Details of the Comparative COVID-19 Research

The unprecedented nature and scope of the crisis caused by a factor exogenous to the political systems of countries, it prompted reflection on why some managed the pandemic more effectively than others — what actions and public policies they implemented, and what consequences followed. However, to a lesser extent, studies have explored the underlying factors behind these actions — in other words, why governments acted as they did, or failed to act as expected, despite recommendations from national and international organizations with recognized expertise in managing epidemics, and despite evidence of what appeared to be more effective responses.

The research presented on this website, supported by the Brazilian Federal Scientific Agency – CNPq -, aims to understand the factors behind government actions and policies during the pandemic. It adopts an empirical and analytical approach to better understand how different responses were shaped. To this end, a comparative study was conducted across 14 Latin American countries, focusing primarily on government actions in managing the pandemic — not only in the health sector, but also in terms of non-pharmaceutical interventions, socioeconomic support measures, and policies for economic recovery.

Secondly, the project intended to identify effects of governmental actions on the pandemic trajectory and on society, particularly epidemiological effects and socioeconomic effects, in this case, only descriptively, without building causal models, based on the assumption of diversity and complexity of factors influencing the greater or lesser success in containing deaths and increased poverty as a result of the pandemic, which escaped the scope of the research.

The assumption is that the pandemic trajectory results from choices, but within certain institutional conditions. In explaining governmental actions in pandemic management – the primary focus of this research -, we identify four sets of factors that potentially influence these public policy decisions:

These economic and institutional conditions provide different types of resources and demands to governments. But still, they are resources that can be mobilized in different ways, being able to be used efficiently or neglected, in addition to always having some elasticity in times of crisis, based on governmental decisions and actions.

To test this model, a comparative study of 14 Latin American countries was carried out, for which there is available and comparable data for all dimensions of the analytical model, which are: Argentina, Bolivia, Brazil, Chile, Colombia, Costa Rica, Ecuador, El Salvador, Mexico, Panama, Paraguay, Peru, Dominican Republic and Uruguay. Cuba, Guatemala, Haiti, Honduras, Nicaragua and Venezuela were excluded, due to unavailability of data in the considered variables.

The elements that compose the research can be understood as factors and conditions that interact in different ways across various national contexts. For each variable and or dimension of the research, indicators were defined, from accessible public sources and with data available for the countries in the sample. For comparison and hierarchization of countries and for aggregation of diverse indicators, original composite indices were created alongside widely recognized existing indices.

The table below presents the values of the constructed indexes for all explanatory dimensions, as well as for the phenomenon under examination, namely government actions (IAG). The indexes range from 0 to 1, where 1 represents the most favourable value or optimal situation, and 0 represents the least favourable. The values are organized in descending order according to the IAG score.”

For the explanatory dimensions HDI, DEM, and HSS, the reference year was 2019, which precedes the onset of the pandemic. This choice sought to capture the situation of countries prior to its advent. For the calculation of the IAG, governmental measures undertaken in 2020 and 2021 were considered, since from that point onward cross-country differences tend to diminish with the arrival of vaccines. The indicator concerning governments’ alignment with WHO recommendations was constructed based on their stance during the pandemic. Finally, the IBS was derived from statistical procedures detailed in a specific technical note.

To test the model, the fuzzy-set QCA method was applied, confirming the validity of the explanatory framework and identifying different configurations leading to the desired outcome—namely, more robust governmental actions.

The results are detailed in the research report, which can be accessed here. We highlight the main ones below:

The research also sought to identify some of the effects of the pandemic and their variations across the countries under study. The absence of comparable data for all countries significantly restricted the analysis, particularly the possibility of causal attribution of these effects, rendering the assessment primarily descriptive. This limitation is reinforced by the difficulty of mobilizing a causal model for each type of effect, since each effect is presumably shaped by a distinct set of factors beyond governmental actions in managing the pandemic—the central focus of this study—which would make the research far more complex than its original objectives allowed.

The most dramatic consequence of the pandemic was mortality resulting from the disease and its complications, which varied across countries due to several factors: some associated with governmental measures to confront the pandemic, others related to the health conditions of the population and its age composition, given that older adults constituted the main risk group. To identify mortality specifically attributable to Covid-19, the indicator of “excess deaths” was employed. This measure compares the number of deaths during the pandemic with the average in preceding years, with excess mortality estimated by the ratio between observed and expected deaths. This procedure adjusts mortality figures to the characteristics of each country, such that the excess may be interpreted as resulting from the pandemic, under the assumption that other factors remained constant (Correia, 2021). The results were further standardized into indices, where a value of one corresponds to the best outcome (lower excess mortality) and zero to the worst outcome (higher excess mortality).

To identify the socioeconomic effects of the Covid-19 pandemic, the indicators employed were extreme poverty, poverty, overall unemployment rate, and the Gini Index. The first two capture the direct effects on the population stemming from restrictions on mobility, which led to the closure of all non-essential economic activities. Most governments implemented economic support measures—both for firms, in order to prevent unemployment, expand credit, and defer debt payments, and directly for the most vulnerable populations. However, these measures varied in scope and duration, depending on countries’ fiscal capacity as well as political choices, and proved insufficient to counterbalance the slowdown in economic activity. The Gini Index, in turn, measures income inequality and was included under the assumption that inequality increased during the most critical phase of the pandemic.

All selected countries—except for the Dominican Republic, which remained virtually stable—experienced an increase in unemployment during the first year of the pandemic (2020). In the second year (2021), all registered a recovery marked by declining unemployment, though without returning to 2019 levels. Brazil, which had the highest unemployment rate in 2019, was overtaken during the pandemic years by Colombia and Costa Rica, both of which were more severely affected. With the exception of Mexico, in every other country unemployment affected more women during the pandemic period.

The study also assessed the evolution of inequality during the pandemic. An analysis of changes in the Gini Index between 2019 and 2020 revealed that inequality increased in six countries (Bolivia, Colombia, El Salvador, Ecuador, Peru, and Uruguay) and declined slightly in five (Argentina, Brazil, Costa Rica, Paraguay, and the Dominican Republic), while for Chile, Mexico, and Panama such comparison was not possible. In 2020, therefore, no clear pattern emerges. Comparing 2019 and 2021, only four countries registered greater inequality in 2021 than in the pre-pandemic period. These findings suggest that the pandemic tended to heighten inequality in its first year in just over half of the countries analysed but did not produce severe effects on income inequality after two years in most cases. This outcome may reflect the mitigating impact of economic support measures adopted by nearly all governments, which to some extent helped sustain the income of those already in lower-income groups. Inequality—which was already high across the region—remains elevated, though it shows signs of rebalancing after the initial pressure of the pandemic.