidemic in terms of medical expenditures or demographic effects seem to be rather small, particularly when compared to other major epidemics such as HIV/AIDS or malaria. A few recent studies—including Chou et al. (2003), Siu and Wong (2003), and Wen (2003)—provide some estimates on the economic effects of SARS on individual Asian regions such as mainland China, Hong Kong (SAR), and Taiwan. But these studies focus mostly on assessing the damages by SARS in affected industries such as tourism and the retail service sector.

However, just calculating the number of canceled tourist trips, declines in retail trade, and similar factors is not sufficient to get a full picture of the impact of SARS because there are linkages within economies, across sectors, and across economies in both international trade and international capital flows. The economic costs from a global disease such as SARS go beyond the direct damages incurred in the affected sectors of disease-inflicted countries. This is not just because the disease spreads quickly across countries through networks related to global travel, but also because any economic shock to one country is quickly spread to other countries through the increased trade and financial linkages associated with globalization. As the world becomes more integrated, the global cost of a communicable disease like SARS can be expected to rise. Our global model is able to capture many of the important linkages across sectors as well as countries through capital flows and the trade of goods and services, thereby providing a broader assessment of disease-associated costs.

The G-Cubed model also incorporates rational expectations and forward-looking intertemporal behavior on the part of individual agents. This feature is particularly important when we are interested in distinguishing the effects of a temporary shock from those of a persistent shock. For example, when foreign investors expect that SARS or other epidemics of unknown etiology can break out in some Asian countries not just this year but persistently for the next few years, they would demand a greater risk premium from investing in affected economies. Their forward-looking behavior would have immediate global impacts.

Needless to say, our empirical assessment is preliminary and relies on our limited knowledge about the disease and constrained methodology. For instance, there is speculation that SARS could reemerge in an even deadlier form in the next influenza season. There is also no consensus yet on the likely developments of any future epidemic and the precise mechanism by which SARS affects economic activities. Although a global model is better than simple back-of-the-envelope calculations, it is a coarse representation of a complex world. Nonetheless, even simple calculations are important inputs into the model. We saw this with the Asian Crisis of 1997, when the transmission of shocks in Asia to the rest of the world and the adjustment within economies in Asia were poorly predicted when only trade flows were considered.1 Thus it is important to go beyond the rough estimates that currently permeate commentary on the economic consequences of SARS. Because we take into account the interdependencies among economies and the role of confidence, our costs are larger than many of the estimates that currently appear in the media.

Economic Impacts of SARS

Despite the catastrophic consequences of infectious diseases such as malaria and HIV/AIDS, the impact of epidemics has been considerably under-researched in economics.2 Traditionally, studies have attempted to estimate the economic burden of an epidemic based on the private and nonprivate medical costs associated with the disease, such as expenditures on diagnosing and treating the disease. The costs are magnified by the need to maintain sterile environments, implement prevention measures, and conduct basic research. Such economic costs can be substantial for major epidemics such as HIV/AIDS. According to UNAIDS (the Joint United Nations Programme on HIV/AIDS), 42 million people globally are living with HIV/AIDS. The medical costs of various treatments of HIV patients, including highly active antiretroviral therapies (HAARTs), are estimated to be more than $2,000 per patient per year. In the Southern African regions, the total HIV-related health service costs, based on an assumed coverage rate of 10 percent, ranges from 0.3 to 4.3 percent of gross domestic product (GDP) (Haacker, 2002).

The costs of disease also include income forgone as a result of disease-related morbidity and mortality. Forgone income is normally estimated by the value of workdays lost due to the illness. In the case of mortality, forgone income is estimated by the capitalized value of future lifetime earnings lost to the diseaserelated death, based on projected incomes for different age groups and age-specific survival rates. This cost can be substantial for some epidemics. Malaria kills more than 1 million people a year, and HIV/AIDS is estimated to have claimed 3.1 million lives in 2002.

Previous researchers have also focused on long-term effects from the demographic consequences of epidemics. The first and foremost impact of epidemics is a negative shock to population and labor force. However, economic theory provides conflicting predictions regarding the economic effects of negative population shocks. A disease that kills mostly children and the elderly without affecting the economically active population aged 15 to 54 can lead to an initial increase in GDP per head. Even when the disease mostly attacks prime earners, its long-term economic consequences are not unambiguous. Standard neoclassical growth models predict that a negative shock to population growth can lead to a faster accumulation of capital and subsequently faster output growth (see Barro and Sala-I-Martin, 1995). Conversely, an exogenous, one-time reduction in labor force raises the capital-labor ratio and lowers the rate of return to capital, which subsequently leads to slower capital accumulation and thereby lower output growth.

Empirical studies also present conflicting results. Brainerd and Siegler (2002) show that the Spanish flu epidemic of 1918–1919, which killed at least 40 million people worldwide and 675,000 in the United States, had a positive effect on per capita income growth across states in the United States in the 1920s. In contrast, Bloom and Mahal (1997) show no significant impact of that epidemic on acreage sown per capita in India across 13 Indian provinces.

Epidemics can have further effects on demographic structures by influencing fertility decisions of households. According to the “child-survivor hypothesis,” parents desire to have a certain number of surviving children. Under this theory, risk-averse households raise fertility by even more than expected child mortality. Evidence shows that high infant and child mortality rates in African regions of intense malaria transmission are associated with a disproportionately high fertility rate and high population growth (Sachs and Malaney, 2002). Thus, the increase in fertility has a further negative impact on long-term growth.

Aside from the direct demographic consequences of an epidemic, another important mechanism by which a disease has an adverse impact on the economy’s long-term growth is the destruction of human capital. Human capital, the stock of knowledge embodied in the population, is considered an important determinant of long-term growth (Barro and Sala-I-Martin, 1995). Furthermore, the decline in “health capital,” as measured in general by life expectancy, has negative effects on economic growth (Bloom et al., 2001). Epidemics also adversely affect labor productivity by inhibiting the movement of labor across regions within a country as well as across countries. Restricted mobility thus inhibits labor from moving to the places where it is most productive. Researchers simulating the effect of AIDS on growth in Southern African countries find that AIDS has had significant negative effects on per capita income growth mainly through the decline in human capital (Haacker, 2002).

While previous studies have emphasized the economic cost of disease associated with private and nonprivate medical costs, this doesn’t seem to be the principal issue in the case of SARS. The number of probable SARS cases is still small in comparison to other major historical epidemics. Furthermore, unlike AIDS, the duration of hospitalization of the infected patients is short, with more than 90 percent of the patients recovering in a relatively short period, thereby rendering the medical costs comparatively very low. The SARS-related demographic or human capital consequences are also currently estimated to be insignificant. The fatality rate of the SARS coronavirus is high, but, with current estimates indicating fewer than 800 deaths from SARS worldwide, the death toll is tiny compared with the 3 million who died of AIDS last year or at least 40 million people worldwide who died in the Spanish flu epidemic of 1918–1919. Therefore, forgone incomes associated with morbidity and mortality as a result of SARS appear to be insignificant. If SARS became endemic in the future, it would substantially increase private and public expenditures on health care and would have more significant impacts on demographic structure and human capital in the infected economies. However, based on information to date, this is unlikely to happen with the SARS epidemic.

Although the medical expenditures and demographic consequences associated with SARS are insignificant, SARS apparently has already caused substantial economic effects by other important channels. We summarize three mechanisms by which SARS influences the global economy.

First, fear of SARS infection leads to a substantial decline in consumer demand, especially for travel and retail sales service. The fast speed of contagion makes people avoid social interactions in affected regions. The adverse demand shock becomes more substantial in regions that have much larger service-related activities and higher population densities, such as Hong Kong or Beijing, China. The psychological shock also ripples around the world, not just to the countries of local transmission of SARS, because the world is so closely linked by international travel.

Second, the uncertain features of the disease reduce confidence in the future of the affected economies. This effect seems to be potentially very important, particularly as the shock reverberates through China, which has been a key center of foreign investment. The response by the Chinese government to the epidemic was fragmented and nontransparent. The greater exposure to an unknown disease and the less effective government responses to the disease outbreaks must have elevated concerns about China’s institutional quality and future growth potential. Although it is difficult to measure directly the effects of diseases on decision making by foreign investors, the loss of foreign investors’ confidence would have potentially tremendous impacts on foreign investment flows, which would in turn have significant impacts on China’s economic growth. This effect is also transmitted to other countries competing with China for foreign direct investment (FDI).

Third, SARS undoubtedly increases the costs of disease prevention, especially in the most affected industries such as the travel and retail sales service industries. This cost may not be substantial, at least in global terms, as long as the disease is transmitted only by close human contact. However, the global cost could become enormous if the disease is found to be transmitted by other channels such as through international cargo.

Simulations Using the G-Cubed (Asia Pacific) Model

Given the important linkages among affected countries in the region through capital flows and the trade of goods and services, any analysis of the implications of SARS on the global economy needs to be undertaken with a model that adequately captures these interrelationships. The G-Cubed (Asia Pacific) model, based on the theoretical structure of the G-Cubed model outlined in McKibbin and Wilcoxen (1998), is ideal for such analysis, having both a detailed country coverage of the region and rich links between countries through goods and asset markets.3 A number of studies—summarized in McKibbin and Vines (2000)—show that the G-Cubed model has been useful in assessing a range of issues across a number of countries since the mid-1980s.4 A summary of the principal characteristics of the G-Cubed model is presented as an annex at the end of this paper.

We make two alternative assumptions in generating a range of possible scenarios under this model. In an earlier analysis, we assumed in the first scenario that the shock lasted for a year. To capture the fact that the shock lasted 6 months, in reality we now scale down the shocks by 50 percent to capture the shorter duration. This is called a temporary shock. The second assumption is that the shocks are the same magnitude in the first year as the temporary shock, but are more persistent in that they fade out equiproportionately over a 10-year period. This illustrates the impact of expectations of the future evolution of the disease on the estimated costs in 2003. It also gives some insight into what might happen to the region if the SARS virus is considered the beginning of a series of annual epidemics emerging from China.

Initial Shock to China and Hong Kong

We first calculate the shocks to the economies of mainland China and Hong Kong (SAR), which were hit most heavily by the disease, and then work out some indexes summarizing how these shocks are likely to occur in other economies. There are three main shocks, based on observations of financial market analysts about the existing data emerging from China and Hong Kong:5

  • A 200 basis-point increase in country risk premium.6
  • A sector-specific demand shock to the retail sales sector, amounting to a 15 percent drop in demand for the exposed industries in the service sector.
  • An increase in costs in the exposed activities in the service sector of 5 percent.

These shocks are then scaled to last only 6 months rather than 1 year.

We could also consider several other shocks, such as the impact on health expenditures and fiscal deficits. It is not clear how large this shock should be for the persistent shock, nor even whether the schock should have a positive or negative sign. Because SARS kills a higher proportion of vulnerable people in a very short period, it may be that the large expenditure for these people will be reduced as a result of SARS. There might also be a reaction by medical authorities to substantially increase investments in public health. Given the current state of information, we would be forced to speculate concerning all of these potential effects on health expenditures. We therefore explicitly ignore such fiscal impacts of SARS in this version of the paper.

Shocks to Other Countries

The transmission of SARS, as distinct from the transmission of economic impacts through global markets, depends on a number of factors. We refer to this as the global exposure to SARS. The speed of spread is likely to depend on (i) tourist flows, (ii) geographical distance to China, (iii) health expenditures and sanitary conditions, (iv) government response, (v) climate, (vi) per capita income, (vii) population density, and so on. Table 2-1 presents indicators on health expenditures, tourist arrivals, and sanitary conditions for selected countries. There are more than 33 million annual visitors to mainland China. Hong Kong (SAR) has annual tourist arrivals that are more than 200 percent of the local population. Overall health expenditure as a ratio to GDP is not small in Asian countries, but health expenditure per capita is only $45 in China.

TABLE 2-1. Health Expenditures, Tourist Arrivals, and Sanitation Indicators for Selected Countries.

TABLE 2-1

Health Expenditures, Tourist Arrivals, and Sanitation Indicators for Selected Countries.

With more data we could do some econometric estimation to capture these influences. Lacking that data, for the purposes of this paper we construct a rough measure of the intensity of exposures to SARS, based on the above information and the cumulative number of cases of SARS for each country. This index of “global exposure to SARS” is contained in Figure 2-1. This will be used to scale down the country risk shocks calculated for all other countries. For example, if a country has an index of 0.5, the country risk premium shock will be the Chinese shock of 2 percent adjusted by the “global exposure to SARS” index, which gives a shock of 1 percent.

FIGURE 2-1. Global exposure to SARS.

FIGURE 2-1

Global exposure to SARS.

For the shocks to the service industries, before applying the global exposure index to each country, we need to adjust the sector-specific shocks. Because we only have an aggregate service sector in the model, we need to take account for structural differences within the service sectors of each country. We do this by creating an “index of sectoral exposure to SARS.” This index is assumed to be proportional to the share of industries affected by SARS within the service sector. Industries such as tourism, retail trade, and airline travel have been impacted severely. We use the GTAP5 database to calculate the share of exposed sectors to total services for each country.7 We define the exposed sectors based on GTAP definitions as wholesale and retail trade (TRD, including hotels and restaurants), land transport (OTP), and air transport (ATP). The “index of sectoral exposure to SARS” is shown in Figure 2-2. This index is applied to the sector-specific shocks we developed for the Chinese economy. We then apply the “global exposure to SARS” to the resulting shocks.

FIGURE 2-2. Sectoral exposure to SARS: share of retail sale and travel industry in service sector.

FIGURE 2-2

Sectoral exposure to SARS: share of retail sale and travel industry in service sector.

The direct impact on any economy will be a function of a number of factors. An important aspect of the impact will be the size of the service sector in the economy as well as the relative indexes of exposure. Figure 2-3 shows the size of the service sector relative to total output in each economy in the model.

FIGURE 2-3. Share of service sector in total output.

FIGURE 2-3

Share of service sector in total output.

Simulation Results

We apply the shocks outlined in the previous section to the global economy. We begin the simulation in 2003, assuming in 2003 that the SARS outbreak was completely unanticipated. Both the temporary and persistent shocks are assumed to be understood by the forward-looking agents in the model. Clearly this is problematic when it comes to a new disease like SARS, when there is likely to be a period of learning about the nature of the shock. In this case, rational expectations might not be a good way to model expectations. Yet an alternative approach is not clear. In our defense, it is worth pointing out that only 30 percent of agents have rational expectations and 70 percent of agents are using a rule of thumb in adjusting to contemporaneous information about the economy. Table 2-2 contains results for the percentage change in GDP in 2003 as a result of the temporary and permanent SARS shocks as well as the contribution of each component (i.e., demand decline for services, cost increase for services, and country risk premium).

TABLE 2-2. Percentage Change in GDP in 2003 Due to SARS.

TABLE 2-2

Percentage Change in GDP in 2003 Due to SARS.

The full dynamics of adjustment will be outlined shortly. Focusing on the GDP results, it is clear that there are interesting differences among the various components of the overall shock as well as between the temporary and permanent shocks. The temporary shock has its largest impact on China and Hong Kong (SAR), as expected. The loss to Hong Kong of 2.63 percent of GDP, however, is much larger than that of 1.05 percent for the remainder of mainland China. This primarily reflects the larger role of the service sector in Hong Kong, the larger share of impacted industries within the service sector in Hong Kong, and the greater reliance on trade within the Hong Kong region. Taiwan is the next most affected area, losing 0.49 percent of GDP in 2003, followed closely by Singapore, with a loss of 0.47 percent of GDP.

For Hong Kong, the increase in costs in the service sector is by far the largest contributing factor to the loss of GDP. In the rest of mainland China it is evenly spread across the three factors. The temporary increase in the country risk premium of 200 basis points is estimated to lower GDP by 0.33 percent for China and by 0.20 percent for Hong Kong. Interestingly, the risk premium shock has very negligible impacts, of less than 0.01 percent of GDP, on Taiwan and Singapore, which adopt floating exchange rate regimes, although they are also subject to a substantial rise in the country risk premium by 150 and 100 basis points, respectively. The difference comes from the fact that exchange rate depreciation helps Taiwan and Singapore to avoid a rise in real interest rate and subsequent output decline.

The calculations when expressed as a percent of each country’s GDP may appear to be small. However, when translated into an absolute dollar amount, these figures imply that the global economic loss from SARS was close to $US 40 billion in 2003. This is a figure much greater than any calculation of the medical costs of treating SARS patients.

The persistent SARS shock is also much more serious for China and Hong Kong. The primary impact is from the persistence in the rise of the country risk premium. Although the same in 2003 as for the temporary shock, the persistence of the country risk premium causes much larger capital outflow from China and Hong Kong. This impacts on short-run aggregate demand through a sharp contraction in investment, as well as a persistent loss in production capacity through a resulting decline in the growth of the capital stock, which reduces the desirability of investment. The extent of capital outflow will be discussed below.

Interestingly, the difference in GDP loss in 2003 when SARS is expected to be more persistent distinguishes between two regions. China, Hong Kong (SAR), Malaysia, the Philippines, Singapore, and Taiwan experience a larger loss in 2003, whereas the OECD economies and others experience a lower GDP loss. This reflects the greater capital outflow from the most affected countries into the least affected countries, which tends to lower the GDP of those countries losing capital and raise the GDP of those countries receiving capital. The countries in the first group that are less affected by SARS are nonetheless worse off with a more persistent disease because of their trade links with China, Hong Kong, and Singapore. The expectation of a more persistent problem with SARS leads to a total GDP loss of roughly $US 54 billion in 2003 alone (this ignores any future years’ losses).

The results for GDP illustrate how the costs of SARS can be very different in 2003, depending on expectations of how the disease will unfold. It is also interesting to examine the change in economic impacts over time.

We present two sets of figures containing six charts within each figure. These results are all expressed as deviation from the underlying baseline of the model projections (which is described in more detail in the annex at the end of this paper). They show how key variables change relative to what would have been the case without SARS. Figures 2-4 and 2-5 describe simulation outcomes for the temporary SARS shock in the three panels on the left and simulation outcomes for the more persistent SARS shock in the three panels on the right. This enables a comparison between the two for the impacts on the real economy and trade flows.

FIGURE 2-4. Real impacts of temporary versus persistent SARS shock.

FIGURE 2-4

Real impacts of temporary versus persistent SARS shock.

FIGURE 2-5. Trade and captial flow impacts of temporary versus persistent SARS shock.

FIGURE 2-5

Trade and captial flow impacts of temporary versus persistent SARS shock.

Figure 2-4 contains results for real GDP, investment, and exports for both the temporary and persistent SARS shock. The loss in GDP from the temporary shock is largely confined to 2003. The persistent shock not only has a larger impact on GDP in 2003—because of expectations about future developments—but has a persistent impact on real GDP for a number of years afterward. Investment falls more sharply in 2003, which is the source of the larger GDP loss.

The results for exports are also interesting. In the case of the temporary shock, exports from Hong Kong fall sharply. Yet, in the more persistent case, exports from Hong Kong rise in 2003. The reason for this difference is that the more persistent the shock, the larger the capital outflow from affected economies. A capital outflow will be reflected in a current account surplus and a trade balance surplus. For this to occur, either exports must rise or imports must fall or both. This can be seen clearly in Figure 2-5.

In the case of the temporary SARS shock, the net capital outflow from China and Hong Kong (relative to base) is around 0.3 percent of GDP. However, when the shock is more persistent, this capital outflow rises sharply (top right panel of Figure 2-5), to 1.4 percent of GDP for Hong Kong and 0.8 percent of GDP for China. This capital outflow is reflected in the trade balance surplus in both. This shift in the trade balance is achieved by the capital outflow depreciating the real exchange rate of both China and Hong Kong substantially.

All of these linkages have many dimensions, but a global model is able to help untangle some of the more important factors. Under this model, the SARS outbreak is predicted to have widespread economic impacts beyond the regions immediately infected with the disease and beyond the decline in the most affected service industries.

Conclusion

The impact of SARS is estimated to be large on the affected economies of China and Hong Kong (SAR). This impact is due not to the consequence of the disease itself for the affected people, but to the impact of the disease on the behavior of many people within these economies. It also depends on the disease-associated adjustment of expectations reflected in integrated real and financial markets. The more persistent SARS is expected to be, the larger the negative economic impacts in 2003 in affected economies, but the smaller the impact in countries outside the core countries. The calculations above suggest that the cost in 2003 of SARS for the world economy as a whole are close to $US 40 billion in the case where SARS is expected to be a single event, versus costs of close to $US 54 billion in 2003 if SARS is expected to recur (this does not include the actual costs of later years if in fact SARS did recur). The higher costs from a persistent shock relate to the loss of investment and the impact on confidence and therefore spending in 2003.

These results illustrate that the true cost of disease is far greater than the cost to a health budget of treatment of the cases involved. The more persistent shock in this paper can be thought of as SARS lasting longer than anyone hopes, but it can also be interpreted as a recurring series of annual epidemics emerging from China and infecting the world through increased globalization. This is not a new phenomenon, since influenza viruses have been emanating from China since at least the 1918–1919 Spanish flu. Fortunately, most have been less devastating than the well-known major outbreaks. A key point of this paper is an attempt to evaluate the true underlying global cost of these diseases. If the threat of recurring SARS or SARS-like diseases from China is real, then the estimated risk to economic activity in this region and the world, as calculated in this paper, might be very large. The estimates in this paper suggest that there is a strong economic case for direct intervention in improving public health in China and other developing countries where there are inadequate expenditures on public health and insufficient investments in research into disease prevention.

As we observed from the Asian financial flu in 1997 and SARS in 2003, there is an important role for global monitoring and coordination mechanisms in containing both economic and microbial epidemics.

Annex 2-1. Characteristics of the G-Cubed (Asia Pacific) Model

Some of the principal features of the G-Cubed (Asia Pacific) model are as follows:

  • The model is based on explicit intertemporal optimization by the agents (consumers and firms) in each economy.8 In contrast to static computable general equilibrium (CGE) models, time and dynamics are of fundamental importance in the G-Cubed model.
  • In order to track the macro time series, however, the behavior of agents is modified to allow for short-run deviations from optimal behavior either due to myopia or to restrictions on the ability of households and firms to borrow at the risk-free bond rate on government debt. For both households and firms, deviations from intertemporal optimizing behavior take the form of rules of thumb, which are consistent with an optimizing agent that does not update predictions based on new information about future events. These rules of thumb are chosen to generate the same steady-state behavior as optimizing agents, so that in the long run there is only a single intertemporal optimizing equilibrium of the model. In the short run, actual behavior is assumed to be a weighted average of the optimizing and the rule-of-thumb assumptions. Thus aggregate consumption is a weighted average of consumption based on wealth (current asset valuation and expected future after-tax labor income) and consumption based on current disposable income. Similarly, aggregate investment is a weighted average of investment based on Tobin’s q (a market valuation of the expected future change in the marginal product of capital relative to the cost) and investment based on a backward-looking version of Q.
  • There is an explicit treatment of the holding of financial assets, including money. Money is introduced into the model through a restriction that households require money to purchase goods.
  • The model also allows for short-run nominal wage rigidity (by different degrees in different countries) and therefore allows for significant periods of unemployment depending on the labor market institutions in each country. This assumption, when taken together with the explicit role for money, is what gives the model its “macroeconomic” characteristics. (Here again, the model’s assumptions differ from the standard market-clearing assumption in most CGE models.)
  • The model distinguishes between the stickiness of physical capital within sectors and within countries and the flexibility of financial capital, which immediately flows to where expected returns are highest. This important distinction leads to a critical difference between the quantity of physical capital that is available at any time to produce goods and services, and the valuation of that capital as a result of decisions about the allocation of financial capital.

As a result of this structure, the G-Cubed model contains rich dynamic behavior, driven on the one hand by asset accumulation, and on the other by wage adjustment to a neoclassical steady state. It embodies a wide range of assumptions about individual behavior and empirical regularities in a general equilibrium framework. The interdependencies are solved out using a computer algorithm that solves for the rational expectations equilibrium of the global economy. It is important to stress that the term “general equilibrium” is used to signify that as many interactions as possible are captured, not that all economies are in a full market-clearing equilibrium at each point in time. Although it is assumed that market forces eventually drive the world economy to a neoclassical steady state growth equilibrium, unemployment does emerge for long periods due to wage stickiness, to an extent that differs between countries on account of differences in labor market institutions.

Baseline Business-as-Usual Projections for G-Cubed Model Simulations

To solve the model, we first normalize all quantity variables by each economy’s endowment of effective labor units. This means that in the steady state, all real variables are constant in these units, although the actual levels of the variables will be growing at the underlying rate of growth of population plus productivity. Next, we must make base-case assumptions about the future path of the model’s exogenous variables in each region. In all regions we assume that the long-run real interest rate is 5 percent, tax rates are held at their 1999 levels, and fiscal spending is allocated according to 1999 shares. Population growth rates vary across regions as per the 2000 World Bank population projections.

A crucial group of exogenous variables are productivity growth rates by sector and country. The baseline assumption in G-Cubed (Asia Pacific) is that the pattern of technical change at the sector level is similar to the historical record for the United States (where data are available). In regions other than the United States, however, the sector-level rates of technical change are scaled up or down in order to match the region’s observed average rate of aggregate productivity growth over the past 5 years. This approach attempts to capture the fact that the rate of technical change varies considerably across industries while reconciling it with regional differences in overall growth. This is clearly a rough approximation; if appropriate data were available, it would be better to estimate productivity growth for each sector in each region.

Given these assumptions, we solve for the model’s perfect-foresight equilibrium growth path over the period 2002–2081. This a formidable task: the endogenous variables in each of the 80 periods number over 7,000 and include, among other things: the equilibrium prices and quantities of each good in each region, intermediate demands for each commodity by each industry in each region, asset prices by region and sector, regional interest rates, bilateral exchange rates, incomes, investment rates and capital stocks by industry and region, international flows of goods and assets, labor demanded in each industry in each region, wage rates, current and capital account balances, final demands by consumers in all regions, and government deficits.9 At the solution, the budget constraints for all agents are satisfied, including both intra-temporal and intertemporal constraints.

Footnotes

*

This paper is adapted from an article that will appear later this year in Asian Economic Papers (MIT Press). An earlier version of the paper was originally presented to the Asian Economic Panel meeting held in Tokyo, May 11–12, 2003, and the Pacific Economic Cooperation Council (PECC) finance forum, Hua Hin, Thailand, July 8–9, 2003. We have updated that original paper to include the last known case of SARS as well as adjusting the scale of some shocks given the knowledge that the SARS epidemic lasted approximately 6 months rather than the full year originally assumed. The authors particularly thank Andrew Stoeckel for interesting discussions and many participants at the conferences, particularly Ifzal Ali, Richard Dorbnick, George Von Furstenberg, Yung Chul Park, Jeffrey Sachs, Wing Thye Woo, and Zhang Wei for helpful comments. Alison Stegman provided excellent research assistance and Kang Tan provided helpful data. See also the preliminary results and links to the model documentation at http://www​.economicscenarios.com. The views expressed in the paper are those of the authors and should not be interpreted as reflecting the views of the institutions with which the authors are affiliated, including the trustees, officers, or other staff of the Brookings Institution.

1

See McKibbin (1998) for a study of the Asia crisis that included the critical role of capital flow adjustment.

2

Exceptions can be found in the Commission on Macroeconomics and Health (2002).

3

Full details of the model, including a list of equations and parameters, can be found online at http://www​.gcubed.com.

4

These issues include Reaganomics in the 1980s, German unification in the early 1990s, fiscal consolidation in Europe in the mid-1990s, the formation of NAFTA, the Asian crisis, and the productivity boom in the United States.

5

These are also consistent with other papers on particular countries presented at the Asian Economic Panel in May 2003.

6

In the May version of this paper we assumed a 300 basis-point shock. We follow the updated research of Australian Treasury (2003) in adjusting this shock to 200 basis points.

7

For more information on this database, see the website of the Global Trade Analysis Project at http://www​.gtap.agecon.purdue.edu/.

8
9

Because the model is solved for a perfect-foresight equilibrium over an 80-year period, the numerical complexity of the problem is on the order of 80 times what the single-period set of variables would suggest. We use software summarized in McKibbin and Sachs (1991), Appendix C, for solving large models with rational expectations on a personal computer.

Copyright © 2004, National Academy of Sciences.
Bookshelf ID: NBK92473

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