This paper starts with a quick overview of results on the classic SIR model and variants allowing for heterogeneity in contact rates. It then notes several implications relevant to model calibrations and policy predictions. Calibrating the classic SIR model to data generated by a heterogeneous model can lead to forecasts that are biased in several ways and to understatement of the forecast uncertainty. Among the biases are that we may underestimate how quickly herd immunity might be reached, underestimate differences across regions, and have biased estimates of the impact of endogenous and policy-driven social distancing.
Ellison, G (2020), ‘Implications of Heterogeneous SIR Models for Analyses of COVID-19‘, COVID Economics 53, CEPR Press, Paris & London. https://cepr.org/publications/covid-economics-issue-53#392514_392929_390648
This paper examines deforestation's effect on the COVID-19 transmission to indigenous peoples and its transmission mechanisms. To that end, I analyze the Brazilian case and use new datasets that cover all the country's municipalities daily. Relying on a fixed-effects model, I find that deforestation is a powerful and consistent variable to explain the transmission of COVID-19 to indigenous populations. The estimates show that one unit increase in deforestation per 100 Km2 Â is associated, on average, with the confirmation of 2.4 to 5.5 new daily cases of COVID-19 in indigenous people 14 days after the deforestation warnings. One Km2 deforested today results in 9.5% more new COVID-19 cases in two weeks. In accumulated terms, deforestation explains at least 22% of all COVID-19 cases confirmed in indigenous people until 31 August 2020. The evidence suggests that the main mechanisms through which deforestation intensifies human contact between indigenous and infected people are illegal mining and conflicts.
Laudares, H (2020), ‘Is deforestation spreading COVID-19 to the indigenous peoples?‘, COVID Economics 53, CEPR Press, Paris & London. https://cepr.org/publications/covid-economics-issue-53#392514_392929_390649
In this study, we estimate the overall impact of the novel Coronavirus pandemic on Chinese exports and differentiate the hypothesized `triple pandemic effect' across its three components: 1) the domestic supply shock; 2) the international demand shock; and 3) the effects of Global Value Chain (GVC) contagion. We find that Chinese exports are very sensitive to the severity of the global Coronavirus outbreaks. Average export elasticity estimates with respect to new Chinese and foreign destination country infections range from -2.5 to -4.6. Against a Covid-19-free counterfactual, our estimates predict that the pandemic has reduced Chinese exports by as much as 40% to 45% during the first half of 2020, but that these losses have peaked and are expected to partially recover by the end of the year. Moreover, we find that all three shocks contribute to the pandemic-induced reduction in Chinese exports, but that GVC contagion exerts the largest and most persistent influence explaining these losses. Among the three shocks, the impact of GVC contagion explains around 75% of the total reduction in Chinese exports, while the domestic supply shock in China accounts for around 10% to 15% and the international demand shock only explains around 5% to 10%. As a result of these varying transmission channels, the pandemic effects appear to be very distinct from those explaining the Great Trade Collapse in 2008-09.
Friedt, F and K Zhang (2020), ‘The Triple Effect of Covid-19 on Chinese Exports: First Evidence of the Export Supply, Import Demand & GVC Contagion Effects‘, COVID Economics 53, CEPR Press, Paris & London. https://cepr.org/publications/covid-economics-issue-53#392514_392929_390650