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Empirical Economics

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07.04.2022

GDP effects of pandemics: a historical perspective

verfasst von: Maciej Stefański

Erschienen in: Empirical Economics | Ausgabe 6/2022

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Abstract

The paper estimates dynamic effects of pandemics on GDP per capita with local projections, controlling for the effects of wars and weather conditions, using a novel dataset that covers 33 countries and stretches back to the thirteenth century. On average, pandemics are found to have prolonged and highly statistically significant effects on GDP per capita—a pandemic killing 1% of the population tends to increase GDP per capita by approx. 0.3% after about 20 years. The study of a more detailed dataset available for the UK reveals that this results mainly from an increase in per capita land and a disproportionate impact of pandemics on low-productivity workers, while monetary expansion, institutional change and innovation could also play some role. At the same time, the effects of pandemics are found to vary with scale and across time and countries, with positive effects present following the Black Death and the Spanish flu pandemics, especially in Northern Europe. This suggests that only the largest and most unexpected pandemics have a positive impact on income.

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The paper studies all significant and documented disease-generated spikes in death rates, whether they have regional or national impact (and thus they can be referred to as epidemics) or they spread over multiple countries or continents (and hence can be termed as pandemics). For simplicity and brevity, the terms “epidemics” and “pandemics” are used interchangeably throughout the paper. For a more detailed definition of studied epidemic events, see the data section.

Long run is defined as the IRF horizon, i.e. 40 years after the pandemic. Throughout the paper, “on impact” refers to the year of the pandemic outbreak (time 0), “short run” to the first few (≤ 5) years following the pandemic, and “medium run” to the period 10–20 years after the pandemic, when the peak effect usually appears.

See "Appendix Section 10.2.6" for a more detailed discussion of this issue.

Argentina, Australia, Austria, Belgium, Canada, Switzerland, Chile, Cuba, Germany, Denmark, Spain, Finland, France, UK, Greece, Indonesia, India, Italy, Japan, Mexico, Malaysia, Netherlands, Norway, New Zealand, Peru, Philippines, Poland, Portugal, Romania, Russia, Sweden, USA, and South Africa.

Data on pandemics and wars are available over the whole 1252–2016 period, while the coverage of tree ring and GDP per capita data is limited. See "Appendix Section 10.1" for details.

Not all such countries are included, however—Bolivia, Brazil, Colombia, Ecuador, Korea, Sri Lanka, Panama, Singapore, Taiwan, Uruguay and Venezuela all have continuous GDP per capita estimates starting before 1914 (though, with the exception of Venezuela, no earlier than 1870), but are not covered.

The 2020 edition of the database has been published recently, but it does not include any important new time series. The main modification is the change back to the 1990 PPP benchmark, which does not have any effect on pre-1950 growth estimates and thus should not substantially influence the results in this paper.

Increasing the number of lags is limiting degrees of freedom much faster in VAR models than in local projections, while making the computation of IRFs much more cumbersome in ARDL models.

Though it should also be noted that Montiel Olea and Plagborg-Møller 2021 find local projections to be robust to non-stationarity.

The numbers from the baseline and the binary variable specifications are not directly comparable, but taking into account that an average pandemic killed about 1.2% of population per year, and there are almost twice as few (169 to 308) pandemic events as observations with positive pandemic size (since only the first year of the pandemic is denoted in the binary variable case), it is clear that the binary variable specification points to significantly larger effects of pandemics than the baseline specification.

They study only the UK, do not include any trends and cover a limited set of pandemics, which probably explains the difference.

Spain, France, UK, Italy, Netherlands, Poland, Portugal, and Sweden. The results for the full sample are reported in "Appendix Section 10.2.5".

3 lags are optimal according to the AIC information criterion, while 30 is the maximum number of lags considered (and optimal according to adjusted R2).

When the maximum period of oscillation is increased further, the CF trend stops to capture well the post-1815 developments in trend growth and thus loses any potential advantage over the piecewise linear trend.

The sum of CCE IRFs shows a reaction of GDP per capita to a pandemic that killed 1% of population in each of the countries in the sample, while the baseline IRF does not consider what happens abroad.

See, for example, Malinowski and van Zanden (2017) for details on the construction of historical GDP per capita estimates.

While the death toll of the Spanish flu (1–2%) was not very large in the context of the whole sample, it has been the largest pandemic of the last 150 years with one of the highest death tolls in absolute terms. Thus, it can be regarded as a large event.

The 2020 edition of the database has been published recently, but it does not include important new time series. The main modification is the change back to the 1990 PPP benchmark, which does not have any effect on pre-1950 growth estimates and thus should not substantially influence the results of this paper.

In the baseline specification, if one country has 400 observations and the other 100, the one with more observations has effectively 4 times as much influence on the results as the one with fewer observations. Using mean group estimator, they are of equal importance. For short sample countries, observations are on average more recent than for long sample countries. Therefore, more recent observations have a relatively larger weight in the mean group estimator than in the baseline specification.

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Titel: GDP effects of pandemics: a historical perspective
verfasst von: Maciej Stefański
Publikationsdatum: 07.04.2022
Verlag: Springer Berlin Heidelberg
Erschienen in: Empirical Economics / Ausgabe 6/2022
Print ISSN: 0377-7332
Elektronische ISSN: 1435-8921
DOI: https://doi.org/10.1007/s00181-022-02227-3

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