Covid Lockdowns and Public Administration

Public Administration Article

Abstract

The present study examines the relationship between governance quality and the effectiveness of lockdown measures in reducing the spread of COVID-19. The study uses a cross-sectional research design and includes a sample of 104 countries. The independent variable is governance quality, while the dependent variable is the effectiveness of lockdown measures in reducing the spread of COVID-19.

To analyze the results, the study employs various statistical methods, including correlation analysis, multiple regression analysis, and sensitivity analysis. The study also controls for potential confounding variables, such as economic development, health infrastructure, and population density.

The research question that the study seeks to answer is whether there is a significant relationship between governance quality and the effectiveness of lockdown measures in reducing the spread of COVID-19, after controlling for potential confounding variables.

Overall, the study finds a positive relationship between governance quality and the effectiveness of lockdown measures in reducing the spread of COVID-19. Countries with higher levels of governance quality were more effective in implementing lockdown measures and reducing the number of daily new COVID-19 cases. This relationship remained significant even after controlling for potential confounding variables.

The study has some limitations, including its cross-sectional design, the sample size and selection, and the use of publicly available data sources. Future research could address these limitations by using a longitudinal design, considering different measures of governance quality and effectiveness of lockdown measures, and including additional variables that could influence the relationship between these variables. Additionally, future research could explore how the relationship between governance quality and the effectiveness of lockdown measures might differ across different regions of the world or between different types of government systems.

Alfano, V., & Ercolano, S. (2021). Stay at home! Governance quality and effectiveness of

lockdown. Social Indicators Research, 159, 101-123. Impact Factor: 1.834 (2020)

Introduction

The article investigates the relationship between the quality of governance and the effectiveness of lockdown measures implemented in response to the COVID-19 pandemic. The authors use a cross-country analysis of data from 104 countries to examine the relationship between the quality of governance, as measured by the Worldwide Governance Indicators, and the effectiveness of lockdown measures, as measured by the reduction in the number of daily new COVID-19 cases.

The study found that countries with higher levels of governance quality, particularly those with stronger government effectiveness and control of corruption, were more successful in reducing the number of daily new COVID-19 cases through lockdown measures. The authors suggest that strong governance may be crucial in facilitating compliance with lockdown measures and effective implementation of public health policies.

Overall, the article provides insights into the importance of governance quality in responding to public health crises and highlights the need for policymakers to prioritize governance reforms to better prepare for future pandemics.

Responses to Major Variables

In this quantitative study, the authors collected data on the quality of governance and the effectiveness of lockdown measures from 104 countries and used statistical analysis to investigate the relationship between these variables. The article also mentions the use of regression analysis to control for potential confounding variables, which is a commonly used statistical technique in quantitative research. The article primarily focuses on examining the relationship between two major variables: the quality of governance and the effectiveness of lockdown measures in reducing the number of daily new COVID-19 cases. The authors collected data on these variables from 104 countries and used statistical analysis to investigate the relationship between them. Therefore, the descriptive questions in the study are appropriately focused on describing the distribution and characteristics of these variables, such as the average level of governance quality and the average effectiveness of lockdown measures across the 104 countries studied.

Additionally, the study’s researchers also sought to describe other variables that may influence the relationship between governance quality and the effectiveness of lockdown measures, such as the level of economic development or the level of health infrastructure in each country. Describing these variables helped control for potential confounding factors and provide a more accurate assessment of the relationship between governance quality and the effectiveness of lockdown measures.

Inferential Questions

It appears that the inferential questions are seeking to relate variables rather than compare groups. Specifically, the authors are interested in investigating the relationship between the quality of governance (as measured by the Worldwide Governance Indicators) and the effectiveness of lockdown measures (as measured by the reduction in the number of daily new COVID-19 cases).

The authors use statistical analysis, including FE analysis, to examine this relationship and control for potential confounding variables. By doing so, they are able to infer whether there is a significant relationship between governance quality and the effectiveness of lockdown measures.

Therefore, the inferential questions in this study are likely to be focused on determining the strength and significance of the relationship between governance quality and the effectiveness of lockdown measures, and identifying any potential moderators or mediators of this relationship.

Theory

The article does not explicitly state a theoretical framework that guides the inferential questions, but it is likely that the study is informed by theories related to public health, governance, and policy implementation.

For instance, the authors probably drew on theories related to the role of governance in promoting effective policy implementation and compliance with public health measures. They also likely drew on theories related to the social determinants of health and the impact of social, economic, and political factors on health outcomes.

Plus, the study was likely guided by theories related to the effectiveness of lockdown measures and the factors that influence their success, such as the timing, duration, and scope of the measures.

Thus, while the article does not explicitly state a theoretical framework, it is likely that the study is informed by relevant theories related to public health, governance, and policy implementation.

Positioning of Variables

The variables are positioned consistently from independent to dependent in the inferential questions. The study is primarily interested in investigating the relationship between the quality of governance (independent variable) and the effectiveness of lockdown measures (dependent variable) in reducing the number of daily new COVID-19 cases.

Therefore, the inferential questions in this study are likely to be focused on examining how changes in the independent variable (governance quality) are related to changes in the dependent variable (effectiveness of lockdown measures). For instance, the authors may ask whether countries with higher levels of governance quality are more effective in reducing the number of daily new COVID-19 cases through lockdown measures, or whether the relationship between governance quality and the effectiveness of lockdown measures is moderated by other factors such as economic development or health infrastructure.

By consistently positioning the variables from independent to dependent, the study is able to provide a clear and focused analysis of the relationship between governance quality and the effectiveness of lockdown measures in reducing the spread of COVID-19.

Data Source and Collection

According to the article, the data source for this study is secondary data collected from various sources. The authors collected data on the quality of governance and the effectiveness of lockdown measures from publicly available sources such as the Worldwide Governance Indicators, a ‘Novel Coronavirus Cases’ dataset compiled by the Johns Hopkins University Center for Systems Science and Engineering, and the World Bank. Additionally, the authors relied on ACAPS data from the ‘COVID-19: Government Measures’ dataset.

The authors used the Worldwide Governance Indicators to measure the quality of governance in each country, which is a well-established instrument that uses expert assessments to measure the quality of governance in six key areas: voice and accountability, political stability, government effectiveness, regulatory quality, rule of law, and control of corruption. The authors also used Johns Hopkins University dataset to measure the effectiveness of lockdown measures in each country, in lieu of an instrument that tracks and scores the stringency of government policies in response to the COVID-19 pandemic.

The sample in this study consists of 104 countries, which were selected based on data availability. The authors note that their sample is not necessarily representative of all countries in the world, but they believe that their sample is diverse enough to provide a meaningful analysis of the relationship between governance quality and the effectiveness of lockdown measures.

The scales of measurement used in this study vary depending on the variable being measured. The quality of governance is measured on a scale from -2.5 to 2.5, where higher scores indicate better governance quality. The effectiveness of lockdown measures is measured on a scale from 0 to 100, where higher scores indicate more effective lockdown measures.

The statistical tools used for analysis in this study included Feasible–Generalized Least Square, and a Fixed Effects (FE) estimator, which captures heterogeneity between countries.  Finally, they performed an Hausman test on this sample, which confirmed that a Fix Effects estimate is to be preferred to a Random Effects one.

FGLS is a statistical method used to estimate the parameters of a linear regression model when the errors are heteroscedastic and/or serially correlated. In this study, FGLS was used to address potential issues of heteroscedasticity and serial correlation that may arise due to the nature of the data.

FE estimator is a statistical method used to control for unobserved heterogeneity in panel data. In this study, FE estimator was used to capture heterogeneity between countries, which may have affected the relationship between governance quality and the effectiveness of lockdown measures. By controlling for unobserved heterogeneity, the study aimed to reduce potential biases and increase the accuracy of the estimates.

Finally, the study performed a Hausman test on the sample, which is a statistical test used to determine whether the FE estimator is preferable to the Random Effects (RE) estimator (Joshi & Wooldridge, 2019). The RE estimator assumes that the unobserved heterogeneity is uncorrelated with the regressors, while the FE estimator allows for correlation between the unobserved heterogeneity and the regressors.

The Hausman test is used to test the null hypothesis that the FE estimator is consistent and efficient, while the RE estimator is consistent but inefficient (Joshi & Wooldridge, 2019). If the null hypothesis is rejected, then the FE estimator is preferred over the RE estimator. In this study, the results of the Hausman test confirmed that the FE estimator is to be preferred to the RE estimator. This suggests that unobserved heterogeneity may be correlated with the regressors, and therefore, the FE estimator is more appropriate for this study.

Research Design

Based on the description in the article, this study used a cross-sectional research design. The study collected data on the quality of governance and the effectiveness of lockdown measures at a single point in time (between April and August 2020), for a sample of 104 countries.

The cross-sectional research design is a type of observational study that involves collecting data at a single point in time, without manipulating any variables. This design allows researchers to examine the relationships between variables and determine whether there are any patterns or trends.

In this study, a cross-sectional research design was used to examine the relationship between governance quality and the effectiveness of lockdown measures during the COVID-19 pandemic. The study collected data on the quality of governance and the effectiveness of lockdown measures for a sample of 104 countries between April and August 2020.

The use of a cross-sectional design in this study allowed the researchers to gather data from a large number of countries and to examine the relationship between governance quality and the effectiveness of lockdown measures at a single point in time. This is particularly relevant for the COVID-19 pandemic, where the situation was rapidly changing, and it was important to collect data at a specific point in time.

However, a limitation of a cross-sectional design is that it cannot establish causality. Because the data is collected at a single point in time, it is not possible to determine whether changes in one variable caused changes in another variable. Therefore, the relationship between governance quality and the effectiveness of lockdown measures observed in this study may be influenced by other variables that were not measured.

To analyze the results, the study employed various statistical methods as described above.

Overall, the study used a cross-sectional design and a variety of statistical methods to examine the relationship between governance quality and the effectiveness of lockdown measures in reducing the spread of COVID-19. The authors used rigorous methods to control for potential confounding factors and to test the robustness of their findings.

Findings, Limitations and Future Research

Findings

The study found that the quality of governance was positively associated with the effectiveness of lockdown measures in reducing the spread of COVID-19. Countries with higher levels of governance quality were more effective in implementing lockdown measures and reducing the number of daily new COVID-19 cases. This relationship remained significant even after controlling for potential confounding variables such as economic development, health infrastructure, and population density.