SARS: Estimating the Economic Impacts Warwick J McKibbin ANU and Brookings Institution Presentation prepared for “Forum on Microbial Threats” Institute of Medicine, Washington DC Sept 30 – Oct 1, 2003

Based on  Lee and McKibbin (2003) “Globalization and Disease: The Case of SARS” forthcoming in Asian Economic Papers, MIT Press.  There is both an earlier version of this paper based on April 2003 data and a revised version based on July 2003 data  See (working papers)

Goals of the Paper  To present a framework for thinking about the likely impacts on the world economy of a disease such as SARS  To present some rough empirical estimates of the impacts of SARS on the global economy

Estimating the Likely Impact of SARS  Short term impact Reduction in demand for travel and other services Psychological impact on consumers Increase in costs in service industries Loss of confidence in affected economies Increase in medical expenses

 Longer term impacts Worries about recurrence of diseases and their transmission – largely from southern China Decrease in human capital through reduction in life expectancy and loss of educate workers Increase in fiscal deficits through higher health expenditures Estimating the Likely Impact of SARS

Attempt to Quantify the Key Issues using a global model Use the G-cubed (Asia Pacific) model version 50n

The G-Cubed (Asia Pacific Model)  Key features Based on optimizing behavior by households and firms in each economy in a dynamic setting (expectations are explicitly modeled) Short run deviation from optimizing behavior due to stickiness in labor markets, myopia Substantial sectoral dis-aggregation within a consistent macroeconomic structure Explicit treatment of financial assets with stickiness in physical capital differentiated from flexibility of financial capital Model has 7,400 equations with 140 jumping (co-state) variables and 263 state variables

Countries  United States Japan  Australia New Zealand  Rest of the OECD Korea  Thailand Indonesia  China Malaysia  Singapore Taiwan  Hong Kong Philippines  India  Oil Exporting Developing Countries  Eastern Europe and the former Soviet Union  Other Developing Countries

Sectors  Energy  Mining  Agriculture  Durable Manufacturing  Non-Durable Manufacturing  Services

The estimates depend on the assumptions of the model as well as the nature of the shocks

How to Design the Shocks?  The basic shocks will differ across countries depending on a range of factors  We assume the shocks start in China and Hong Kong calibrating the size of these shocks to observed or predicted data from other experts  We then assume that the size of the shocks across countries will depend: A country’s “global exposure to SARS” A country’s “sectoral exposure to SARS”

How to Design the Shocks?  For China and Hong Kong: Shift in the share of consumption expenditure on most affected service sectors  benchmarked to a 15% decline in demand for travel and retail trade in China and H/K. Reduction in overall consumption demand  Equal to the fall in demand for travel and retail trade Increase in costs in affected service industries  Assumed to be 5% in China and H/K implemented as a productivity shock Increase in country risk  Assumed to be 200 basis points based on a survey of financial market experts  Shocks are scaled to 50% to capture the period of impact of 6 months rather than a year

How to Design the Shocks?  Note that the ultimate impact on a country will depend on the shocks within that country as well as the way the shocks are transmitted through trade and capital flows between countries.

How to Design the Shocks?  Need to construct an index on the “Global exposure to SARS”  This will depend on Cumulative cases already reported Size of tourist flows Geographical distance from China Sanitary conditions Government response Climate Per capita income Population density etc

How to Design the Shocks?  Because we have an aggregate service sector we need to scale the shocks to the service sector for each country – (i.e. the demand shock and cost shock).  To do this we construct an index of sectoral exposure to SARS which is the share of the vulnerable service industries in the total service sector within each country (ie travel, tourism, retail trade, restaurants hotels etc – TRD+OTP+ATP from GTAP)

Key factor  The composition of the service sectors in each country matter for how the shocks are fed into the model  The role of services in the overall economy will be an important factor in the size of the impacts

The Simulations  After constructing the country specific shocks as outlined we then apply to each country’s shocks the index of “global exposure to SARS”

How to Design the Shocks?  Two scenarios A single year shock A shock that decays proportionately over 10 years

The Results

The Scenarios  1) Baseline 2002 Assumptions about  population growth by country  Productivity growth by sector catching up by 2% per year to US leading sector  Given tax rates, monetary growth rates etc in all countries Solve for the full path for the future of the global economy  2) apply the temporary shock  3) apply the persistent shock

Conclusion  SARS is estimated to have had an significant effect on China and Hong Kong  It is not the disease per se that has the largest impact, but the affect on expectations and the behavior of consumers and investors  The cost of SARS is not just the number of cases multiplied by the cost of each case

Conclusion  We have considered a temporary and persistent shock.  It is not unreasonable to argue that although SARS might be temporary, there could be a perceived increase in the likelihood of a series of these types of diseases emanating from China and being transmitted globally.  Thus the temporary SARS shock might indeed be a permanent disease transmission shock which through expectations might have significant economic implications for the Asian region – a different kind of contagion.

Background Papers