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Impacts of a Potential Influenza Pandemic on New Zealand's Macroeconomy - PP 06/03

Other Studies of the Economic Impacts of Pandemics

The economic impacts of potential influenza pandemics have been estimated in a number of studies using a range of methods. The scenarios investigated as well as the type of effects taken into account in each analysis vary widely. There are broadly two types of analysis used, the first looks at what society would pay to avoid a pandemic, usually in terms of how much is likely to be spent on health care, and how much will be lost from the economy in terms of lost work days and lost life. Examples include the papers by Meltzer et al. (1999) and by Balicer et al. (1999) which undertake cost benefit analyses of interventions including vaccination and use of antiviral drugs. The second type of analysis looks at the effect on GDP, typically taking into account the effect on labour supply and reductions in demand due to “social distancing” effects. The analysis contained in this paper falls within the second genre and for comparison purposes we briefly review recent similar studies undertaken for other countries, of which there are a growing number (see e.g. International Monetary Fund 2006)

Kennedy, Thomson and Vujanovic(2006) evaluate the channels a pandemic will affect the Australian economy by imposing a sequence of supply and demand shocks on the Australian Treasury macroeconomic model (TRYM). The pandemic they investigate assumes a population mortality of 0.2% and that 20% of the labour force is absent from work during the pandemic quarter. Kennedy et al. find that the most significant GDP reductions are due to confidence effects on household consumption and business investment, followed by the reduction in labour supply due to sickness and absenteeism. Over a year they estimate that Australian GDP would be around nine percent less than trend because of the pandemic. This result is towards the top of our range of estimates despite their lower assumed death rate. This is largely because of greater (in proportion to the death rate) confidence effects in the Australian analysis. Kennedy et al. believe that even “… with only a small number of deaths, the confidence effects on consumption are likely to be large and immediate and are likely to overshadow all other factors in the short-run.”

Sinclair and Blake of the University of Nottingham evaluate the potential impact of a pandemic on the UK’s GDP, although a full report of their work is yet to be produced (Nottingham University News Archive 2005). This study predicts an 8% GDP decline for a pandemic that directly affects 25% of the population, where affects means "contracting avian flu, having a family member infected, contracting another form of flu and being restricted from normal activities as a precaution, or being in an area of high incidence and being quarantined as a result." Their potential pandemic has approximately 0.08% population mortality. The authors also modelled smaller impacts: the first being a contained local impact scenario that would see only a small number of deaths, and the second a SARS-like impact with widespread anxiety about catching the disease and people changing their living and working habits to avoid unnecessary contact with other people. These scenarios cause respective 0.2% and 0.4% reductions in GDP.

Bloom, de Wit and Carangal-San Jose (2005) evaluate two scenarios for the Asian region based on a “relatively mild pandemic”, with an infection rate of 20% and a case fatality rate of 0.5%. The first scenario assumes a demand shock for two quarters followed by a milder demand shock in the following six quarters, and a supply shock due to sick workers being away for two weeks, with no additional absenteeism. The demand side shock reduces annual GDP growth in the Asian region by 2.3ppts and the supply side shock reduces growth by 0.3ppts. This aggregate Asian region reduction results from reductions in GDP that vary significantly for each Asian country, from 0.5ppts for Indonesia to 10.4ppts for Singapore, and depend on the openness of each economy and the size of the service export sector. The second scenario assumes that the psychological impact of the pandemic lasts longer and demand is seriously affected for four quarters with a milder demand shock in the following four quarters. In this second scenario the reduction in Asian annual GDP growth is 6.5ppts from the demand side and 0.3ppts from the supply side. The country specific reductions now range from 2.6ppts for Indonesian to 22.4ppts for Singapore.

The Congressional Budget Office (2005) has performed an evaluation of the impact on the United States’ GDP for two scenarios: a severe pandemic of 1918/1919 proportions and a mild pandemic of 1957 and 1968 proportions. The severe pandemic has a gross infection rate of 30% and a case fatality rate of 2.5%, whereas the mild pandemic has a gross infection rate of 20% and case fatality rate of 0.1%. In the severe pandemic there is a labour supply effect from those dying and people being away from work an average of three weeks, for which the CBO calculates a total GDP reduction of 3%. The CBO then looked at the demand side assuming for the severe pandemic an 80% reduction (for three months) in the entertainment, arts, recreation, lodging, and restaurant industries. Most other industries suffer a 10% reduction in demand except for the government and education sectors, which have no demand side effect, and health for which demand increases by 15%. Combining the demand side effects with the supply side effects gives the CBO a total 5% reduction in GDP. For the mild scenario the total effect is a decline in the level of GDP of 1.5%.

McKibbin and Sidorenko (2006) have provided one of the most comprehensive treatments of a pandemic’s potential macroeconomic consequences and provide a valuable benchmark for our estimates. They estimate the effects of four different scenarios on twenty economies, including New Zealand, that interact through trade and capital flows. McKibbin and Sidorenko explicitly attempt to evaluate the risk in investing in each country due to financial instability, health policy, government quality geographic location and international connectedness. New Zealand’s risk indicator is in the middle of the group, notably being higher than Australia, the United States, Europe and the UK.

The McKibbin and Sidorenko model calculates reductions in GDP for each country following from shocks to labour supply, demand for service sector output, to the financial risk, to costs of production and to demand. The results for New Zealand include a first year 1.4% GDP reduction in a 1968 type mild pandemic scenario, a 9.4% reduction for a severe 1918 type scenario and a 17.7% reduction for the even more severe “ultra” scenario. These results compare well with our own estimates. The main cause of reductions in GDP for New Zealand in the McKibbin and Sidorenko analysis is increased costs of doing business, followed by labour force reduction. The shock to the risk of investing in New Zealand has a minimal effect. In contrast to our results and the other studies, McKibbin and Sidorenko attribute a much smaller proportion of the reduction in GDP to reduced demand.

A significant result of the McKibbin and Sidorenko paper is that there could be a “flight to quality” of investment, where countries that have low financial risk could benefit from capital inflows. This highlights the importance of adequate preparation for a pandemic; if a country is seen to have prepared well and has a sound financial system, it may benefit from investment diverted from countries with higher risk.

These studies give a large range for the impacts of a pandemic on GDP. The difference in estimated reduction between our study and others is due to a number of differences in assumptions that highlight the uncertainties in modelling the economic effects of a pandemic. To begin with, the assumed characteristics of the pandemic are different in each study. Many of the studies assume death rates similar to that of the 1957 or 1968 pandemics, and all assume gross infection rates that are lower than assumed in the Ministry of Health standard planning model. Also the other studies have assumed little or no additional absenteeism, the exception being McKibbin and Sidorenko who assume absenteeism due to woman taking time off to care for children. This variation gives a different labour supply reduction in each study.

The judgements formed about demand side effects also vary between studies. Our demand side effects are broadly comparable with the CBO’s severe pandemic scenario. Another difference between our work and other studies comes in the treatment of the recovery. We have included a recovery path that approximately doubles the reduction in GDP in the first year. CBO in particular includes no recovery time and it is unclear how some other studies treat the recovery. For rough comparison, if we ignore the loss of output during the recovery and use parameter values roughly similar to the CBO, our result is a 4% reduction in GDP for the first year (CBO result is 5%). Including the recovery path increases the first year loss to 9%.

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