Methods
Data Source, Sample Selection and Variables
All data are as of April 17th, 2020. Data on confirmed
cases and deaths come from the John’s Hopkins Coronavirus
Resource Center (5). Data on
population, geography, income and expenditures, and B Hepatitis, Measles
and Diphtheria/Pertussis/Tetanus (DPT) vaccines come from World
Development Indicators database
(https://databank.worldbank.org/source/world-development-indicators) and
from the United Nations Comtrade statistics (https://comtrade.un.org/).
Data about foreign direct investments to and from China come from the
International Monetary Fund Coordinated Direct Investment Survey
(https://data.imf.org/?sk=40313609-F037-48C1-84B1-E1F1CE54D6D5).
Detailed information on tuberculosis vaccination policies comes from the
Bacille Calmette-Guérin (BCG) vaccine Atlas (14), last updated in 2017
in the online version (http://www.bcgatlas.org/index.php). Data about
human freedom comes from the 2019 Human Freedom Report by the Fraser
Institute
(https://www.cato.org/sites/cato.org/files/human-freedom-index-files/human-freedom-index-2018-revised.pdf).
Data from a total of 121 countries, out of the 209 that reported cases
of Covid19, accounting for about 99% of both confirmed cases and
deaths, have been used. The countries in the analysis, listed in
supplementary appendix, have been chosen in view of the availability of
observations relative to covariates.
The set of dependent and independent variables is reported in Table 1.
In particular, we used confirmed cases per million inhabitants as a
proxy for the intensity of contagion; the number of cases 15 days
earlier as a proxy for the stage of the diffusion of the virus;
population in the largest city as a proxy for density and the degree of
urbanization; life expectancy at birth as a comprehensive health
indicator, and as a proxy for the share of aged people in the
population; the latitude to define both the season as of April
17th (above or below the Equatorial line) and tropical
countries (those countries whose latitude as defined by the
corresponding variable in the World Development Indicators lies in
between the two tropics). As for BCG vaccination policy, two alternative
continuous measures were constructed, and used for robustness checks:
the BCG coverage, as reported in national surveys in various years, and
the years of absence of mandated vaccination, until 2020.
Coverage rates for different vaccines (B Hepatitis, Measles and DPT)
were also used, to disambiguate the effects of BCG from those of a more
general vaccination policy.
Among the variables proxying for economic ties with China, where the
epidemic first appeared, we include imports from China, and the levels
of inward and outward Foreign Direct Investment (FDI) relative to China.
Finally, to proxy for the compliance with the lockdown measures
implemented by the various governments, we use the Index of Human
Freedom (HFI), a weighted average of 79 distinct indicators (37 for the
personal freedom subindex and 42 for the economic freedom subindex),
each one ranging from 0 to 10, with 10 representing the most freedom.
The HFI ranges therefore from 0 to 10, in increasing order of freedom
(https://www.cato.org/sites/cato.org/files/human-freedom-index-files/human-freedom-index-2018-revised.pdf).
We used Gross Domestic Product (GDP) per capita, and private and general
government health expenditure to proxy for countries’ level of
development (general and of their health system) and for the countries’
testing capability (more income and a richer health system should be
positively correlated to more Covid-19 testing).
Statistical Analysis
To model our dependent variables, we used both ordinary least squared,
as a reference estimator, and nonlinear estimation methods. In
particular Tobit regressions, estimating both the impacts of covariates
on the probability of a country reporting more than 100 cases as of
April 17th, and their effect on relative diffusion,
was our preferred estimation method.
The reported coefficients in the Tobit regression represent the marginal
effects of the explanatory variables on the outcome variable, after
accounting for the inclusion of countries in the high incidence group.
The second and third outcome variables, i.e. CFRs and MRs, were first
modelled by ordinary least squares to obtain benchmark estimations, and
then by Probit fractional regression methods to account for the
fractional nature of the dependent variables (15). When the dependent
variable is a fraction, as with CFRs and MRs, using log-odds
transformation or Tobit regressions with lower and upper limits set to 0
and 1 may yield biased results (15, 16). Therefore, fractional
regressions will be our preferred estimation method for fatality and
mortality rates.
For consistency and comparison purposes all models included the same set
of explanatory variables. Moreover, ordinary least squares and
fractional regressions also account for heteroskedasticity, by using
robust variance-covariance estimators.
All statistical analyses have been performed by using Stata/MP 16 for
Windows.