Are there common tacit drivers for European Recovery and Resilience grants distribution and allocation in Health?

doi:10.21203/rs.3.rs-3327115/v1

Download PDF

Research Article

Are there common tacit drivers for European Recovery and Resilience grants distribution and allocation in Health?

https://doi.org/10.21203/rs.3.rs-3327115/v1

This work is licensed under a CC BY 4.0 License

Version 1

posted

You are reading this latest preprint version

This work aims to identify drivers for the distribution and allocation of EU Recovery and Resilience Facility (RRF) grants for Health. To achieve this, 29 variables were collected and divided into four domains. A Principal Component Analysis was done, and a linear regression analysis was estimated to test the existence of drivers of RRF distribution and allocation. Our main findings demonstrate that factors like avoidable mortality and self-reported poor health status drive grants per capita for Health, while Bismarck-type health systems, number of beds and health expenditures discourage the allocation of those grants. We have also found that certain political characteristics, such as governments constituted by a single party or a majority in parliament, may affect the distribution and allocation of RRF money for health.

Health Policy

Tacit drivers

RRF

European Funds

Allocation

Health

the only formal rules for the distribution of RFF grants in Health that is significant is GDP per capita.
there are tacit drivers for the distribution and allocation of RFF grants in health related with population health, health resources and government political characteristics.

The public health and economic crisis that emerged from the COVID-19 pandemic and the mitigating measures, in 2020, has created an exceptional situation across the world. In the European Union, the economic recession defined by the decline of the Gross Domestic Product (GDP) called for exceptional measures from the European Union institutions. The response of the European Union was to create an emergency plan, based on the idea of generating expansionary measures to promote economic growth and reduce health inequalities. So, in 2020, NextGenerationEU was launched to address the socioeconomic impacts of COVID-19 and to boost reforms with long-term effects in the EU (EU Commission 2021, 2022). At the core of this emergency plan is the EU Recovery and Resilience Facility (RRF), which comprises grants and loans for EU members for reforms and public investment to be implemented up to the year 2026.

The investment priorities under RRF cover six areas, of which the fifth is “Health and economic, social and institutional resilience, with the aim of, inter alia, increasing crisis preparedness and crisis response capacity” (European Commission 2021, 2022); within these priorities several flagships are to be considered such as digitalization of public administration, data cloud capacities and sustainable processors. The intertwined nature of the priorities and flagships of the investments and reforms may create some difficulties in labelling and categorizing them.

Member states had to submit their national Recovery and Resilience Plans to the European Commission, which were then approved for implementation. Each national plan outlines the investments and reforms that will be carried out over the next 4–6 years. Despite the clear establishment of priorities areas and flagships that RRF plans must comply with to access funds, it is up to the national governments to draft their own plan according to what they consider to be their key national challenges and priorities. An overall comparison across RRF plans may identify several horizontal themes that have attracted the attention of national RRF plans because of their contribution to the improvement of health and reduction of health inequities: public health, health promotion and disease prevention, primary and community care, mental care, health and social workforce, monitoring and surveillance systems, digital inclusion, and support for families and equal opportunities to education for children (EuroHealthNet 2021).

The distribution of RRF funds is based on a rule defined in the RRF regulation (Regulation EU 2021/241, Annexes II and III), which accounts for the size of the population, inverse of GDP per capita, average unemployment rate and the fall of the real GDP. The amounts to be made available to countries ought to increase with the size of the population, unemployment rate and decrease of GDP per capita, and ought to decrease for large GDP per capita (EuroHealthNet 2021). Despite these criteria to distribute the RRF grants, Guillamón et al. (2021) argued that there are other non-explicit factors that may influence that distribution, such as the number of COVID-19 cases and mortality rate.

Focusing on the RRF grants aimed at Health, this analysis seeks to find the potential tacit drivers, on top of the formal rules described in RRF regulation, for the distribution and allocation of the grants. We focus on the grants aimed at the health sector because at the origin of the RRF plan lies a public health crisis which revealed the weaknesses of European countries when it came to health and health systems (European Health Observatory 2022; PHSSR 2022). We consider four different domains of possible drivers and conduct a statistical analysis aiming to test the importance of the different influences in the RRF distribution and allocation. By distribution of RFF grants, we mean the relative amount of grants provided to a country and by allocation we mean the share of the total grant given to a country which is applied in Health. We use Health to express the related area with population health and health systems.

Our main findings show that drivers for the grants per capita for the health sector include avoidable mortality and self-reported bad-health status, while factors like number of beds and the health expenditures in Bismarck-type countries dissuade the allocation of grants; the drivers for a larger percentage of grants aimed at Health include unmet health needs reported by the country’s population. Last but not least, we also found that some political features may influence the distribution and allocation of RRF funds for Health, such as governments formed by a single party and governing with a majority in parliament.

Overview on the factors explaining allocation of European funds

The distribution and allocation of European funds usually follows established rules or criteria formally described in European regulation. Nevertheless, the are other non-formal or tacit factors that influence the distribution and allocation of European funds. Unfortunately, there are very few studies that focus on this issue and set out to identify and comprehend these tacit drivers. Concerning the RRF funds, Guillamón et al. (2021) confirmed that countries with larger populations and higher unemployment rates were entitled to larger RRF grants. But these authors also found the larger grants were associated with larger GDP per capita, despite the formal rule described in RRF regulation. Additionally, grants were associated with health factors such as the number of COVID-19 deaths and COVID-19 cases per capita.

Analysis applied to other European funds show the existence of non-formal drivers in parallel with formal rules for the distribution and allocation of the funds.

The first factor taken into consideration for the distribution of European funds is the size of the population, so that all socioeconomic measures may be reported in per capita units. In this way, larger populations are associated with a larger share of funds, at least up to a point (Kalo et al. 2019); in terms of GDP per capita, lower levels are usually associated with higher levels of funds (Becker 2012; Bouvet and Dall’Erba 2010; European Parliament 2022 (2)). Despite the lack of scientific interest in the role that age and education levels may play in the distribution of funds due to the indirect path of influence, Kisiala et al. (2018) found that, in Poland, demographic features of the population are associated with different spatial preferences for the spending the European funds.

Socioeconomic rules are the basis for the distribution of European funds because these are easily measurable, well-known and available from Eurostat. For instance, Structural Funds and the Cohesion Fund are directed to regions with a GDP per capita at or below 75% of the EU average (European Parliament 2022) and so, less developed regions are expected to receive more funding than regions that are more developed. However, this statement may not be observable in real allocations; furthermore, some differences may arise from expertise attracting the EU funds or in the absorption capabilities of the funds (Heijman and Koch 2011). Nevertheless, economic criteria (usually formal factors) are generally relevant factors which explain the allocation of European Structural Funds (Bouvet and Dall’Erba 2010; Kalo et al. 2019).

There is one particular socioeconomic characteristic related with the modern principles of efficiency, transparency, merit, equity and objectivity of governance. These features are the only accepted norms for governance quality, which assumes the absence of corruption (Mungiu-Pippidi 2013). This has long been a major concern for the EU. In some cases, EU funds contribute to a higher risk of corruption, in others this may not be the case (Fazekas and Toht 2016; Teichmann et al 2020). Despite the improvements in controlling corruption, it is still relevant and entrenched in some EU countries (Toth and Palocz 2022; Popescu 2014).

Achim and Borlea (2015) focused their attention on the political and public governance of countries to explain the absorption of European funds and confirmed that better public governance resulted in higher absorption of funds. In the same line of research Charron (2016) explains the determination of Structural funds based on formal and informal institutions of the regions. He found that higher quality of government is associated with greater transfers in regions with higher autonomy, while lower quality of government is correlated with greater amount of funds in regions with lower levels of regional autonomy.

Finally, researchers have paid some attention to the political factors that influence the distribution of funds in the EU. Bouvet and Dall’Erba (2010) distinguish the influence of the political factors at the national and regional levels and found that there is a different influence on the level of the political factors according to the cohesion objectives. At this stage it is worth referring to the work by Bellido (2019) on the relevance of political factors at the level of national and public decision. Specifically, Bellido focused on the relationship between public healthcare expenditures and political and electoral factors, and confirmed the association with government characteristics and the growth of public health expenditures (left-wing governments incentivize spending while minority governments do not).

To end on the topic about the importance of political factors it is worth mentioning that Muntaner et al. (2011) reviewed the importance of politics on population health and concluded that countries with a left, egalitarian and/or liberal tradition are more likely to perform better in terms of population health.

In summary, it may be concluded that in addition to the formal rules for assigning European funds to member countries, there are other tacit drivers which contribute to explaining their distribution and allocation. These drivers may be grouped into four domains: i) Demographic, Education, and Population Health (DEPH), 2) Social and Economic (SE), 3) Expenditures and Resources in health (ER), and 4) Political Characteristics of the Government (PCG).

Analytical framework

The analytical framework we have constructed to conduct our analysis may be represented by Fig. 1. Our approach is based on a large set of variables, grouped into four thematic domains, which provide statistical information to explain the tacit drivers of the distribution and allocation of the RRF grants in Health across EU member countries.

Recovery and Resilience Facility funds related to health

The purpose of this analysis is to find possible common decision drivers for the allocation of RRF funds on Health. We therefore looked at grants applied in Health per capita and the share of these grants in the total amount of grants awarded to each EU member. These data are collected from Bruegel (2022), from the EU (2022), and from the approved plans (in the case of Hungary). The label for these two variables is H_RRFpc and H_RRFperc, respectively.

Explanatory variables

Explanatory variables are the potential drivers associated with the allocation of RRF grants in Health. These variables have been grouped into 4 domains as described in Table 1: 1) Demographic, Education and Population Health, 2) Social and Economic, 3) Expenditures and Resources in health, and finally 4) Political characteristics of the Government. For the three first domains, we have taken the last 5 years (2016–2020), or the available years within that period, and obtained an average to smooth time variations. The source for these variables is the Eurostat Database. The source for the variable measuring the perception of corruption is Dalhberg et al. (2022). For the domain concerning political characteristics of the government in the country, we have considered the prevailing features of 2020, and the sources for these variables are Fernando Casal Bértoa (2022), Bakker et al. (2020) and Dahlberg et al. (2022).

Table 1

Explanatory variables by domain
Variable	Description	Abbreviation
DOMAIN: Demographic, Education and Population Health
Ageing population	Percentage of population older than 65	POP65
Secondary education	Percentage of population, aged 15–74, with secondary level of schooling (levels 3–4 of International Standard Classification of Education)	MID_EDU
Tertiary education	Percentage of population, aged 15–74, with higher level of education (levels 5–8 of International Standard Classification of Education)	UP_EDU
Life expectancy at birth	Life expectancy at birth	LE
Avoidable mortality	Percentage of preventable and treatable deaths, excluding 2020 due to excess mortality caused by COVID-19.	AVOID_M
Healthy life years at 65	Number of expected healthy life years at 65	HLY65
Infant mortality	Infant mortality rate (per 1000 births)	INF_M
Bad or very bad health status	Percentage of population older than 16 who reports a self-assessed health as Bad or Very Bad	BAD_H
Unmet healthcare needs	Percentage of population older than 16 reporting unmet health needs due to financial constraints, long distance and waiting lists.	UNMET

Variable	Description	Abbreviation
DOMAIN: Social and Economic
GDP per capita	Real GDP per capita (chain linked volumes 2010)	GDPpc
GDP per capita growth	Percentual change of GDP per capita between two sequential years	VGDPpc
Unemployment	Unemployment rate of active population aged 15–74	UNEMPL
Social expenditures	Social protection expenditures as a share of GDP	SOCIAL_EX
Investment	Share of GDP for gross fixed capital formation	INVEST
Poverty	Percentage of people in risk of poverty or social exclusion	RISK_POV
Public debt	Public debt as a share of GDP	DEBT_GDP
Transparency and corruption	Index for corruption perception. This index measures the perception of people to the corruption in the public sector. The index varies from 0 to 100, where 0 means extremely high perception of corruption in the public sector and 100 means perception of corruption absence.	NO_CORR
Income inequality	Gini coefficient measures inequality of equivalised disposable income. The index varies from 0 to 100, where 0 means no inequality and 100 means the maximum level of inequality.	GINI

Variable	Description	Abbreviation
DOMAIN: Expenditures and Resources in health
Bismark-type health expenditures	Percentage of total health expenditure financed by compulsory social health insurance schemes, common among Bismark-type of health systems.	HE_BIS
Beveridge-type health expenditures	Percentage of total health expenditure financed by taxes or government schemes, common among Beveridge-type of health systems.	HE_BEV
Direct payments	Percentage of total health expenditure paid by households’ direct payments as out-of-pocket payments.	OOP
Doctors	Number of physicians per 100 000 inhabitants (this number is overestimated for Portugal and Greece)	DOC
Nurses	Number of nurses per 100 000 inhabitants	NUR
Beds	Number of hospital beds per 100 000 inhabitants	BEDS

Variable	Description	Abbreviation
DOMAIN: Political Characteristics of Government
Left or right	Political orientation of the government. This variable takes value 1 if government is left-oriented and 0 if it is right-oriented.	LEFT
Liberal of conservative	Political orientation of the government. This variable takes value 1 if government is liberal-oriented and 0 if it is conservative-oriented.	LIBER
State intervention or market orientation	Political orientation of the government towards its intervention in the market. This variable takes value 1 if government is for state-intervention and 0 if it prefers a market orientation.	STATE
Single party or coalition	Government formation is single party or a coalition of parties. This variable takes value 1 if government is formed by a single party and 0 if it is a coalition.	SINGLE
Majority of minority in the parliament	Government support in the parliament. This variable takes value 1 if government has a parliament majority seats or 0 if it has a minority parliament seats.	MAJOR

Strategy Design

The analysis performed in STATA 15 (StataCorp 2017) comprises 3 sequential stages:

Stage 1 – Clusters of countries

Using the ordinal variables of domains 1) Demographic, Education and Population Health, 2) Social and Economic and 3) Expenditures and Resources in healthcare, we have performed a cluster analysis. This analysis is based on a hierarchical analysis using Ward method (Ward 1963) with the Gower dissimilarity measure (Gower 1971). The construction of these clusters is useful mainly for stage 3 to account for the heterogeneity of countries and mitigate heteroscedasticity in linear regression estimations.

Stage 2 – Principal Component Analysis (PCA)

A Principal Component Analysis (PCA) for three domains of variables is performed, but not for the Political Characteristics of the Government domain because all its variables are binary. All ordinal variables were standardized to be used for PCA. This approach has the advantage of allowing the reduction of the number of variables into components created as a linear combination of a set of variables. This procedure allows to keep the relevant information and discard redundant information (Watkins 2021, 69).

The criterion used to select the number of principal components for the PCA was that components ought to explain at least 50% of the variables’ variation or the first two extracted components, so that the analysis remains feasible and parsimonious (Glen 2022). We also include the KMO test to check for the strength of the partial correlation between variables, as well as the Bartlett’s test of sphericity to test whether the correlation matrix is an identity matrix.

Stage 3 – Linear regression analysis

In this last stage, we test the potential relationship between the RRF grants distribution and allocation, in values per capita and in percentage of total grants (H_RRFpc and H_RRFperc), and the extracted components in each domain and the political government characteristics. To minimize countries heterogeneity effects in the inference, we consider a robust variance-covariance matrix of standard errors and adjust it to the country’s clusters found in stage 1.

Empirical findings

Empirical findings are presented sequentially following the three stages described before.

Stage 1 – Clusters of countries

The cluster analysis resulted in the creation of 5 clusters of countries, listed in Table 1A, in appendix, and shown in Picture 1. We have named the clusters according to the countries composing the cluster and its geographic position. The correspondent dendrogram is shown in Graph 1A, in the appendix.

Stage 2 – Principal Component Analysis (PCA)

We have undertaken a Principal Component Analysis for the three domains, after standardizing all variables, since the variance is the most relevant information obtained from each variable. We have considered the first two components which usually capture most of the variable’s variance and which allow for a bi-dimensional, feasible and parsimonious analysis.

The correlation matrix for the set of variables for the three domains under analysis are presented in Table 2A–4A, in appendix. Additionally, the components loadings graphs for each domain are presented in the appendix (Graph 2A-4A).

Domain – Demographic, Education and Population Health (DEPH)

The two extracted components in this domain explain about 63% of the variance of the set of variables. The stronger influence in both principal components comes from disadvantageous health indicators and strong negative influence comes from advantageous health indicators (Graph 2A, in appendix). Component 1 is strongly influenced by avoidable mortality and the percentage of people reporting bad or very bad health status, while is strongly negatively influenced by favorable health indicators such as life expectancy and healthy life years. Component 2 is pulled up by the percentage of people reporting unmet health needs and pulled down by infant mortality. This result is summarized in Table 2, as well as the Bartlett test for sphericity and KMO measure. These two statistics show that it is reasonable to perform a PCA.

Table 2

PCA for DEPH domain
	Variables		Explained variance
	Sign (+)	Sign (-)
Component 1	AVOID_M	LE	0.467
	BAD_H	HLY65
Component 2	UNMET	INF_M	0.166
Bartlett test of sphericity	161.304 (0.000)
KMO Measure	0.648

From the country score variables, it can be observed that the importance of component 1 is clear and relevant for countries in Eastern Europe clusters as displayed in Graph 1.

Domain – Social and Economic variables (SE)

In the domain of socioeconomic variables, the PCA is reasonable to perform according to Bartlett and KMO test and first two components explain nearly 55% of variables variability (Table 3). The extracted component 1 is strongly influenced by the corruption indicator and the value of the GDP per capita (which would be expected due to RRF regulations) and it is negatively influenced by disadvantageous social indicators such as the percentage of people in risk of poverty or social exclusion and the income inequality measured by GINI coefficient. Component 2, on its turn, is strongly determined by the percentage of GDP for social expenditures, by the share of country debt and by the percentage of unemployment (being unemployment one of the formal rules of RRF regulation). But this component is pull down by the GDP share aimed to investment in gross capital and by the average variation of the GDP per capita (also expected from RRF regulations), as graphically shown in Graph 3A, in appendix.

Table 3

PCA for SE domain
	Variables		Explained variance
	Sign (+)	Sign (-)
Component 1	NO_CORR	RISK_POV	0.355
	GDPpc	GINI
Component 2	SOCIAL_EX	INVEST	0.224
	DEBT_GDPt	VGDP
	UNEMPL
Bartlett test of sphericity	150.423 (0.000)
KMO Measure	0.575

Observing Graph 2 with country score variables, Southern countries are more influenced by component 2 and negatively influenced by component 1. Northern and Central European countries register a larger weight for component 1 and much less influence from component 2. For instance, countries with low public perception of corruption are situated on the right-hand side of the graph and those with high income disparity are on the left-hand side of the graph.

Domain – Expenditures and Resources in health (ER)

Concerning the domain for Expenditures and Resources in Health, the extracted components may be described as follows (Table 4). Components 1 and 2 can capture almost 78% of the variable’s variance. Component 1 is advantaged by Bismarck-type health expenditures and by the number of beds and it is disadvantaged by Beveridge-type of health expenditures. Component 2 is positively influenced by out-of-pocket expenditures and by the number of doctors while it is negatively influenced by the number of nurses per capita. Graph 4A, in appendix, makes clear the opposing positions of health expenditures in both Bismarck and Beveridge-type of health systems, but also between doctors and beds in component 1; in component 2, the opposing variables are doctors and nurses.

Table 4

PCA for ER domain
	Variables		Explained variance
	Sign (+)	Sign (-)
Component 1	HE_BIS	HE_BEV	0.454
	BEDS
Component 2	OOP	NUR	0.325
	DOC
Bartlett test of sphericity	98.309 (0.000)
KMO Measure	0.583

Graph 3 for the country scores for the expenditures and resources in healthcare allows us to clearly identify the type of financing of the health system. So, countries with a Beveridge-type health system are situated on the left-hand side of the graph and the countries from the Central Europe cluster with a Bismarck-type health system on the right-hand side. Countries with high OOP and high percentage of doctors, where component 2 has a considerable weight are Greece, Latvia, and Cyprus.

Domain – Political Characteristics of the Government

The domain for the political government characteristics includes binary variables which describe the main features of the government, namely left/right, liberal/conservative, market/state oriented, and majority/coalition government.

The correlation (measured by tetrachoric correlation) between these characteristics is presented in Table 5. So, as expected, there is a strong correlation between left, liberal and state-oriented governments.

Table 5

Tetrachoric correlation
	LEFT	LIBERAL	STATE	SINGLE
LEFT	1.00
LIBERAL	0.769	1.00
STATE	0.869	0.499	1.00
SINGLE	-0.183	-0.032	0.18	1.00
MAJORITY	-0.401	0.032	-0.455	-0.065

Stage 3 – Linear regression analysis

After predicting the components for each country, we have estimated linear regressions for each domain to explain the distribution of the amount of grants per capita for health and the share of grants directed to health sector. Most of the results found are not statistically significant and so they are not reported here but are available in Table 5A, in appendix. Below, in Table 6, we present the results with statistical significance up to 10%.

Table 6

Linear regressions explaining distribution and allocation of RRF grants to Health
					Health grants per capita H_RRFpc	Health grants share H_RRFperc
Domain	Component		sign in component	estimated coefficient sign
DEPH	Component 1	(+) AVOID_M; (+) BAD_H (-) LE; HLY65			+	ns
	Component 2	(+) UNMET (-) INF_M			ns	+
ER	Component 1	(+) HE_BIS; (+) BEDS (-) HE_BEV			ns	-

Note: ns- non-significant

Results point to the significance of component 1 of DEPH explaining the distribution of grants to health area per capita (H_RRFpc). This implies that the grants distribution per capita accounts for some health indicators. While it weights positively for preventable mortality and bad health self-assessed health status by larger shares of the population, it weights health indicators such as life expectancy and healthy life years at 65 negatively.

On the other hand, when looking for the drivers explaining the percentage of grants allocated to health (H_RRFperc), we found that component 2 of DEPH contributes to higher shares grants aimed at health while component 1 of ER reduces share of grants allocated to health (for a p-value equal to 0.101). That is, a higher share of unmet health care needs and lower infant mortality rate motivate government to allocate grants to health. Additionally, countries closer to Bismarck-type health systems and higher number of beds per capita are less interested in allocating grants to health, while countries with a Beveridge-type health system are more interested in applying grants to health.

Finally, concerning the political government characteristics, we also present the statistically significant results (Table 7). Countries with governments formed with a single party are more prone to distributing more grants to the health area. On the other hand, countries governing with a majority government are allocating smaller shares of the RFF grants to health.

Table 7

Linear regression for Political Characteristics of Government
	Health Grants per capita H_RRFpc	Health grants share H_RRFperc
Variable	Coefficient sign
SINGLE = 1/COALLITION = 0	+	+
MAJORITY = 1/MINORITY = 0	ns	-

Note: ns- non-significant

To conclude the regression analysis section, we have estimated a linear regression for H_RRFpc and H_RRFperc on the three variables expressing the formal rules of RFF (GDP per capita, unemployment rate and variation of the GDP per capita), without any other control variable in the regression. The results for these estimations are in Table 8. The inverse relationship between the level of GDP per capita and the distribution and allocation of RFF grants to Health is statistically significant. The remaining estimated coefficients are not statistically significant, despite the correct sign for the estimated coefficient. An exception is the sign of the unemployment rate and the percentage of RRF directed to Health. In this case, the sign is negative with a p-value equal to 0.108, meaning that higher unemployment rates are related with lower share of RRF grants to Health.

Table 8

Linear regression for RRF grants distribution formal rules
	Health grants per capita H_RRFpc		Health grants share H_RRFperc
	coefficient sign	P > t	coefficient sign	P > t
GDPpc	-	0.011	-	0.006
VGDPpc	- (ns)	0.754	+ (ns)	0.964
UNEMPL	+ (ns)	0.815	-	0.108
R-squared	0.331		0.244

Note: ns- non-significant

The main findings point to the existence of tacit drivers for the distribution and allocation of the RRF grants. Concerning the distribution of RRF grants across countries, we found tacit drivers that contribute to a higher amount of grants per capita like rates of preventable mortality and self-assessing bad or very bad health status, and we found other drivers have the opposite effect like life expectancy and healthy life years at 65. The countries where these drivers may be more relevant are situated in Eastern Europe clusters. Additionally, we found that single-party government are more likely to increase the amount of RRF grants per capita.

Concerning the allocation of RRF grants to Health as a percentage of the total allocated grants, we found that while the component of unmet health needs per capita is positively associated with that percentage, infant mortality and number of beds are negatively associated. We also found that countries with a Beveridge-type health system are more likely to assign larger percentages of RFF grants to health than countries with a Bismarck-type health system. Furthermore, governments formed by a single party are associated with higher allocation of RRF in health while governments with a majority prefer smaller shares of RRF in the health area.

Firstly, across European governments there seems to be a common purpose on pursuing the 3rd Sustainable Development Goal (SDG), “Good Health and Well-Being”, specifically the target of reducing premature mortality from non-communicable diseases and ensuring universal coverage. These are tacit drivers for both the distribution and allocation of RRF grants. This common concern about population health outcomes was previously found by Guillamón et al. (2021) to some extent, who concluded that RRF funds were related to the cases and deaths from COVID-19. Countries where these results were unfavourable tended to receive larger shares of the RFF funds, which is indirectly related to the concern for improving health outcomes arising from avoidable (both treatable and preventable) mortality. Nevertheless, our results clearly confirm the alignment of the European government plans for investment and reforms in health with the 3rd SDG.

Secondly, unlike some other empirical analyses on the distribution of European funds, we did not find evidence for the application of the formal rules defined in the European regulation for the distribution and allocation of RRF grants. Even though, from a general perspective, it was, at least, observable that countries with larger GDP per capita were more likely to receive smaller amounts of RRF grants and were assigning smaller shares of these to the Health area. It may be argued that countries with a larger GDP per capita may achieve health outcomes from all other non-health-related policies and, in this way, these governments choose other priority areas to invest and implement reforms with RFF grants.

Thirdly, the quality and some political characteristics of the government such as political spectrum (left/right), values (egalitarian and liberal) and quality of the governance (Mungiu-Pippidi 2013, Achim and Borlea 2015) were not relevant in our analysis in contrast to other works (Muntaner et al. 2011). We have obtained some new results. We found that governments formed by a single party, meaning governments following a cohesive political idea and governing according to a strategic plan, due to the discipline within political parties and its natural internal cohesion, were more likely to get higher RFF grants and allocate higher share to Health. However, governments with a majority of parliamentary seats are less likely to endorse RFF grants for Health. This possibly may reflect a lower level of dialogue with political and social partners or other tacit political objectives aimed at securing positive election outcomes. The uniqueness of this result calls for future research to further understand how social and health topics are treated by governments with these different characteristics, as well as how the interaction of these characteristics may influence government plans and decisions.

Fourthly, we found a significant difference between the choices between countries with Beveridge-type health systems and those with Bismarck-type health systems. Beveridge-type health systems are financed by taxes and therefore investments and reforms are also financed by public money, meaning that access to European funds may speed up and increase the investments and reforms in these countries. Bismarck-type health systems rely on non-tax contributions and health insurance to cover the provision, which means that investment is mainly supported by the private sector. On the other hand, while Beveridge-type health systems are more likely to face problems related with increased choice and reduced waiting time, the challenge for Bismarck-type health systems has more to do with cost containment (Or et al. 2010). These differences call for different approaches in response: while in Beveridge-type health systems, investments funded by RFF funds may mitigate health system issues, in Bismarck-type health systems the approach to control costs may not include investments and reforms advocated by RRF regulation.

The strength of this work lies in its uniqueness and, therefore, its value-added contribution to the literature. Nevertheless, several limitations may be identified. Firstly, it may be difficult to classify one reform, or an investment planned in Health, given the intricate correlation among different priority areas and flagships. Secondly, from a statistical point of view, this analysis is challenging as it only accounts for 27 observations, meaning that we have a small sample. This implies that the robustness of results is influenced by the number of observations. Thirdly, due to the limited time to draft and submit RRF plans to the European Commission, shareholders and socioeconomic partners were not fully consulted (Vanhercke and Verdun 2021), which may reflect the missing sensibility of the government decisions to socioeconomic features such as poverty and income inequity. Fourthly, the component of the RRF loans was excluded but it may influence the strategy of some governments to design the RRF plan.

The potential policy implications of this work are linked to the ongoing discussion on the creation of a European Health Union (Bucher 2022). One of the priority areas of the RRF funds is the digital transformation and this may indeed be a step towards the Health Union. The digital transformation of health systems together with the European Health Data Space are critical infrastructures for the construction of common health research, regulation and policy making. Additionally, another RFF priority area is health and economic, social and institutional resilience, which raises the question of monitoring and progress on health inequalities across the EU. A common understanding and framework to measure these could thus be placed under the umbrella of a Health Union. Finally, future research work will focus on the drivers of the distribution, allocation and execution of the EU4Health programme which runs for the period 2021-27 and which will pave the way to the Health Union.

Competing interests: The authors declare no competing interests.

Ethic statement

Not applicable.

Credit Author Statement

Aida Isabel Tavares: Conceptualization, Investigation, Writing Original Draft, Data Curation, Methodology, Software, Visualization, Supervision.

Joao Vieira: Investigation, Formal analysis, Validation, Reviewing and Editing.

Joao Marques: Writing, Investigation, Data Curation, Visualization.

Achim MV and Borlea NS (2015) Determinants of the European funds absorption 2007–2013 in European Union member states. Proceeding of WEI International Academic Conference in Viena, Austria:12–15.
Bakker R, Edwards E, Hooghe L et al. (2020) 2019 Chapel Hill Expert Survey. Version 2019.1. Chapel Hill, NC: University of North Carolina, Chapel Hill. Available at: www.chesdata.eu (accessed 10/12/2022).
Becker SO (2012) EU structural funds: Do they generate more growth? The CAGE–Chatham House Series 3.
Bellido H, Olmos L and Roman‑Aso JA (2019) Do political factors influence public health expenditures? Evidence pre- and post-great recession. The European Journal of Health Economics 20: 455–474.
Bouvet F and Dall’Erba S (2010) European regional structural funds: How large is the influence of politics on the allocation process? JCMS: Journal of Common Market Studies 48(3): 501–528.
Bruegel (2022) European Union countries’ recovery and resilience plan. Available at: https://www.bruegel.org/publications/datasets/european-union-countries-recovery-and-resilience-plans/ (accessed at 10/12/2022).
Bucher A (2022) Does Europe need a Health Union? Bruegel-Policy Contributions. Available at: https://www.bruegel.org/policy-brief/does-europe-need-health-union (accessed 10/01/2023).
Charron N (2016) Explaining the allocation of regional Structural Funds: The conditional effect of governance and self-rule European Union Politics 17(4): 638–659.
Dahlberg S, Sundström A, Holmberg S et al. (2022) The quality of government basic dataset, version Jan22. University of Gothenburg: The Quality of Government Institute. Available at: http://www.qog.pol.gu.se (accessed 07/09/2022).
European Parliament (2022) European Regional Development Fund (ERDF), Fact Sheets on the European Union . Available at: https://www.europarl.europa.eu/factsheets/en/sheet/95/european-regional-development-fund-erdf (accessed 06/01/2023).
European Commission (2021), “Questions and Answers: The Recovery and Resilience Facility”. Available at: https://ec.europa.eu/commission/presscorner/detail/it/qanda_21_481 (accessed 10/01/2023).
European Commission (2023) Recovery Plan for Europe. Available at: https://commission.europa.eu/strategy-and-policy/recovery-plan-europe_en (accessed 06/01/2023)
European Health Observatory (2022) Policy Responses to COVID-19. Available at https://eurohealthobservatory.who.int/covid-19/policy-responses-to-covid-19/resilience (accessed 15/12/2022).
EuroHealth (2021) Recovery and Resilience Plans: drivers to promote health and wellbeing in the European Union?. Available at: https://eurohealthnet.eu/publication/recovery-and-resilience-plans-drivers-to-promote-health-and-wellbeing-in-the-european-union/ (accessed 10/06/2022).
Fazekas M, Tóth IJ (2016) From Corruption to State Capture: A New Analytical Framework with Empirical Applications from Hungary. Political Research Quarterly 69(2): 320–334.
Fernando Casal Bértoa (2022) School of Politics & International Relations. Nottingham University. Available at: https://whogoverns.eu/ (accessed 17/05/2022).
Glen S (2023) Parsimonious Model: Definition, Ways to Compare Models. In: StatisticsHowTo.com: Elementary Statistics for the rest of us! Available at: https://www.statisticshowto.com/parsimonious-model/ (accessed 05/01/2023).
Gower JC (1971) A general coefficient of similarity and some of its properties. Biometrics: 857–871.
Guillamón MD, Ríos AM and Benito B (2021) An assessment of post-COVID-19 EU recovery funds and the distribution of them among member states. Journal of Risk and Financial Management 14(11): 549.
Heijman W, Koch T (2011) The allocation of financial resources of the EU Structural Funds and Cohesion Fund during the period 2007-2013. Agricultural Economics 57(2): 49–56.
Kaló Z,, Hendrik L, van den Akker M et al. (2019). Is there a fair allocation of healthcare research funds by the European Union? PloS One 14(4): e0207046.
Kisiala W, Bajerski A, and Stepin´ski B (2018) Preferences of poles concerning the shape of regional policy and the allocation of European funds. Transylvanian Review of Administrative Sciences 14(54): 55–72.
Mungiu-Pippidi, Alina (2013). The good, the bad and the ugly: Controlling corruption in the European Union. Advanced Policy Paper for Discussion in the European Parliament 9.
Muntaner C, Borrell C, Ng E , et al. (2011). Politics, welfare regimes, and population health: controversies and evidence. Sociology of health & illness 33(6): 946–964.
Or Z, Cases C, Lisac M, et al. (2010). Are health problems systemic? Politics of access and choice under Beveridge and Bismarck systems. Health Economics, Policy and Law 5(3): 269–293.
PHSSR - Partnership for Health Systems Sustainability and Resilience (2022). Available at https://www.phssr.org/home (accessed 27/12/2022)
Popescu AI (2014). Corruption in Europe: Recent developments. CES Working Papers 6(2A): 150–160.
StataCorp (2017) Stata Statistical Software: Release 15. College Station, TX: StataCorp LLC.
Teichmann F, Falker MC, and Sergi B (2020) Gaming environmental governance? Bribery, abuse of subsidies, and corruption in European Union programs. Energy Research & Social Science 66: 101481.
Tóth IJ and Palócz E (2022) Firm Size, Productivity, EU Funds, and Corruption. Emerging European Economies after the Pandemic. Springer: 113–153.
Vanhercke B, Verdun AC (2021) The recovery and resilience facility: have social actors been side lined?. Leiden University. Policy Brief 08/2021. Vienna. Available at: https://hdl.handle.net/1887/3249877 (accessed 10/01/2023).
Ward Jr, Joe H (1963) Hierarchical grouping to optimize an objective function. Journal of the American Statistical Association 58(301): 236–244.
Watkins MW (2021) A Step-by-step Guide to Exploratory Factor Analysis with Stata. New York: Routledge.

Graphs 1-3 are available in the Supplementary Files section.

Picture 1 is available in the Supplementary Files section.

Download PDF

Version 1

posted

You are reading this latest preprint version

Are there common tacit drivers for European Recovery and Resilience grants distribution and allocation in Health?

Status:

Version 1

Abstract

Figures

Highlights

Introduction

Overview on the factors explaining allocation of European funds

Analytical framework

Methods, Data and Variables

Recovery and Resilience Facility funds related to health

Strategy Design

Domain – Demographic, Education and Population Health (DEPH)

Domain – Social and Economic variables (SE)

Domain – Expenditures and Resources in health (ER)

Domain – Political Characteristics of the Government

Discussion

Conclusion

Declarations

References

Graphs

Picture 1

Supplementary Files

Status:

Version 1