The relationship between government health expenditure and economic growth: Evidence from western Balkan countries
Selvete Shuleta QEHAJA1
Driton QEHAJA2*
Arber HOTI3
Erolinda Marovci4
1College of Medical Sciences "Rezonanca", Kosovo. |
AbstractThis paper aims to investigate the effect of government expenditure on health and other relevant factors like health insurance, longevity, average age and death rate on economic growth in Western Balkan countries. Countries with higher levels of government expenditure on health tend to have higher levels of economic growth. Investment in healthcare may result in a greater supply of health incentives which may help human capital and enhance productivity and the economy’s performance. This paper uses annual data from 2000-2020 for the following Western Balkan countries: Albania, Bosnia, Herzegovina, Kosovo, Montenegro, Northern Macedonia and Serbia. This region represents a diverse set of countries at different stages of development with varying government expenditures on health. This provides an opportunity to study the impact of health on economic growth in an environment with a lot of "real-world" variation. The data is collected from the World Bank, National Statistical Offices and Eurostat. The dependent variable is economic growth, measured as Gross Domestic Product (GDP) per capita growth. The independent variables are government expenditure on health as a percentage of GDP, health insurance, longevity, the average age of the population, health expenditure per capita and the death rate. In order to measure the impact of individual factors, the study uses econometric models with fixed effects and random effects. The regression analysis results show that government expenditure on health has a positive and significant impact on economic growth in Western Balkans countries. |
Licensed: |
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Keywords: JEL Classification: |
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Received: 15 September 2022 |
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(* Corresponding Author) |
Funding: This study received no specific financial support. |
Competing Interests: The authors declare that they have no competing interests. |
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1. Introduction
A growing economy requires both physical and human capital. As Romer (1990) emphasized, human capital accumulation promotes economic growth. The components of human capital formation include education, health and training. When examining the details of the endogenous growth hypothesis, health and education expenditures are seen as means of increasing human capital which would stimulate internal technological advancement and accelerate economic growth.
Government Expenditure on Health (GEH) as a percentage of GDP indicates how much a government devotes to health from its total resources. It includes all current and capital expenditures on health at all levels of government including primary, secondary and tertiary care as well as public health on (i) the provision of health services (preventive and curative), including spending on public health and environmental protection activities, (ii) medical products, vaccines and medical equipment, (iii) the training and education (GEH) of health personnel, (iv) government subsidies or reimbursements to private entities for healthcare services.
The Western Balkan (WB) is a region in Southeastern Europe that comprises Albania, Bosnia, Herzegovina, Kosovo, Montenegro, North Macedonia and Serbia. Over the course of the past two decades, the region has been subjected to several changes. The most notable are the collapse of Yugoslavia and the war in Kosovo. Despite the challenges, these countries have made significant progress in their economic growth and integration into the European Union having a population of 17.5 million and a GDP per capita of $6,164 (World Bank, 2022c).
According to the findings, low and middle income countries are more affected by GEH’s than in high-income countries (Bustamante & Shimoga, 2018; Rhee, 2014). Since low- and middle-income countries have lower levels of health care as a result, they need more enhancement. However, the amount spent on health care should be spent efficiently so that resources are not wasted and to enhance the macroeconomic effects. Medeiros and Schwierz (2015) highlight four main causes of the non-optimal use of resources that are applicable to most European Countries (EU) : inefficient delivery of healthcare services, inefficient provision of health care, corruption and inefficient distribution of preventive and curative care.
While the evidence on the relationship between GEH and economic growth is relatively clear, the mechanisms through which this relationship operates, and the direction of causality are less well understood. The first mechanism is the direct effect of health on labor productivity (Santiago, Joseph, & Tubayan, 2016; Sengupta, 2017). One possibility is that good health allows workers to be more productive and thus earn higher incomes. This ultimately results in higher economic growth. Another possibility is that GEH promotes economic growth by providing insurance against health risks (Ihori, Kato, Kawade, & Bessho, 2011; Plianbangchang, 2018; Welfens, 2020; Zhao, Jia, & Chen, 2020; Zheng, 2019) . This is because people ensured against health risks can invest more in productive activities, such as education and training without worrying about the costs of potential illnesses. A second mechanism is the indirect effect of health on human capital formation due to better nutrition and health care (Gumbau, 2021; Kotschy, 2021; Sokolskaya et al., 2019) . Third, GEH may lead to economies of scale in producing health care goods and services. Fourth, GEH may improve the allocation of resources across sectors, leading to higher productivity in the economy as a whole (Aladejare 2022; Althawaini, Elmulthum, & Morsi, 2022; Behera & Dash, 2019; Jakovljevic, 2013) . Finally, good health can also lead to increased savings and investment. Healthy individuals are less likely to need to spend their income on medical care and are more likely to be able to save for retirement or other investments (Fioroni, 2010; Wang, Wang, & Ma, 2019).
Establishing the direction of causality is likely to be complex and multi-directional (Boussalem, Boussalem, & Taiba, 2014; Rana, Alam, & Gow, 2020). Some studies suggest that economic growth causes GEH while others find evidence for the opposite. For example, higher levels of GEH may lead to higher levels of economic growth. However, it is also possible that higher levels of economic growth may lead to higher levels of GEH. This is because countries with better health outcomes may also have other characteristics that promote economic growth, such as higher levels of education or more effective institutions.
Several studies have used instrumental variable (IV) techniques to estimate the direction of causality. Instrumental variable estimation aim is to find a variable that affects health but not economic growth (or vice versa) and use it to "instrument” health in the growth equation. One of the most commonly used IVs is climate which has a direct impact on health but is unlikely to affect economic growth directly. Another option is to use measures of historical mortality rates which are likely to be correlated with current health outcomes but not with current economic conditions. A number of studies have used IVs, such as military spending, and found evidence that improvements in health lead to higher economic growth (Adams, Hurd, McFadden, Merrill, & Ribeiro, 2003; Alsan, Bloom, & Canning, 2006; Cooper & Robert, 2004; Easterly & Rebelo, 1993; Ikegami & Wang, 2022) . However, there are several problems with using IVs to estimate the direction of causality. First, it is often difficult to find valid IVs. Finally, even if valid IVs are found, there is often a problem of endogeneity, in which case the estimates may be biased (Arelleno & Bover, 1995).
Healthcare systems have been severely affected by the COVID-19 pandemic, resulting in an unprecedented demand for healthcare services and supplies. The pandemic has unprecedented impact on the health, social and economic systems throughout the WB, the European Union and the rest of the world. It has highlighted the interdependence of lives and livelihoods, society, the economy and the urgency of improving health, wellbeing and sustainable development within and beyond the current crisis context. A pandemic has shown the need of considering how to utilize limited resources to promote regional economies and support sustainable development. With this challenge, WB countries have been working together to reduce COVID-19 spread and protect their citizens.
The study continues with an analysis of health expenditure across WB countries followed by a review of literature in which theoretical and empirical studies are discussed. The paper will then test this relationship using data from the World Bank Indicators database, national statistical offices and Eurostat. The analysis results will be discussed and outlined followed by a section that includes the study's conclusion and limitations.
2. Health Expenditures in WB Countries
WB countries have made considerable progress in recent years regarding access to health status and health care although the indicators are still low compared to other European countries. There are differences among the six WB countries. Per capita health spending ranges from a low of 270 United States dollar (USD) for Albania to a high of 693 USD in Serbia or 683 USD in Montenegro. The average per capita health spending in the Balkan countries is USD 484 which is still low compared to the Organisation for Economic Co-operation and Development (OECD) with above USD 5000 or the EU average with above USD 3500 (World Bank, 2022b).
In addition, the GDP spent on health care varies significantly across the Balkan countries (Table 1). The Balkan countries spend between 5.15% and 9.36% of their GDP on health. However, there is a continuing discussion over the forms of healthcare expenditure and the ideal amount of investment for economic growth (Agénor, 2008; Boucekkine, Diene, & Azomahou, 2008; Guo & Meei, 2015) . According to Mcintyre, Meheus, and Røttingen (2017) 5% of GDP is commonly mentioned as the minimum level of public health spending required for universal coverage. GEH levels in all WB countries exceed the 5% threshold.
Several countries spend at levels observed in the universal coverage systems of high-income countries which typically spend between 7% to 10% of their GDP on health. However, higher levels of public spending on health are associated with lower out-of-pocket expenditures and related financial hardship and impoverishment. For example, the total spending in Kosovo and Albania is driven by large shares of out-of-pocket and private voluntary insurance expenditures, whereas publicly mandated and supported financing in Serbia, Montenegro and Northern Macedonia (World Bank, 2022c).
Another essential factor that can impact economic growth is health insurance coverage. Kosovo is the only country in the region that does not provide public health insurance as illustrated in Table 1. In all six countries, the percentage of the population with health insurance coverage increased between 2000 and 2020. The increase in health insurance coverage is attributable to several factors, including the expansion of government-sponsored health insurance programs and the growth of the private health insurance market. In 2020, the average percentage of the WB population covered by health insurance was 77% compared to 93% in the EU. The smallest increase was in Kosovo, where population health insurance coverage rose from 72.8% in 2000 to 75.6% in 2020. The largest increase was in Northern Macedonia, where health insurance coverage rose from 50.8% in 2000 to 67.2% in 2020 (World Bank, 2022a).
There is significant variation in how the health budgets of WB countries are financed. The mix of sources varies considerably across nations with a few exceptions. On the one hand, Kosovo and Albania rely mainly on out-of-pocket payments (57% and 41%, respectively) and private voluntary insurance (19% and 20%) to finance their health care systems. On the other hand, Serbia (80%), Montenegro (67%), Bosnia and Herzegovina (62%) and Northern Macedonia (61%) have a higher reliance on mandatory social insurance contributions as their main source of financing. This finding is consistent with evidence from earlier studies which found that tax-based systems are associated with more equitable access to health care (Stuckler & Basu, 2009).
Another factor that can impact the relationship between GEH and economic growth is longevity. Generally, countries with higher life expectancies tend to have higher levels of economic development. This is because a longer lifespan indicates that citizens are healthy and productive. In all six countries, life expectancy at birth increased between 2000 and 2020. The smallest increase was in Kosovo, where life expectancy at birth rose from 68 years in 2000 to 71.1 years in 2020. The most significant increase was in Northern Macedonia, where life expectancy at birth rose from 72.9 years in 2000 to 75.7 years in 2020. The increase in life expectancy is attributable to a reduction in mortality rates and several factors, including improved access to healthcare, medical technology advances and lifestyle and diet changes (World Bank, 2022d).
The death rate is another factor that can impact the relationship between GEH and economic growth. The death rate has been increasing in WB in recent years (Table 1). A lower death rate indicates that citizens are healthy and productive for longer. Generally, countries with lower death rates tend to have higher levels of economic development.
The average age is another factor that can impact the relationship between GEH and economic growth. Generally, countries with higher average ages tend to have higher levels of economic development. This is because older citizens are typically more experienced and productive than younger citizens. The average age in WB has been increasing in recent years. The increase in average age is attributable to several factors, including an aging population and improvements in life expectancy.
Despite all these developments, health outcomes in the WB remain relatively poor compared to other European countries. For example, life expectancy at birth in the region is lower than the EU average of 80 years. In addition, infant mortality rates in the WB are higher than the EU average of 3.0 deaths per 1,000 live births. These findings suggest that the WB region faces significant challenges in terms of health outcomes. Other important factors for poor health outcomes in the WB region include the health workforce, distribution of the health workforce, number of hospital beds per 10,000 people, health care facilities, immunization rates and disease prevalence.
2000 |
2001 |
2002 |
2003 |
2004 |
2005 |
2006 |
2007 |
2008 |
2009 |
2010 |
2011 |
2012 |
2013 |
2014 |
2015 |
2016 |
2017 |
2018 |
2019 |
2020 |
||
GDP per capita (USD) |
Albania | 1127 |
1282 |
1425 |
1846 |
2374 |
2674 |
2973 |
3595 |
4371 |
4114 |
4094 |
4437 |
4248 |
4413 |
4579 |
3953 |
4124 |
4531 |
5288 |
5396 |
5246 |
| Bosnia and H. | 1484 |
1545 |
1790 |
2259 |
2698 |
2981 |
3417 |
4193 |
5091 |
4715 |
4636 |
5093 |
4777 |
5130 |
5330 |
4730 |
4995 |
5394 |
6070 |
6120 |
6080 |
|
| Kosovo | 2845 |
2765 |
2678 |
2678 |
2897 |
2789 |
2789 |
2897 |
2965 |
2848 |
3010 |
3541 |
3411 |
3705 |
3903 |
3521 |
3760 |
4009 |
4384 |
4416 |
4347 |
|
| Montenegro | 1627 |
1910 |
2107 |
2789 |
3380 |
3675 |
4426 |
5976 |
7368 |
6727 |
6688 |
7329 |
6587 |
7186 |
7378 |
6514 |
7029 |
7784 |
8846 |
8911 |
7677 |
|
| N. Macedonia | 1862 |
1823 |
1989 |
2440 |
2796 |
3073 |
3363 |
4079 |
4841 |
4585 |
4578 |
5098 |
4728 |
5241 |
5496 |
4862 |
5150 |
5450 |
6109 |
6070 |
5917 |
|
| Serbia | 915 |
1727 |
2284 |
3005 |
3503 |
3720 |
4383 |
5848 |
7101 |
6169 |
5735 |
6809 |
6016 |
6755 |
6600 |
5589 |
5765 |
6293 |
7252 |
7417 |
7721 |
|
Government expenditure on health per capita (USD) |
Albania | 81 |
90 |
96 |
126 |
157 |
165 |
175 |
222 |
220 |
186 |
192 |
211 |
214 |
237 |
251 |
193 |
202 |
226 |
275 |
269 |
270 |
| Bosnia and H. | 113 |
112 |
127 |
181 |
245 |
263 |
304 |
388 |
483 |
425 |
418 |
467 |
453 |
484 |
505 |
442 |
462 |
482 |
540 |
554 |
432 |
|
| Kosovo | 91 |
94 |
110 |
147 |
177 |
199 |
210 |
218 |
236 |
218 |
242 |
288 |
290 |
327 |
352 |
301 |
336 |
326 |
361 |
359 |
407 |
|
| Montenegro | 83 |
99 |
116 |
162 |
206 |
286 |
353 |
479 |
592 |
548 |
551 |
578 |
542 |
604 |
586 |
583 |
602 |
616 |
731 |
735 |
683 |
|
| N. Macedonia | 164 |
152 |
175 |
217 |
234 |
233 |
246 |
265 |
314 |
297 |
305 |
330 |
311 |
350 |
344 |
307 |
327 |
358 |
399 |
437 |
417 |
|
| Serbia | 57 |
112 |
173 |
230 |
274 |
308 |
372 |
547 |
672 |
575 |
545 |
619 |
560 |
628 |
609 |
491 |
486 |
516 |
617 |
641 |
693 |
|
Government expenditure on health as a percentage of GDP |
Albania | 4 |
5 |
4 |
4 |
5 |
6 |
6 |
6 |
5 |
5 |
5 |
5 |
5 |
5 |
6 |
5 |
5 |
5 |
5 |
5 |
5 |
| Bosnia and H. | 5 |
5 |
4 |
5 |
5 |
5 |
5 |
5 |
5,5 |
6 |
6 |
6 |
7 |
6 |
6 |
7 |
7 |
7 |
7 |
7 |
7 |
|
| Kosovo | 3 |
3 |
4 |
6 |
6 |
7 |
8 |
8 |
8 |
8 |
8 |
8 |
9 |
9 |
9 |
9 |
9 |
8 |
8 |
8 |
9 |
|
| Montenegro | 5 |
5 |
6 |
6 |
6 |
8 |
8 |
8 |
8 |
8 |
8 |
9 |
8 |
8 |
9 |
8 |
9 |
8 |
8 |
9 |
9 |
|
| N. Macedonia | 6 |
6 |
6 |
6 |
7 |
8 |
7 |
7 |
7 |
7 |
7 |
7 |
7 |
7 |
6 |
6 |
6 |
7 |
7 |
7 |
7 |
|
| Serbia | 7 |
7 |
7 |
7 |
7 |
8 |
8 |
9 |
9 |
9 |
10 |
9 |
9 |
9 |
9 |
9 |
8 |
8 |
9 |
9 |
9 |
|
Health insurance |
Albania | No |
No |
No |
No |
No |
No |
No |
No |
No |
No |
No |
No |
Yes |
Yes |
Yes |
Yes |
Yes |
Yes |
Yes |
Yes |
Yes |
| Bosnia and H. | No |
No |
No |
No |
No |
No |
No |
No |
No |
Yes |
Yes |
Yes |
Yes |
Yes |
Yes |
Yes |
Yes |
Yes |
Yes |
Yes |
Yes |
|
| Kosovo | No |
No |
No |
No |
No |
No |
No |
No |
No |
No |
No |
No |
No |
No |
No |
No |
No |
No |
No |
No |
No |
|
| Montenegro | No |
No |
No |
No |
No |
No |
No |
No |
No |
Yes |
Yes |
Yes |
Yes |
Yes |
Yes |
Yes |
Yes |
Yes |
Yes |
Yes |
Yes |
|
| N. Macedonia | No |
No |
No |
No |
No |
No |
No |
No |
No |
No |
Yes |
Yes |
Yes |
Yes |
Yes |
Yes |
Yes |
Yes |
Yes |
Yes |
Yes |
|
| Serbia | No |
No |
No |
No |
No |
No |
No |
No |
No |
Yes |
Yes |
Yes |
Yes |
Yes |
Yes |
Yes |
Yes |
Yes |
Yes |
Yes |
Yes |
|
Longevity |
Albania | 74 |
74 |
75 |
75 |
75 |
75 |
75 |
76 |
76 |
76 |
77 |
77 |
77 |
78 |
78 |
78 |
78 |
78 |
79 |
79 |
79 |
| Bosnia and H. | 74 |
75 |
75 |
75 |
75 |
75 |
75 |
76 |
76 |
76 |
76 |
76 |
76 |
77 |
77 |
77 |
77 |
77 |
77 |
77 |
78 |
|
| Kosovo | 68 |
68 |
68 |
68 |
69 |
69 |
69 |
69 |
69 |
70 |
70 |
70 |
71 |
71 |
71 |
71 |
72 |
72 |
73 |
73 |
71 |
|
| \Montenegro | 73 |
73 |
73 |
73 |
73 |
74 |
74 |
74 |
75 |
75 |
76 |
76 |
76 |
77 |
76 |
76 |
76 |
77 |
77 |
77 |
76 |
|
| N. Macedonia | 73 |
73 |
73 |
73 |
74 |
74 |
74 |
74 |
74 |
74 |
75 |
75 |
75 |
75 |
76 |
75 |
75 |
76 |
77 |
77 |
76 |
|
| Serbia | 72 |
72 |
72 |
72 |
73 |
73 |
73 |
74 |
74 |
74 |
74 |
75 |
75 |
75 |
75 |
75 |
76 |
76 |
76 |
76 |
74 |
|
Average age |
Albania | 29 |
29 |
29 |
30 |
29 |
29 |
33 |
31 |
32 |
32 |
32 |
30 |
33 |
32 |
33 |
36 |
35 |
37 |
37 |
38 |
38 |
| Bosnia and H. | 30 |
29 |
29 |
31 |
31 |
31 |
32 |
33 |
33 |
34 |
34 |
35 |
35 |
35 |
35 |
36 |
36 |
35 |
36 |
36 |
35 |
|
| Kosovo | 22 |
22 |
23 |
24 |
25 |
25 |
26 |
26 |
26 |
27 |
26 |
27 |
27 |
27 |
28 |
27 |
27 |
27 |
26 |
27 |
29 |
|
| Montenegro | 32 |
33 |
33 |
34 |
35 |
35 |
35 |
36 |
36 |
36 |
36 |
37 |
37 |
38 |
37 |
38 |
38 |
39 |
39 |
39 |
31 |
|
| N. Macedonia | 32 |
33 |
34 |
34 |
34 |
34 |
34 |
35 |
35 |
36 |
36 |
36 |
36 |
37 |
37 |
37 |
38 |
38 |
38 |
39 |
39 |
|
| Serbia | 35 |
35 |
36 |
36 |
37 |
37 |
38 |
38 |
38 |
38 |
39 |
39 |
39 |
39 |
40 |
40 |
40 |
41 |
41 |
40 |
41 |
|
Death rate per 1,000 population |
Albania | 6 |
6 |
6 |
6 |
6 |
6 |
6 |
7 |
7 |
7 |
7 |
7 |
7 |
7 |
7 |
7 |
8 |
8 |
8 |
8 |
8 |
| Bosnia and H. | 8 |
8 |
8 |
9 |
9 |
9 |
9 |
9 |
9 |
10 |
10 |
10 |
10 |
10 |
10 |
10 |
10 |
11 |
11 |
11 |
11 |
|
| Kosovo | 7 |
7 |
7 |
7 |
7 |
7 |
7 |
7 |
7 |
7 |
7 |
7 |
7 |
7 |
7 |
7 |
7 |
7 |
7 |
7 |
8 |
|
| Montenegro | 9 |
9 |
9 |
9 |
9 |
10 |
10 |
10 |
9 |
10 |
9 |
9 |
10 |
10 |
10 |
10 |
10 |
11 |
11 |
11 |
12 |
|
| N. Macedonia | 9 |
8 |
9 |
9 |
9 |
9 |
9 |
10 |
9 |
9 |
9 |
10 |
10 |
9 |
10 |
10 |
10 |
10 |
10 |
10 |
12 |
|
| Serbia | 14 |
13 |
14 |
14 |
14 |
14 |
14 |
14 |
14 |
14 |
14 |
14 |
14 |
14 |
14 |
15 |
14 |
15 |
15 |
15 |
17 |
3. Literature Review
The relationship between GEH and economic growth is likely to be context-specific depending on factors like the level of development of the healthcare system, the structure of the economy and the government's fiscal and monetary policies, which may account for the conflicting findings. In addition, the contradictory results from the literature may be due to methodological differences, data quality, sample selection and econometric methods. Some studies use cross-country data, while others use panel data. Some studies include a limited number of countries, while others include a large number of countries. Some studies only take into account a few variables, while others take into account a lot more. These studies can be broadly classified into two groups. The first group of studies uses cross-country data and different econometric methods, such as ordinary least squares (OLS), panel data methods, instrumental variables (IV) estimation and Granger causality tests. The second group of studies uses time series data for a single country and employs autoregressive distributed lag (ARDL) models.
A large body of literature on the relationship between GEH and economic growth present. A review of the literature reveals several different perspectives on the topic. The literature on the relationship between GEH and economic growth can be divided into two main categories: empirical studies and theoretical studies. Empirical studies use data from countries worldwide to examine the relationship between GEH and economic growth. Theoretical studies develop models to explain how GEH affects economic growth.
3.1. Empirical Studies
Empirical evidence on the relationship between GEH and economic growth is contradictory with some studies finding a positive relationship between the two variables (Khan & Bashar, 2015; Mushkin, 1962; Pasara, Mutambirwa, & Diko, 2020; Sethi, Mohanty, Das, & Sahoo, 2020; Shen, Chang, Yin, & Wang, 2020; Zhao et al., 2020) . Others found a negative relationship because GEH may crowd out private investment and reduce the economy's efficiency (Eggoh, Houeninvo, & Sossou, 2015; Tobing & Jeng, 2012; Yang, 2020) . Several studies have also found no significant relationship between GEH and economic growth (Di Matteo, 2010; Giannoni & Hitiris, 2002; Mehrara, 2011; Tobing & Jeng, 2012) .
Most empirical studies suggest that GEH has a positive impacts on economic growth. This is because investment in healthcare can lead to improved health outcomes which can lead to increased productivity and earnings. When a country invests more resources in healthcare, more people benefit from those investments. Mushkin (1962) proposed the “health-led growth hypothesis” which argues that a country's spending on healthcare is crucial to that country's economic development.
According to research conducted in the USA between 1980 and 2004, every one percent increase in GEH led to a 0.38% increase in GDP per capita (Moscone & Tosetti, 2010). Nghiem and Connelly (2017); Panopoulou and Pantelidis (2012) and Wang (2015) used data from 19 OECD countries and found a positive relationship between public expenditure on health and economic growth.
According to evidence from the South Asian countries, public expenditure on health has a positive and significant impact on economic growth (Sethi et al., 2020). The study found that an increase in GEH of 1% is associated with an increase in economic growth of 0.17%. The results also showed that the impact of GEH on economic growth is higher in low-income countries than in high-income countries. Sethi et al. (2020) examine the relationship between GEH and economic growth in China. They find a positive and significant relationship between these two variables. Evidence from China suggests that GEH has a positive and significant influence on economic growth. The study found that an increase in GEH of 1% is associated with an increase in economic growth of 0.13%. The results also showed that the impact of GEH on economic growth is higher in provinces with a higher level of development (Zhang, Zong, & Xiao, 2020).
Aboubacar and Xu (2017) study the impact of health expenditure on economic growth in sub-Saharan Africa. They discovered that health expenditure has a positive and significant impact on economic growth. Esen and Celik (2022) and Kurt (2015) provided data on health expenditure in Turkey. According to their research, health spending by the government has increased over the years. This increase in spending has coincided with an increase in life expectancy and a decrease in the death rate. These developments suggest that GEH has a positive impact on economic growth in Turkey.
Konatar, Kaštelan, Kaštelan, Đurašković, and Radović (2021) investigated the determinants of healthcare expenditure in Central and Eastern Europe countries. They found a positive and significant relationship between public expenditure on health and economic growth in Central and Eastern European countries.
However, several studies also find a negative relationship between GEH and economic growth (Baird, Hicks, & Kremer, 2016; Crivelli, Filippini, & Mosca, 2006; Yang, 2020) . These writers generally find that GEH can crowd out private investment and lead to higher taxes, reducing economic growth. Yang (2020) explored the effects of GEH on economic growth in 21 developing countries from 2000 to 2016. He found a negative relationship between health expenditure and economic growth.
The literature on the relationship between public expenditure on health and economic growth in the WB is very limited. Writers have examined the relationship between GEH and economic growth in the WB have found a positive relationship between the two variables (Bredenkamp & Gragnolati, 2008; Bredenkamp, Mendola, & Gragnolati, 2011; Jakovljevic, 2013; Rancic, Kovacevic, & Dragojevic-Simic, 2015; Stanculescu & Neculau, 2014; Stepovic et al., 2020; Stojic, Jevto, & Nela, 2021) .
3.2. Theoretical Studies
Two main theoretical approaches have been used to examine the relationship between public expenditure on health and economic growth. The first approach is based on the neoclassical growth model while the second approach is based on the endogenous growth model. The neoclassical growth model assumes that economic growth is determined by exogenous factors, such as technological progress. This model implies that public expenditure on health does not have a direct impact on economic growth. In contrast, the endogenous growth model suggests that public expenditure on health can affect economic growth through its impact on human capital formation.
A substantial body of evidence connects health and economic growth at both the micro and macro levels (Bloom, Canning, Kotschy, Prettner, & Schünemann, 2021; Weil, 2007; Weil, 2014) . At the microeconomic level, the empirical evidence suggests that there are fundamental relationship between health and various dimensions of economic performance such as earnings, job tenure or labor market participation rates. Several studies found a positive relationship between individual health status and earnings (Weil, 2007). The magnitude of this effect varies depending on the particular context but it is typically found to be large. For example, Weil (2014) estimates that a 10-percentage-point increase in adult survival rates raises labor productivity by 6.7 percent.
At the macroeconomic level, this relationship can be explained by the impact of health on the three main drivers of growth: labor productivity, human capital accumulation and total factor productivity (TFP). GEH can improve health outcomes and lead to higher labor productivity (Bucci, Carbonari, & Trovato, 2019; Rivera & Currais, 1999; Sengupta, 2017; Wahab & Kefeli, 2017) . A study by Culyer and Wagstaff (1993) found that a 10% increase in GEH was associated with a 0.5% increase in labor productivity. The effect is often marginal but statistically significant. Another channel is human capital accumulation. According to Azarnert (2020); Grossman (1972) and Raghupathi and Wullianallur (2020) , GEH can help in human capital development by increasing the supply of skilled workers. GEH has a positive impact on TFP (Du, Yaojun, & Xiang, 2021; Isreal, Kaliappan, & Hamzah, 2019).
4. Research Methodology
This paper is based on empirical research methodologies that analyze the impact of allocated health expenditures on economic growth per capita. We shall use total GEH as a percentage of GDP, health insurance, longevity, average age and mortality as independent variables due to their widespread use in measuring GEH and their ability to be compared across countries. We shall also employ a panel data method that considers country-specific fixed effects.
This paper focuses on the following 6 WB countries: Albania, Bosnia, Herzegovina, Kosovo, Montenegro, Northern Macedonia and Serbia from 2000 to 2020. Secondary data was collected by credible institutions that publish statistics annually, such as the World Bank, the National Statistical Offices of Western Balkan (WB) countries, Eurostat and others.
We used the following econometric model to measure the impact of independent factors on economic growth:
The Statistical Package for the Social Sciences (SPSS) software will be used to assess the data. The econometric model's dependent variable is per capita economic growth, while the other variables are independent. The sophisticated element or standard error term is shown at the end of the equation (µ), whereas a and β are parameters estimated by the model's completion.
| Nr. | Variables | Dep./Indep. | Shortcuts | Origin | The measure |
| 1 | GDP growth per capita | Depended | GDPG_C | World Bank | USD |
| 2 | Government expenditure on health | Independent | GS_H | World Bank | % of GDP |
| 3 | Health insurance | Independent | H_I | Eurostat | Dummy (1- state provides health insurance) |
| 4 | Average life expectancy | Independent | ALE_M | World Bank | Years |
| 5 | Average age | Independent | A_A | World Bank | Years |
| 6 | Mortality | Independent | MOR | World Bank | 1000 residents |
Table 2 presents the description of six variables (depended and independent) used in this research paper.
5. Research Results
The research findings are presented graphically and in tabular form. The collected data first organised and filtered before being extracted using the SPSS program.
| Variables | N |
Minimum |
Maximum |
Mean |
Std. deviation |
| GDP growth per capita | 126 |
914.78 |
8,910.65 |
4,419.38 |
1,813.66 |
| Government expenditure on health | 126 |
3.2 |
9.53 |
6.83 |
1.56 |
| Health insurance | 126 |
0 |
1 |
0.42 |
0.49 |
| Average life expectancy | 126 |
67.94 |
78.69 |
74.42 |
2.5 |
| Average age | 126 |
22.1 |
41.2 |
33.64 |
4.55 |
| Mortality | 126 |
5.87 |
16.9 |
9.5 |
2.54 |
Table 3 presents the descriptive statistics of the six study variables. The average per capita income in the five study countries is USD 4,419.38, which is very low in comparison to developed or European countries. These six countries, on average, spend 6.83% of their gross output on health expenditures, while their population has an average life expectancy of 74.42 years. In comparison, these countries are characterized by a young population with an average age of 33.64 years. The final variable represents mortality, measured in units of 1000 inhabitants and we can see that the deaths in these five countries are 9.5 inhabitants on average (per 1000 inhabitants).
Montenegro has the highest per capita income with 8,910.65 dollars in 2019. Serbia had the highest health expenditures in 2010 with 9.53% of GDP spent on health. In the following, we will present the ratio between the variables through the correlation coefficient.
According to the correlation results in Table 4, there is a positive relationship between GEH and per capita income with a coefficient r = 0.396 which is significant at the 1% significance level. As a result, rising GEH leads to higher per capita income and vice versa. At the 1% significance level, we also find a perfect positive correlation (r = 0.637) between health insurance coverage and per capita income. Therefore, countries that are more likely to provide health insurance are also more likely to have a higher per capita income or standard of living. The correlation between average life expectancy and per capita income has a positive relationship between these two variables (r = 0.729, significant at the 1% level).
| Variables | GDPG_C |
GS_H |
H_I |
LE_M |
A_A |
MOR |
|
| GDP growth per capita | Pearson correlation | 1 |
0.396** |
0.637** |
0.449** |
0.729** |
0.609** |
| Sig. (2-tailed) | 0 |
0 |
0 |
0 |
0 |
||
| N | 77 |
77 |
77 |
77 |
75 |
77 |
|
| Government expenditure on health | Pearson correlation | 0.396** |
1 |
-0.042 |
-0.469** |
0.106 |
0.560** |
| Sig. (2-tailed) | 0 |
0.713 |
0 |
0.356 |
0 |
||
| N | 77 |
80 |
80 |
80 |
78 |
80 |
|
| Health insurance | Pearson correlation | 0.637** |
-0.042 |
1 |
0.670** |
0.720** |
0.434** |
| Sig. (2-tailed) | 0 |
0.713 |
0 |
0 |
0 |
||
| N | 77 |
80 |
80 |
80 |
78 |
80 |
|
| Average life expectancy | Pearson correlation | 0.449** |
-0.469** |
0.670** |
1 |
0.691** |
0.164 |
| Sig. (2-tailed) | 0 |
0 |
0 |
0 |
0.145 |
||
| N | 77 |
80 |
80 |
80 |
78 |
80 |
|
| Average age | Pearson correlation | 0.729** |
0.106 |
0.720** |
0.691** |
1 |
0.764** |
| Sig. (2-tailed) | 0 |
0.356 |
0 |
0 |
0 |
||
| N | 75 |
78 |
78 |
78 |
78 |
78 |
|
| Mortality | Pearson correlation | 0.609** |
0.560** |
0.434** |
0.164 |
0.764** |
1 |
| Sig. (2-tailed) | 0 |
0 |
0 |
0.145 |
0 |
||
| N | 77 |
80 |
80 |
80 |
78 |
80 |
|
Note: ** At the 1% level, correlation is significant. |
Three econometric models, the ordinary least squares method (OLS), the fixed effects method (FEM) and the random effects method (REM) are summarized in Table 5. Assuming that the coefficients from the model with random effects are generally regarded as the most trustworthy, we will use them to interpret the results below.
| Model | OLS |
Fixed effect |
Random effect |
|||
Coefficient |
P value |
Coefficient |
P value |
Coefficient |
p value |
|
| Government expenditure on health | 638.72 |
0 |
288.37 |
0.001 |
638.72 |
0.000 |
| Health insurance | 1330.93 |
0 |
565.39 |
0.015 |
1330.93 |
0.000 |
| Average life expectancy | 232.68 |
0.001 |
510.55 |
0 |
232.68 |
0.001 |
| Average age | 10.39 |
0.041 |
11.25 |
0.866 |
10.39 |
0.032 |
| Mortality | -18.08 |
0.105 |
594.75 |
0 |
-18.08 |
0.891 |
Note: Dependent variable - GDP growth per capita. |
For every 1% increase in GEH, we estimate that per capita income will increase by an average of USD 638.72. GEH has a positive impact on per capita income which is significant at the 1% level of importance (p = 0.000).
At the 1% importance level, health insurance raises per capita income and has a significant impact (p = 0.000). Compared to countries without health insurance, countries with health insurance have a higher per capita income of USD 1,330.93 on average.
The average life expectancy of the population has a positive impact on living standards and has a significant impact at the level of importance of 1% (P = 0.001). We estimate that for every 1-year increase in average life expectancy, per capita income will increase by USD 232 on average. In addition to life expectancy, the model was tested for average age and the results show a positive and significant impact at the significance level of 5% (P = 0.032), with an estimated increase in per capita income of USD 10.39 for every additional year in the average age.
The mortality rate is unfavorable to per capita income. According to random effects model estimations, per capita income would decrease by USD 18.08 for each extra unit of measurement rise in death rate.
6. Conclusion and Limitations
This paper has examined the relationship between GEH and economic growth in the WB. Because our data consists of observations about various cross sections across time, the ordinary least squares (OLS) model, econometric models with fixed effects and random effects are used in this research. According to this research, GEH has a positive impact on economic growth in WB countries during the last two decades. These results are consistent with previous studies on both developed and developing countries.
The relationship between GEH and economic growth is complex and context-specific. Several factors, such as health insurance coverage, longevity, average age and death rate can impact this relationship. The regression analysis results show that healthy life expectancy at birth and total expenditure on health per capita positively and significantly affect economic growth. This means that investment in healthcare leads to an increase in human capital and productivity which leads to an increase in economic growth. The regression analysis results also show that the infant mortality rate negatively and significantly affects economic growth. The increase in GEH is expected to continue, albeit slower, in line with economic growth and the expansion of social protection systems.
Many researchers have investigated the relationship between public expenditure on health and economic development but most focus on developed countries. Few studies have been conducted in developing countries and even fewer in transition economies. Therefore, this research fills an important gap in the literature. The study's findings contribute to a better understanding of GEH's impact on WB countries' economic growth. This research is important and relevant to the current global pandemic of COVID-19 which has a profound effect on economies worldwide.
The WB region has significantly improved health outcomes in recent years. However, much work still needs to be done to close the gap with other European countries. The WB region faces many challenges when it comes to health care. These include ensuring access to quality healthcare for all, reducing inequalities in health care and improving health outcomes. Addressing these challenges will require a sustained commitment from all stakeholders, including governments, health care providers, civil society organizations and the private sector. Improving health outcomes in the WB region will require a holistic and multi-sectoral approach, focusing on prevention and treatment. It is also important to note that health outcomes are determined by the health care system and various other factors, such as education, nutrition, housing and the environment. Addressing the WB region's health care system's challenges will require a comprehensive and integrated approach.
The COVID-19 pandemic has shown how urgent it is to improve health and how important it is to be smart about how to use limited resources to stimulate economies and promote sustainable development in the region.
This study has several limitations. Our research relies on secondary data which has limitations regarding availability and factuality. Statistics from the 2000s may be inaccurate due to a lack of national statistical entities, particularly in Kosovo which was a fledgling country after the war and the Kosovo Statistics Agency (KSA) was not established until 2008. A study with a larger number of observations would yield more qualitative results and we could provide a comparative basis with the results presented in this study. The data only includes information on GEH and does not include private expenditures. This study excludes variables expected to be determinants of economic growth, such as the rule of law index, the democracy index and the corruption index. Finally, the effects of healthcare spending on different groups within a country (such as different age groups) were not studied. Despite these limitations, the study shed light on the relationship between GEH and economic growth.
References
Aboubacar, B., & Xu, D. (2017). The impact of health expenditure on the economic growth in Sub-Saharan Africa. Theoretical Economics Letters, 7(03), 615-622. https://doi.org/10.4236/tel.2017.73046
Adams, P., Hurd, M. D., McFadden, D., Merrill, A., & Ribeiro, T. (2003). Healthy, wealthy, and wise? Tests for direct causal paths between health and socioeconomic status. Journal of Econometrics, 112(1), 3-56.https://doi.org/10.1016/s0304-4076(02)00145-8
Agénor, P.-R. (2008). Health and infrastructure in a model of endogenous growth. Journal of Macroeconomics, 30(4), 1407-1422. https://doi.org/10.1016/j.jmacro.2008.04.003
Aladejare , S. A. (2022). Population health, infrastructure development and FDI inflows to Africa: A regional comparative analysis. Asian Journal of Economic Modelling, 10(3), 192–206. https://doi.org/10.55493/5009.v10i3.4568
Alsan, M., Bloom, D. E., & Canning, D. (2006). The effect of population health on foreign direct investment inflows to low-and middle-income countries. World Development, 34(4), 613-630. https://doi.org/10.1016/j.worlddev.2005.09.006
Althawaini, H., Elmulthum, N., & Morsi, H. (2022). A study on the impact of the Saudi Citizen account as a compensation program to achieve food security for low-income Citizens Under the Kingdom’s Vision 2030. Asian Development Policy Review, 10(2), 77–87. https://doi.org/10.55493/5008.v10i2.4461
Arelleno, M., & Bover, O. (1995). Another Look at instrumental variable estimation of error component models. Journal of Econometrics, 68(1), 29-51. https://doi.org/10.1016/0304-4076(94)01642-d
Azarnert, L. V. (2020). Health capital provision and human capital accumulation. Oxford Economic Papers, 72(3), 633-650. https://doi.org/10.1093/oep/gpaa004
Baird, S., Hicks, J. H., & Kremer, M. E. (2016). Worms at work: Long-run impacts of a child health investment. The Quarterly Journal of Economics, 131(4), 1637-1680. https://doi.org/10.1093/qje/qjw022
Behera, D. K., & Dash, U. (2019). Impact of macro-fiscal determinants on health financing: Empirical evidence from low-and middle-income countries. Global Health Research and Policy, 4(1), 1-13.https://doi.org/10.1186/s41256-019-0112-4
Bloom, D. E., Canning, D., Kotschy, R., Prettner, K., & Schünemann, J. (2021). Health and economic growth: Reconciling the micro and macro evidence. Retrieved from NBER Working Paper, No. (w26003):
Boucekkine, R., Diene, B., & Azomahou, T. (2008). Growth economics of epidemics: A review of the theory. Mathematical Population Studies, 15(1), 1-26. https://doi.org/10.1080/08898480701792410
Boussalem, F., Boussalem, Z., & Taiba, A. (2014). The relationship between public spending on health and economic growth in Algeria: Testing for co-integration and causality. International Journal of Business and Management, 2(3), 25-39.
Bredenkamp, C., & Gragnolati, M. (2008). Sustainability of health care financing in the western Balkans: An overview of progress and challenges. South East Europe Review, 11(2), 151-184. https://doi.org/10.5771/1435-2869-2008-2-151
Bredenkamp, C., Mendola, M., & Gragnolati, M. (2011). Catastrophic and impoverishing effects of health expenditure: New evidence from the Western Balkans. Health Policy and Planning, 26(4), 349-356. https://doi.org/10.1093/heapol/czq070
Bucci, A., Carbonari, L., & Trovato, G. (2019). Health and income: Theory and evidence for OECD countries. In Human Capital and Economic Growth. In (pp. 169-207). Cham: Palgrave Macmillan.
Bustamante, A. V., & Shimoga, S. V. (2018). Comparing the income elasticity of health spending in middle-income and high-income countries: The role of financial protection. International Journal of Health Policy and Management, 7(3), 255. https://doi.org/10.15171/ijhpm.2017.83
Cooper, R. N., & Robert, W. F. (2004). The escape from hunger and premature death, 1700-2100: Europe, America, and the third world. Foreign Affairs.
Crivelli, L., Filippini, M., & Mosca, I. (2006). Federalism and regional health care expenditures: An empirical analysis for the Swiss cantons. Health Economics, 15(5), 535-541. https://doi.org/10.1002/hec.1072
Culyer, A., & Wagstaff, A. (1993). Equity and equality in health and health care. Journal of Health Economics, 12(1), 431-457. https://doi.org/10.1016/0167-6296(93)90004-x
Di Matteo, L. (2010). The sustainability of public health expenditures: Evidence from the Canadian federation. The European Journal of Health Economics, 11(6), 569-584. https://doi.org/10.1007/s10198-009-0214-x
Du, Y., Yaojun, F., & Xiang, H. (2021). The efficiency of provincial government health care expenditure after China’s new health care reform. Macroeconomics and Finance in Emerging Market Economies. https://doi.org/10.1080/17520843.2021.1979328
Easterly, W., & Rebelo, S. (1993). Fiscal policy and economic growth. Journal of Monetary Economics, 32(3), 417-458.
Eggoh, J., Houeninvo, H., & Sossou, G.-A. (2015). Education, health and economic growth in African countries. Journal of Economic Development, 40(1), 93-111.https://doi.org/10.35866/caujed.2015.40.1.004
Esen, E., & Celik, K. M. (2022). Economic growth and health expenditure analysis for Turkey: Evidence from time series. Journal of the Knowledge Economy, 13(3), 1786-1800. https://doi.org/10.1007/s13132-021-00789-8
Fioroni, T. (2010). Optimal savings and health spending over the life cycle. The European Journal of Health Economics, 11(4), 355-365. https://doi.org/10.1007/s10198-009-0183-0
Giannoni, M., & Hitiris, T. (2002). The regional impact of health care expenditure: The case of Italy. Applied Economics, 34(14), 1829-1836. https://doi.org/10.1080/00036840210126809
Grossman, M. (1972). On the concept of health capital and the demand for health. Journal of Political Economy, 80(2), 223-255. https://doi.org/10.1086/259880
Gumbau, A. M. (2021). The impact of health status and human capital formation on regional performance: Empirical evidence. Papers in Regional Science, 100(1), 123-139. https://doi.org/10.1111/pirs.12561
Guo, J. J., & Meei, Y. K. (2015). How much should countries spend on health? African and Asian Studies.
Ihori, T., Kato, R. R., Kawade, M., & Bessho, S.-i. (2011). Health insurance reform and economic growth: Simulation analysis in Japan. Japan and the World Economy, 23(4), 227-239. https://doi.org/10.1016/j.japwor.2011.07.003
Ikegami, M., & Wang, Z. (2022). Does military expenditure crowd out health-care spending? Cross-country empirics. Quality & Quantity, 1-16. https://doi.org/10.1007/s11135-022-01412-x
Isreal, A. I. O., Kaliappan, S. R., & Hamzah, H. Z. (2019). Impact of health capital on total factor productivity in Singapore. Malaysian Economic Journal, 53(2), 83-98. https://doi.org/10.17576/jem-2019-5302-7
Jakovljevic, M. B. (2013). Resource allocation strategies in Southeastern European health policy. The European Journal of Health Economics, 14(2), 153-159. https://doi.org/10.1007/s10198-012-0439-y
Khan, H., & Bashar, O. K. (2015). Social expenditure and economic growth: Evidence from Australia and New Zealand using cointegration and causality tests. The Journal of Developing Areas, 49(4), 285-300. https://doi.org/10.1353/jda.2015.0137
Konatar, M., Kaštelan, S., Kaštelan, U., Đurašković, J., & Radović, M. (2021). What drives healthcare expenditure growth?: Evidence from central and eastern European economies. Economic Journal, 69(7), 750-765. https://doi.org/10.31577/ekoncas.2021.07.05
Kotschy, R. (2021). Health dynamics shape life-cycle incomes. Journal of Health Economics, 75, 102398. https://doi.org/10.1016/j.jhealeco.2020.102398
Kurt, S. (2015). Government health expenditures and economic growth: A Feder-Ram approach for the case of Turkey. International Journal of Economics and Financial Issues, 5(2), 441-447.
Mcintyre, D., Meheus, F., & Røttingen, J.-A. (2017). What level of domestic government health expenditure should we aspire to for universal health coverage? Health Economics, Policy and Law, 12(2), 125-137. https://doi.org/10.1017/s1744133116000414
Medeiros, J., & Schwierz, C. (2015). Efficiency estimates of health care systems (No. 549). Retrieved from Directorate General Economic and Financial Affairs (DG ECFIN), European Commission, pp 549-558:
Mehrara, M. (2011). Health expenditure and economic growth: An ARDL approach for the case of Iran. Journal of Economics and Behavioral Studies, 3(4), 249-256. https://doi.org/10.22610/jebs.v3i4.277
Moscone, F., & Tosetti, E. (2010). Health expenditure and income in the United States. Health Economics, 19(12), 1385-1403. https://doi.org/10.1002/hec.1552
Mushkin, S. J. (1962). Health as an investment. Journal of Political Economy, 70(5, Part 2), 129-157.
Nghiem, S. H., & Connelly, L. B. (2017). Convergence and determinants of health expenditures in OECD countries. Health Economics Review, 7(1), 1-11. https://doi.org/10.1186/s13561-017-0164-4
Panopoulou, E., & Pantelidis, T. (2012). Convergence in per capita health expenditures and health outcomes in the OECD countries. Applied Economics, 44(30), 3909-3920. https://doi.org/10.1080/00036846.2011.583222
Pasara, M. T., Mutambirwa, T. K., & Diko, N. (2020). The trivariate causality among education, health, and economic growth in Zimbabwe. Sustainability, 12(4), 1357. https://doi.org/10.3390/su12041357
Plianbangchang, S. (2018). Universal health coverage (UHC). Journal of Health Research, 32(4), 322-324. https://doi.org/10.1108/jhr-05-2018-029
Raghupathi, V., & Wullianallur, R. (2020). Healthcare expenditure and economic performance: Insights from the United States data. Frontiers in Public Health, 8. https://doi.org/10.3389/fpubh.2020.00156
Rana, R. H., Alam, K., & Gow, J. (2020). Health expenditure and gross domestic product: Causality analysis by income level. International Journal of Health Economics and Management, 20(1), 55-77. https://doi.org/10.1007/s10754-019-09270-1
Rancic, N., Kovacevic, A., & Dragojevic-Simic, V. (2015). Long-term health expenditure changes in selected Balkan countries. Frontiers in Public Health, 3, 152.
Rhee, H.-J. (2014). The relationship between healthcare expenditure and national income in different income groups. Information: An International Interdisciplinary Journal, 17(12), 6063-6070.
Rivera, B., & Currais, L. (1999). Income variation and health expenditure: Evidence for OECD countries. Review of Development Economics, 3(3), 258-267. https://doi.org/10.1111/1467-9361.00066
Romer, P. (1990). Endogenous technical progress. Journal of Political Economy, 98(5), 71-103.
Santiago, R. D., Joseph, E., & Tubayan, C. (2016). The relationship of health-related factors to labor productivity of the ASEAN-5. Review of Integrative Business and Economics Research, 5(4), 138.
Sengupta, K. (2017). Health and its impact on labour productivity and labour market. International Journal of Health and Medicine, 2(1), 13-16. https://doi.org/10.24178/ijhm.2017.2.1.13
Sethi, N., Mohanty, S., Das, A., & Sahoo, M. (2020). Health expenditure and economic growth nexus: Empirical Evidence from South Asian countries. Global Business Review, 1, 097215092096306. https://doi.org/10.1177/0972150920963069
Shen, Q., Chang, B., Yin, G., & Wang, W. (2020). The impact of health investment on economic growth: Evidence from China. Iranian Journal of Public Health, 49(4), 684-692. https://doi.org/10.18502/ijph.v49i4.3174
Sokolskaya, M., Madzhuga, A., Babieva, N., Khvostunov, K., Moskvina, A., Abdullina, L., & Golovneva, E. (2019). The effect of personal health on the formation of human capital: A metasystem approach. International Journal of Public Health Science, 8(1), 134-142. https://doi.org/10.11591/ijphs.v8i1.17837
Stanculescu, M. S., & Neculau, G. (2014). The performance of public health-care systems in South East Europe. In (pp. 1). Belgrade: Friedrich Ebert Stiftung.
Stepovic, M., Rancic, N., Vekic, B., Dragojevic-Simic, V., Vekic, S., Ratkovic, N., & Jakovljevic, M. (2020). Gross domestic product and health expenditure growth in Balkan and East European countries—three-decade horizon. Frontiers in Public Health, 8, 492. https://doi.org/10.3389/fpubh.2020.00492
Stojic, V., Jevto, E., & Nela, E. (2021). Indicators of the effectiveness of the healthcare financing system in the Western Balkan countries - critical analysis. Military Medical Examination, 78(12), 1303-1310.
Stuckler, D., & Basu, S. (2009). The International Monetary Fund's effects on global health: Before and after the 2008 financial crisis. International Journal of Health Services, 39(4), 771-781. https://doi.org/10.2190/hs.39.4.j
Tobing, E., & Jeng, J.-L. (2012). Long-run growth and welfare effects of rising us public health expenditure. Public Finance Review, 40(4), 470-496. https://doi.org/10.1177/1091142112442238
Wahab, A., & Kefeli, Z. (2017). Modeling the effect of healthcare expenditure and education expenditure on labour productivity: A study on OIC countries. Journal of Business and Economics Review, 2(2), 31-37. https://doi.org/10.35609/jber.2017.2.2(5 )
Wang, R., Wang, R., & Ma, H. (2019). The effect of healthy human capital improvement on savings and growth: An empirical study based on China’s inter-provincial panel data. Proceedings of the Faculty of Economics in Rijeka: Magazine for Economic Theory and Practice, 37(1), 29-54. https://doi.org/10.18045/zbefri.2019.1.29
Wang., F. (2015). More health expenditure, better economic performance? Empirical evidence from OECD countries. INQUIRY: The Journal of Health Care Organization, Provision, and Financing, 52, 0046958015602666. https://doi.org/10.1177/0046958015602666
Weil, D. N. (2007). Accounting for the effect of health on economic growth. The Quarterly Journal of Economics, 122(3), 1265-1306. https://doi.org/10.1162/qjec.122.3.1265
Weil, D. N. (2014). Health and economic growth. In Handbook of economic growth. In (Vol. 2, pp. 623-682): Elsevier.
Welfens, P. J. (2020). Macroeconomic and health care aspects of the coronavirus epidemic: EU, US and global perspectives. International Economics and Economic Policy, 17(2), 295-362. https://doi.org/10.1007/s10368-020-00465-3
World Bank. (2022a). Coverage of social insurance programs (% of Population). World Development Indicators. Retrieved from https://data.worldbank.org/indicator/per_si_allsi.cov_pop_tot .
World Bank. (2022b). Current health expenditure per capita (Current US$). World Development Indicators. Retrieved from https://data.worldbank.org/indicator/SH.XPD.CHEX.PC.CD .
World Bank. (2022c). GDP per capita (Current US$).” World Development Indicators. Retrieved from https://data.worldbank.org/indicator/NY.GDP.PCAP.CD .
World Bank. (2022d). Life expectancy at birth, 2000-2020.” World Development Indicators. Retrieved from https://data.worldbank.org/indicator/SP.DYN.LE00.IN .
Yang, X. (2020). Health expenditure, human capital, and economic growth: An empirical study of developing countries. International Journal of Health Economics and Management, 20(2), 163-176. https://doi.org/10.1007/s10754-019-09275-w
Zhang, X., Zong, G., & Xiao, D. (2020). Effects of government healthcare expenditure on economic growth based on spatial Durbin model: Evidence from China. Iranian Journal of Public Health, 49(2), 283-293.
Zhao, Z., Jia, H., & Chen, M. (2020). Major socioeconomic driving forces of improving population health in China: 1978–2018. Population and Development Review, 46(4), 643-676. https://doi.org/10.1111/padr.12370
Zheng, L. (2019). Social health insurance reform and economic growth: Simulation analysis in China. International Journal of Business and Economics Research, 8(6), 458. https://doi.org/10.11648/j.ijber.20190806.26