How Do Gender Inequalities in Health Relate to the Competitiveness of Developed Countries? An Empirical Study

Abstract

Public health plays an important role in the spectrum of economic indicators. Simultaneously, the competitiveness of countries is a key economic indicator, and this interconnection requires special attention, especially in the decision-making process of public policies. The main objective of the presented study was to evaluate the effects of selected health indicators on the competitiveness of developed countries. The following health variables were collected for the analysed period from 2010 to 2018: four variables of Life Expectancy (LE) and three variables of Perceived Health Status (PHS). Each variable was observed from the perspective of gender differentiation and gender inequalities. The Global Competitiveness Index (GCI) was also used in the analyses as an economic variable. The research sample included all countries of the Organisation for Economic Cooperation and Development (OECD). The analytical data processing consisted of descriptive analysis, difference analysis (Wilcoxon Test), regression analysis (Panel regression model) and correlation analysis (Spearman's ρ). Regarding the results, all health variables showed differences between men and women. The results of the regression analysis revealed a very strong and significant effect of LE and PHS on the competitiveness of individual economies. The correlation analysis confirmed all relations. Improving the health of men and women is likely to increase the competitiveness of developed countries. As a result, it provides space for further research and policy-making in each country.

Full text

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HOW DO GENDER INEQUALITIES IN HEALTH
RELATE TO THE COMPETITIVENESS OF
DEVELOPED COUNTRIES? AN EMPIRICAL STUDY
▪Beata Gavurova, Viera Ivankova, Martin Rigelsky, Iveta Kmecova
Abstract
Public health plays an important role in the spectrum of economic indicators. Simultaneously,
the competitiveness of countries is a key economic indicator, and this interconnection requires
special attention, especially in the decision-making process of public policies. The main ob-
jective of the presented study was to evaluate the effects of selected health indicators on the
competitiveness of developed countries. The following health variables were collected for the
analysed period from 2010 to 2018: four variables of Life Expectancy (LE) and three variables of
Perceived Health Status (PHS). Each variable was observed from the perspective of gender dif-
ferentiation and gender inequalities. The Global Competitiveness Index (GCI) was also used in
the analyses as an economic variable. The research sample included all countries of the Organi-
sation for Economic Co-operation and Development (OECD). The analytical data processing
consisted of descriptive analysis, difference analysis (Wilcoxon Test), regression analysis (Panel
regression model) and correlation analysis (Spearman's ρ). Regarding the results, all health vari-
ables showed differences between men and women. The results of the regression analysis re-
vealed a very strong and significant effect of LE and PHS on the competitiveness of individual
economies. The correlation analysis confirmed all relations. Improving the health of men and
women is likely to increase the competitiveness of developed countries. As a result, it provides
space for further research and policy-making in each country.
Keywords: Competitiveness, Global Competitiveness Index, Health Inequalities, Life Expectancy, Perceived
Health Status, Gender
JEL Classification: I14, O47
Received: March, 2020
1st Revision: August, 2020
Accepted: August, 2020
1. INTRODUCTION
In economic theory, it is proclaimed that good health is very important in supporting the eco-
nomic situation of countries. Many research findings have confirmed a positive impact of health
on various aspects of economic life in countries, including productivity, income, consumption,
as well as economic growth (Bloom et al., 2004; French, 2012; Herzer & Nagel, 2019; Kalemli-
Gavurova, B., Ivankova, V., Rigelsky, M., & Kmecova, I. (2020). How Do Gender Inequalities in Health
Relate to the Competitiveness of Developed Countries? An Empirical Study. Journal of Competitiveness,
12(3), 99–118. https://doi.org/10.7441/joc.2020.03.06
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Journal of Competitiveness 100
Ozcan et al., 2000; Sharma, 2018; Weil, 2007). However, the question of whether there is an
explicit relation between the health of the population and the competitiveness of countries re-
mains open.
The health status of the population varies considerably from country to country and within each
of them, while differences in health are evident in terms of geographical aspects as well as in
terms of gender differentiation (Birader & Öztüren, 2019; Štefko et al. 2020). On the other hand,
there is a lack of research activities examining health inequalities and their effect on the competi-
tiveness of countries. For this reason, research that fills this gap is necessary and beneficial, as
health and competitiveness are considered to be key aspects of a country's economic life. Based
on this, the main objective of the presented study was to evaluate the effects of selected health
indicators on the competitiveness of developed countries. In other words, the study verified the
assumption about the existence and nature of the economic effects of health indicators and gen-
der health inequalities on global competitiveness. The research included analytical procedures to
achieve this objective, namely a descriptive analysis, an analysis of differences between men and
women in life expectancy and perceived health status, and a regression analysis that examined
the effects of these health indicators on the competitiveness of OECD countries. At its proce-
dural and result level, the presented research study offers a new perspective on the population’s
health as a key factor in the country’s competitiveness.
The structure of the study is as follows: the theoretical background provides an overview of
international studies dealing with research in the examined issue, while their main findings
were used to create the analytical framework and to determine the main research trajectories. In
the methodology section, the study specifies the fundamental methodological procedures and
describes the variables used in the research. The next section consists of the analytical methods
used to examine predetermined research trajectories. This section also interprets the results of
the analyses in order to create a wider framework for discussion. Finally, the main findings are
summarized and the research platform for future research in this area is formed.
2. THEORETICAL BACKGROUND
Public health is currently one of the most discussed social topics in the world, policy mak-
ers seek to understand health from different perspectives, while the economic perspective of
health has an irreplaceable place in decision-making processes (Nemec et al., 2020; Šumpíková &
Ďurčeková, 2019). From a macroeconomic point of view, the health of the population is of great
importance, as it is a source of economic and social development and the effects of health on
various areas of life are considerable. For these reasons, improving the health of the population
and reducing health inequalities should be one of the main strategic objectives of any country
(OECD, 2019; WHO, 2013). On the contrary, the poor health of the population can weaken the
economic condition of countries.
In recent years, many international research studies have examined the effect of health on eco-
nomic processes at both macroeconomic and microeconomic level. For instance, the study
conducted by Monterubbianesi et al. (2017) revealed that healthier population brings greater
economic benefits in terms of increased productivity and long-term income of countries. In
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101
the studies of Khan et al. (2016) and Li & Huang (2009), it was proved that a co-integration con-
nection between health and education has positive effects on the economic growth. The effect of
the health status of the population can also be found in other economic and social aspects, such
as countries' innovation performance (Herzer, 2020; Pacáková & Kopecká, 2019), knowledge
transfer (Bar & Leukhina, 2010), technological progress (Gehringer & Prettner, 2019; Lechman,
2019), labour supply (Jiang et al., 2019), and poverty (Kim, 2018; Rakauskienė & Volodzkienė,
2017).
These facts prove that health has an effect on various areas of economic life in countries. Also,
Madsen (2016) and Mihalache (2019) emphasized the importance of healthy human capital in
economic terms, while both believe that economic growth is represented by population health.
Healthy human capital has a significant effect on the growth of the manufacturing sector in
countries (Hena et al., 2019), which emphasizes a productive contribution to the economic per-
formance of countries (Bloom & Canning, 2003).
Regarding the health outcomes, life expectancy is a health indicator which is very frequently
used. In the study conducted by Onsel et al. (2008), the relation between this indicator and com-
petitiveness is evident. The authors consider life expectancy to be the fourth most important
criterion of the countries’ competitiveness, especially of those with a low level of competitiveness
(factor-driven economies). The following countries are the examples provided by the authors:
Croatia, Mexico, Poland, and Romania. Also, Salahodjaev & Nazarov (2013) revealed that an
increase in life expectancy increases the competitiveness of human capital, as well as the position
of developing countries in assessing their competitiveness represented by the GCI index. The
GCI index represents a commonly used tool which identifies a sustainable economic develop-
ment of the countries (Lall, 2001). Bucher (2018) revealed its high correlation with human devel-
opment index (HDI), gross domestic product (GDP) and the level of gender inequality.
Gender inequalities represent a serious element that can affect economic life in countries, as well
as their competitiveness. Kroupova & Rezankova (2016) confirmed a moderate positive correla-
tion between gender gap index (GGI) representing the gender inequalities in four main areas
(economic participation and opportunity, educational attainment, health and survival, political
empowerment) and the GCI indicator in OECD countries, with the exception of Japan, Korea,
and Iceland. Significant inequalities in health may represent a major obstacle for countries to
achieve the required level of competitiveness. These inequalities are a result of differences in
health outcomes achieved among various groups of the population, leading to an economic
burden in the form of considerable social and economic costs (WHO, 2017). At the same time,
socio-economic aspects such as employment status, level of education, income level, place of
residence, behavioural and living conditions are key factors in creating these differences (Adler
et al., 2016; Balaj et al., 2017; Mackenbach et al., 2019; Omotoso & Koch, 2018; Oversveen &
Eikemo, 2018).
Differences in health occur in various groups, and can be also seen from different perspectives,
for example in terms of colour (Patterson & Veenstra, 2016) or ethnicity (Allik et al., 2019).
From a territorial point of view, it can be stated that the population in Northern and Southern
Europe shows better health outcomes than the population in Eastern Europe (Pinillos-Franco
& Somarriba-Arechavala, 2019). Health inequalities are also often discussed in terms of differ-
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Journal of Competitiveness 102
ences between men and women (Crimmins et al., 2011; Denton et al., 2004; Heise et al., 2019).
Gender and health inequalities are evident in many health indicators, such as mortality or mor-
bidity (Singh-Manoux et al., 2008). Self-rated health represents a frequently used health indicator,
while Roxo & Perelman (2019) revealed that European women report poorer health than men.
Similar results have also been confirmed by many other studies (Caroli & Weber-Baghdiguian,
2016; Gerritsen & Deville, 2009; Singh et al., 2013), while Boerma et al. (2016) emphasized that
biological and social gender factors can contribute to the creation of such a gender gap. Life
expectancy represents another health outcome that makes gender inequalities evident. There
is much evidence that men live shorter than women (Barford et al., 2006; Belon et al., 2014;
Haeberer et al., 2015; Le et al., 2015). However, the research findings have shown a reduction in
gender inequality in life expectancy (Van Oyen et al., 2010). In 1970, the average life expectancy
of women was 73 years, in 2015, it increased to 83.2 years. For men, the average life expectancy
increased from 66.7 years to 77.9 years, indicating some elimination of differences (Megyesiova
& Lieskovska, 2018). These facts were also confirmed by research studies that deal with gender
inequalities in life expectancy and self-rated health, while the findings showed a longer life for
Indian women, but their perceptions of health were worse (Bora & Saikia, 2015).This phenom-
enon is called the male-female health-survival paradox, which expresses the fact that, despite the
poor health status of women, men show a higher level of mortality due to biological, behavioural
and social differences between the sexes (Oksuzyan et al., 2010; Van Oyen et al., 2013).
All of the above-mentioned research studies and their findings point to an increase in public
awareness and importance in evaluating the effect of health indicators on the economic pros-
perity of countries. International organizations, as well as research teams in various countries,
highlight the importance of eliminating health inequalities between the social, demographic and
economic characteristics of the population, which also affect health inequalities in individual
countries. The presented study addresses this issue from the perspective of global competitive-
ness.
3. RESEARCH OBJECTIVE, METHODOLOGY AND DATA
The theoretical basis provided evidence on the need to address gender inequalities in health and
global competitiveness. A healthy population can be expected to be able to generate competitive
economic outputs from which the economy can prosper and compete with other economies. In
any case, it should be noted that this issue has been overlooked in the research area. Despite the
fact that many studies have confirmed the importance of health and competitiveness, it is still
unclear what effect gender inequalities can have on a country's economic life in terms of com-
petitiveness. This was the main impetus for this study to conduct research in this area. Based on
the above-mentioned, the main objective of the presented study was to evaluate the effects of
selected health indicators on the competitiveness of developed countries.
In order to meet this objective, health and economic data on OECD countries were collected. All
of the OECD member countries till the end of 2019 (36 countries) were included in the analyses.
Due to the high frequency of missing data from 2019, the latest data from 2018 and the oldest
data from 2010 were analysed. Health data were obtained from the OECD databases (OECD,
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103
2020a), while two main groups were created for the analytical process, namely the life expectancy
(LE) group, and the perceived health status (PHS) group. Also, the Global Competitiveness
Index (GCI) was used in the analytical processes as economic data from the Global Competi-
tiveness Reports published by the World Economic Forum (WEF, 2020). The period for the
GCI (from 2010–2011 to 2018–2019) can be explained by the fact that Global Competitiveness
Reports provide year-on-year observation data. The analytical process also included data on
the health care system (HCS) as a panel variable. This variable was represented by four catego-
ries: a multiple insurance model (MI), a national health system covering the country as a whole
(NHS), a regional health system (RHS), and a single payer model (SPM). The countries included
in the analyses were classified into the above-mentioned health care systems on the basis of
data from the OECD database (OECD, 2016) - (Australia – NHS; Austria – MI; Belgium – MI;
Canada – NHS; Chile – MI; Czech Republic – MI; Denmark – NHS; Estonia – SPM; Finland –
NHS; France – SPM; Germany – MI; Greece – SPM; Hungary – SPM; Iceland – NHS; Ireland
– NHS; Israel – MI; Italy – NHS; Japan – MI; Korea – SPM; Latvia – NHS; Lithuania – NHS;
Luxembourg – SPM; Mexico – MI; Netherlands – MI; New Zealand – NHS; Norway – NHS;
Poland – SPM; Portugal – NHS; Slovak Republic – MI; Slovenia – SPM; Spain – NHS; Sweden –
RHS; Switzerland – MI; Turkey – SPM; United Kingdom – NHS; United States – MI).
The LE group consisted of four variables (each variable was gender-differentiated): life expect-
ancy at birth (LE_I), life expectancy at the age of 40 years (LE_II), life expectancy at the age of
65 years (LE_III), and life expectancy at the age of 80 (LE_IV). These variables may be defined
as the average number of years that a person at that age may be expected to live, assuming that
age-specific mortality levels remain constant (OECD, 2020b). The PHS group consisted of three
variables (each variable was gender-differentiated): good/very good health (PHS_I), fair (not
good, not bad) health (PH_II), and bad/very bad health (PH_III). All these variables included
the population over 15 years of age and were expressed as a percentage. Consequently, these
variables may be defined as a percentage of the population, aged 15 years old and over, who
report their health to be ‘good/very good' (or excellent) (all positive response categories), ‘fair’
(not good, not bad), ‘bad/very bad’ (all negative response categories) (OECD, 2020c). The com-
petitiveness of countries was represented by the indicator Global Competitiveness Index (GCI),
which ranges from 1 to 7. This range was used in the index methodology until 2017 (inclusive),
while from 2018, the range from 0 to 100 is used. These data were standardized to a uniform
range, i.e. from 1 to 7. Based on the theoretical framework, the higher the number, the higher the
competitiveness of the country (WEF, 2020).
The analytical research process consisted of the following steps. Firstly, a descriptive analysis
and an analysis of differences were applied. The Wilcoxon Rank Sum test, a nonparametric test
of the difference between two independent samples, was used to analyse the differences. The
Shapiro-Wilk test helped select this particular test based of normality tests. LE and PHS in men,
women, as well as inequality (difference) between men and women in absolute value were used in
a regression analysis as independent variables. A multiple linear regression analysis (panel model)
was used to evaluate the effects of selected health variables on the GCI indicator. Before applying
the multiple linear regression, several tests of assumptions were carried out in order to select the
most appropriate model. The F Test for Individual and/or Time Effects verified the significance
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of the effect of time series data. The Hausman test was applied to select the correct panel model.
Simultaneously, the Bonferroni Outlier Test verified the outliers. Based on the Gauss-Markov
theorem, the assumptions of the absence of significant heteroscedasticity and the absence of
multicollinearity were primarily tested. The multicollinearity was verified using the Variance
Inflation Factors (VIF) method. The homogeneity of variability of residuals (homoscedasticity)
was tested using the Breusch-Pagan test. If the assumptions were met, a multiple linear regres-
sion model was used. However, if significant heteroscedasticity occurred, the coefficients were
estimated using a robust estimator. In conclusion, a correlation analysis was applied using the
Spearman’s correlation coefficient ρ that was chosen on the basis of the Royston's multivariate
normality test. This analysis was selected in order to assess the relationships between health vari-
ables (LE, PHS) and competitiveness. The programming language R v. 3.6.1. (nickname: Action
of the Toes) in the R Studio was used in the analyses.
4. RESULTS AND DISCUSSION
The analytical processes were conducted in three consecutive parts in order to achieve the objec-
tive of the study. The difference analysis together with the descriptive analysis were applied to
describe the analysed variables, as well as to show the statistically significant existence of differ-
ences (inequalities). Subsequently, the regression analysis was carried out in the second part. Its
aim was to evaluate the effects of the LE and PHS indicators on the competitiveness of OECD
countries in three separate models for each health variable. Also, the correlation analysis was
applied to complete a univariate concept of the relations between health and competitiveness.
Table 1 presents the basic outputs of descriptive analysis as well as the results of the normality
test using the Shapiro-Wilk test (S-W value, p-value).
Tab. 1 – Health indicators – gender differences. Source: own research
Characteristic LE_1 LE_2 LE_3 LE_4 PHS_1 PHS_2 PHS_3
Female Mean 82.9752 43.9006 21.0826 9.76031 65.7378 23.4546 10.605
Median 83.5 44.3 21.3 9.8 68.5 22.2 9.55
Std.
Dev.
2.13392 1.90238 1.44386 0.86587 14.3161 9.98321 5.59362
S-W
value
0.9444† 0.9505† 0.9585† 0.9648† 0.9581† 0.9527† 0.9602†
Male Mean 77.4375 38.9872 17.686 7.94781 70.1922 20.9307 8.173
Median 78.7 40 18.2 8.1 71.85 19.3 7.8
Std.
Dev.
3.35553 2.90156 1.68168 0.84508 12.7579 9.47395 4.03642
S-W
value
0.8526† 0.8557† 0.8767† 0.9700† 0.9492† 0.9189† 0.9614†
Wilcoxon W 97791† 96102† 98034† 95834† 34743† 50991† 56194†
Note: † - p-value < 0.001, S-W - Shapiro-Wilk normality test; Wilcoxon W - Wilcoxon Rank Sum test
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105
Higher values of life expectancy (LE) were identified in women rather than in men. These results
are in accordance with many research findings confirming that women live longer compared
to men (Barford et al., 2006; Belon et al., 2014; Haeberer et al., 2015; Le et al., 2015). From the
results of another analysed variable representing the self-rated health status on the scale from
good to very good (PH_1), it is clear that men perceived their health status more positively than
women. This fact was also confirmed by other authors such as Roxo & Perelman (2019) or Singh
et al. (2013). Higher values of fair and negative health status perceived (PH_2 and PH_3) were
typical for women, while men reported their health status more positively. Also, these results cor-
respond with other research findings (Caroli & Weber-Baghdiguian, 2016; Gerritsen & Deville,
2009). On the other hand, the findings indicate a certain discrepancy; men die at a younger age
due to higher morbidity, while women, paradoxically, perceive their health status worse. Bora &
Saikia (2015) revealed similar research findings. In this case, it is possible to discuss the male-
female health-survival paradox, which expresses the fact that despite the poorly perceived health
status of women, men achieve a higher level of mortality, and consequently, they live shorter
(Van Oyen et al., 2013). Simultaneously, it should be pointed out that life expectancy (LE) in de-
veloped countries is considered to be a relatively overall benchmark. However, it is considered to
be the most complex benchmark that plays an important role in comparing differences between
men and women. The perceived health status can also be considered to be a useful criterion in
gender-oriented analyses, which was also confirmed by differences in the real health status and
the perceived (self-rated) health status of individuals. In the last lines of Table 1, the results of
normality test for men and women are shown. The p-values are lower than 0.05 in all of the cases.
Therefore, it is not possible to confirm a significant difference between the distribution of the
analysed variables and the normal distribution. The given test verified the difference between the
observed values (results for men and women) from zero. It is obvious that the difference was con-
firmed in all of the cases. In general, the LE variables showed more positive values for women,
thus women live longer. On the other hand, the PHS variables showed more positive values for
men, thus men perceive their health status more positively. Based on the above-mentioned, it can
be concluded that there are health inequalities between men and women, which may be caused by
various biological and social gender factors (Boerma et al., 2016). Some research studies confirm
the narrowing of these differences (Megyesiova & Lieskovska, 2018; Van Oyen et al., 2010), but
they are still significant in OECD countries.
In the following analytical part of the study, the analyses also include these gender inequalities
in health, i.e. the gender differences in LE and PHS in absolute value. Table 2 provides the basic
outputs of the descriptive analysis.
Tab. 2 – Gender inequalities in health – descriptive statistics. Source: own research
Character-
istic
LE_I LE_II LE_III LE_IV PHS_I PHS_II PHS_III
Mean 5.537771 4.913438 3.396584 1.836875 4.471429 2.633101 2.497959
Median 5.2 4.6 3.3 1.7 4.15 2.7 2.2
Std. Dev. 1.809961 1.646267 0.889899 0.912959 2.790488 1.531871 1.858803
Min 2.6 1.9 1.4 0 0 0 0
Max 11.3 9.6 5.5 4.2 11.3 7 8.5
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Journal of Competitiveness 106
The study is primarily focused on the average values in interpreting Table 2. The LE_I variable
acquired the value of a significant difference, and based on the values provided in Table 1, higher
values can be found in women. Accordingly, it can be assumed that women live 5.7 years longer
than men in terms of the average life expectancy. Other analysed variables can be interpreted in
a similar way.
Based on the results of the analyses, significant differences in health indicators have been identi-
fied between men and women, while health inequalities can play a key role in shaping economic
life in countries. This fact was confirmed within other economic dimensions with regard to
inequalities among population groups according to race and ethnicity (LaVeist et al., 2011), or
among socio-economic groups (Politzer et al., 2019).
Up to this point, only the health dimension of the research objective has been evaluated. The fol-
lowing parts of the study are focused on examining the interconnection of the selected indicators
of health and the competitiveness of countries. Firstly, it is important to assess the characteristics
of the GCI indicator in order to provide a more detailed picture of the dependent variable.
Fig. 1 – Distribution indicators – GCI and HCS. Source: own research
The average value of the GCI indicator during the analysed period was 4.94, while the median
value was 5.04. This difference in favour of the higher median value indicates the presence of
the outliers shown in Figure 1 (Switzerland values in 2012-2013 and 2013-2014). Standard devia-
tion was 0.50, skewness was -0.25 and kurtosis was at the level of 0.76. Figure 1 also shows the
absolute frequency of health care systems as a panel variable in panel regression models in the
following part of the analyses.
The regression analysis consisted of 6 models, while the specificities of the models were deter-
mined by the independent variables. The dependent variable, the GCI indicator, was identical for
all models. The first model consisted of the independent variables of LE (LE_I, LE_II, LE_III,
LE_IV) in female variant (Model_LE_F). The second model consisted of the independent vari-
ables of LE in male variant (Model_LE_M), the third model included the gender inequalities in
LE, i.e. the difference between men and women in absolute value (Model_LE_GI). The fourth
model contained the independent variables of PHS (PHS_I, PHS_II, PHS_III) in female variant
0
10
20
30
40
3
4
5
6
count
4
5
GCI
MI
NHS
RHS
SPM
Health care systems
10
8
12
6
9
81
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107
(Model_PHS_F), while the fifth model consisted of the independent variables of PHS in male
variant (Model_PHS_M). The sixth model included the gender inequalities in PHS, i.e. the dif-
ference between men and women in absolute value (Model_PHS_GI). Based on the verification
of the effects of years in the individual models using the F test for individual effects, years were
not considered to be a variable with a significant effect. The individual countries classified in the
health care system variable proved to be significant in all the above-mentioned models; therefore,
the application of a panel model was required.
Prior to the application of the regression analysis, it was necessary to verify the assumptions. The
Model_LE_F showed questionable values in several variables when verifying the multicollinear-
ity. For this reason, the variables LE_II and LE_III were removed. Subsequently, the VIF value
was at the level of 3.38. The Bonferroni test detected two values as significant outliers (Switzer-
land). Also, the Gauss-Markov theorem suggests that it is appropriate to verify the presence of
significant heteroscedasticity. The Hausman test (chisq = 0.39, p-value = 0.8222) recommended
the use of a random effect model, and the Breusch-Pagan test (BP = 17.53, p-value = 0.0002)
confirmed the presence of significant heteroscedasticity; therefore, the White 1 estimator was
used to estimate the coefficients.
Tab. 3 – Regression analysis – Model_LE_F. Source: own research
Random
- HC3
White 1
Estimate CI 2.5 % CI 97.5 % Std. Error z value Pr(>|t|)
(Intercept) -1.8842 -4.2435 0.4750 1.2483 -1.5094 0.1312
LE_I 0.0692 0.0330 0.1054 0.0200 3.4689 0.0005
LE_IV 0.1181 0.0301 0.2060 0.0472 2.5029 0.0123
Note: Multiple R-squared: 0.4437, Adjusted R-squared: 0.4401
As can be seen in Table 3, the significant coefficients acquired positive values. Based on this, it
can be concluded that if life expectancy at birth (LE_I) and at the age of 80 years (LE_IV) in
women increases, the competitiveness of countries is also expected to increase.
The second Model_LE_M also showed questionable values in several variables when analysing
the multicollinearity. Therefore, the variables LE_II and LE_III were removed. The value of
VIF was at the level of 1.06 after this correction. The Bonferroni test identified two significant
outliers (Switzerland). The Hausman test (chisq = 3.98, p-value = 0.1372) recommended the use
of a random effect model, and the Breusch-Pagan test (BP = 19.239, p-value = 0.0001) confirmed
the presence of significant heteroscedasticity. Therefore, even in this case, the White 1 estimator
was used to estimate the coefficients.
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Journal of Competitiveness 108
Tab. 4 – Regression analysis – Model_LE_M. Source: own research
Random -
HC3 White 1 Estimate CI 2.5 % CI 97.5 % Std. Error z value Pr(>|t|)
(Intercept) -1.3314 -2.3091 -0.3536 0.3907 -3.4077 0.0007
LE_I 0.0774 0.0649 0.0900 0.0050 15.5511 < 0.0001
LE_IV 0.0376 -0.0116 0.0868 0.0221 1.7009 0.0890
Note: Multiple R-squared: 0.63, Adjusted R-squared: 0.6276
Table 4 provides the results of regression analysis in the Model_LE_M. Even in this case, the sig-
nificant coefficients showed positive values. On this basis, it can be stated that if life expectancy
at birth in men (LE_I) increases, an increase in the competitiveness of countries is expected.
The results of the previous two models care consistent with the findings of Onsel et al. (2008)
who identified the life expectancy as one of the most important criteria of the competitiveness
for the factor-driven economies. Life expectancy, as a potential factor in increasing competitive-
ness in developing countries, was also used in the study of Salahodjaev & Nazarov (2013).
In analysing the multicollinearity, the Model LE_GI showed one questionable value in the case
of the LE_II variable, and this variable was removed. Subsequently, the VIF value was 5.62 in
LE_I, 5.62 in LE_III and 1.47 in LE_4. These outputs were considered acceptable. The Bon-
ferroni test revealed two significant outliers (Switzerland) that were excluded from the analysis.
The Hausman test (chisq = 6.97, p-value = 0.0727) recommended the use of a random effect
model, and the Breusch-Pagan test (BP = 0.43, p-value = 0.9333) did not confirm the presence
of significant heteroscedasticity.
Tab. 5 – Regression analysis – Model_LE_GI. Source: own research
Random Estimate CI 2.5 % CI 97.5 % Std. Error z-value Pr(>|t|)
(Intercept) 5.271455 5.037147 5.505764 0.116269 45.3384 < 0.0001
LE_I -0.25885 -0.31239 -0.2053 0.023916 -10.8234 < 0.0001
LE_III 0.333313 0.225104 0.441522 0.046872 7.1111 < 0.0001
LE_IV 0.000674 -0.05466 0.056011 0.024578 0.0274 0.9809
Note: Multiple R-squared: 0.6106, Adjusted R-squared: 0.6068
Based on the results in Table 5, a negative coefficient can be found in the LE_I variable. Therefore,
a reduction in gender inequalities in life expectancy at birth can lead to increased competitiveness.
The opposite situation can be seen in the case of LE_III with a positive coefficient, which indicates
that if gender inequality in life expectancy at the age of 65 years increases, countries' competitive-
ness is also expected to increase. In general, men who live longer may offer their competitive ad-
vantage in the economy. On the other hand, the population group covered by the life expectancy
at the age of 65 years is represented by economically unproductive people with higher social and
health costs. Therefore, if the gender inequality increases, it will mean greater differences in this
group limited by age, indicating higher mortality on one side of the sex, and accordingly, lower
costs in countries. This fact may be reflected in the economic condition of countries. Also, it is
important to emphasize that the LE_I coefficient is more significant for the competitiveness index
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109
than LE_III, suggesting that life expectancy at birth is a very important health indicator in the
examined problem.
The above-mentioned findings reveal not only that reducing gender inequalities in life expectancy
at birth can lead to greater competitiveness, but also that there is a need to focus on men in this ef-
fort, as this gender group showed a higher significance of the effect on competitiveness compared
to women. In other words, increasing the average life expectancy at birth in the case of men will
reduce gender inequality with a greater effect on competitiveness.
In analysing the multicollinearity, the Model_PHS_F showed a questionable value in the PHS_I
variable that was removed. The VIF value was at the level of 3.41 in other two independent vari-
ables (PHS_II and PHS_III). The Bonferroni test revealed two significant outliers (Switzerland)
and these values were removed from the analysis. The Hausman test (chisq = 4.47, p-value = 0.1073)
recommended the use of a random effect model, and the Breusch-Pagan test (BP = 22.02, p-value
< 0.0001) confirmed the presence of significant heteroscedasticity; therefore, the White 1 estimator
was used to estimate the coefficients.
Tab. 6 – Regression analysis – Model_PHS_F. Source: own research
Random -
HC3 White 1
Estimate CI 2.5 % CI 97.5 % Std. Error z value Pr(>|t|)
(Intercept) 5.1590 5.0040 5.3139 0.0982 52.5322 < 0.0001
PHS_II 0.0340 0.0264 0.0417 0.0053 6.3962 < 0.0001
PHS_III -0.0956 -0.1101 -0.0811 0.0086 -11.0595 < 0.0001
Note: Multiple R-squared: 0.6736, Adjusted R-squared: 0.6713
The results in Table 6 point to the fact that if the proportion of women who report a fair percep-
tion of their health status (PHS_II) increases, the competitiveness of countries is also expected to
increase. On the other hand, a decrease in the proportion of women who report a negative percep-
tion of their health status (PHS_III) may lead to an increase in the competitiveness of countries.
When verifying the multicollinearity, the Model_PHS_M showed a questionable value in the
PHS_I variable, which was removed. The VIF value was at the level of 3.08 in other two inde-
pendent variables (PHS_II and PHS_III). The Bonferroni test detected two significant outliers
(Switzerland) that were also excluded from the analysis. The Hausman test (chisq = 2.05, p-value =
0.3579) recommended the use of a random effect model, and the Breusch-Pagan test (BP = 31.86,
p-value < 0.0001) confirmed the presence of significant heteroscedasticity. Accordingly, the White
1 estimator was used to estimate the coefficients.
Tab. 7 – Regression analysis – Model_PHS_M. Source: own research
Random -
HC3 White 1
Estimate CI 2.5 % CI 97.5 % Std. Error z value Pr(>|t|)
(Intercept) 5.2369 5.0782 5.3956 0.0999 52.4044 < 0.0001
PHS_II 0.0347 0.0266 0.0428 0.0046 7.5205 < 0.0001
PHS_III -0.1249 -0.1456 -0.1043 0.0111 -11.2074 < 0.0001
Note: Multiple R-squared: 0.6479, Adjusted R-squared: 0.6454
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Journal of Competitiveness 110
The results in Table 7 point to the fact that if the proportion of men who report a fair perception
of their health status increases (PHS_II), the competitiveness of countries is also expected to in-
crease. On the other hand, a decrease in the proportion of men who report a negative perception of
their health status (PHS_III) may lead to an increase in the competitiveness of countries. Overall,
a higher rate of outputs’ significance was measured in the case of men.
When verifying the multicollinearity, the Model_PHS_GI also showed one questionable value in
the PHS_I variable that was removed. The VIF value was 1.10 for the other two independent vari-
ables (PHS_II and PHS_III). The Bonferroni test revealed two significant outliers (Switzerland)
and these values were removed from the analysis. The Hausman test (chisq = 29.58, p-value <
0.0001) recommended the use of a fixed effect model, and the Breusch-Pagan test (BP = 13.189,
p-value < 0.0014) confirmed the presence of significant heteroscedasticity. Therefore, in this case,
the Arellano estimator was used to estimate the coefficients.
Tab. 8. – Regression analysis – Model_PHS_GI. Source: own research
Fixed - HC3
Arellano
Estimate CI 2.5 % CI 97.5 % Std. Error t value Pr(>|t|)
PHS_II -0.10032 -0.1297 -0.0710 0.0241 -4.1615 < 0.0001
PHS_III -0.10653 -0.1306 -0.0824 0.0315 -3.3860 0.0008
Note: Multiple R-squared: 0.3939, Adjusted R-squared: 0.3831
From the results in Table 8, it is evident that if the gender inequality in perceived health status
decreases, the competitiveness of countries is expected to increase. The above-mentioned find-
ings confirm the general considerations that a reduction in health inequalities in the economy
brings economic benefits (Nanney et al., 2019). Also, this fact can be confirmed in terms of gender
inequalities in perceived health status (PHS), indicating if a gender gap narrows and the popula-
tion perceives its own health more positive, the competitive advantage in various economic areas
will increase. People feel healthier, physically and emotionally stronger, tougher, as well as more
productive (Doan et al., 2020; WHO, 2002). The results confirm this interconnection from the
perspective of competitiveness.
The previous regression analysis provides a multivariate view of the relations between selected
health and economic outputs. Table 9 shows the results of the correlation analysis that offers a uni-
variate view and it includes all independent variables. The primary assumption for selecting an
appropriate test was a normality verified by the Royston's multivariate normality test, which did not
show a p-value higher than 0.05 in none of the analysed cases. It means that the assumption of nor-
mality was not met. The Spearman’s nonparametric ρ coefficient was considered the appropriate.
Tab. 9 – Correlation analysis (Spearman’s correlation coefficient ρ). Source: own research
GCI x Health LE_I LE_II LE_III LE_IV PHS_I PHS_II PHS_III
Female 0.37† 0.38† 0.39† 0.43† 0.50† -0.31† -0.54†
Male 0.53† 0.55† 0.51† 0.26† 0.41† -0.25† -0.48†
Gender Inequal. -0.55† -0.48† -0.34† 0.17*** -0.62† -0.44† -0.62†
Note: *** - p-value < 0.01; † - p-value < 0.001
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111
The results in Table 9 may be divided into three parts. The first line shows the output of the cor-
relation coefficient ρ, while the second line shows the p-value. However, it is important to state
that all tested relations appeared to be significant. As for gender inequalities in health, all outputs
of the correlation coefficients are negative, which is a positive output for the competitiveness of
countries. This suggests that increasing competitiveness may be associated with reducing gender
inequalities in health. When comparing the outputs of men and women, the variables LE_I,
LE_II and LE_III show higher values of the coefficients for men. Women were dominant in the
LE_IV variable. It can be stated that the activities which increase life expectancy could be more
effective in the case of men than women in terms of increasing the competitiveness of countries.
The correlation analysis also revealed a significant relationship between the competitiveness of
countries and the positive perception of health status. This relationship was stronger in the case
of women than men; however, these differences were not significant.
The overall results indicate that the health of the population, as well as the gender inequalities
in health, are key factors that can affect the competitive position of countries around the world.
Accordingly, it is important to emphasize that the countries should focus on health of their own
residents, as well as on reducing the gender inequalities in health, in order to increase their own
competitiveness. The above-mentioned facts provide space for active health and social policy-
making. This study revealed the fact that public policies should focus on increasing the life
expectancy of productive population as well as improving the self-rated health status of individu-
als. This can be achieved through an improved health and social system in a country. Also, the
financing of health care plays an important role in this effort, as health expenditure is positively
associated with population longevity (Bein et al., 2017) and social support (Reynolds & Aven-
dano, 2018). Last but not least, other evidence showed that increased health expenditure can lead
to a reduction in longevity inequalities (Obrizan & Wehby, 2018). Another recommendation for
policy makers, based on the findings, is health prevention and education, which should be pro-
moted among the population. A healthy lifestyle is considered to be a key determinant of popula-
tion longevity (Li et al., 2018) and health perceptions (Marques et al., 2019), while unhealthy and
risky behaviour (such as smoking, poor diet, alcohol consumption and physical inactivity) may
result in reduced health, life expectancy (Mehta & Myrskyla, 2017), as well as self-rated health
status (Chan et al., 2015). Efforts to support the social and health systems, prevention and educa-
tion, would be transformed into economic life, the competitiveness of countries in particular.
5. CONCLUSION
Population health has an effect on a number of economic aspects, while each of these economic
areas creates a picture of competitiveness among the individual countries. However, the relation
between the population health and the competitiveness of the countries in which these people
live requires broader research. The main reason is especially the methodological difficulty of the
research process, as well as the absence of research trajectories that would have an implicational
character for relevant types of policies. Also, it is important to choose relevant variables wisely as
the main representatives of the population health that would accurately quantify the effect and
dependence of health and economic categories. The main objective of the presented study was to
evaluate the effects of selected health indicators on the competitiveness of developed countries.
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Journal of Competitiveness 112
This objective was achieved through various analyses and their main findings are summarized
in the following text.
The results of the analysis of differences showed significant gender differences in all analysed
health variables, while life expectancy showed considerable values indicating the well-known
fact that women live longer than men. As expected, the differences in perceived health status
revealed positive results for men. The regression analysis provided very interesting results that
have added value, especially when comparing them with each other. The effect of life expectancy
at birth was considered to be very significant for the competitiveness of economies, in terms of
male outputs, female outputs, as well as outputs of gender inequality. Similarly, a relatively strong
effect on competitiveness was also found in the perceived health status. These results were also
confirmed by the correlation analysis that revealed significant negative coefficients in almost
all of the relations between competitiveness and gender inequalities in health indicators. Based
on these all findings, it can be concluded that a reduction in gender inequalities can lead to an
increase in the competitiveness of countries. In any case, this study confirms economic theories
that consider health to be an important economic factor.
Potential limitation of this study may be a fact that data from the OECD database were not en-
tirely used. The main reason was the assumption that the larger timeline, the higher chance that
the given data will be influenced by various economic, social, and other effects (shocks). The
sample is considered sufficient, and consequently, relevant results may be presented. In future
research, it is planned to link the examined variables with education and other socio-econom-
ic indicators. Future research will also include various geographical aspects, and their link to
healthcare accessibility and healthcare costs will offer further insight into this issue.
Acknowledgments
This research was funded by the Slovak Research and Development Agency under the contract No. APVV-17-
0360: “Multidimensional analysis of signicant determinants of public procurement efciency with emphasis
on the application of Health Technology Assessment in the procurement preparation phase”.
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Contact information
prof. Ing. Beata Gavurova, PhD., MBA
Technical University of Košice
Faculty of Mining, Ecolog y, Process Control and Geotechnologies
Institute of Earth Resources
Slovakia
E-mail: beata.gavurova@tuke.sk
ORCID: 0000-0002-0606-879X
Mgr. Viera Ivankova
University of Presov
Faculty of Management
Department of Economics and Economy
Slovakia
E-mail: viera.ivankova@smail.unipo.sk
ORCID: 0000-0002-6164-1165
Mgr. Martin R igelsky
University of Presov
Faculty of Management
Department of Marketing and International Trade
Slovakia
E-mail: martin.rigelsky@smail.unipo.sk
ORCID: 0000-0003-1427- 4689
Ing. Iveta Kmecova, PhD.
Institute of Technolog y and Business
Faculty of Cor porate Strateg y
Czech Republic
e-mail: kmecova@mail.vstecb.cz
ORCID: 0000-0002-0406-2861
joc2020-3-v3.indd 118 24.9.2020 16:27:07