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Privatization and Pandemic: A Cross-Country Analysis of COVID-19 Rates and
Health-Care Financing Structures
Jacob Assa* and Cecilia Calderon
UNDP/HDRO
30 May 2020
Abstract
The outbreak of coronavirus and the infectious disease it causes (COVID-19) have taken different
paths around the world, with countries experiencing different rates of infection, case prevalence and
mortality. This simultaneous yet heterogenous process presents a natural experiment for
understanding some of the reasons for such different experiences of the same shock. This paper looks
at the privatization of healthcare as one key determinant of this pattern. We use a cross-section dataset
covering 147 countries with the latest available data. Controlling for per capita income, health
inequality and several other control variables, we find that a 10% increase in private health expenditure
relates to a 4.3% increase in COVID-19 cases and a 4.9% increase in COVID-19 related mortality.
Globalization also has a small positive effect on COVID-19 prevalence, while higher hospital capacity
(in beds per 1,000 people) is significant in lowering COVID-19 mortality. The findings suggest caution
regarding policies which privatize healthcare systems in order to boost efficiency or growth in the
short-run, as these reduce countries' long-term preparedness for dealing with pandemics.
* Corresponding author (jacob.assa@undp.org). The authors would like to thank Christina Lengfelder on an earlier
version of this paper.
1
1. Introduction
The global infection and mortality rates of COVID-19 have increased dramatically within the space
of a few months, from around 12,000 cases (and 259 deaths) on February 1 - mostly in China - to 5.2
million cases and 338,000 deaths by May - now mostly concentrated in North America and Western
Europe. Unlike previous epidemics or pandemics - such as SARS or TB - the latest data on the
coronavirus - which causes COVID-19 - show that richer countries have been hit as hard as, on
average, or harder than, poorer countries. The United States, Italy and Spain are among the countries
most affected, for example.
Given the enormous pressure the virus has put on national health systems, the structure of
funding for these systems is one potential explanatory factor for the different effects of this pandemic
across countries. The uneven speeds of spread of COVID-19 around the world and the differential
rates of infection, death and recovery present us with a natural experiment in this regard.
Given the drastic changes in health-care systems since the 1980s, private health expenditure
in many countries has increased faster than public spending on health, due to neoliberal policies such
as deregulation and privatization, usually promising to increase efficiency and economic growth. While
there are indeed some advantages to private health facilities such as reduced waiting times, there are
many concerns about quality of private health care, especially relating to the relative lack of regulation,
over-prescription of antibiotics or unnecessary treatments (Basu, Andrews, Kishire, Panjabi and
Stuckler 2012, Collyer and White 2011).
Much of the literature on the privatization of healthcare involves case studies, and there is thus
a lack of comparative studies across countries in this area. In this paper we look at structural
determinants of COVID19 rates across 147 countries, and link it to the privatization of healthcare
systems. Section 2 provides an overview of COVID-19’s differential impact, especially among the
richest countries. Section 3 surveys the debates in the literature on the relative strengths of private vs.
2
public health care. Section 4 presents our data and methodology, while Section 5 states our hypotheses
and predictions. Section 6 provides the results of OLS regressions of COVID-19 prevalence and
mortality on health care financing as well as several control variables. Section 7 concludes with a
discussion of policy implications for making health systems sustainable.
2. The Differential Spread of COVID-19
Covid-19 is an infectious disease caused by the most recently discovered strand of coronavirus. This
virus belongs to a family of viruses which can lead to respiratory infections in both humans and
animals. Mild infections include the common cold, while more severe illnesses include the Severe
Acute Respiratory Syndrome - SARS - as well as the Middle East Respiratory Syndrome - MERS -
(Chan et. al. 2020, Chen et. al. 2020, Guan et. al. 2020, Huang et. al. 2020, Li et. al 2020). The last two
resulted in 1632 deaths (Mahase 2020), combined, compared to almost 350,000 COVID-19 related
deaths as of the end of May 2020.
Covid-19 is transmitted between people via sneezing, coughing or speaking, or by touching
surfaces and objects with droplets from an infected person and then touching their nose, mouth or
eyes. At the time of writing, there is not yet a cure or preventive medication for COVID-19.
People with pre-existing conditions such as high blood pressure, diabetes, cancer, and heart
or lung disease) at any age are more likely than others to become seriously ill as a result of the virus,
as are older people and people with limited access to healthcare (Guan et. al. 2020, Huang et. al. 2020,
Zhou et. al. 2020).
While COVID-19 first appeared in Wuhan, China and peaked there in February, it then spread
to other regions rapidly, but differentially. As of 20 May 2020, there are 5.2 million confirmed cases
around the world, of which 2.3 million are in the Americas, 2 million in Europe, and the rest in the
eastern Mediterranean, South-East Asia, the Western Pacific and Africa.
3
Deaths from COVID-19 are approaching 350,000 at this point, but the death rates also vary
greatly across countries, whether measured per 1 million population or as a ratio of cases (i.e. the case
fatality ratio). Unlike previous epidemics or pandemics, the mortality rate at this point is highest in the
richer countries. Of the top 20 countries in terms of deaths per million population, 16 are OECD
members:
Table 1: Top 20 Countries in COVID-19 Mortality
Country Name
Income per capita
Deaths per million population
Death Ratio
Belgium
43,582
790
16.3
Spain
34,831
596
12.0
Italy
35,828
535
14.2
United Kingdom
40,522
526
14.4
France
39,556
430
15.7
Sweden
47,718
379
12.2
Netherlands
49,787
335
12.9
Ireland
70,855
318
6.5
United States
55,719
282
6.0
Switzerland
59,317
219
6.2
Luxembourg
96,793
174
2.7
Ecuador
10,412
164
8.3
Canada
44,078
163
7.5
Portugal
28,999
124
4.3
Germany
45,936
97
4.6
Denmark
48,419
96
5.0
Peru
12,794
92
2.9
Brazil
14,283
89
6.5
Iran (Islamic Republic of)
19,098
86
5.7
Austria
46,260
70
3.9
Source: COVID-19 deaths data from the Johns Hopkins University COVID-19 Dashboard (downloaded May 20, 2020).
As the table shows, however, there are also significant differences between the mortality rates
and case fatality rates among the rich countries. In particular, Canada, Portugal, Germany, Denmark
4
and Austria have far less COVID-19 related deaths - either per million or per cases - than the US, UK
and several other countries at the top of the list.
The difference in COVID-19 prevalence and mortality is especially stark between several
neighboring countries, which share not only a border but also a similar level of economic development
and some cultural affinities. As shown in Table 1 above, The U.S. has nearly double the mortality rate
of Canada (and more than double its prevalence rate). An even more dramatic comparison can be
made between Spain and Portugal, with the former showing nearly six times the mortality rates of the
latter. Belgium and France are another such example. In all these cases, it is the richer countries in per
capita terms that are the worst affected.
Given that the pandemic is still ongoing, as well as the speed of its spread and the heterogeneity
of public measures to contain it, there is not yet much cross-country research on the structural
determinants of COVID-19 prevalence and mortality. One recent study of 118 countries found that
the extent of a country’s globalization is positively related to the scale and speed of the virus in it, but
negatively to fatality rates (Zimmermann 2020). However, given the fact that many of the top 20
countries in Table 1 above are highly globalized, we find this explanation incomplete.
Instead, we look at the structure of healthcare systems - specifically the balance between
private and public expenditures on health care - as a potential explanatory variable for the international
heterogeneity in COVID-19 prevalence and mortality. A cursory look at the latest data shows, for
example, that per capita public expenditure on healthcare in Canada is nearly three times its per capita
private health care, while in the U.S. private health care expenditures per person are as high as public
health spending.
Previous research has found a significant and negative effect of healthcare privatization on
tuberculosis (TB) rates (Austin, DeScisciolo and Samuelsen 2016), although TB - unlike COVID-19 -
has been mostly non-existent in developed countries in recent decades. As we have seen, however,
5
COVID-19 affects nearly all countries in the world, and this paper therefore examines the implications
of health care privatization for the differential trends of this pandemic.
3. The Debate on Privatizing Healthcare
The 1980s and 1990s witnessed a rise in the support for and implementation of neoliberal policies -
often referred to under term “Washington Consensus” - which include deregulation and privatization
of previously state-owned sectors, liberalization of trade and financial markets, and an overall
withdrawal of the state from public service provision (Arrieta et al. 2011, Bundey 2014, Collyer &
White 2011, Larbi 1998, Maclean 2011, McMichael 2012).
These policies have been promoted by international financial institutions such as the IMF and
World Bank, especially following the debt crises in many developing countries, and partly justified as
a necessary step to increase economic growth and thus revenue for the payments of external debts.
These policies have often had adverse effects on domestic conditions, including water, sanitation and
healthcare (Herrera, 2014; Maclean 2011, Navarro et al. 2006, Shandra, Shandra, & London 2011,
Wilder & Lankao, 2006).
Some studies have linked neoliberal policies to a deceleration in life-expectancy increases in
poor countries (Cornia et al. 2009, Navarro 2002, 2007), while others link economic conditionality
policies to higher rates of various diseases such as tuberculosis (Austin 2015, Maynard et al., 2012,
Stuckler, King, & Basu, 2008). These studies, however, all look at neoliberal reforms broadly, and so
far, only one paper has specifically studied the role of healthcare privatization on rates of infectious
disease (Austin, DeScisciolo & Samuelsen 2016). Furthermore, there is lack of research on the effects
of privatization on health care outcomes across all countries - both developed and developing.
Overall, private and public health care systems have different strengths and weaknesses. Some
of the advantages of private health facilities in developing countries include shorter waiting times and
better interaction with staff compared to public facilities. Their disadvantages include lower accuracy
6
in diagnostics, lower adherence to medical management standards, lower-level staff (e.g. assistant
physicians, pharmacists or midwives rather than doctors), and sometimes over-prescription of
antibiotics (Basu et al. 2012, Das, Hammer, & Gbotosho et al. 2009, Leonard 2008, Lonnroth et al.
2001, Lu et al. 2010, Maclean 2011, Naterop & Wolffers 1999).
Public health expenditure and universal healthcare systems, by contrast, have been linked to
higher well-being in several studies (Anderson 2010, Bokhari et al. 2007, Dekker & Wilms 2010,
Oglobin 2011, Palmer 2014, Pfutze 2014).
Privatization also has distributional effects, as private clinics and doctors often charge user
fees which the poor cannot pay, a situation which deters some people from seeking medical testing
and treatment (Baker 2014, Basu et al. 2012, Blumenthal & Hsiao 2005, Herrera 2014, Navarro et al.
2006, Palmer 2014).
This adverse effect of privatizing health care on health outcomes is not limited to developing
countries. Many countries in transition from communism to a market-system have experienced mass
privatization of many sectors, including health-care. This has led to treatments becoming
unaffordable, denial of services or health insurance for people with pre-existing conditions, and a
decrease in people’s willingness to visit a doctor when ill (Balabanova et al. 2004, King and Stuckler
2007, Reiss et al. 1996).
This positive relationship between private health-care provision and health inequality is
confirmed by the latest data for 147 countries on inequality in life-expectancy (UNDP 2019) and the
ratio of private to public health expenditures (WHO 2020):
7
Figure 1: Inequality in Health (life-expectancy) and the Ratio of Private to Public Health Expenditures (ln)
This positive relationship between more privatized healthcare and inequality is critical in the
case of COVID-19, as this disease has an unequal impact on more vulnerable populations. First,
poorer people are more likely to suffer from chronic conditions and thus be at higher-risk of COVID-
19 mortality. Poorer people without medical insurance or the means to pay private health care fees
may also disregard social distancing in order to keep working, thus reducing the efficacy of control
measures (Ahmed et. al. 2020).
4. Predictions
We predict that the relative importance of private and public funding of health care systems plays a
major role in relation to the recent impacts of COVID-19 across countries. Literature on other
pandemics suggests that privatization weakens countries’ ability to provide sufficient preparedness to
and coping capacity for pandemics. We predict that higher private health expenditures relative to
8
public health expenditures are, ceteris paribus, related to a higher incidence (cases per million
population) and mortality (deaths per one million population) of COVID-19.
Figure 2 shows the association between the natural logarithm (ln)
1
of COVID-19 prevalence
(cases per million population) and the ln of private health expenditure
2
.
Figure 2: Covid-19 Prevalence (ln) and Private Health Expenditure (ln)
Note: covariates included in the regression are the ones from model 6 in Table 3 and they were held constant at the mean value. When
all the covariates are removed, the slope of the regression line is steeper, yet in both cases statistically significant.
Figure 3 shows the same for COVID-19 mortality (deaths per million population) and private
health expenditure (both in ln).
9
Figure 3: Covid-19 Mortality (ln) and Private Health Expenditure (ln)
Note: covariates included in the regression are the ones from model 7 in Table 4 and they were held constant at the mean value. When
all the covariates are removed, the slope of the regression line is steeper, yet in both cases statistically significant.
We also aim to understand the role of other factors on the differential impacts of COVID-19,
in particular on mortality. Based on the literature surveyed above, we hypothesize that countries with
older populations will have higher mortality rates, while health systems with better infrastructure (e.g.
more hospital beds relative to population size) would have lower mortality than others.
5. Methods and Variables
The sample includes 147 countries (see Appendix) for which the latest data on both the dependent
and independent variables were available from reliable international sources. The countries included
in this dataset account for 93% of the world population. To account for the effect of different health-
10
care financing structures (private vs. public) on COVID-19 prevalence rates, we use several ordinary
least squares (OLS) models.
The key dependent variables are COVID-19 prevalence (P) and mortality rates (M), defined
as follows:
Prevalence (P) = COVID-19 confirmed cases (C) / population (P) * 1,000,000
Mortality (M) = COVID-19 related deaths (D) / population (P) * 1,000,000
These variables are based on data for confirmed COVID-19 cases and deaths by country from
the Johns Hopkins University COVID-19 Dashboard (last accessed on 20 May 2020). The data on
cases were transformed into a rate of prevalence using data on total population for 2020
3
, where
COVID-19 prevalence is equal to the rate of the virus per one million people. The variable was further
transformed using the natural logarithm because the COVID-19 cases per million population indicator
follows a log-normal distribution. Since the original COVID-19 cases per million population variable
is highly skewed, the ln transformation helps diminish the effect of extreme values (outliers) and also
provides an intuitive interpretation of the regression coefficients (see Figure 4).
Figure 4: Distributions of Covid-19 Prevalence (ln) and Mortality (ln)
11
The main aim of this paper is to analyze how the financing structure of health care systems
affects COVID-19 trends in different countries. Therefore, the two key independent variables in the
analysis are Domestic private health expenditure (PVT-D) per capita in US$ and Domestic general
government health expenditure (GGHE-D) per capita in US$ from WHO (2020). To smooth the
effects of annual fluctuations, five year averages were used in both cases (2013-2017). Also, as the
distributions of these two types of health expenditures are skewed, they enter the regressions in
logarithm transformation (see Figure 5).
Figure 5: Distributions of private and public health expenditures, both in natural logarithm scale.
We also include several important control variables in all models to account for factors
identified in the literature, such as the level of economic development, urbanization, inequality,
globalization and democracy. For the mortality models we further add the percentage of population
over 65 years old and hospital capacity (beds per 1,000 people).
Economic development is measured using GDP per capita, PPP (constant 2011 international
$). We divide this indicator by 1,000 to make the interpretation of the regression coefficient more
intuitive, as thousand units of per capita GDP. Urbanization has also been identified as critical in the
12
rate of spread of infectious diseases, and we measure it using the percentage of the population living
in urban areas. Both these variables are from the World Bank (2020).
Globalization has been specifically identified as a key determinant of COVID-19 prevalence
and mortality (Zimmerman et. al. 2020). While we suspect that much of this effect will be captured by
our privatization variable, we nonetheless include an overall measure of globalization - the KOF index
(Gygli et. al. 2019) to account for this dimension. We also include a variable on democracy, given
some recent debate on the comparative abilities of states with different political regimes to cope with
spread of coronavirus. For example, Frey, Chenand Presidente (2020) found that governments more
democratically accountable to the citizenry were less strict in imposing lockdowns but were able to
reduce people’s mobility by 20% more. We measure democracy using the EIU Democracy Index (EIU
2019).
COVID-19 has been especially fatal for people over 65 years of age (Zhou et. al. 2020,
Hopman, Allegranzi & Mehtar 2020), so we include a variable on the proportion of the population
age 65 and over (United Nations 2020). We also include a variable on hospital capacity. This was
identified as a critical factor in affecting mortality, e.g. in the case of Italy, where a very high load of
cases overwhelmed the hospital system capacity and resulted in very high mortality rates (Onder, Rezza
& Brusaferro 2020). We measure this with the indicator Hospital beds per 1,000 people (World Bank
2020).
The analyzed baseline equation analyzed in this paper is therefore:
Coronavirus Variables i = α + β Health Expenditure Variables i + γ Xi + ε i
13
6. Results
The correlation matrix and summary statistics for all variables are shown in Table 2. At this stage, it
seems that COVID-19 prevalence is negatively correlated with health inequality, but positively with
all other variables.
Table 2: Correlation matrix and univariate statistics
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
(10)
(11)
(1) COVID-19 cases per million (ln)
1
(2) Per capita income (th)
0.688*
1
(3) Percentage urban
0.697*
0.667*
1
(4) Inequality in life expectancy (%)
-0.648*
-0.668*
-0.635*
1
(5) Private health expenditure (ln)
0.734*
0.758*
0.738*
-0.793*
1
(6) Public health expenditure (ln)
0.713*
0.807*
0.777*
-0.865*
0.910*
1
(7) Globalization (KOF)
0.693*
0.710*
0.655*
-0.866*
0.832*
0.875*
1
(8) Democracy (EIU)
0.394*
0.447*
0.424*
-0.592*
0.635*
0.673*
0.714*
1
(9) COVID-19 deaths per million (ln)
0.839*
0.587*
0.577*
-0.619*
0.740*
0.726*
0.731*
0.530*
1
(10) Population Age 65+ (percent)
0.555*
0.517*
0.473*
-0.758*
0.737*
0.763*
0.839*
0.698*
0.688*
1
(11) Hospital beds (per 1,000 people)
0.391*
0.398*
0.361*
-0.610*
0.511*
0.569*
0.593*
0.367*
0.388*
0.728*
1
Mean
5.44
20.07
61.10
14.07
4.86
5.06
65.95
5.70
2.23
9.66
3.02
S.D.
2.08
20.34
21.95
10.31
1.53
2.08
14.18
2.15
1.72
6.86
2.49
N=147; * p<0.01
Table 3 reports the OLS estimates of COVID-19 prevalence, starting with the baseline regression in
Model 1. At this stage only health inequality (%), the proportion of population living in urban areas
and GDP per capita (in thousands of PPP dollars) are included. This model already has significant
explanatory power (with an R2 of 0.598). It shows a small but statistically significant positive coefficient
for income and urbanization, and a negative coefficient for health inequality. To minimize the risk of
multicollinearity, we add further independent variables one by one in each subsequent model.
Models 2 and 3 add the two measures of health care expenditure - first private and then public.
As expected, the log of private health expenditure has a large and negative coefficient (statistically
significant), while public health expenditure has a much smaller coefficient which is also not
statistically significant. Model 2 predicts that a 10% increase in private health expenditure results in a
14
3.81% increase in COVID-19 cases. Health inequality also ceases to be significant from this point
onwards, presumably as its effects are captured by the private health expenditure variable.
Model 4 combines the two measures of health expenditure together, and the coefficients have
the same sign, size and significance as above. Model 5 adds the KOF globalization index, which is not
significant on its own. But when controlling for democracy in model 6, globalization has a small
positive (and significant) coefficient, confirming the insight of Zimmerman et. al. (2020) that
globalization matters. However, globalization in this model explains a very small part of COVID-19
prevalence, while private health expenditures explain the most.
The most saturated regression, Model 6, predicts that a 10% increase in private health
expenditure results in a 4.85% increase in COVID-19 cases.
Table 3. OLS regression predicting COVID-19 prevalence - Cases per 1,000,000 (ln)
COVID-19 prevalence - Cases per 1,000,000 (ln)
(1)
(2)
(3)
(4)
(5)
(6)
Per capita income (thousands)
0.031***
0.023***
0.030***
0.028***
0.027***
0.022**
(0.006)
(0.006)
(0.007)
(0.007)
(0.007)
(0.008)
Percent urban
0.034***
0.026**
0.033***
0.030***
0.031***
0.028**
(0.008)
(0.008)
(0.008)
(0.008)
(0.009)
(0.009)
Health inequality
-0.044**
-0.02
-0.042
-0.039
-0.019
-0.01
(0.016)
(0.019)
(0.026)
(0.024)
(0.023)
(0.023)
Private health expenditure (ln)
0.381**
0.527***
0.467**
0.485**
(0.130)
(0.148)
(0.164)
(0.160)
Public health expenditure (ln)
0.021
-0.275
-0.35
-0.242
(0.165)
(0.186)
(0.201)
(0.208)
Globalization (KOF)
0.033
0.048*
(0.018)
(0.021)
Democracy (EIU)
-0.151
(0.082)
Constant
3.383***
1.803*
3.293**
2.399*
0.573
-0.029
(0.585)
(0.778)
(1.013)
(0.954)
(1.121)
(1.174)
R2
0.598
0.617
0.598
0.624
0.633
0.643
Degrees of freedom
143
142
142
141
140
139
BIC
516.9
514.8
521.9
517.2
518.6
519.4
Number of observations
147
147
147
147
147
147
* p<0.05, ** p<0.01, *** p<0.001
15
Table 4 shows the OLS results for COVID-19 mortality where the dependent variable is the
log of COVID-19 related deaths per million people. The coefficients of per capita income and urban
population are positive and significant (but small), as in Table 3, and the coefficient for health
inequality is negative. Model 2 predicts that a 10% increase in private health expenditure results in a
6.91% increase in COVID-19 deaths.
While public health expenditures have a positive and significant coefficient in model 3, adding
it to models 4, 5, 6, and 7 produces positive signs for both private expenditures and public
expenditures. However, private expenditures have positive coefficients in all of these models, while
public expenditures are never significant. So we do not add new information by adding public health
expenditures from model 4 onwards
4
.
In models 4 through 7 we introduce the percentage of the population aged 65 years or older
as this group has been identified as especially high-risk for COVID-19 mortality (Guan et. al. 2020,
Huang et. al. 2020, Zhou et. al. 2020). On average, these models predict that a 10% increase in the
percentage of older people results in a 1.18% increase in COVID-19 deaths.
Neither globalization nor democracy seem to affect COVID-19 mortality in models 6 and 7.
However, hospital capacity is critical. Models 5 through 7 predict that, on average, a 10% increase in
the percentage of hospital beds per 1,000 people results in a 1.67% decrease in COVID-19 deaths,
confirming tragic lessons such as that of Italy (Onder, Rezza & Brusaferro 2020).
Table 4. OLS regression predicting COVID-19 mortality - Deaths per 1,000,000 (ln)
COVID-19 mortality - Cases per 1,000,000 (ln)
(1)
(2)
(3)
(4)
(5)
(6)
(7)
Per capita income (thousands)
0.018
0.003
0
(0.012)
(0.009)
(0.010)
Percent urban
0.017**
0.004
0.002
0.009
0.011
0.011
0.008
(0.006)
(0.006)
(0.007)
(0.006)
(0.006)
(0.006)
(0.006)
Health inequality
-0.056***
-0.012
0.006
(0.015)
(0.013)
(0.018)
Private health expenditure (ln)
0.691***
0.457***
0.426***
0.451***
0.379***
(0.118)
(0.113)
(0.105)
(0.102)
(0.107)
16
COVID-19 mortality - Cases per 1,000,000 (ln)
(1)
(2)
(3)
(4)
(5)
(6)
(7)
Public health expenditure (ln)
0.610***
(0.152)
Population Age 65+ (percent)
0.083***
0.127***
0.143***
0.119***
(0.022)
(0.023)
(0.028)
(0.030)
Hospital beds (per 1,000 people)
-0.154**
-0.172**
-0.175**
(0.055)
(0.057)
(0.055)
Democracy (EIU)
-0.073
-0.102
(0.058)
(0.063)
Globalization (KOF)
0.028
(0.015)
Constant
1.589**
-1.276*
-1.075
-1.372***
-1.266***
-1.080**
-1.955***
(0.502)
(0.612)
(0.765)
(0.268)
(0.265)
(0.332)
(0.555)
R2
0.461
0.552
0.528
0.598
0.622
0.625
0.634
Degrees of freedom
143
142
142
143
142
141
140
BIC
504.7
482.5
490.1
461.5
457.7
461.3
462.7
Number of observations
147
147
147
147
147
147
147
* p<0.05, ** p<0.01, *** p<0.001
Taken together, the results shown in Tables 3 and 4 suggest that higher rates of private health
expenditure are associated with both higher prevalence and higher mortality related to COVID-19
across countries, controlling for differences in level of development, urbanization, age structure,
political regime and the extent of globalization of a country.
7. Conclusions
The current COVID-19 pandemic may be unprecedented in its impacts on global societies and
economies, but it is unlikely to be the last. Recent research has shown that the increasing pressure of
human activities on natural habitats and the resulting decline in wildlife populations have increased
the transmission of zoonotic diseases from animals to humans (Johnson et. al. 2020).
This implies that we could expect more and perhaps worse pandemics as time goes by unless
immediate action is taken to reduce the impact of human activity on nature. Given the glacial pace of
progress on this front, countries need to prepare themselves for this grim scenario.
17
Globalization and neoliberal policies have contributed to changes in countries’ healthcare
systems in recent decades, with more privatization and commercialization justified as means to
improve efficiency and boost economic growth. This paper adds to a literature that questions the
ability of privately-financed healthcare systems to cope with the scope and magnitude of infectious
diseases, including COVID-19.
The results presented above indicate that private spending on health care significantly raises
the rates of COVID-19 prevalence and mortality across countries, controlling for their income,
urbanization, demographic structure, exposure to globalization and political system. These findings
add to the existing literature showing the inadequacy of private healthcare systems in addressing other
infectious diseases such as TB.
Another effect of globalization and cost-cutting policies - a reduction in the number of
hospital beds per 1,000 people - has been shown to be critical in worsening mortality rates across
countries, as hospitals are overwhelmed by case-loads and infected patients require urgent access to
specific equipment and treatment.
This paper contributes to the emerging literature on COVID-19 as well as the lengthy debates
about the relative effectiveness of private vs. public healthcare systems. Our findings suggest that, to
make health systems sustainable at various levels of development and given the expectation of
worsening environmental conditions, there is an urgent need to reconsider the neoliberal impulse to
privatize health care systems. The short-term benefits from such privatization policies - e.g. reduced
costs, shorter waiting times - must be weighed against the long-term damage such policies can do to
countries’ ability to cope with a rapidly-spreading infectious disease.
18
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Appendix: Countries Included in the Analysis (N = 147)
Afghanistan
Djibouti
Kenya
Poland
Algeria
Dominican Republic
Korea (Republic of)
Portugal
Angola
Ecuador
Kuwait
Qatar
Argentina
Egypt
Kyrgyzstan
Romania
Armenia
El Salvador
Lao PDR
Russian Federation
Australia
Equatorial Guinea
Latvia
Rwanda
Austria
Estonia
Lebanon
Saudi Arabia
Azerbaijan
Eswatini
Liberia
Senegal
Bahrain
Ethiopia
Lithuania
Serbia
Bangladesh
Fiji
Luxembourg
Sierra Leone
Belarus
Finland
Macedonia
Singapore
Belgium
France
Madagascar
Slovakia
Benin
Gabon
Malawi
Slovenia
Bhutan
Gambia
Malaysia
South Africa
Bolivia
Georgia
Mali
Spain
Bosnia and Herzegovina
Germany
Malta
Sri Lanka
Botswana
Ghana
Mauritania
Suriname
Brazil
Greece
Mauritius
Sweden
Bulgaria
Guatemala
Mexico
Switzerland
Burkina Faso
Guinea
Moldova
Tanzania
Burundi
Guinea-Bissau
Mongolia
Thailand
Cabo Verde
Guyana
Morocco
Timor-Leste
Cambodia
Haiti
Mozambique
Togo
Cameroon
Honduras
Myanmar
Trinidad and Tobago
Canada
Hungary
Namibia
Tunisia
Central African Republic
Iceland
Nepal
Turkey
Chad
India
Netherlands
Uganda
Chile
Indonesia
New Zealand
Ukraine
China5
Iran
Nicaragua
United Arab Emirates
Colombia
Iraq
Niger
United Kingdom
Congo, Rep.
Ireland
Norway
United States
Costa Rica
Israel
Oman
Uruguay
Côte d'Ivoire
Italy
Pakistan
Uzbekistan
Croatia
Jamaica
Panama
Vietnam
Cyprus
Japan
Paraguay
Zambia
Czech Republic
Jordan
Peru
Zimbabwe
Denmark
Kazakhstan
Philippines
End Notes:
1
Throughout the paper, ln refers to the natural logarithm, i.e. loge x.
2
See Section 6 for the rationale for transforming the variables into logarithms.
3
Population size for 2020 extracted from World Population Prospects: The 2019 Revision. New York.
https://population.un.org/wpp/. Accessed May 2020.
4
Additional estimates are available upon request.
5
Data for China do not include Hong Kong Special Administrative Region of China, Macao Special Administrative Region
of China or Taiwan Province of China.
