Government Effectiveness and the COVID-19 Pandemic

Abstract

The COVID-19 pandemic threatens to derail progress achieved in sustainable development. This study investigates the effectiveness of government policy responses to the COVID-19 pandemic, namely the number of deaths. Using the Oxford COVID-19 Government Response Tracker (OxCGRT) dataset for a global sample of countries between March and September 2020, we find a non-linear association between government response indices and the number of deaths. Less stringent interventions increase the number of deaths, whereas more severe responses to the pandemic can lower fatalities. The outcomes are similar for a sample of countries disaggregated by regions. These findings can be informative for policymakers in their efforts to mitigate the spread of the virus and save lives.

Full text

sustainability
Article
Government Effectiveness and the COVID-19 Pandemic
Carolyn Chisadza *,† , Matthew Clance †and Rangan Gupta †


Citation: Chisadza, C.; Clance, M.;
Gupta, R. Government Effectiveness
and the COVID-19 Pandemic.
Sustainability 2021,13, 3042. https://
doi.org/10.3390/su13063042
Academic Editor: Mbodja Mougoué
Received: 13 February 2021
Accepted: 7 March 2021
Published: 10 March 2021
Publisher’s Note: MDPI stays neutral
with regard to jurisdictional claims in
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iations.
Copyright: © 2021 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
Department of Economics, Hatfield Campus, University of Pretoria, Lynnwood Road, Pretoria 0002, South Africa;
matthew.clance@up.ac.za (M.C.); rangan.gupta@up.ac.za (R.G.)
*Correspondence: carolyn.chisadza@up.ac.za
† These authors contributed equally to this work.
Abstract:
The COVID-19 pandemic threatens to derail progress achieved in sustainable development.
This study investigates the effectiveness of government policy responses to the COVID-19 pandemic,
namely the number of deaths. Using the Oxford COVID-19 Government Response Tracker (OxCGRT)
dataset for a global sample of countries between March and September 2020, we find a non-linear
association between government response indices and the number of deaths. Less stringent inter-
ventions increase the number of deaths, whereas more severe responses to the pandemic can lower
fatalities. The outcomes are similar for a sample of countries disaggregated by regions. These findings
can be informative for policymakers in their efforts to mitigate the spread of the virus and save lives.
Keywords: COVID-19 pandemic; OxCGRT; policies
1. Introduction
The risks to sustainable development progress are significantly increased during
health pandemics. Such crises make the development, implementation, and effectiveness
of national intervention policies critical for alleviating the adverse effects on society. The
COVID-19 pandemic has already resulted in widespread economic and social implications
across the world, with close to 83 million cases recorded and just under 2 million deaths
reported as of (https://www.worldometers.info/coronavirus/, accessed on 29 December
2020).
For many governments in the world, the primary mandate became the preservation
of lives, which entailed introducing several interventions to bring down the COVID-
19-related infections and deaths. Such interventions, among others, included national
lockdowns, travel restrictions, quarantine and isolation for the infected, social distancing,
and wearing of personal protective equipment (PPE), such as face masks. Given the various
interventions that have been implemented, it is important to provide initial evaluations of
the current policy responses that are in place to determine their efficacy in reducing the
loss of lives and mitigating the pandemic.
The purpose of this study is twofold. First, we investigate the effects of the combined
government policy responses to the pandemic to determine if the policies were effective
in reducing the number of deaths. Second, we disaggregate the government responses
to determine which of the policies were most effective. Using the Oxford COVID-19
Government Response Tracker (OxCGRT) dataset [
1
] for a global sample of countries
between the March and September 2020, we observe a non-linear effect of government
responses on the number of deaths. The findings suggest that the number of deaths
increases when the implementation of policies is less strict. However, as the policies become
stricter, the number of deaths start to decline. The disaggregation of the government policy
responses reveals that these results are largely driven by policies on containment and
health.
As we conduct this study, several countries in the world are entering second waves
of the pandemic. Even more disconcerting is that pandemics, such as that of COVID-19,
Sustainability 2021,13, 3042. https://doi.org/10.3390/su13063042 https://www.mdpi.com/journal/sustainability

Sustainability 2021,13, 3042 2 of 15
can pose serious threats in undoing progress achieved towards sustainable development
in many countries, especially in developing ones, which often struggle with capacity and
capabilities to respond effectively. According to the Global Economic Prospects report, the
economic damage from the COVID-19 pandemic represents the largest economic shock that
the world has experienced in decades [
2
]. In addition, the COVID-19 shock has exacerbated
the growth and development challenges that many developing countries were already
facing prior to the crisis [
2
]. This highlights the necessity for policy responses that will
reduce the adverse consequences and prevent further delay in progress towards sustainable
development. It is therefore imperative to understand the mechanisms that can slow down
the spread of the virus in order to alleviate the pressure on health systems and government
resources, as well as to maintain the delicate balance between saving lives and protecting
livelihoods.
1.1. Related Literature
Although the COVID-19 pandemic is a new virus with limited evidence in the litera-
ture, we can draw some insights from previous global pandemics that have occurred. From
as early as the Black Death in the fourteenth century, which almost halved the European
labor force, pandemics in general imply a huge cost in terms of economic activity. For
example, the authors of [
3
] found that pandemics dating back to the fourteenth century
negatively affected the real interest rates for years after the pandemics ended. The Spanish
flu of 1918–1920 reduced real per capita Gross Domestic Product (GDP) by 6% for affected
countries [
4
]. According to [
5
], the Severe Acute Respiratory Syndrome (SARS) in 2003
caused significant economic costs associated with excessive preventative interventions by
some governments in the affected countries, despite the small death toll. Moreover, the
authors of [
6
] showed that the Ebola virus decreased the competitiveness of Africa, particu-
larly in the West region, as a tourist destination during its peak period. Findings from [
7
]
showed that the HIV/AIDS pandemic impacted labor supply through increased mortality
and morbidity, as well as reducing labor productivity, more so in Africa. Currently, the
World Economic Outlook Update on the impact of COVID-19 reports that the 2021 global
growth is projected at 5.4%, about 6.5 percentage points lower than in the pre-COVID-19
projections of January 2020 [8].
While the pandemics may be different in nature and severity, the objective of saving
lives remains the same. As such, the role of the state becomes pivotal in periods of
health-related disasters. In the wake of the SARS pandemic, a report by [
9
] highlighted
the importance of the state remaining transparent given that the market structures were
unsupportive and inefficient in dealing with SARS at the time of the outbreak. They
maintained that managing uncertainty can ease some of the disruptions to the economic
activity. Similarly, the authors of [
10
] found that higher levels of knowledge about the
SARS disease indicated higher levels of public trust in Singapore. Such lessons learned
from previous pandemics can be valuable today in government responses to the COVID-19
outbreak. Most governments have implemented mixed approaches that combine, among
others, awareness campaigns in media, health notices in all shops, welfare programs for
the poor, income support for households incurring lost salaries, testing and contact tracing,
closure or reduced capacity of public areas that involve crowds (e.g., parks, nightclubs,
restaurants), and support for frontline health workers (i.e., provision of PPE, ventilators,
and field hospitals to accommodate COVID-19 patients). However, not only is it necessary
for governments to remain transparent in the implementation of these interventions,
but there also needs to be a collective response from the community at large to these
interventions if we hope to flatten the COVID-19 curve for the number of recorded deaths,
as seen in Figure 1.

Sustainability 2021,13, 3042 3 of 15
Figure 1.
Figure 1shows the daily count of cases and deaths recorded across the world. Source:
Oxford COVID-19 Government Response Tracker (OxCGRT) dataset.
2. Materials and Methods
To test the impact of government policy responses on the number of COVID-19-related
deaths, we estimate the following Poisson model:
E[Vit |xit,φi,eit]=exp(γ+αzit +βxit +eit), (1)
where
Vit
is the number of deaths per million in country
i
,
zit
is a vector of the main
explanatory variables,
xit
is a vector of control variables, and
eit
is unobserved regional
heterogeneity. We also estimate dynamic regressions using
Vi,t−1
to account for the persis-
tence of deaths. We use the Poisson model because the dependent variable, the number of
deaths, is a non-negative count variable.
The dependent variable is the daily count of total deaths attributed to COVID-19
per million people. The data were obtained from the COVID-19 Data Repository by the
Center for Systems Science and Engineering (CSSE) at Johns Hopkins University (JHU
CSSE COVID-19 Data) [
11
]. We used data recorded from March to September 2020 for
a global sample of 144 countries. During this period, the COVID-19 pandemic affected
most countries around the world, requiring governments to implement various levels of
interventions to slow down the rate of infection.
The main explanatory variables are collected by the OxCGRT [
1
]. The data track
governments’ policies and interventions across a standardized series of indicators and
creates composite indices to measure the extent of these responses. For the purpose of this
study, we use the government response index, as well as the sub-indices that make up the
composite government response index, namely the economic support index, containment
and health index, and the stringency index. The economic support index shows how much
economic support has been made available to households (such as income support to
people who lost their jobs or cannot work and debt relief, e.g., freezing loan repayments).
The containment and health index indicates how many and how strict the measures for
containing the virus and protecting citizens’ health are. These measures combine lockdown
restrictions and closures (schools, workplace, public gatherings, public transport, and
local and international travel) with health measures (testing policy, contact tracing, public
information campaigns, and investments in vaccines and healthcare). The stringency
index records the strictness of “lockdown style” for closure and containment policies that
primarily restrict people’s behavior. The overall government response index records how
the responses of countries have varied over all indicators, therefore capturing the full range
of government responses. All of these indicators measure policies on an ordinal scale of
severity, with 0 being the least severe and a higher number being the most severe.

Sustainability 2021,13, 3042 4 of 15
The control variables measure other social and economic factors that can affect the
number of deaths during the pandemic. The number of hospital beds per thousand peo-
ple was logged and obtained from the World Bank and national governments’ statistics.
The healthcare systems come under strain during pandemics, especially the capacity at
hospitals [
12
]. According to [
13
], existing health inequalities in countries due to insuffi-
cient health capacity can affect the vulnerable people. We therefore expect the increased
availability of hospital beds to be negatively correlated with the number of deaths.
The Gross Domestic Product (GDP) at purchasing power parity (constant 2011 interna-
tional dollars) was taken from the World Development Indicators (WDIs) and logged. The
authors of [
14
] found that the poverty rate is positively associated with a higher probability
of fatality. We expect wealthier countries to have the resources to provide early preventative
measures to reduce the number of deaths.
The median age of the population was obtained from the United Nations Population
Division. People above the age of 65 years are more susceptible to the COVID-19 virus
than the younger population [
14
,
15
], although unhealthy behavior, such as obesity and
smoking, can make the younger population equally vulnerable to the virus. We also control
for underlying health conditions by including the percentage of the population aged 20 to
79 with diabetes prevalence from the WDIs. Findings from [
16
,
17
] show that people with
pre-existing health conditions and non-communicable diseases (NCDs) are associated with
increased incidence of virus and mortality rate.
3. Results and Discussion
The initial results in Table 1show positive associations for the overall government
response index and its sub-indices with the number of deaths.
Table 1. Government effectiveness.
(1) (2) (3) (4)
Total Deaths
per million
Total Deaths
per million
Total Deaths
per million
Total Deaths
per million
Govt Response 0.0221 ∗∗∗
(0.0019)
Economic Support 0.0249 ∗∗∗
(0.0032)
Containment Health 0.0178 ∗∗∗
(0.0017)
Stringency 0.0130 ∗∗∗
(0.0016)
ln(GDPpc) 0.2500 ∗∗ 0.2058 ∗0.2956 ∗∗∗ 0.3266 ∗∗∗
(0.1072) (0.1246) (0.1056) (0.1050)
ln(Hospital Beds per 1000) 0.0445 0.0838 0.0265 0.0444
(0.1548) (0.1668) (0.1560) (0.1463)
Diabetes prevalence 0.0124 0.0260 0.0063 0.0040
(0.0210) (0.0242) (0.0209) (0.0202)
Median Age 0.0376 ∗∗ 0.0279 0.0393 ∗∗ 0.0400 ∗∗
(0.0174) (0.0179) (0.0176) (0.0172)
Total Deaths per milliont−10.0057 ∗∗∗ 0.0050 ∗∗∗ 0.0059 ∗∗∗ 0.0061 ∗∗∗
(0.0007) (0.0006) (0.0007) (0.0007)
Chamberlain Yes Yes Yes Yes
LogLik −243,301.674 −220,033.856 −254,352.726 −263,453.359
Obs 27,796 27,804 27,796 27,809
Coefficients reported. Robust standard errors in parentheses. ∗p<0.10, ∗∗ p<0.05, ∗∗∗ p<0.01
.
However, we observe statistically significant non-linear associations with the number
of deaths when we include the squared terms in Table 2. The findings suggest that the
implementation of less strict intervention measures is not effective in reducing the number
of deaths, whereas interventions at higher levels of severity reduce deaths. In addition,

Sustainability 2021,13, 3042 5 of 15
the authors of [
18
] found that the greater the strength of government interventions is at an
early stage, the more effective the interventions are in decreasing or reversing the mortality
rate. Findings from [
19
] also suggest that higher government stringency is a key predictor
for the cumulative number of cases. Therefore, quick and early action by the government
in imposing strict measures is important in slowing down the spread of the virus.
Table 2. Government effectiveness—non-linear results.
(1) (2) (3) (4)
Total Deaths
per million
Total Deaths
per million
Total Deaths
per million
Total Deaths
per million
Govt Response 0.1258 ∗∗∗
(0.0139)
Govt Response Sq. −0.0009 ∗∗∗
(0.0001)
Economic Support 0.0662 ∗∗∗
(0.0118)
Economic Support Sq. −0.0004 ∗∗∗
(0.0001)
Containment Health 0.1205 ∗∗∗
(0.0172)
Containment Health Sq. −0.0009 ∗∗∗
(0.0002)
Stringency 0.0874 ∗∗∗
(0.0111)
Stringency Sq. −0.0007 ∗∗∗
(0.0001)
ln(GDPpc) −0.0354 0.1693 0.0682 0.1732
(0.1188) (0.1303) (0.1180) (0.1085)
ln(Hospital Beds per 1000) 0.0927 0.0582 0.0509 0.0965
(0.1393) (0.1672) (0.1407) (0.1331)
Diabetes prevalence 0.0177 0.0231 0.0040 −0.0024
(0.0189) (0.0279) (0.0187) (0.0182)
Median Age 0.0572 ∗∗∗ 0.0313 ∗0.0601 ∗∗∗ 0.0525 ∗∗∗
(0.0174) (0.0175) (0.0174) (0.0160)
Total Deaths per milliont−10.0051 ∗∗∗ 0.0049 ∗∗∗ 0.0050 ∗∗∗ 0.0050 ∗∗∗
(0.0006) (0.0006) (0.0005) (0.0006)
Chamberlain Yes Yes Yes Yes
LogLik −210,701.111 −201,951.388 −215,056.338 −226,469.993
Obs 27,796 27,804 27,796 27,809
Coefficients reported. Robust standard errors in parentheses. ∗p<0.10, ∗∗ p<0.05, ∗∗∗ p<0.01
.
In terms of the sub-indices, we observe that the containment and health index has a
relatively larger coefficient compared to the economic support and stringency indices. This
finding implies that a combination of interventions related to a strict lockdown environment
and public awareness (such as closures of schools and workplaces, cancellations of public
events, travel restrictions, keeping the public informed, testing and contact tracing) was
most likely a more effective measure of slowing down the spread of the virus and the
related number of deaths. We observe similar non-linear results for the total number of
COVID-19-related cases. These results are reported in Appendix Aunder Table A3. The
control variables are mostly statistically insignificant, apart from the age of the population,
which is in line with [
14
], who found that older people are at a high risk of COVID-19-
related deaths. The lagged dependent variable is positive and significant, indicating the
persistence of fatalities. We also estimate Equation (1) with a two-step generalised method
of moments GMM for Poisson models. The effects are similar to those of the base Poisson
model and the conclusions in the paper remain unchanged. The results are available upon
request.

Sustainability 2021,13, 3042 6 of 15
3.1. Additional Analysis
As an additional analysis, we split the sample of countries by regions as classified
by “Our World in Data” geographical locations. We combined North and South America
together, as well as Asia and Oceania. In Table 3, we observe non-linear results that are
similar to our main findings for the overall government response index across the different
global regions.
Table 3. Regional effects—Overall government response index.
(1) (2) (3) (4)
Africa Asia Europe Americas
Total Deaths per million
Govt Response 0.1434 ∗∗∗ 0.1303 ∗∗∗ 0.1208 ∗∗∗ 0.1986 ∗∗∗
(0.0353) (0.0229) (0.0148) (0.0655)
Govt Response Sq. −0.0013 ∗∗∗ −0.0010 ∗∗∗ −0.0009 ∗∗∗ −0.0014 ∗∗∗
(0.0003) (0.0002) (0.0001) (0.0005)
ln(GDPpc) −0.0003 0.3606 −0.0908 −0.0429
(0.2908) (0.2430) (0.2191) (0.4063)
ln(Hospital Beds per 1000) 0.2113 0.1549 −0.2944 0.2144
(0.1817) (0.3220) (0.2105) (0.3721)
Diabetes prevalence 0.0264 0.0535 0.0383 0.0004
(0.0280) (0.0651) (0.0519) (0.0590)
Median age 0.0061 −0.0748 ∗0.0155 −0.0207
(0.0419) (0.0423) (0.0430) (0.0487)
Total Deaths per milliont−10.0204 ∗∗∗ 0.0170 ∗∗∗ 0.0042 ∗∗∗ 0.0051 ∗∗∗
(0.0048) (0.0039) (0.0005) (0.0008)
Chamberlain Yes Yes Yes Yes
LogLik −17,505.631 −31,313.826 −77,022.193 −59,259.656
Obs 5940 9068 7911 4877
Coefficients reported. Robust standard errors in parentheses. ∗p<0.10, ∗∗ p<0.05, ∗∗∗ p<0.01.
While the sub-indices also show similar non-linear associations with the number of
deaths, we notice that the containment and health index in Table 4is again relatively larger
in coefficient size in relation to the other sub-indices across the regions (see Tables 5and 6).
Table 4. Regional effects—containment index.
(1) (2) (3) (4)
Africa Asia Europe Americas
Total Deaths per million
Containment Health 0.1554 ∗∗∗ 0.1162 ∗∗∗ 0.1065 ∗∗∗ 0.2119 ∗∗
(0.0351) (0.0192) (0.0189) (0.0842)
Containment Health Sq. −0.0013 ∗∗∗ −0.0009 ∗∗∗ −0.0008 ∗∗∗ −0.0015 ∗∗
(0.0003) (0.0002) (0.0002) (0.0006)
ln(GDPpc) 0.0992 0.4108 ∗0.0568 −0.0857
(0.2503) (0.2310) (0.2020) (0.4367)
ln(Hospital Beds per 1000) 0.1979 0.1211 −0.2535 0.0980
(0.1852) (0.3172) (0.2207) (0.3502)
Diabetes prevalence 0.0217 0.0452 0.0327 −0.0059
(0.0267) (0.0646) (0.0506) (0.0511)
Median age 0.0062 −0.0746 ∗0.0137 0.0116
(0.0385) (0.0445) (0.0422) (0.0502)
Total Deaths per milliont−10.0188 ∗∗∗ 0.0169 ∗∗∗ 0.0043 ∗∗∗ 0.0051 ∗∗∗
(0.0043) (0.0039) (0.0005) (0.0007)
Chamberlain Yes Yes Yes Yes
LogLik −16,756.492 −32,168.453 −82,180.944 −59,089.920
Obs 5940 9068 7911 4877
Coefficients reported. Robust standard errors in parentheses. ∗p<0.10, ∗∗ p<0.05, ∗∗∗ p<0.01.

Sustainability 2021,13, 3042 7 of 15
Table 5. Regional effects—economic index.
(1) (2) (3) (4)
Africa Asia Europe Americas
Total Deaths per million
Economic Support 0.0311 ∗∗ 0.0758 ∗∗∗ 0.0605 ∗∗∗ 0.0705 ∗∗∗
(0.0148) (0.0187) (0.0184) (0.0171)
Economic Support Sq. −0.0000 −0.0004 ∗∗∗ −0.0004 ∗∗ −0.0005 ∗∗∗
(0.0002) (0.0001) (0.0001) (0.0001)
ln(GDPpc) 0.0249 0.2816 0.1185 0.1717
(0.2319) (0.2427) (0.2200) (0.4066)
ln(Hospital Beds per 1000) 0.3672 ∗∗ 0.2462 −0.3895 ∗0.2154
(0.1758) (0.2980) (0.2219) (0.4065)
Diabetes prevalence 0.0002 0.0773 0.0677 −0.1004 ∗∗
(0.0296) (0.0651) (0.0525) (0.0490)
Median age 0.0169 −0.0896 ∗∗ −0.0107 −0.0318
(0.0491) (0.0430) (0.0444) (0.0474)
Total Deaths per milliont−10.0175 ∗∗∗ 0.0153 ∗∗∗ 0.0043 ∗∗∗ 0.0051 ∗∗∗
(0.0067) (0.0033) (0.0006) (0.0010)
Chamberlain Yes Yes Yes Yes
LogLik −16,801.643 −27,031.301 −77,990.057 −61,516.709
Obs 5948 9067 7911 4878
Coefficients reported. Robust standard errors in parentheses. ∗p<0.10, ∗∗ p<0.05, ∗∗∗ p<0.01.
Table 6. Regional effects—stringency index.
(1) (2) (3) (4)
Africa Asia Europe Americas
Total Deaths per million
Stringency 0.1246 ∗∗∗ 0.1090 ∗∗∗ 0.0726 ∗∗∗ 0.1727 ∗∗∗
(0.0256) (0.0184) (0.0116) (0.0587)
Stringency Sq. −0.0012 ∗∗∗ −0.0009 ∗∗∗ −0.0006 ∗∗∗ −0.0012 ∗∗∗
(0.0002) (0.0002) (0.0001) (0.0004)
ln(GDPpc) 0.1645 0.4787 ∗∗ 0.1722 −0.0436
(0.2404) (0.2157) (0.1950) (0.3371)
ln(Hospital Beds per 1000) 0.2283 −0.0155 −0.1337 0.2010
(0.1746) (0.3372) (0.2141) (0.3632)
Diabetes prevalence 0.0178 0.0240 0.0297 0.0408
(0.0242) (0.0605) (0.0494) (0.0379)
Median age 0.0284 -0.0620 0.0130 0.0625
(0.0505) (0.0506) (0.0402) (0.0499)
Total Deaths per milliont−10.0178 ∗∗∗ 0.0168 ∗∗∗ 0.0045 ∗∗∗ 0.0049 ∗∗∗
(0.0038) (0.0038) (0.0006) (0.0008)
Chamberlain Yes Yes Yes Yes
LogLik −16,061.114 −31,846.813 −91,586.176 −58,733.588
Obs 5940 9079 7911 4879
Coefficients reported. Robust standard errors in parentheses. ∗p<0.10, ∗∗ p<0.05, ∗∗∗ p<0.01.
The results by regions are somewhat unexpected. First, we expected some regional
variations in the government effectiveness in responding to the pandemic, especially
between poor regions, such as Africa, and wealthier regions, such as Europe. Second,
given that the virus was first detected in Asia, we also expected the results to only show
significant effects for those regions that were first affected by the pandemic, as their policies
should be progressively stringent in relation to those regions that were affected later.
However, it appears that the overall conclusion drawn from these findings is that it comes
down more to the severity of the intervention measures implemented, particularly the
closure and public awareness policies, which are key in slowing down the spread of the
virus and related deaths.

Sustainability 2021,13, 3042 8 of 15
We also specify the sample of countries by economic classifications in Table
??
: ad-
vanced (developed), emerging and least-developed countries. We used the United Nations
classification for the advanced and the least-developed countries [
20
]. We used the Morgan
Stanley Capital International (MSCI) Emerging Markets Index to classify the emerging
countries [21].
Table 7. Economic Classification - List of Countries
Advanced Emerging LDC
Australia Argentina Afghanistan
Austria Brazil Angola
Belgium Chile Bangladesh
Bulgaria China Benin
Canada Colombia Bhutan
Croatia Egypt Burkina Faso
Cyprus Hong Kong Burundi
Czech Republic India Cambodia
Denmark Indonesia Central African Republic
Estonia Jordan Chad, Comoros
Finland Korea DR of the Congo
France Kuwait Djibouti
Germany Malaysia Eritrea
Greece Mexico Ethiopia
Hungary Pakistan Gambia
Iceland Peru Guinea
Ireland Philippines Guinea-Bissau
Italy Qatar Haiti
Japan Russia Kiribati
Latvia Saudi Arabia Lao PDR
Lithuania Singapore Lesotho
Luxembourg South Africa Liberia
Malta Taiwan Madagascar
Netherlands Thailand Malawi
Norway Turkey Mali
New Zealand United Arab Emirates Mauritania
Poland Vietnam Mozambique
Portugal Myanmar
Slovakia Nepal
Slovenia Niger
Spain Rwanda
Sweden Sao Tome and Principe
Switzerland Senegal
United Kingdom Sierra Leone
United States Solomon Islands
Somalia
South Sudan
Sudan
Timor-Leste
Togo
Tuvalu
Uganda
Tanzania
Vanuatu
Yemen
Zambia
Developed countries are usually characterized by more advanced economies with de-
veloped infrastructure, developed capital markets, and higher standards of living. Emerg-
ing countries are those in the process of rapid growth and development, but they still
have lower incomes per capita and less-developed infrastructure, and are prone to high

Sustainability 2021,13, 3042 9 of 15
market volatility in currency, commodity prices, and domestic policies. Notably, emerg-
ing countries are moving away from their reliance on agriculture and the export of raw
materials towards industrialization. The least-developed countries, on the other hand,
are characterized by poor economic growth, rudimentary infrastructure, underdeveloped
capital markets, and low standards of living. Several countries in the emerging economies
have characteristics that could place them in more than one category; however, for the
purpose of this analysis, the groupings have been made mutually exclusive.
The results for the overall government response index in Table 8still show a non-
linear association between government effectiveness and the number of deaths across the
different economic classifications of countries. However, we observe that the effectiveness
of government responses is not as significant in reducing the number of deaths in the
least-developed countries compared to the advanced and emerging groups of countries.
These findings reveal that the least-developed countries may be struggling to respond
aggressively to the pandemic as compared to the richer countries. This may be due to
their weak economic capabilities. Least-developed countries, which are mostly situated
in Africa and East Asia, tend to rely heavily on bilateral and multilateral institutions to
boost their already limited domestic resources. The lockdowns and closures of borders
will have hampered economic activity that would have contributed to people’s livelihoods.
For example, trade restrictions and supply chain disruptions may exacerbate food secu-
rity, inequality, and poverty issues in the least-developed countries [
2
]. Such increased
risks to the economies can only serve to worsen economic growth and delay sustainable
development for vulnerable countries.
Table 8. Economic classification—overall government response index.
(1) (2) (3)
Advanced Emerging LDC
Total Deaths per million
Govt Response 0.1222 ∗∗∗ 0.2730 ∗∗∗ 0.0381
(0.0150) (0.0810) (0.0303)
Govt Response Sq. −0.0009 ∗∗∗ −0.0020 ∗∗∗ −0.0003
(0.0001) (0.0006) (0.0003)
ln(GDPpc) 1.0610 ∗∗∗ 0.9588 −1.0584
(0.3408) (0.6387) (0.7484)
ln(Hospital Beds per 1000) −0.5320 ∗0.3131 −0.4070
(0.2861) (0.3739) (0.3207)
Diabetes prevalence −0.0055 −0.0770 0.2122 ∗∗
(0.0558) (0.0627) (0.0981)
Median age 0.1254 ∗∗ −0.1380 0.0680
(0.0500) (0.1037) (0.1063)
Total Deaths per milliont−10.0042 ∗∗∗ 0.0051 ∗∗∗ 0.0841 ∗∗∗
(0.0004) (0.0011) (0.0280)
Chamberlain Yes Yes Yes
LogLik −64,247.637 −47,840.717 −7769.830
Obs 7561 5345 4969
Coefficients reported. Robust standard errors in parentheses. ∗p<0.10, ∗∗ p<0.05, ∗∗∗ p<0.01.
From the sub-indices, we observe that the effectiveness of economic policies and
health and containment policies is not significant in the least-developed countries (see
Tables 9and 10
), but what is driving the overall government response is the stringency
sub-index in
Table 11
. The stringency sub-index records the strictest measures of the
containment and closure restrictions for schools, workplaces, public gatherings, public
transport, intercity, interregional, and international travel, and staying at home. The
findings suggest that this type of policy response may be most effective in stemming the
number of deaths in the least-developed countries. However, governments would have to
factor in the loss to livelihoods in implementing such severe closures.
As robustness checks, we used 7- and 14-day moving averages, as well as the Ordinary
Least Squares (OLS) estimator. The overall conclusions of our findings remain consistent.
The results are available in Appendix Ain Tables A4–A6.

Sustainability 2021,13, 3042 10 of 15
Table 9. Economic classification—containment index.
(1) (2) (3)
Advanced Emerging LDC
Total Deaths per million
Containment Health 0.1001 ∗∗∗ 0.3239 ∗∗∗ 0.0306
(0.0178) (0.0860) (0.0225)
Containment Health Sq. −0.0007 ∗∗∗ −0.0024 ∗∗∗ −0.0003
(0.0002) (0.0006) (0.0002)
ln(GDPpc) 1.0972 ∗∗∗ 0.9287 −1.1951
(0.3328) (0.5924) (0.9703)
ln(Hospital Beds per 1000) −0.5033 ∗0.2324 −0.3695
(0.2845) (0.3180) (0.3816)
Diabetes prevalence −0.0104 −0.0878 0.2147
(0.0596) (0.0633) (0.1422)
Median age 0.1203 ∗∗ −0.1291 0.0239
(0.0493) (0.1003) (0.0854)
Total Deaths per milliont−10.0044 ∗∗∗ 0.0050 ∗∗∗ 0.0831 ∗∗∗
(0.0005) (0.0009) (0.0278)
Chamberlain Yes Yes Yes
LogLik −72,145.225 −45,344.604 −7789.105
Obs 7561 5345 4969
Coefficients reported. Robust standard errors in parentheses. ∗p<0.10, ∗∗ p<0.05, ∗∗∗ p<0.01.
Table 10. Economic classification—economic index.
(1) (2) (3)
Advanced Emerging LDC
Total Deaths per million
Economic Support 0.0846 ∗∗∗ 0.1064 ∗∗∗ 0.0151
(0.0092) (0.0230) (0.0172)
Economic Support Sq. −0.0005 ∗∗∗ −0.0007 ∗∗∗ −0.0001
(0.0001) (0.0002) (0.0002)
ln(GDPpc) 1.1074 ∗∗∗ 1.0170 −1.1742
(0.3450) (0.7477) (1.1239)
ln(Hospital Beds per 1000) −0.4767 0.2079 −0.1275
(0.3123) (0.6626) (0.2679)
Diabetes prevalence 0.0211 −0.0894 0.1178 ∗
(0.0452) (0.1051) (0.0703)
Median age 0.0982 −0.1579 −0.0472
(0.0651) (0.1906) (0.0704)
Total Deaths per milliont−10.0039 ∗∗∗ 0.0045 ∗∗∗ 0.0838 ∗∗∗
(0.0004) (0.0009) (0.0253)
Chamberlain Yes Yes Yes
LogLik −59,564.850 −43,714.656 −7741.987
Obs 7561 5345 4977
Coefficients reported. Robust standard errors in parentheses. ∗p<0.10, ∗∗ p<0.05, ∗∗∗ p<0.01.
Table 11. Economic classification—stringency index.
(1) (2) (3)
Advanced Emerging LDC
Total Deaths per million
Stringency 0.0667 ∗∗∗ 0.1734 ∗∗∗ 0.0508 ∗∗
(0.0108) (0.0352) (0.0246)
Stringency Sq. −0.0005 ∗∗∗ −0.0013 ∗∗∗ −0.0005 ∗∗
(0.0001) (0.0003) (0.0002)
ln(GDPpc) 1.1694 ∗∗∗ 0.8301 −1.2336
(0.3320) (0.6075) (1.1679)
ln(Hospital Beds per 1000) −0.3679 0.3416 −0.4268
(0.2838) (0.3340) (0.5771)
Diabetes prevalence −0.0129 −0.0481 0.2119
(0.0556) (0.0645) (0.1783)
Median age 0.1151 ∗∗ −0.1079 0.0431
(0.0500) (0.0994) (0.1095)
Total Deaths per milliont−10.0046 ∗∗∗ 0.0047 ∗∗∗ 0.0781 ∗∗∗
(0.0005) (0.0009) (0.0285)
Chamberlain Yes Yes Yes
LogLik −81,171.943 −48,069.052 −7737.934
Obs 7561 5347 4979
Coefficients reported. Robust standard errors in parentheses. ∗p<0.10, ∗∗ p<0.05, ∗∗∗ p<0.01.
4. Conclusions
The COVID-19 pandemic has necessitated quick action from policymakers across the
world to save lives. This study investigates the effectiveness of current government policy
responses to COVID-19-related deaths. We find that the overall government response

Sustainability 2021,13, 3042 11 of 15
index has a non-linear association with the number of deaths—driven by the containment
and health interventions—for the aggregated sample of countries. The number of deaths
increases with partially relaxed lockdown restrictions, but decreases with severe restrictions.
We observe similar non-linear outcomes when we disaggregate the sample by global
regions. We also find support for strict government lockdown policies in controlling the
number of cases related to COVID-19. However, when we split the sample of countries by
economic classifications, we find that effectiveness of the current government responses in
place is not significantly associated with reducing the number of deaths in least-developed
countries compared to developed and emerging countries. Only for the most severe
restrictions do we observe the number of deaths declining in least-developed countries.
As several countries enter the second wave of the pandemic, the valuable insights
drawn from this study and other related studies should not be taken lightly. The im-
plications of these findings emphasize the importance of maintaining strict containment
measures to slow the spread of the virus. However, the trade-off is that should the pan-
demic persist for a longer period, these prolonged restrictions will continue to disrupt
economic activities, reversing years of progress toward sustainable development goals.
Secondly, the weak economic growth of the developed countries due to the pandemic
is bound to have negative spillovers into the least-developed countries, who depend on
foreign financing and trade for exporting commodities. Pandemics tend to exacerbate
already existing health inequalities in society, and COVID-19 is no different. While the
primary goal for policymakers in the short term is to slow down the spread of the virus
and simultaneously mitigate the harm to the economy, in the long term, some thorough
policy reforms need to be implemented to avoid further economic disparities among the
population and delays in economic development. Therefore, it is also vital for policy
reforms to be targeted for the impoverished population to facilitate better self-care. These
reforms can include increasing programs for early detection and contact tracing, increasing
testing centers and public awareness, and investing in health services and equipment, such
as more beds, ventilators, and PPE. These reforms are key in the least-developed countries
that are struggling with resources, and should be structured so as to provide affordable
health service delivery. Consequently, global coordination of measures that are necessary
for attenuating the spread of the pandemic, such as strengthening health services and
supporting households and firms with stimulus packages, may provide us with the upper
hand in the battle over the COVID-19 crisis.
Author Contributions:
C.C., M.C., and R.G. contributed to the conceptualization, data analysis, and
design of the research. M.C. collated the data and prepared the tables and figures. C.C. drafted the
manuscript. All authors contributed to the revision of the manuscript. All authors have read and
agreed to the published version of the manuscript.
Funding: This research received no external funding.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement:
COVID-19 data were obtained from https://github.com/CSSEGISandData/
COVID-19, accessed on 2 September 2020.
Acknowledgments:
We acknowledge the assistance from our research assistant Anneri Oosthuizen
in the data analysis and obtaining relevant literature related to the study.
Conflicts of Interest: The authors declare no conflict of interest.
Appendix A
Appendix A.1
In Tables A1 and A2, we report the variable definitions and statistics. Table A3 reports
findings for total cases attributed to COVID-19 per million people, while
Tables A4 and A5
report results for the seven-day and 14-day moving averages. Table A6 shows the OLS

Sustainability 2021,13, 3042 12 of 15
estimator results. Our findings remain consistent with the main results reported in the
paper. Global region effects by total cases and seven- and 14-day moving averages, as well
as those using the OLS estimator, also remain robust. The results are available upon request
from the authors.
Table A1. List of variables and definitions.
Variable Description Source
Total deaths per million Total deaths attributed to COVID-19
per 1,000,000 people
COVID-19 Data Repository by the Center for
Systems Science and Engineering (CSSE) at
Johns Hopkins University
Govt Response Overall Government Response Index Oxford COVID-19 Government Response
Tracker, Blavatnik School of Government
Economic Support Economic Support Index Oxford COVID-19 Government Response
Tracker, Blavatnik School of Government
Containment Health Containment and Health Index Oxford COVID-19 Government Response
Tracker, Blavatnik School of Government
Stringency Stringency Index Oxford COVID-19 Government Response
Tracker, Blavatnik School of Government
GDPpc
Gross domestic product at purchasing
power parity (constant 2011 international
dollars), most recent year available
World Bank World Development Indicators,
source from World Bank, International
Comparison Program database
Hospital Beds per 1000 Hospital beds per 1000 people, most recent
year available since 2010
OECD, Eurostat, World Bank, national
government records and other sources
Diabetes prevalence Diabetes prevalence (% of population aged
20 to 79) in 2017
World Bank World Development Indicators,
sourced from International Diabetes
Federation, Diabetes Atlas
Median age Median age of the population, UN
projection for 2020
UN Population Division, World Population
Prospects, 2017 Revision
Table A2. Descriptive statistics.
Obs Mean Std.Dev. Min. Max.
Total Deaths per million 34,740 50.69 140.60 0.00 1237.55
Govt Response 33,966 55.32 24.02 0.00 96.15
Economic Support 33,458 40.41 32.61 0.00 100.00
Containment Health 34,091 58.06 25.41 0.00 100.00
Stringency 34,105 57.84 28.16 0.00 100.00
GDPpc 32,987 21,561.03 21,235.55 661.24 116,935.60
Hospital Beds per 1000 30,254 3.03 2.46 0.10 13.05
Diabetes prevalence 34,003 7.77 3.96 0.99 22.02
Median age 33,163 31.30 9.21 15.10 48.20
Sources: JHU CSSE COVID-19 Data, OxCGRT, World Bank, United Nations.
Table A3. Total cases per million—government effectiveness.
(1) (2) (3) (4)
Total Cases per million Total Cases
per million
Total Cases
per million
Total Cases
per million
Govt Response 0.1540 ∗∗∗
(0.0139)
Govt Response Sq. −0.0012 ∗∗∗
(0.0001)
Economic Support 0.0599 ∗∗∗
(0.0095)
Economic Support
Sq. −0.0004 ∗∗∗
(0.0001)
Containment Health 0.1323 ∗∗∗
(0.0117)
Containment Health
Sq. −0.0010 ∗∗∗
(0.0001)
Stringency 0.1046 ∗∗∗
(0.0102)
Stringency Sq. −0.0009∗∗∗
(0.0001)
ln(GDPpc) 0.0899 0.3071 ∗∗ 0.2341 ∗∗ 0.3188 ∗∗∗
(0.1153) (0.1344) (0.1139) (0.1179)
ln(Hospital Beds per
1000) 0.2305 ∗0.1948 0.1949 ∗0.2625 ∗∗
(0.1179) (0.1355) (0.1136) (0.1104)
Diabetes prevalence −0.0002 −0.0082 −0.0158 −0.0218
(0.0199) (0.0248) (0.0193) (0.0189)
Median age 0.0317 ∗∗ 0.0043 0.0311 ∗∗ 0.0203
(0.0145) (0.0154) (0.0148) (0.0141)
Total Cases per
milliont−10.0001 ∗∗∗ 0.0001 ∗∗∗ 0.0001 ∗∗∗ 0.0001 ∗∗∗
(0.0000) (0.0000) (0.0000) (0.0000)
Chamberlain Yes Yes Yes Yes
LogLik −6,849,649.631 −7,234,408.436 −7,149,392.860 −7,371,750.750
Obs 27,796 27,804 27,796 27,809
Coefficients reported. Robust standard errors in parentheses. ∗p<0.10, ∗∗ p<0.05, ∗∗∗ p<0.01.

Sustainability 2021,13, 3042 13 of 15
Table A4. Total deaths per million (seven-day moving average (MA)).
(1) (2) (3) (4)
Total Deaths (7-day
MA)
Total Deaths (7-day
MA)
Total Deaths (7-day
MA)
Total Deaths (7-day
MA)
Total Deaths (1 Week
MA)
Govt Response 0.1212 ∗∗∗
(0.0136)
Govt Response Sq. −0.0009 ∗∗∗
(0.0001)
Economic Support 0.0638 ∗∗∗
(0.0117)
Economic Support
Sq. −0.0004 ∗∗∗
(0.0001)
Containment Health 0.1180 ∗∗∗
(0.0171)
Containment Health
Sq. −0.0009 ∗∗∗
(0.0002)
Stringency 0.0863 ∗∗∗
(0.0112)
Stringency Sq. −0.0007 ∗∗∗
(0.0001)
ln(GDPpc) −0.0270 0.1757 0.0748 0.1785∗
(0.1186) (0.1303) (0.1177) (0.1082)
ln(Hospital Beds per
1000) 0.0866 0.0546 0.0459 0.0920
(0.1395) (0.1673) (0.1411) (0.1332)
Diabetes prevalence 0.0165 0.0223 0.0030 −0.0035
(0.0189) (0.0278) (0.0187) (0.0181)
Median age 0.0573 ∗∗∗ 0.0316 ∗0.0601 ∗∗∗ 0.0526 ∗∗∗
(0.0174) (0.0175) (0.0174) (0.0160)
Total Deathst−1(1
Week MA) 0.0050 ∗∗∗ 0.0048 ∗∗∗ 0.0050 ∗∗∗ 0.0049 ∗∗∗
(0.0006) (0.0006) (0.0005) (0.0006)
Chamberlain Yes Yes Yes Yes
LogLik −205,123.251 −197,363.853 −207,796.534 −218,102.169
Obs 27,008 27,016 27,008 27,021
Coefficients reported. Robust standard errors in parentheses. ∗p<0.10, ∗∗ p<0.05, ∗∗∗ p<0.01.
Table A5. Total deaths per million (14-day moving average).
(1) (2) (3) (4)
Total Deaths
(14-day MA)
Total Deaths
(14-day MA)
Total Deaths
(14-day MA)
Total Deaths
(14-day MA)
Govt Response 0.1195 ∗∗∗
(0.0134)
Govt Response Sq. −0.0009 ∗∗∗
(0.0001)
Economic Support 0.0624 ∗∗∗
(0.0112)
Economic Support
Sq. −0.0004 ∗∗∗
(0.0001)
Containment Health 0.1178 ∗∗∗
(0.0175)
Containment Health
Sq. −0.0009 ∗∗∗
(0.0002)
Stringency 0.0858 ∗∗∗
(0.0113)
Stringency Sq. −0.0007 ∗∗∗
(0.0001)
ln(GDPpc) −0.0221 0.1845 0.0788 0.1822 ∗
(0.1188) (0.1313) (0.1178) (0.1085)
ln(Hospital Beds
per 1000) 0.0761 0.0437 0.0364 0.0838
(0.1409) (0.1687) (0.1426) (0.1344)
Diabetes prevalence 0.0149 0.0211 0.0017 −0.0048
(0.0188) (0.0280) (0.0186) (0.0181)
Median age 0.0584 ∗∗∗ 0.0326 ∗0.0612 ∗∗∗ 0.0536 ∗∗∗
(0.0174) (0.0176) (0.0174) (0.0160)
Total Deathst−1(2
Week MA) 0.0049 ∗∗∗ 0.0048 ∗∗∗ 0.0048 ∗∗∗ 0.0048 ∗∗∗
(0.0005) (0.0005) (0.0005) (0.0005)
Chamberlain Yes Yes Yes Yes
LogLik −194,709.841 −188,724.385 −196,368.352 −206,054.168
Obs 26,058 26,063 26,058 26,067
Coefficients reported. Robust standard errors in parentheses. ∗p<0.10, ∗∗ p<0.05, ∗∗∗ p<0.01.

Sustainability 2021,13, 3042 14 of 15
Table A6. Ordinary Least Squares (OLS) results—government effectiveness.
(1) (2) (3) (4)
Total Deaths
per million
Total Deaths
per million
Total Deaths
per million
Total Deaths
per million
Govt Response −0.00359
(0.00687)
Govt Response Sq. 0.00022 ∗∗
(0.00009)
Economic Support 0.00846
(0.00651)
Economic Support
Sq. 0.00003
(0.00010)
Containment
Health 0.00617
(0.00714)
Containment
Health Sq. 0.00009
(0.00008)
Stringency 0.00618
(0.00630)
Stringency Sq. 0.00009
(0.00007)
ln(GDPpc) 0.11763 ∗0.20647 ∗∗∗ 0.15461 ∗∗ 0.17885 ∗∗∗
(0.07044) (0.07298) (0.06972) (0.06676)
ln(Hospital Beds
per 1000) −0.10087 −0.13248 ∗−0.11027 −0.08741
(0.07253) (0.07584) (0.07083) (0.06838)
Diabetes
prevalence −0.03441 ∗∗ −0.02933∗−0.03756 ∗∗ −0.03636 ∗∗
(0.01527) (0.01554) (0.01511) (0.01444)
Median age 0.01309 0.00403 0.01404 0.01222
(0.01088) (0.01198) (0.01033) (0.00997)
Total Deaths per
milliont−11.00222 ∗∗∗ 1.00193 ∗∗∗ 1.00233 ∗∗∗ 1.00248 ∗∗∗
(0.00140) (0.00151) (0.00140) (0.00137)
Chamberlain Yes Yes Yes Yes
Overall R2 0.9997 0.9997 0.9997 0.9997
Obs 27,796 27,804 27,796 27,809
Coefficients reported. Robust standard errors in parentheses. ∗p<0.10, ∗∗ p<0.05, ∗∗∗ p<0.01.
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