Do European Social Funds Matter in Achieving the Sustainable Development Goals?

Abstract

The aim and novelty of this study lie in analyzing the role of the European Social Fund (ESF) in supporting the implementation of the Sustainable Development Goals (SDGs) within European Union (EU) countries over the 2015–2023 period. EU Member States were grouped into two clusters: Cluster A (15 lower-income states) and Cluster B (12 higher-income states). The study used ESF payments as the explanatory variable and 17 SDG indicators as dependent variables. The methodology combined exploratory factor analysis (EFA) and robust regression to analyze the relationship between the ESF and the SDGs. The ESF has a significant impact on reducing poverty (SDG1), fostering economic growth (SDG8), and enhancing industry and innovation (SDG9), particularly in less-developed regions where its interventions address structural challenges through vocational training, job creation, and infrastructure development. However, its contributions to sustainability goals (SDGs 7, 12, and 13) are limited or even negative in some cases, as economic priorities often overshadow environmental objectives, especially in more-developed regions where climate and energy transitions rely on other funding sources. The ESF excels in fostering partnerships (SDG17) in less-developed regions by mobilizing resources and promoting collaboration, but its role is less impactful in developed regions where institutional frameworks are already well-established.

1. Introduction

In the current context of global development, the 17 Sustainable Development Goals (SDGs) established by the United Nations through the 2030 Agenda [1] represent a universal framework for addressing the most pressing economic, social, and environmental challenges. These goals are central to building a sustainable future, aiming to eradicate poverty, reduce inequality, ensure access to quality education, combat climate change, and promote a circular and resilient economy. The European Union (EU) has consistently demonstrated its dedication to sustainable development, acknowledging its essential role in meeting social, economic, and environmental objectives. Financial support from the EU significantly contributes to promoting sustainable development across Member States, influencing diverse societal dimensions both directly and indirectly [2].

The European Union, as a major global actor and a leader in sustainable policies, has taken a central role in the implementation of these goals, both within its borders and internationally [3]. To this end, the Union has developed a number of strategic financial instruments, through which it supports Member States to reduce regional disparities and achieve, among other objectives, the SDGs’ agreed targets [4]. These resources are instrumental in aiding Member States to pursue a sustainable and prosperous future, while also aligning with global sustainability objectives, including the United Nations’ Sustainable Development Goals (SDGs). The most important mechanisms include the European Social Fund (ESF), the European Regional Development Fund (ERDF), the Cohesion Fund (CF), and the European Agricultural Fund for Rural Development (EAFRD).

These funds are designed to respond to the diverse needs of Member States with a wide range of aims, including reducing unemployment, promoting social inclusion, reducing social inequalities (ESF) [5,6,7,8], developing infrastructure, modernizing the rural economy and encouraging sustainable agricultural practices (ERDF and EAFRD) [9,10], and delivering sustainable infrastructure projects capable of bridging the gaps (CF). While the EU provides significant support through these resources, Member States are responsible for absorbing the funds and integrating them into coherent national policies that support sustainable development.

However, the absorption of funds varies considerably between Member States, with some countries having the capacity to implement funded projects quickly and efficiently, while others face administrative and logistical difficulties [11,12]. For example, Eastern European countries, while receiving considerable amounts, often face management problems, which most likely also limits the impact of the funds on the SDGs [8,13,14]. In addition, there are questions about the strategic targeting of these resources and their ability to address the structural problems of European economies [2,15,16,17].

In the global context of complex challenges, the SDGs require integrated solutions to address the interconnections between climate change, economic inequalities, and environmental pressures, which affect all Member States of the European Union, albeit in different ways. European funds play a crucial role in bridging the gaps between Member States, in particular supporting countries in Central and Eastern Europe which have limited domestic resources to implement the SDGs. The European Social Fund plays a crucial role in supporting both more-developed and less-developed countries in the European Union in achieving the Sustainable Development Goals (SDGs). The ESF represents one of the most concrete expressions of European integration policies, embodying the ideal of European multilevel governance. While the priorities of the ESF, including spending conditions, are set at the European level, their implementation is largely delegated to national, regional, and local authorities within each member state [18].

In the current context, marked by significant economic and social challenges, funding from the European Social Fund Plus (ESF+) becomes crucial in supporting the implementation of the Sustainable Development Goals (SDGs). These funds support initiatives aimed at social inclusion, reducing inequalities, and improving access to education and vocational training, directly impacting the creation of sustainable and inclusive economies [19]. In a time when sustainable development is more important than ever, the ESF plays a key role in facilitating global progress on the 2030 Agenda, contributing to the creation of a more equitable and resilient future.

While in more-developed states, the funds are directed towards innovation, lifelong learning, and reducing gender inequalities, in less-developed countries, they focus on poverty reduction, enhancing social inclusion, and improving access to education and employment. The European Union uses the SDGs as a compass for aligning Member States’ public policies and national strategies. Mainstreaming the goals across all areas—from education, health, green energy, and agriculture to digitalization—is becoming essential for the overall success of the 2030 Agenda. European funds provide a financial incentive, but also strategic direction for coherent and innovative policies.

The aim of this paper is to analyze the role of European Social Funds in supporting the implementation of the SDGs in the European Union, highlighting their contribution to reducing regional disparities, promoting economic and social sustainability, and addressing global challenges such as climate change and inequality.

This research situates itself at the intersection of sustainable development policies and European financial mechanisms, offering an in-depth exploration of the multifaceted impact of the European Social Fund (ESF) on achieving the Sustainable Development Goals (SDGs). By analyzing its role in bridging socioeconomic disparities, fostering social cohesion, and addressing systemic challenges such as poverty, inequality, and access to quality education, this paper aims to provide a nuanced understanding of how EU financial instruments are leveraged to align national and regional priorities with the global sustainability objectives outlined in the 2030 Agenda.

To achieve this aim, a series of research questions were formulated as follows:

How does the ESF contribute to achieving the SDGs in the Member States of the European Union?

Are there significant differences between the effects of ESF funding on SDGs in more-developed countries compared to less-developed ones within the EU?

Which are the most relevant SDGs influenced by ESF-funded projects in different Member States?

Which are the main SDGs benefiting from ESF funding based on the development level of Member States, and how do investment priorities differ?

This paper is organized as follows: Section 2 provides a literature review on the links between the ESF and its impacts on the SDGs. Section 3 outlines the variables included in the analysis and the methodology employed. Section 4 and Section 5 present the empirical findings and discussion, and the Section 6 presents the conclusions of the study.

2. Literature Review

To provide an overview of the main theoretical frameworks and research trends related to the impact of European Social Funds on sustainable development goals, we conducted a bibliometric analysis of recent studies focusing on both developed and less-developed countries within the EU. We analyzed a sample of 211 scientific articles published between 1992 and 2024, indexed in the Web of Science database. We chose to use the Web of Science database due to its rigor and selectivity in indexing scientific publications, ensuring a superior quality of the included data. Their selection was based on the keywords: European Social Funds, SDGs, social inclusion, social inequalities, and unemployment, in line with our research objective. The resulting dataset was processed using the VOSviewer 1.6.20 software to identify the main papers related to European Social Fund financing and its impact on the SDGs.

To conduct the literature review, we used a citation analysis of authors to identify the key works in the field, as the most cited articles tend to have a significant influence on the studied topic. Thus, from the citation analysis conducted using VOSviewer, out of 435 authors, 27 met the criterion of having at least two cited works. Among them, only six are interconnected, meaning they are cited in works within the same field, grouped into 18 clusters, as shown in Figure 1. Of the 18 clusters, only 5 contain at least two authors.

The authors in Cluster 1—Joensuu Matti, Koivonen Mani, and Wikstrom Mila—have collaborated on studies analyzing the impact of funding from the European Social Fund (ESF) on the work capacity and functioning of individuals in vulnerable positions in the labor market. They use the Abilitator—a validated assessment tool (PROM) for the target group in these studies. Cluster 2 consists of authors Nakrosis Vitalis, Vanhercke, and Zimmermann Katharina, who examine aspects related to the administrative capacity of local governance and local responses to ESF funding in the context of national activation policies. Cluster 3 includes authors Bruha Jan, Potluka Oto, and Spacek Martin, who have co-authored studies investigating the effects of the ESF on labor market participation, applying the regression discontinuity method to assess its impact. Cluster 4 brings together authors Carlsson Vanja, Tome Eduardo, and Zartaloudis Sotirios, who explore the ideological consequences of governance structures on gender equality policies. They also emphasize that the ESF enables local autonomy but limits the influence of local public actors in formal and informal decision-making processes. Moreover, the authors state that the ESF has significantly contributed to building the vocational training system in certain countries and bringing them closer to the high skill levels of their Western European neighbors. Cluster 5 is composed of authors Cornford Tony and Ianacci Federico, who examine the success of multiple cases of information systems adopted for monitoring the allocation and use of resources within the ESF, utilizing fuzzy-set qualitative comparative analysis and process tracing to understand the mechanisms underlying their success.

The author with the most citations (49) is Verschraegen, G., with two works: “The European Social Fund and domestic activation policies: Europeanization mechanisms” [20] and “Policy learning, aid conditionality or domestic politics? The Europeanization of Dutch and Spanish activation policies through the European Social Fund” [21]. Along with his collaborators, Verschraegen highlights how the ESF has catalyzed innovation in activation instruments, influenced employment policy governance, and shaped policy framing. The second article [21] examines the variations in the domestic impact of the European Union’s largest financial instrument, the ESF, in the Netherlands and Spain. It was found that despite significant differences between the two countries in terms of “fit”, the ESF had notable effects in both the Netherlands and Spain. However, these effects unfolded through rather different dynamics: intermediate variables such as leverage, learning, and aid conditionality play a crucial role in determining how the ESF impacts national policies, in addition to the degree of institutional, political, and policy (mis)fit.

Another relevant work in the researched field is by Potluka, O. et al. [22], which examines the influence of the ESF in the Czech Republic by estimating the impact of ESF assistance in Czech companies through employee training interventions and the number of jobs created. Another work by Pelucha, M., Kveton, V. and Potluka, O. [23] aimed to identify the impact of support for professional training in Czech companies through the ESF on employment and profitability, using both qualitative and quantitative methods. The quantitative methods have not proven the impacts of subsidized training, either on employment or on profit. In a more recent paper, Potluka [24] analyzed the effects of participating in EU-funded entrepreneurship programs on the chances of being employed, identifying a positive effect of the social entrepreneurship program in the Czech Republic.

Iannacci, Cornford et al. [25] conducted an analysis of the monitoring systems for the use of ESF funds and emphasized that simplifying data entry and reducing opportunity costs, trans-forming participants in monitoring systems into partners who jointly develop the ESF strategy, providing regular feedback to project providers, as well as offering financial incentives for sharing information—such as linking project payments to information exchange—could dramatically improve existing monitoring processes. Nakrošis et al. [26] examine how European Union (EU) support contributes to administrative reforms and capacity building at the national level, focusing on projects financed by the European Social Fund during the 2014–2020 programming period. The authors explore the influence of EU support, domestic politicization, and initial administrative capacity on organizational change, using qualitative content analysis of 29 case studies. The research findings indicate that progress in project implementation was more influenced by domestic factors than by EU conditionalities, and that initial administrative capacity played a crucial role in the success of organizational change, while domestic politicization had a mixed impact.

The implementation of the ESF involves combining financial incentives, regulatory requirements, and programmatic conditions. Unlike previous studies that analyze the impact of this tool on administrative and social reforms, Zimmermann [27] emphasizes the importance of differentiating between the use of funds and the actual changes occurring within local organizations. Additionally, the results suggest that the success of ESF implementation depends not only on the conditions imposed by the EU but also on internal factors such as domestic politicization and the initial administrative capacity of the Member States. In this regard, the need for an appropriate theoretical approach is highlighted to understand the causal processes between the use of funds and organizational changes, considering that their impact may vary significantly depending on the local context and the actors involved.

Battistin and Meroni [28] analyzed the effects of ESF funding for additional instruction time in lower secondary schools in Italy using a difference-in-differences analysis method. They concluded that the ESF intervention led to higher math scores for students from disadvantaged backgrounds, along with better results for the best students. Another study in the field of youth education and employment [5] highlighted a positive impact of ESF funding on the population with lower secondary and tertiary education, and a negative impact on those with upper secondary education. In terms of employment, it identified a positive response for youth at all education levels.

In another study on the topic of the ESF, Zartaloudis [29] examined how ESF funding and EU membership enhanced welfare in Greece and Portugal by promoting gender equality in employment policies.

In an article analyzing the effects of the ESF in Portugal, Tomé [30] highlighted that the impact of this investment was relatively modest. At the macroeconomic level, the ESF’s contribution to GDP was limited, and from a microeconomic perspective, no significant effects on wages were recorded. However, notable results were observed in terms of employment. The most evident indicator of the ESF’s limited effectiveness in Portugal is its modest impact on improving the skill levels of the local population.

In an article studying the effectiveness of the ESF in Spanish regions regarding active labor market policies (ALMP), González-Alegre [31] concluded that the European Social Fund is not effective in its goal of promoting ALMP spending, especially when regions compete against each other for funding.

Articles examining the implications of the ESF have predominantly focused on the analysis of governance and the monitoring processes of fund distribution, with most studies conducted at the regional or national level, focusing on specific regions and Member States of the European Union. Furthermore, the majority of research has concentrated on the financial impact of the ESF on specific areas such as education, unemployment, and job creation, leaving less attention to exploring the ESF’s effects on broader dimensions of sustainable development, such as social inclusion, reducing inequalities, and promoting sustainable economic development. In this context, there is a significant gap in the literature regarding the contribution of the ESF to the achievement of the broader goals of the 2030 Agenda for Sustainable Development. However, there is a lack of comprehensive research addressing the ESF’s broader role in advancing the multidimensional objectives of the Sustainable Development Goals (SDGs). Specifically, the ESF’s contributions to areas like social inclusion, inequality reduction, and sustainable economic development have not been thoroughly examined within the context of the 2030 Agenda.

This study seeks to fill this gap by examining how the ESF supports progress toward the SDGs in a holistic manner, integrating its various elements into a broader sustainable development framework. By moving beyond traditional analyses, the research offers a more inclusive perspective on the transformative potential of the ESF in addressing global challenges.

Our study thus arises as a necessity, aiming to explore the effects of the ESF on progress toward the SDGs, considering the importance of a more comprehensive vision that integrates all components of this European fund within the framework of global sustainable development.

In this context, there is a clear need to further explore the relationship between the ESF and the achievement of the SDGs, specifically identifying which SDGs are most influenced by ESF-funded projects and how investment priorities differ between Member States based on their level of development. Therefore, to guide this research endeavor, the following hypotheses have been formulated:

H1. 

The European Social Fund (ESF) contributes to advancing the achievement of the Sustainable Development Goals (SDGs) in EU Member States by addressing key economic and social challenges.

H2. 

There are significant differences in the impact of ESF funding on SDGs between more-developed and less-developed EU Member States.

H3. 

ESF investments have diverse outcomes in Member States, highlighting the need for tailored approaches to maximize their contribution to sustainable development.

The novelty brought by this study lies in its focus on examining the broader impact of the ESF beyond traditional areas such as education, unemployment, and job creation. While the existing literature predominantly addresses governance, fund distribution, and financial impacts at the regional or national level, there is a lack of research on the ESF’s role in achieving the wider goals of sustainable development. Specifically, this study aims to bridge the gap by exploring the ESF’s contribution to the achievement of the SDGs, integrating the various components of the ESF within the global framework of sustainable development.

3. Materials and Methods

3.1. Data

To optimally identify the impact of the ESF on sustainable development goals, EU Member States were grouped into two clusters. Cluster A consists of 15 EU Member States with a gross national income (GNI) per capita below 90% of the EU average: Bulgaria, Croatia, Cyprus, Czechia, Estonia, Greece, Hungary, Latvia, Lithuania, Malta, Poland, Portugal, Romania, Slovakia, and Slovenia. Cluster B includes 12 Member States with a GNI per capita above 90% of the EU average: Austria, Belgium, Denmark, Finland, France, Germany, Ireland, Italy, Luxembourg, Netherlands, Spain, and Sweden. This study relies on a comprehensive dataset spanning the 2015–2023 period to explore the relationship between ESF payments and the SDGs. The dataset integrates several key sources to ensure robust analysis and meaningful conclusions. The primary data on ESF payments were sourced from the European Commission’s Cohesion Policy database [32]. These data, expressed in euros, represent annual payments to each EU member state. The payments reflect financial support allocated to projects aimed at reducing inequalities, fostering social inclusion, and improving education and vocational training, making them a critical variable in assessing progress towards SDG-related outcomes.

The study also incorporates data on SDG indicators [33], which were retrieved from two primary sources: the United Nations Sustainable Development Goals Global Database and Eurostat. These indicators encompass a broad spectrum of metrics, such as poverty reduction (SDG1), quality education (SDG4), economic growth (SDG8), and climate action (SDG13). Together, these indicators capture the multifaceted nature of sustainable development, covering social, economic, and environmental dimensions. As the explanatory variable, we used ESF payments in euros to Member States during 2015–2024, while the dependent variables included indicators for the 17 SDGs: SDG1—no poverty, SDG2—zero hunger, SDG3—good health and well-being, SDG4—quality education, SDG5—gender equality, SDG6—clean water and sanitation, SDG7—affordable and clean energy, SDG8—decent work and economic growth, SDG9—industry, innovation, and infrastructure, SDG10—reduced inequalities, SDG11—sustainable cities and communities, SDG12—responsible consumption and production, SDG13—climate action, SDG14—life below water, SDG15—life on land, SDG16—peace, justice, and strong institutions, and SDG17—partnerships for the goals.

To contextualize the analysis, data on gross national income (GNI) per capita for EU Member States were obtained from the World Bank database. These data facilitated the grouping of countries into two clusters: Cluster A, comprising lower-income states, and Cluster B, consisting of higher-income states. This clustering was essential for assessing regional disparities in ESF impacts on SDG outcomes.

To facilitate a comprehensive understanding of the data and to ensure methodological rigor, descriptive statistics were computed and presented in

Table 1. Descriptive statistics of the variables.

	Mean	Skewness	Kurtosis	Shapiro–Wilk	p-Value of Shapiro–Wilk	Minimum	Maximum	Valid
ESF	1.432 × 10+9	2.424	6.429	0.657	<0.001	377,057	1.281 × 10+10	270
SDG1	21.671	1.238	1.949	0.914	<0.001	10.8	46	243
SDG2	113.37	3.008	9.561	0.558	<0.001	0.138	1121.618	243
SDG3	66.957	−0.757	0.081	0.943	<0.001	42.8	84.5	242
SDG4	8.83	0.65	0.101	0.965	<0.001	2	20	243
SDG5	9.73	0.842	0.255	0.935	<0.001	0.2	26.8	243
SDG6	9.783	4.004	16.655	0.463	<0.001	0.14	117.33	135
SDG7	22.74	1.048	1.013	0.926	<0.001	4.987	66.002	236
SDG8	27,508.4	1.487	2.34	0.855	<0.001	5850	86,540	243
SDG9	1.66	0.596	−0.886	0.912	<0.001	0.44	3.5	216
SDG10	32,198.77	2.134	5.35	0.788	<0.001	13,200	90,600	243
SDG11	38.815	−0.122	−0.699	0.978	0.002	9.1	70.3	211
SDG12	9.279	1.097	0.698	0.898	<0.001	1.3	30.6	243
SDG13	78.952	0.309	0.314	0.979	0.002	15.1	156.9	216
SDG14	653.258	2.594	6.809	0.597	<0.001	16	4871	198
SDG15	8246.519	7.071	72.728	0.411	<0.001	0	240,140	243
SDG16	63.955	0.251	−1.18	0.942	<0.001	41	91	243
SDG17	68.739	1.131	1.297	0.918	<0.001	8.5	209.4	243

. These statistics offer valuable insights into the characteristics and distributions of the variables analyzed in this study. Measures such as mean values and ranges were utilized to describe central tendencies and variability, providing a summary of ESF payments and SDG-related indicators across the studied period. This initial exploration helps to contextualize the data and identify patterns or discrepancies between Member States. Additionally, skewness and kurtosis were calculated to assess the symmetry and concentration of the data distributions. Skewness provides information on the direction and extent of asymmetry, while kurtosis measures the presence of extreme values or outliers. These metrics revealed significant deviations in the distributions of several variables, emphasizing the need for robust statistical techniques. Furthermore, the Shapiro–Wilk test for normality was conducted, with results indicating that most variables deviated from a normal distribution (p < 0.001). This confirmed the appropriateness of using non-parametric and robust analytical methods, such as exploratory factor analysis (EFA) and regression with the M-estimator, to minimize the impact of outliers and ensure the validity of the findings.

The descriptive statistics in Table 1 serve as the foundation for subsequent analyses, highlighting the heterogeneity in ESF funding allocation and its diverse impacts on SDG outcomes. By characterizing these statistical properties, the study provides a transparent and methodologically sound basis for interpreting the relationships between ESF payments and progress toward achieving the SDGs. The data reveal that most variables deviate notably from a normal distribution according to the Shapiro–Wilk test [34], with p-values < 0.001 in almost all cases, except for SDG11 and SDG13 (p = 0.002). These results suggest caution in applying parametric statistical methods and may necessitate data transformations or robust statistical approaches [35].

The skewness analysis reveals strong positive skewness for several variables, such as ESF (2.424), SDG2 (3.008), SDG6 (4.004), SDG10 (2.134), SDG14 (2.594), and SDG15 (7.071), driven by high outliers. Conversely, SDG3 (−0.757) and SDG16 (−0.251) show mild negative skewness, indicating more balanced distributions. High kurtosis values for SDG6 (16.655) and SDG15 (72.728) reflect leptokurtic distributions, with data concentrated near the mean but influenced by extreme values. In contrast, variables like SDG9 (−0.886), SDG11 (−0.699), and SDG16 (−1.18) exhibit low kurtosis, indicating a more uniform spread.

The range of values also highlights variability, with ESF spanning from 377,057 to 1.281 × 10⁺¹⁰, while SDG15 varies from 0 to 240,140. In contrast, SDG3 (42.8–84.5) and SDG9 (0.44–3.5) demonstrate narrower ranges, suggesting more homogeneity. These findings underscore the heterogeneity of variable distributions, from moderate to extreme deviations in skewness and kurtosis. This calls for appropriate statistical techniques, such as non-parametric methods or logarithmic transformations, especially for variables with severely distorted distributions [36]. As data interpretation progresses, these deviations should be considered to ensure the validity and robustness of conclusions.

3.2. Methodology

To rigorously analyze the role of the ESF in achieving the SDGs, this study employed a combination of exploratory factor analysis (EFA) and robust regression using the M-estimator. These tools were selected for their ability to manage complex datasets characterized by non-normal distributions and outliers, ensuring the robustness and reliability of results. The EFA method identified latent structures and grouped correlated variables, enabling a focused investigation of the relationships between ESF payments and SDG outcomes. Subsequently, the robust regression approach accounted for data variability and outlier effects, providing more accurate estimations of the relationships.

Compared to methodologies employed in similar studies, such as conventional regression or descriptive analyses, our approach demonstrates significant advantages. Previous studies, like those by Verschraegen et al. [20], analyzed the ESF’s influence on national policies but did not employ dimensionality reduction techniques like EFA to uncover underlying factors. Other research, such as Potluka et al. [22], utilized regression discontinuity to assess ESF impacts but lacked the robust mechanisms needed to address non-linearities and data anomalies as provided by the M-estimator.

This dual-method approach enhances the depth and reliability of findings, particularly by revealing distinct impacts of ESF funds across different regional clusters (Cluster A and Cluster B). The robust regression ensured that relationships between ESF interventions and SDG outcomes remained valid even under variable data conditions. For example, the methodology highlighted disparities in ESF impacts, with more significant contributions in less-developed regions for goals such as poverty reduction (SDG1) and economic growth (SDG8).

The inclusion of these advanced analytical tools also allowed us to integrate socioeconomic and environmental dimensions of SDG outcomes, which are often analyzed separately in the literature. The ability to differentiate impacts across clusters provided actionable insights for policymakers, emphasizing the need for tailored interventions based on regional development levels. This approach not only strengthens the validity of our conclusions but also contributes to a broader understanding of how financial instruments like the ESF can effectively support the 2030 Agenda for Sustainable Development. By employing these methods, this study provides a more nuanced and robust analysis of the ESF’s impact, offering a methodological framework that can inform future research and policy-making.

Exploratory factor analysis (EFA) is a multivariate technique used to identify latent structures within complex datasets, aiming to reduce the number of observed variables and uncover essential relationships underlying them. EFA identifies groups of correlated variables, known as factors, considered common sources of variation [37]. This technique is fundamental in research seeking to understand relationships among multiple variables, particularly in behavioral and psychological studies [38]. The first step involves assessing data suitability for factor analysis using the Kaiser–Meyer–Olkin (KMO) test, which evaluates sampling adequacy, with a recommended minimum value of 0.6 [39]. Additionally, Bartlett’s test of sphericity examines the significance of the correlation matrix, ensuring that variables are sufficiently intercorrelated to justify factor analysis [40].

Next, factor extraction methods, such as Principal Component Analysis (PCA) or Maximum Likelihood (ML) factorization, are applied to identify factors explaining most of the variance in the dataset [41]. The choice of method depends on the data characteristics and analysis objectives. PCA is traditionally used to reduce data dimensionality and extract principal components, while ML is preferred for more precise estimates of latent relationships. The optimal number of factors is determined using the eigenvalue criterion and scree plot analysis [42]. The eigenvalue criterion suggests retaining factors with eigenvalues greater than 1, as they explain a significant proportion of variance. The scree plot visually identifies the point where the decline in eigenvalues becomes flatter, indicating the appropriate number of factors. After extraction, factor rotation techniques, such as orthogonal (e.g., varimax) or oblique (e.g., promax) rotation, are used to achieve interpretable solutions. Orthogonal rotation assumes factor independence, while oblique rotation allows for factor correlations, with promax often chosen for its flexibility in handling correlated factors [43].

Robust regression with the M-estimator evaluates relationships between latent and observed variables, providing accurate estimates even in the presence of outliers or model specification errors. The M-estimator is a robust estimation method that minimizes the influence of extreme data points on parameter estimates, making it less sensitive to non-uniform data distributions [44]. This technique is particularly valuable in economic and social data analysis, where outliers can significantly distort traditional regression results. The M-estimator calculates regression coefficients robustly by minimizing a loss function that penalizes large errors less severely [45].

Combining exploratory factor analysis with robust regression using the M-estimator creates a powerful methodology for analyzing complex relationships between latent and observed variables. EFA uncovers the latent structure of data and reduces dimensionality, while robust regression ensures accurate and reliable estimates, even in the presence of outliers.

Alternative methods, such as Structural Equation Modeling (SEM) or generalized linear models, were considered but deemed less suitable for this study. SEM, while capable of handling latent constructs, requires larger sample sizes and assumes stricter model specifications, which may not align with the variability and heterogeneity of the dataset. Similarly, generalized linear models are effective for specific types of dependent variables but do not address issues of dimensionality or outlier sensitivity as effectively as the combination of EFA and robust regression. The integration of EFA and regression with the M-estimator thus represents an optimal methodological choice for this study, balancing the need for dimensionality reduction, robustness, and interpretability. This combination ensures that the analysis captures the complex relationships between ESF interventions and SDG outcomes while maintaining methodological rigor.

4. Results

The results of the exploratory factor analysis (EFA) indicate a complex structure with four main factors explaining a significant proportion of the total variance in the data. The Chi-square test presented in

Table 2. Chi-squared test results.

Chi-Squared Test
	Value	df	p
Model	1308.475	74	<0.001

revealed a significant value (χ² = 1308.475, p < 0.001), confirming that the proposed model fits the data well, suggesting that factor analysis is appropriate for the examined dataset.

Regarding factor loadings, the results suggest a clear separation of variables associated with each factor (

Table 3. Factor loadings.

	Factor 1	Factor 2	Factor 3	Factor 4	Uniqueness
SDG10	0.972				0.133
SDG8	0.947				0.068
SDG3	0.520				0.472
SDG16	0.494	−0.519			0.225
SDG17	−0.403		0.471	0.728	0.322
SDG5		0.710			0.331
SDG1		0.622			0.520
SDG9		−0.610	0.500		0.234
SDG4		0.567			0.713
SDG7		−0.439			0.660
SDG11		−0.424	0.502		0.279
SDG14			0.662		0.420
SDG2			0.645		0.575
SDG12			0.473		0.680
SDG13				0.801	0.407
SDG6				0.552	0.588
SDG15					0.876

Note. Applied rotation method is promax.

). Factor 1 is dominated by SDG10 (0.972) and SDG8 (0.947), with strong loadings and low uniqueness values (0.133 and 0.068, respectively). This indicates a strong relationship between these indicators, grouping them into a common factor that reflects a specific domain or theme related to the SDGs. Factor 2 includes SDG16 and SDG17 with significant loadings, while Factor 3 comprises indicators such as SDG5, SDG1, and SDG9, appearing to reflect social and economic aspects. Finally, Factor 4 encompasses SDG15, SDG13, and SDG6, pointing to factors related to environmental and sustainability issues, with high loadings on respective variables, such as SDG15 (0.876).

The promax rotation used in the factor analysis facilitated a better interpretation of the relationships between variables and helped achieve a clearer solution, with higher loadings on specific factors and a more concentrated sum of squared loadings (

Table 4. Factor characteristics.

		Unrotated Solution			Rotated Solution
	Eigenvalues	SumSq. Loadings	Proportion Var.	Cumulative	SumSq. Loadings	Proportion Var.	Cumulative
Factor 1	4.579	4.290	0.252	0.252	2.852	0.168	0.168
Factor 2	2.864	2.431	0.143	0.395	2.618	0.154	0.322
Factor 3	2.168	1.696	0.100	0.495	2.147	0.126	0.448
Factor 4	1.480	1.080	0.064	0.559	1.880	0.111	0.559

). The rotation results indicate that Factor 1 explains approximately 16.8% of the total variability, followed by Factor 2, which accounts for 15.4%, while Factor 3 and Factor 4 explain 12.6% and 11.1%, respectively, collectively accounting for about 55.9% of the total variance. In conclusion, the exploratory factor analysis revealed a clear structure with four relevant factors aligned with the SDGs and their associated indicators. This model can be used to understand the complex relationships between different dimensions of sustainability and to guide the analysis of how these factors impact organizational performance or economic policies related to sustainable development.

Figure 2 represents a complex network of interactions between thematic factors (referred to as RC—key resources) and the SDGs. The connections highlight the relationships between each factor and specific goals, marked by varying intensities and distinct polarities.

These relationships are graphically represented by lines of different thicknesses and colors, symbolizing the intensity and nature (positive or negative) of the interdependencies. Green lines illustrate positive relationships, indicating favorable contributions of the respective factors toward achieving the SDGs. In contrast, red lines indicate negative relationships, representing potential conflicts, trade-offs, or adverse effects on the targeted goals. The thickness of the lines signifies the intensity of the relationship, suggesting stronger impacts of certain interactions compared to others.

Analyzing the relationships between thematic factors and SDGs, RC1 shows strong positive connections with goals such as SDG1 (no poverty) and SDG10 (reduced inequalities). This suggests that activities or priorities within RC1 significantly contribute to reducing economic and social disparities. Additionally, RC1 has more limited or less intense relationships with other SDGs, indicating a focused impact. In the case of RC2 and RC3, the relationships are more complex, including both positive contributions and conflicts. For instance, RC2 exhibits negative interactions with SDG7 (affordable and clean energy), which may signal adverse impacts or challenges in balancing sustainable energy priorities. Conversely, RC2 shows notable positive contributions to other SDGs, such as SDG9 (industry, innovation, and infrastructure), reflecting a focus on technological development. RC4 stands out with predominantly positive relationships, making significant contributions to SDG17 (partnerships for the goals) and SDG13 (climate action). This highlights its crucial role in fostering international collaboration and addressing climate change.

Next, robust regression with an M estimator is applied to evaluate the impact of the ESF on each SDG. The results presented in

Table 5. Robust regression with M estimator results.

	Cluster A				Cluster B
	Coefficient	Std. Error	z-Statistic	Prob.	Coefficient	Std. Error	z-Statistic	Prob.
SDG1	−0.20021	0.076732	−2.60919	0.0091	−0.14515	0.032537	−4.46107	0.0000
SDG2	0.43927	0.214708	2.045899	0.0408	0.042864	0.202997	0.211157	0.8328
SDG3	0.105933	0.0453	2.33849	0.0194	−0.01299	0.016464	−0.78925	0.4300
SDG4	0.191751	0.141897	1.35134	0.1766	−0.32344	0.054235	−5.96367	0.0000
SDG5	0.117828	0.160353	0.734803	0.4625	−0.08384	0.107026	−0.78337	0.4334
SDG6	6.390892	4.744795	1.346927	0.1780	0.991704	2.769573	0.358071	0.7203
SDG7	−0.19147	0.075992	−2.5196	0.0117	−0.54733	0.137174	−3.99004	0.0001
SDG8	0.335045	0.061665	5.433327	0.0000	0.2198	0.051847	4.239377	0.0000
SDG9	0.280262	0.11229	2.495878	0.0126	0.26379	0.108052	2.441319	0.0146
SDG10	0.078045	0.052452	1.487939	0.1368	0.223405	0.03956	5.647248	0.0000
SDG11	−0.03439	0.093309	−0.36856	0.7125	0.261059	0.041104	6.351208	0.0000
SDG12	−0.27881	0.156338	−1.78337	0.0745	0.688832	0.135353	5.089147	0.0000
SDG13	0.223008	0.109025	2.045474	0.0408	−0.21845	0.050787	−4.30139	0.0000
SDG14	0.417888	0.294952	1.416802	0.1596	−1.30711	0.346136	−3.77628	0.0002
SDG15	−1.35316	0.660575	−2.04845	0.0405	−0.74469	0.571736	−1.30251	0.1960
SDG16	0.006801	0.039648	0.171527	0.8638	0.098509	0.02923	3.370082	0.0008
SDG17	0.65115	0.122502	5.315436	0.0000	−0.17299	0.103456	−1.67208	0.0945

show significant differences between the two clusters, indicating distinct impacts depending on the economic and social characteristics of the Member States included in each cluster.

For SDG1, in Cluster A, the coefficient of −0.20021, with a significant p-value of 0.0091, suggests a negative relationship between the implementation of ESF-funded projects and poverty levels. This result suggests that the ESF significantly contributes to poverty reduction in the lower-income or more vulnerable EU Member States. ESF funds concretely support initiatives for vocational training, labor market integration, and education, helping to improve access to economic opportunities for vulnerable groups. Thus, ESF-supported measures can help these states overcome major economic challenges and reduce social inequalities, having a positive impact on poverty reduction. In contrast, Cluster B, which includes states with a higher degree of economic development, shows a smaller coefficient (−0.14515) and a p-value of 0.0000, signifying a significant negative relationship between the ESF and poverty, but in a more evident and stronger form. This result suggests that in the more-developed EU states, the ESF has a significant impact on reducing poverty and social inequalities. These states benefit from ESF projects targeting the integration of vulnerable individuals into the labor market and reducing regional or group disparities through initiatives promoting social inclusion, continuous training, and ensuring decent living standards for disadvantaged categories. When comparing the two clusters, we observe that the ESF’s impact on poverty reduction is stronger and more significant in the more-developed states (Cluster B) than in the less-developed ones (Cluster A). This could reflect that in higher-income states, the ESF has a direct and visible effect in combating poverty within the context of more stable and well-integrated economies. On the other hand, in less-developed states, the ESF supports education and training programs targeting the economic integration of disadvantaged groups, but its effect may be subtler and longer-term, given deeper structural challenges.

Regarding SDG2, for Cluster A, the coefficient of 0.43927 and a p-value of 0.0408 suggest a significantly positive relationship between ESF interventions and progress toward achieving objectives related to food security and sustainable agriculture development. This result indicates that the ESF plays an important role in improving living conditions and the economic integration of rural communities in lower-income Member States. ESF funds primarily support vocational training and education initiatives in rural areas, which can directly contribute to developing more sustainable agricultural practices and increasing their capacity to address challenges related to climate change, natural resources, and market demands. Additionally, the ESF may support innovation in agriculture, promoting technologies and production techniques that reduce environmental impact and improve food sustainability in these regions. On the other hand, Cluster B presents a significantly smaller coefficient (0.042864) and a very high p-value (0.8328), suggesting that the ESF’s impact on this SDG is not statistically significant. This result may indicate that in more-developed states, where agriculture is already more modernized and integrated into global supply chains, ESF interventions do not have an immediate impact on food security or sustainable agriculture. Although the ESF continues to support vocational training and the integration of vulnerable groups into the labor market, its impact on the agricultural sector and food security in these states is less pronounced, being less directly linked to the measures supported by the funds. In this situation, the ESF’s priority may focus more on consolidating economic and social cohesion policies, which do not directly target sustainable agriculture. Comparing the two clusters, a clear difference is observed in the efficiency and significance of the ESF’s impact on SDG2. While in Cluster A, the ESF significantly contributes to food security and the development of a more sustainable agricultural sector through support for training and innovation, in Cluster B, the impact is much more reduced. This can be explained by the fact that in the more-developed states of the European Union, agriculture is already well-established, and ESF interventions cannot produce the same direct effects as in less-developed regions, where sustainable agriculture is often a priority and a key area for economic and social development.

For SDG3—good health and well-being, the impact of the ESF varies significantly between the two clusters. In Cluster A, the coefficient of 0.105933 and a p-value of 0.0194 suggest a significantly positive relationship between ESF interventions and improvements in the health and well-being of the population in lower-income Member States. The ESF supports projects that include vocational training in healthcare, access to health services for vulnerable groups, and the promotion of healthy lifestyles in disadvantaged regions. These measures can directly contribute to improving public health, reducing inequalities in access to medical care, and preventing diseases. Additionally, the ESF supports educational programs aimed at educating communities to adopt healthy behaviors, which can lead to lower mortality rates and increased life expectancy. Thus, the European Social Fund plays a significant role in strengthening healthcare systems in poorer regions, contributing to the achievement of SDG3. In Cluster B, the coefficient for SDG3 is negative, −0.01299, and the p-value is 0.4300, indicating a statistically insignificant relationship between the ESF and public health. This suggests that in more-developed states, where healthcare infrastructure is already well-established, ESF interventions do not have a significant impact on improving health and well-being. In these countries, health issues are already addressed through consolidated national policies and systems, and the contributions of the ESF may be less visible or relevant in this context. Although the ESF may support training for healthcare personnel or awareness activities, its impact on overall public health is not as direct as in less-developed regions. Comparing the two clusters, it is clear that the ESF has a significant impact on SDG3 in Cluster A, acting as a catalyst for improving the health and well-being of the population in poorer regions, while in Cluster B, the impact is much smaller and insignificant. This phenomenon can be explained by the fact that in Cluster B states, which have more developed economies and established healthcare systems, ESF interventions are less effective in bringing about significant changes. In these regions, funds are more focused on strengthening social or economic cohesion policies, and public health issues are primarily addressed through national mechanisms.

For SDG4, in Cluster A, the coefficient of 0.191751 is positive, but the p-value of 0.1766 is higher than the significance threshold of 0.1, suggesting that the ESF’s influence on education is not statistically significant in less-developed regions. Although the ESF clearly supports educational initiatives aimed at improving access to quality education, combating school dropout, and promoting vocational and technical education, their impact is not strong or rapid enough to produce visible and significant effects across all Cluster A states. This result may be explained by the fact that Cluster A states face multiple economic and social challenges, including insufficient educational infrastructure, inequalities in access to education, and low educational funding, which limit the ESF’s ability to effectively address these issues and achieve SDG4 goals in the short term. In Cluster B, the coefficient is significant, with a value of −0.32344 and an extremely low p-value of 0.0000, indicating a significant and negative influence of the ESF on SDG4. This result suggests that in the EU Member States in Cluster B, ESF interventions in education have more notable effects, likely contributing to improving the quality of education. While the ESF supports a wide range of educational and vocational training projects, in more-developed regions, it seems that more attention needs to be paid to diversifying and perfecting the educational system. For example, in some Cluster B states, the ESF may contribute to increasing competitiveness and access to quality education for various social groups, especially for young people from disadvantaged backgrounds or those from marginalized communities. These results suggest that in more-developed regions, there is more emphasis on adapting education to labor market requirements and developing advanced skills, but this may address smaller inequalities compared to poorer regions. Comparing the two clusters, a significant difference is observed in how the ESF influences education: in Cluster A, although the ESF supports investments in education, the effects are not significant due to major structural and economic challenges, while in Cluster B, the ESF has a significant impact, although this may indicate the need for more complex and sophisticated educational policies. In more-developed regions, the ESF supports several projects aimed at improving the quality of education and continuous workforce training but also highlights the need for more strategic educational reforms.

The analysis of the influence of ESF on SDG5 reveals that, in Cluster A, the coefficient of 0.117828 indicates a positive relationship between ESF interventions and progress in gender equality. However, the p-value of 0.4625 shows that this relationship is not statistically significant, suggesting that the impact of the ESF on SDG5 in less-developed regions is limited. This situation can be explained by the presence of deeply rooted structural gender inequalities, such as workplace discrimination, reduced access to education and economic opportunities for women, or cultural and traditional norms that discourage the active participation of women in public and economic life. Although the ESF supports projects aimed at reducing gender disparities, such as mentoring programs for women, promoting female entrepreneurship, and advocating for equality in the educational and professional sectors, these efforts fail to generate significant short-term impact in less-developed regions. In Cluster B, the coefficient of −0.08384 suggests a negative relationship, but the p-value of 0.4334 also indicates a lack of statistical significance. This result is surprising, considering that the more-developed regions in Cluster B benefit from greater financial resources and a social environment that supports gender equality. However, this result may reflect persistent challenges, such as wage gaps between men and women, the under-representation of women in leadership positions, or the difficulties women face in reconciling professional life with family responsibilities. Furthermore, in more-developed regions, gender equality policy may not be an immediate priority compared to other domains, limiting the effectiveness of ESF-supported interventions. Comparing the two clusters, the results suggest that ESF interventions for promoting gender equality have not yet achieved significant impact in either the less-developed or the more-developed regions. In Cluster A, the lack of statistical significance may reflect major structural barriers, while in Cluster B, the insignificant negative result may suggest that progress in this area faces more subtle challenges or that ESF resources are being used in other priority areas.

For SDG6, in Cluster A, the coefficient of 6.390892 indicates a substantial positive association between ESF funding and progress in SDG6. However, the p-value of 0.1780 suggests that this relationship is not statistically significant, limiting conclusions about the direct impact of the ESF in this area. In less-developed regions, water and sanitation infrastructure is often underdeveloped, and intervention programs generally focus on immediate needs, such as education, vocational training, and labor market integration, at the expense of direct investments in water and sanitation infrastructure. This reality may explain why the impact of the ESF in this area is less evident, even if some indirect interventions, such as promoting social inclusion, could have collateral benefits for access to clean water. In Cluster B, the coefficient of 0.991704 is significantly lower, and the p-value of 0.7203 indicates a clear lack of statistical significance. This suggests that in more-developed regions, the impact of the ESF on SDG6 is almost nonexistent. This result is expected, given that in these regions, water and sanitation infrastructure is already well developed, and ESF interventions focus on other priority areas, such as vocational training and integrating vulnerable groups into the labor market. In this context, the need for direct interventions related to SDG6 is reduced. Comparing the two clusters, although the coefficient is higher in Cluster A than in Cluster B, neither result is statistically significant. This situation highlights the fact that objectives related to clean water and sanitation are not a direct priority for programs funded through the ESF, regardless of the region’s level of development.

The analysis of SDG7—affordable and clean energy highlights that in Cluster A, the coefficient is −0.19147, and the p-value of 0.0117 suggests a statistically significant negative relationship between ESF interventions and SDG7. This result may be explained by the fact that less-developed regions have urgent needs in other areas, such as education and employment, which are prioritized in ESF programs. Affordable and clean energy is often supported through other funds, such as the Cohesion Fund or the European Regional Development Fund (ERDF), which could lead to an apparent negative correlation between ESF support and progress on SDG7. Additionally, ESF interventions, which do not directly target clean energy, may have limited or even negative side effects on this goal if indirect economic support promotes industries incompatible with ecological transition. In Cluster B, the coefficient is −0.54733, and the p-value of 0.0001 indicates a stronger and statistically significant negative relationship compared to Cluster A. This result may be correlated with the fact that in more-developed regions, ESF interventions also prioritize other areas, such as human capital development and integrating vulnerable groups. The direct impact of the ESF on infrastructure and energy transition is limited, which may enhance the perception of a negative relationship between ESF support and progress towards affordable and clean energy. Moreover, in developed regions, sustained economic growth supported by ESF interventions could contribute to increased energy consumption, which may put pressure on the transition to renewable sources, especially in the absence of clear regulatory measures. Comparing the two clusters, it is evident that the negative relationship is more pronounced in Cluster B than in Cluster A, suggesting that in developed regions, the indirect impact of ESF interventions on energy transition may be more problematic. This result reflects that although the ESF plays an important role in supporting social and economic inclusion, its contribution to SDG7 is minimal or even counterintuitive under certain circumstances.

Regarding SDG8—decent work and economic growth, the results emphasize the relevance of this fund in promoting the goal, with a focus on reducing unemployment and increasing economic inclusion. In Cluster A, the coefficient is 0.335045, and the p-value is 0.0000, indicating a statistically significant positive relationship. This result suggests that ESF interventions have significantly contributed to stimulating economic growth and job creation in less-developed regions. This positive impact can be explained by the ESF’s focus on funding vocational training programs, supporting entrepreneurship, and integrating disadvantaged groups into the labor market. In less-developed regions, such interventions have a significant multiplier effect, contributing to increased productivity and strengthening the local economy. In Cluster B, the coefficient is 0.2198, with a p-value of 0.0000, confirming a positive and significant relationship, although less in magnitude than in Cluster A. This difference can be attributed to the characteristics of more-developed regions, where the labor market is already better consolidated, and ESF interventions are less transformative. Nevertheless, the result indicates that the fund continues to play an important role in supporting initiatives related to social inclusion and sustainable economic growth, even if its marginal impact is smaller. Comparing the two clusters, it is evident that ESF interventions have a greater impact in less-developed regions (Cluster A) than in developed ones (Cluster B). This difference can be explained by the more urgent needs and lack of economic infrastructure in Cluster A, which allows the ESF to have a more visible effect. In contrast, in Cluster B, ESF interventions are often complementary and contribute to marginal adjustments, especially for vulnerable groups. In conclusion, the European Social Fund has a significantly positive influence on SDG8 in both clusters, confirming its essential role in supporting local economies and employment. However, the intensity of the impact varies depending on the region’s level of development, being more pronounced in less-developed regions. Future policies should consider this dynamic, maximizing the ESF’s impact through interventions tailored to the specific needs of each regional context.

For SDG9—industry, innovation, and infrastructure, in Cluster A, the estimated coefficient is 0.280262, and the p-value of 0.0126 demonstrates that the relationship is significant at a high confidence level (p ≤ 0.05). This result highlights that the ESF contributes substantially to the development of basic infrastructure and innovation initiatives in less-developed regions. This positive impact can be explained by the fact that less-developed regions benefit from essential investments to modernize social and economic infrastructure, which are indispensable for attracting additional investments and supporting industrial activities. For instance, in these regions, ESF funds could be directed toward modernizing local transportation networks, improving access to clean energy, and strengthening production capacities in emerging industrial sectors. Moreover, supporting vocational training programs in the technological field is crucial for preparing the workforce to sustain new industries. In Cluster B, the coefficient is 0.26379, and the p-value of 0.0146 also indicates a positive and significant relationship. This result suggests that, in more-developed regions, the ESF mainly contributes to improving innovation infrastructure and strengthening the capacity of enterprises to develop new and sustainable technologies. Unlike Cluster A, where the focus is on building basic infrastructure, in Cluster B, funds are used to support economic competitiveness through innovation and technologization. This may include projects that help SMEs adopt more efficient and eco-friendly production processes, collaborations between universities and industry for technology transfer, and the creation of industrial parks focused on advanced technologies. In these regions, the ESF plays a strategic role in maintaining international economic competitiveness. Although the coefficients for the two clusters are relatively close in magnitude, the ESF impact is directed differently based on the needs of each region. In Cluster A, the funds are used to reduce economic and social disparities by improving access to infrastructure and supporting basic industrial initiatives. In Cluster B, the emphasis is on innovation and sustainability, helping regions maintain a competitive advantage and adapt to the requirements of the digital economy. This difference highlights the flexibility of the ESF in responding to the specific challenges of each region. While less-developed regions receive support for economic growth and industrialization, more-developed regions benefit from investments that encourage continuous innovation and economic resilience.

The impact of the ESF on SDG10—Reduced Inequalities shows that, in Cluster A, the estimated coefficient for the impact of the ESF on SDG10 is 0.078045, and the p-value of 0.1368 indicating a relationship that is not statistically significant at the threshold of p ≤ 0.1. This result suggests that although the ESF may contribute positively to reducing inequalities, its effects are limited in less-developed regions. This may be because, in these regions, the ESF focuses more on developing infrastructure, education, and basic industrialization (other SDGs), with less emphasis on direct inequality reduction measures. Another factor could be the persistence of major economic and social gaps, which require longer periods to show significant progress. Thus, the effects of the ESF on reducing inequalities may appear in the long term, once other essential development components (such as infrastructure and access to education) are consolidated. In Cluster B, the coefficient is 0.223405, and the p-value of 0.0000 indicates a highly significant and positive relationship. This result suggests that the ESF plays a key role in reducing inequalities in more-developed regions. The measures funded by the ESF in these regions are often focused on social inclusion, combating discrimination, and supporting vulnerable groups. Additionally, the emphasis is on integrating marginalized individuals into the labor market and improving access to quality social services. This positive impact can be explained by the increased efficiency of fund utilization in these regions, where basic infrastructure and services are already well developed. In this context, funds can be directed directly toward programs aimed at reducing economic and social inequalities without needing to prioritize other fundamental areas. The differences between the clusters reflect regional priorities and stages of economic and social development. In Cluster A, the effects of the ESF on reducing inequalities are indirect and depend on other interventions addressing deeper structural problems. In Cluster B, the funds are effectively directed toward specific initiatives to reduce inequalities, benefiting from an already well-developed institutional and infrastructural framework.

For SDG11—sustainable cities and communities, in Cluster A, the estimated coefficient is −0.03439, and the p-value of 0.7125 indicates that the relationship is not statistically significant. This lack of significance may reflect differing regional priorities, where ESF interventions are less oriented towards urban transformation or sustainable communities. In less-developed regions, funds are often allocated to other significant needs such as basic infrastructure, education, and poverty alleviation, which are essential for supporting overall development. Moreover, this result may indicate that less-developed regions face systemic challenges in implementing sustainable urban development initiatives. These challenges may include a lack of qualified human resources, insufficient infrastructure, or limited institutional capacity. In Cluster B, the estimated coefficient is 0.261059, and the p-value of 0.0000 suggests a highly statistically significant and positive relationship. This indicates that the ESF has a notable impact on promoting sustainable cities and communities in more-developed regions. Funds are used to support projects such as urban regeneration, improving public transport infrastructure, reducing emissions in cities, and supporting community initiatives that foster social cohesion. Another important factor in these regions is the presence of a more robust institutional framework, which allows for the effective implementation of complex projects. Furthermore, developed regions already have solid basic infrastructures, enabling funds to be directed toward initiatives that have a direct impact on urban sustainability. Significant differences between the clusters can be attributed to divergences in regional priorities and capacities. In Cluster A, ESF resources are mainly used to address basic needs such as poverty reduction and education, leaving less room for initiatives dedicated to sustainable urban development. Conversely, in Cluster B, existing infrastructure and complementary resources allow for fund allocation towards urban transformation, yielding visible and quantifiable results. These findings highlight the need to adapt funding strategies to address the specific challenges of each cluster. In less-developed regions, an integrated approach that includes elements of urban development could significantly improve the ESF’s impact on SDG11. The impact of the ESF on SDG11 differs substantially between less-developed and more-developed regions. In Cluster B, funds contribute directly and significantly to creating more sustainable cities and communities due to a combination of well-defined strategic priorities and strong institutional capacities. In contrast, in Cluster A, the lack of statistical significance suggests ESF interventions are focused on other priority areas. These findings underscore the need for better-adapted policies to promote sustainable urban development in less-developed regions.

The impact of the ESF on SDG12—responsible consumption and production shows a significant divergence between the two clusters. While in Cluster B the relationship is positive and highly statistically significant, Cluster A displays a marginally significant negative relationship. These results reflect essential differences in regional priorities and capacities to integrate sustainability principles into consumption and production practices. The estimated coefficient for Cluster A is −0.27881, and the p-value of 0.0745 indicates a marginally significant negative relationship. This result may suggest that ESF interventions fail to effectively promote the transition to sustainable consumption and production models in less-developed regions. This situation can be attributed to factors such as the lack of adequate infrastructure, modern technology, or qualified human resources, which limit the regions’ ability to implement sustainability initiatives. Moreover, priorities in these regions are often oriented toward basic economic and social needs, which may undermine efforts to reduce the ecological impact. Consequently, less-developed regions tend to rely on traditional production models with limited integration of circular economy principles. In Cluster B, the estimated coefficient is 0.688832, and the p-value of 0.0000 indicates a highly statistically significant positive relationship. This result reflects that the ESF supports the transition to sustainable consumption and production models in more-developed regions. These regions benefit from advanced infrastructure, modern technologies, and well-regulated environmental policies, allowing for efficient use of funds in projects that promote the circular economy, waste reduction, and renewable resource utilization. In more-developed regions, ESF funds can be directed towards initiatives such as educating citizens and companies about sustainable practices, supporting businesses adopting green technologies, and fostering innovation in sustainability. These initiatives contribute to reducing the ecological footprint and promoting a greener economy. The significant differences between clusters highlight the role of regional context in influencing ESF outcomes on SDG12. In Cluster A, ESF funds face structural and contextual limitations, making the transition to sustainable practices challenging. In contrast, in Cluster B, favorable conditions enable the use of funds for advanced sustainability projects with a significant positive impact. These findings underscore the need for differentiated strategies for each cluster. In less-developed regions, it may be necessary to combine support for basic economic needs with measures encouraging the gradual adoption of sustainability principles. The impact of the ESF on SDG12 highlights stark differences between less-developed and more-developed regions. In Cluster A, a marginally significant negative relationship indicates challenges in implementing sustainability initiatives. In contrast, in Cluster B, ESF funds clearly support the transition to responsible consumption and production. These findings emphasize the importance of adapting ESF interventions to the specific needs of each cluster to maximize their impact on SDG12.

The analysis of the impact of the ESF on SDG13—climate action highlights significant differences between the two clusters. While Cluster A reports a statistically significant positive relationship, Cluster B demonstrates an extremely significant negative relationship. These results suggest regional variations in the use of ESF funds to support adaptation and mitigation measures for climate change. The estimated coefficient for Cluster A is 0.223008, with a p-value of 0.0408, indicating a statistically significant positive relationship. This result suggests that the ESF contributes favorably to supporting climate action in less-developed regions. These funds can be used for projects aimed at increasing community resilience to the impacts of climate change, such as strengthening basic infrastructure to withstand natural disasters or supporting reforestation initiatives. Less-developed regions are often more vulnerable to climate change, and ESF funds appear to be effectively directed at reducing this vulnerability. However, progress may be limited by financial and technological constraints, underlining the need for additional support to develop local capacities. In Cluster B, the estimated coefficient is −0.21845, with a p-value of 0.0000, indicating an extremely statistically significant negative relationship. This negative relationship may reflect a diversion of ESF funds to other economic priorities, particularly in developed regions that already have advanced climate policies supported by other financing instruments. These regions may view the ESF as a secondary source of support for climate measures, preferring to use funds for initiatives more closely related to the labor market or social inclusion. Another factor could be the complexity of implementing climate projects in developed regions, where standards and expectations for such initiatives are higher, potentially leading to delays or reduced efficiency in the use of funds. The results for SDG13 reflect a clear difference in regional priorities and needs. In Cluster A, the positive impact of the ESF indicates that funds play a crucial role in supporting vulnerable regions in addressing climate challenges. In contrast, in Cluster B, the negative relationship shows that the ESF is not perceived as a primary tool for supporting climate action, likely due to the availability of other, more relevant funding sources for this domain. These differences suggest that the allocation of ESF funds needs to be better tailored to the needs of each cluster. In developed regions, the ESF could support innovative green technology and energy efficiency projects, while in less-developed regions, it should continue to support basic projects aimed at community resilience.

For SDG14—life below water, the estimated coefficient for Cluster A is 0.417888, with a p-value of 0.1596, indicating a positive relationship, but not statistically significant. Although the result does not meet the significance threshold of p ≤ 0.1, the positive coefficient value indicates that, in less-developed regions, the ESF contributes to projects that have an indirect impact on SDG14, such as improving education on environmental protection and supporting economic activities that can prevent water pollution or promote sustainable management of aquatic resources. These regions are more susceptible to issues related to water pollution and marine environmental degradation, and the ESF may support education and raise awareness among locals about the importance of protecting underwater ecosystems. However, achieving a significant impact in this area would require additional investments and clearer policies integrating water protection measures into ESF-funded projects. In Cluster B, the coefficient is −1.30711, with a p-value of 0.0002, indicating a significant negative relationship. This suggests that in developed regions, the ESF has a negative impact on SDG14, which can be explained by funds being directed more toward initiatives not directly related to marine environmental protection but rather other social or economic priorities. For instance, the ESF might support professional training projects in fields not including ecological sustainability or aquatic environmental conservation. In developed regions already addressing marine environmental protection through other policies and funds, the ESF may be considered a less relevant tool for directly supporting SDG14. Projects aimed at developing infrastructure or integrating economic measures in other sectors may not have a direct connection with objectives related to life below water. Comparing the two clusters, we observe that in Cluster A, the ESF has a positive but insignificant impact on SDG14, suggesting that funds are used to a lesser extent to support projects contributing indirectly to the protection of water and marine ecosystems. Conversely, in Cluster B, there is a significant negative relationship, reflecting fund allocation to areas other than aquatic life, such as social and economic infrastructure development. Differences between the two clusters highlight that in less-developed regions, the ESF could play a more significant role in supporting initiatives to educate and train local communities on marine environment protection and aquatic resource management. In developed regions, however, these initiatives are already supported by other financial mechanisms, and the ESF does not play a central role in supporting activities related to SDG14.

The analysis of the impact of the ESF on SDG15—life on land provides a clearer understanding of how this fund can directly or indirectly support biodiversity conservation and the protection of terrestrial ecosystems. In Cluster A, the estimated coefficient is −1.35316, with a p-value of 0.0405, suggesting a significant negative relationship between the ESF and SDG15. This indicates that in less-developed regions, the European Social Fund may have a negative impact on objectives related to life on land, despite its intention to support biodiversity conservation activities and sustainable management of terrestrial ecosystems. This negative relationship can be explained by the fact that in less-developed regions, funds are more frequently directed toward social and economic infrastructure projects, which do not focus on environmental protection. In these regions, where there is often an urgent need for investments in education, healthcare, and infrastructure, environmental protection may be considered a secondary priority. Thus, despite a possible intention to support activities related to SDG15, the ESF may be used more efficiently to address the immediate needs of local communities. In Cluster B, the coefficient is −0.74469, and the p-value of 0.1960 suggests a negative but statistically insignificant relationship. This indicates that while there is a negative impact, it is not significant enough to substantially influence SDG15 in developed regions. This situation might indicate that ESF funds are used for projects not directly targeting land conservation and biodiversity, such as urban development initiatives or professional training programs unrelated to the environment. Additionally, developed regions may benefit from more effective policies in environmental protection and have access to other dedicated funding sources specifically for protecting the environment and conserving biodiversity. Therefore, in these regions, the ESF is not seen as the primary tool for supporting SDG15, and its impact is less significant. In Cluster A, the ESF has a significant negative impact on SDG15, while in Cluster B, this impact is negative but statistically insignificant. This suggests that in less-developed regions, the ESF might indirectly support projects that do not directly contribute to environmental protection, which reduces its effectiveness in supporting SDG15. In contrast, in developed regions, ESF funds are not prioritized for environmental protection, and their impact on SDG15 is minor, reflecting greater access to other dedicated environmental funding mechanisms. These differences suggest that in less-developed regions, where there is a greater need for social and economic development, the ESF can indirectly support environmental protection measures, but there is no clear direction of the funds toward specific activities for biodiversity conservation and ecosystem protection. In developed regions, by contrast, the ESF is insufficient to effectively address challenges related to SDG15, and other funding mechanisms may be more suitable for this purpose.

Regarding SDG16—peace, justice, and strong institutions, in Cluster A, the estimated coefficient for SDG16 is 0.006801, with a p-value of 0.8638, indicating that the ESF’s impact on this goal is nearly statistically insignificant. This suggests that in less-developed regions, the ESF does not significantly contribute to strengthening institutions and promoting effective governance, either positively or negatively. Such a result can be explained by the fact that the ESF in less-developed regions is often concentrated on domains such as education and professional training, healthcare, and social inclusion, rather than on strengthening government institutions and justice. In these regions, other funding sources and external policies are often more influential in supporting SDG16 objectives, and ESF interventions are not sufficiently oriented to address these challenges directly. Thus, the ESF may contribute indirectly to SDG16-related goals, but without a significant impact on governance and justice. In Cluster B, the coefficient for SDG16 is 0.098509, with a p-value of 0.0008, indicating a statistically significant positive impact. This suggests that in more-developed regions, the ESF significantly contributes to promoting a more efficient institutional framework and supporting better governance practices. This positive relationship may reflect a greater emphasis on projects aimed at institutional reforms, training public administration personnel, improving access to justice, and strengthening the judicial system. In developed regions, the ESF is often used to support administrative reforms and to promote the creation of more efficient institutions, which can have a direct effect on SDG16. These regions benefit from greater resources and have better-developed infrastructures to implement projects aimed at strengthening the rule of law, reducing corruption, and promoting transparency in governance. Additionally, developed regions may implement conflict prevention measures and support more equitable justice systems. The differences between the two clusters regarding the ESF’s impact on SDG16 emphasize the importance of regional context in efficiently utilizing funds. In Cluster A, the almost negligible impact suggests that the ESF is not an effective tool for supporting governance and justice, likely due to a greater focus on social and economic development rather than institutional strengthening. Conversely, in Cluster B, the positive significant impact highlights the use of the ESF for projects aimed at improving administrative efficiency and promoting a stronger institutional framework. These results indicate that the ESF can have a more substantial impact on SDG16 in developed regions, where institutional infrastructure is already better established and where there are more opportunities to use the funds for institutional reform and supporting an effective justice system. In less-developed regions, the ESF may not be optimally utilized to address SDG16-related objectives, and implementing policies targeting institutional strengthening may require additional funding sources and specific interventions.

The analysis of the impact of the ESF on SDG17—Partnerships for the Goals reveals a significant contrast between less-developed regions (Cluster A) and more-developed regions (Cluster B). These differences reflect how the ESF contributes to fostering collaboration and mobilizing the resources needed to achieve the SDGs. In Cluster A, the coefficient for SDG17 is 0.65115, with a p-value of 0.0000, indicating a significant positive impact of the ESF on promoting partnerships for development. This is a remarkable result, as it suggests that the ESF can be an important instrument in mobilizing resources and creating partnerships between the public and private sectors, as well as among various civil society organizations, to support the implementation of the SDGs. In less-developed regions, the ESF can play a vital role in stimulating cooperation among different levels of governance, including local authorities, non-governmental organizations, and economic actors. ESF funds can be used to support transnational projects and build networks of cooperation between less-developed and more-developed regions within the EU. Additionally, the ESF can support the development of partnerships across various economic sectors, such as education, health, and infrastructure, to maximize the impact of sustainable development at local and regional levels. In Cluster B, the coefficient for SDG17 is −0.17299, with a p-value of 0.0945, suggesting a negative but statistically significant impact of the ESF on partnership development in more-developed regions. This result indicates that, while the ESF supports initiatives for development partnerships, in advanced regions, its use may be less efficient, and partnerships in these regions are not as strong or well-coordinated as those in less-developed regions. One possible reason for this negative impact could be related to the fact that developed regions already have a strong institutional infrastructure and may no longer need external interventions to encourage partnerships. In these regions, resources are often already efficiently mobilized, and other funding sources and policies may play a more significant role in fostering partnerships. Additionally, competition for ESF funds may lead to a more unequal distribution of resources among various initiatives, potentially undermining the efficiency of collaboration. Comparing the two clusters, it is evident that the ESF’s impact on SDG17 is much more favorable in Cluster A than in Cluster B. In less-developed regions, the ESF significantly contributes to strengthening development partnerships and fostering cooperation among various stakeholders. This can be an essential factor in stimulating economic and social development in these regions, especially in the context of SDG implementation. In contrast, in Cluster B, the ESF appears to have a negative impact on partnerships for development, suggesting that in more-developed regions, there is less need for external interventions to support inter-institutional and cross-sectoral collaboration. These regions may benefit from more natural and well-established partnerships, and the ESF might not be perceived as a necessary tool for strengthening these connections.

The complex relationships between the ESF and the SDGs revealed in Figure 2 and statistical analysis through robust regression with M-estimator provide an integrated perspective on the impact of these interventions. The figure suggests a moderate positive link between RC1 and SDG1, indicating that certain interventions within this thematic cluster contribute to poverty reduction. However, the robust regression analysis shows that some thematic clusters exert a negative influence on SDG1. This could reflect how unequal resource distribution or prioritization of other economic objectives might diminish the positive impact of ESF policies on poverty alleviation. These findings are supported by studies emphasizing the importance of context in implementing social policies. For example, prior research has shown that social assistance programs, if not well-targeted, may exacerbate existing inequalities, particularly in marginalized communities [46]. In the case of the ESF, prioritizing initiatives exclusively focused on employment without sufficient attention to social inclusion could explain these negative effects.

Figure 2 shows a positive connection between RC3 and SDG2, and the robust regression analysis confirms the contribution of interventions from certain clusters to reducing food insecurity. This reflects the positive impact of programs supporting rural development, sustainable agriculture, and access to basic resources. The literature highlights that sustained investments in agriculture and rural infrastructure are essential for achieving SDG2. Studies conducted by the Food and Agriculture Organization (FAO) underline that policies aimed at building local capacity in agriculture significantly contribute to reducing hunger, but only when accompanied by measures ensuring equitable resource distribution and integrating small markets into global supply chains [47]. The results confirm the importance of these interventions and the role of ESF in funding them.

Also, the figure and regression analysis clearly show a strong positive influence between thematic clusters and SDG8. This relationship underscores the significant impact of ESF funds in promoting job creation, employment, and sustainable economic growth. This finding is well-documented in the literature. For instance, the European Commission’s report on the European Social Fund indicated that ESF funding has directly supported millions of people through professional training, labor market integration, and entrepreneurship initiatives [48]. Additionally, programs supporting the circular economy and green transition, funded by the ESF, can contribute to economic growth without compromising natural resources. However, some studies caution that the positive effects on SDG8 may vary depending on regional development levels and local administrative capacity. Areas with underdeveloped infrastructure and limited access to resources may experience slower progress [49].

Moreover, Figure 2 shows a significant positive relationship between RC4 and SDG13, but the regression analysis suggests that certain thematic clusters also exert negative influences. This dual impact may reflect the conflict between economic development goals and climate priorities. For instance, infrastructure projects funded through the ESF, if not implemented with sustainability criteria, may negatively impact the environment, undermining climate change objectives. This phenomenon is well-documented in studies addressing trade-offs between economic development and environmental protection. Research indicates that policies failing to integrate carbon emission reduction measures during planning may adversely affect the environment, even if they contribute to short-term economic growth [50]. Nevertheless, when correctly utilized, ESF funds can support the green transition, as demonstrated by successful programs in renewable energy and environmental education [51].

The figure indicates a clear positive relationship between RC4 and SDG17, emphasizing the ESF’s contribution to promoting partnerships and international cooperation. The regression analysis confirms this, showing a significant positive impact from certain clusters. However, marginal negative influences from other clusters may reflect challenges in multilateral coordination or aligning regional and global objectives. The literature highlights that successful partnerships are essential for achieving the SDGs. They enable resource mobilization, knowledge exchange, and coordinated efforts at national and international levels [52]. However, studies also indicate that partnership efficiency depends on governance quality and trust levels among partners. Lack of transparency and coordination issues can limit the positive impact of such initiatives [53].

5. Discussion

The findings of this study provide valuable insights into the relationships between European Social Fund (ESF) payments and various Sustainable Development Goals (SDGs) while providing opportunities to compare these results with the existing literature. This discussion will explore how the results align with or depart from previous research, identify some inherent limitations of the analysis, and highlight gaps in the existing body of knowledge that warrant further investigation.

The study identifies significant positive impacts of ESF on reducing poverty (SDG1), stimulating economic growth (SDG8), and strengthening industry and innovation (SDG9), particularly in less-developed regions. These findings are in line with Verschraegen et al. [20], who demonstrated that the ESF catalyzed innovation in activation instruments and positively influenced employment policy governance. Furthermore, the role of ESF in addressing structural challenges through vocational training and job creation aligns with Potluka et al. [22,24], who found positive employment effects of ESF-funded training programs, albeit with varying degrees of success in different contexts.

However, the limited or negative contributions of ESF to the sustainability goals (SDGs 7, 12, and 13) resonate with Tomé [30], who reported a relatively modest macroeconomic impact of the ESF in Portugal and limited progress in skills development. These findings also parallel González-Alegre [31], who identified challenges in the effectiveness of ESF interventions in active labor market policies, suggesting potential inefficiencies or misalignments in resource allocation.

Furthermore, the study’s focus on regional variations—observing greater impact in less-developed regions compared to developed ones—is confirmed by Nakrosis et al. [26], who emphasized the significant role of internal factors, including initial administrative capacity and politicization, in shaping the outcomes of ESF-funded projects. Similarly, Zimmermann [27] stressed the importance of differentiating between the use of funds and actual organizational change, a notion that the findings of this study implicitly support by revealing differential effects across regions.

Nevertheless, the study’s observation on the success of ESF in promoting partnerships is echoed by Iannacci et al. [25], who identified strategies for improving ESF monitoring processes, such as improving collaboration and information sharing among stakeholders. These mechanisms are likely to contribute to the capacity of the ESF to effectively mobilize resources, especially in less-developed regions.

Although the study uses sound methodological approaches such as exploratory factor analysis (EFA) and robust regression, some limitations must be recognized. First, the reliance on ESF payments as the sole explanatory variable may neglect other contextual factors, such as domestic policies, cultural differences, or parallel financing mechanisms, that influence partnership outcomes. For example, Nakrosis et al. [26] and Zimmermann [27] emphasize the importance of internal factors and institutional capacity in shaping project outcomes, which this study cannot fully capture. In addition, aggregating data at the regional level may mask intra-regional disparities or localized effects of ESF. Pelucha et al. [23] suggest that the impact of training programs can vary significantly even within the same national or regional context, which highlights the need for more granular analysis.

Furthermore, the focus of this study on 17 SDG indicators may not capture the full extent of the potential contributions of ESF, particularly in areas such as gender equality and social inclusion, which Zartaloudis [29] emphasizes as significant but under-explored dimensions. In addition, the observed negative impact on sustainability objectives may be partly attributable to the limitation of available data or methodological constraints, rather than reflecting the true extent of ESF contributions.

The findings reveal several gaps in the literature that warrant further investigation. The limited impact of the ESF on sustainability objectives (SDGs 7, 12, and 13) suggests the need for future studies examining the integration of environmental objectives into ESF-funded programs. In addition, future research could explore how economic growth priorities can be balanced with environmental sustainability, particularly in developed regions. Also, future research should focus on more localized analyses to uncover intra-regional disparities and identify factors that enhance or hinder the effectiveness of ESF interventions at the EU level. Nevertheless, investigating how the ESF interacts with other EU funding instruments, such as the European Regional Development Fund (ERDF) or Horizon Europe, could provide a more comprehensive understanding of the complementarities and synergies between the different funding streams.

Similarly, given the findings of Zartaloudis [29] and Tomé [30], future studies should deepen the role of the ESF in promoting social inclusion and gender equality and reducing inequalities, which are essential components of sustainable development. Finally, long-term studies that track the sustained impact of ESF-funded projects on the SDGs would provide valuable insights into the sustainability and transformative potential of these interventions.

Thus, this study highlights the multidimensional impact of the ESF on the SDGs, with notable successes in reducing poverty, promoting economic growth, and enhancing innovation, particularly in less-developed regions. However, it also highlights areas where ESF contributions are limited, such as the sustainability goals, suggesting the need for more integrated and context-sensitive approaches. By addressing the identified limitations and following the proposed future research directions, researchers and policymakers can deepen their understanding of the role of the ESF in achieving the broader goals of the 2030 Agenda for Sustainable Development.

6. Conclusions

The findings of this study provide a clear answer to the research question: What role does the European Social Fund (ESF) play in the implementation of the Sustainable Development Goals (SDGs)? The analysis demonstrates that the ESF serves as a critical financial mechanism for promoting progress toward the SDGs within the European Union (EU). Specifically, the ESF significantly contributes to reducing poverty (SDG1), fostering economic growth (SDG8), and supporting industry, innovation, and infrastructure (SDG9), particularly in less-developed regions. These contributions are achieved through investments in vocational training, job creation, and infrastructure development. Additionally, the ESF facilitates partnerships (SDG17), especially in regions with weaker institutional frameworks, highlighting its role in fostering collaboration across diverse stakeholders. However, the study also identifies limitations in the ESF’s impact on environmental objectives, such as affordable and clean energy (SDG7) and climate action (SDG13), particularly in more-developed regions where economic priorities often overshadow environmental goals. These findings suggest that while the ESF is instrumental in addressing social and economic dimensions of sustainable development, its role in advancing environmental sustainability requires further enhancement.

The European Social Fund (ESF) significantly contributes to achieving the Sustainable Development Goals (SDGs) in EU Member States by addressing economic disparities, promoting social inclusion, and enhancing workforce skills. Through its targeted interventions, the ESF supports SDG1 (No Poverty) by reducing unemployment and fostering economic participation, particularly among marginalized groups. It also strongly impacts SDG8 (Decent Work and Economic Growth) by funding initiatives that improve employability, create jobs, and support entrepreneurship. While its influence on SDG10 (Reduced Inequalities) is important in reducing socioeconomic disparities, the ESF also indirectly supports other SDGs, such as SDG4 (Quality Education), by improving access to and the quality of education systems, and SDG2 (Zero Hunger), through investments in rural development and sustainable agriculture. These contributions establish the ESF as a key instrument in the EU’s efforts to align regional development with the SDGs.

Significant differences are evident in how ESF funding impacts SDGs across more-developed (Cluster B) and less-developed (Cluster A) Member States. In more-developed countries, ESF funding is more efficiently utilized due to well-established institutional frameworks and advanced infrastructure. This leads to a stronger and more immediate impact on SDG1 (No Poverty) and SDG10 (Reduced Inequalities), as these regions can efficiently address social inclusion and economic disparities. In contrast, less-developed states experience a slower, foundational impact, as ESF interventions focus on overcoming structural challenges, such as underdeveloped infrastructure and capacity constraints. For instance, while less-developed countries show incremental progress in SDG4 (Quality Education) and SDG8 (Decent Work and Economic Growth), developed countries utilize ESF funding to improve the alignment of education systems with labor market needs and foster innovation under SDG9 (Industry, Innovation, and Infrastructure).

The most relevant SDGs influenced by ESF-funded projects vary between Member States based on their development levels. SDG1 (No Poverty), SDG8 (Decent Work and Economic Growth), and SDG10 (Reduced Inequalities) emerge as universally important across both clusters. In less-developed countries, the ESF significantly supports SDG2 (Zero Hunger) by fostering sustainable agricultural practices and improving food security, as well as SDG4 (Quality Education), through initiatives aimed at reducing educational disparities. In more-developed states, SDG9 (Industry, Innovation, and Infrastructure) and SDG4 see marked advancements, as ESF projects prioritize enhancing competitiveness, fostering technological innovation, and promoting lifelong learning.

The main SDGs benefiting from ESF funding reflect the distinct priorities of Member States based on their development status. In less-developed countries, ESF primarily supports SDG1, SDG8, SDG4, and SDG2, addressing poverty alleviation, job creation, education access, and rural development. In contrast, more-developed countries prioritize SDG10, SDG4, SDG9, and SDG8, focusing on reducing persistent inequalities, improving educational quality, fostering innovation, and supporting upskilling initiatives. These variations underscore the adaptability of ESF funding, as it addresses foundational needs in less-developed states while advancing strategic enhancements in more-developed regions. Such differentiation ensures that ESF investments are aligned with regional challenges, fostering balanced progress toward the SDGs across the EU.

The findings of this study reveal important practical implications and provide actionable policy recommendations for optimizing the impact of the ESF on achieving the SDGs. First, the study underscores the need for tailored regional approaches, given the substantial differences in how ESF interventions affect less-developed (Cluster A) and more-developed (Cluster B) EU regions. Policymakers should adapt funding strategies to address region-specific challenges, focusing on foundational infrastructure and long-term outcomes in less-developed regions while prioritizing innovation and sustainability in more-developed areas. Furthermore, the ESF’s gradual impacts on poverty reduction, education, and health outcomes highlight the importance of designing interventions with a long-term perspective in mind, particularly in regions facing deep structural barriers.

Another practical implication involves balancing economic and environmental objectives. The study identifies potential trade-offs between development and sustainability goals, as some ESF-funded projects risk negatively impacting SDGs related to clean energy (SDG7) and climate action (SDG13). To mitigate this, sustainability criteria should be integrated into all ESF-funded projects, ensuring alignment with the EU’s Green Deal and circular economy principles. Additionally, the study emphasizes the role of strong partnerships and governance in achieving SDG17, particularly in less-developed regions. Governments should actively foster collaboration between public, private, and civil society actors to mobilize resources effectively and enhance the outcomes of ESF interventions. High-quality data collection and robust monitoring systems are also essential for evaluating the effectiveness of these projects, supporting data-driven decisions, and addressing the heterogeneity of regional contexts.

To maximize the ESF’s impact, several policy recommendations emerge. Regional differentiation in ESF allocations is crucial to addressing specific needs. For less-developed regions, funding should focus on strengthening infrastructure, reducing education and health disparities, and fostering economic integration. In more-developed regions, ESF projects should target innovation, advanced training, and sustainability initiatives. Policymakers should also prioritize capacity building in less-developed regions by investing in institutional and administrative capabilities to enhance project implementation. Furthermore, social inclusion and equality programs should be bolstered, with targeted measures to reduce gender disparities (SDG5) and economic inequalities (SDG10) through initiatives supporting vocational training, entrepreneurship, and inclusive education. Strengthening long-term evaluation frameworks and monitoring systems will be significant for assessing the delayed impacts of interventions, particularly on foundational goals like poverty reduction (SDG1) and sustainable agriculture (SDG2).

Lastly, collaboration and partnerships should be reinforced through cross-border and transnational projects to encourage knowledge exchange and collective problem-solving, especially in less-developed regions. These recommendations, if implemented, could significantly enhance the ESF’s contribution to achieving the SDGs, fostering inclusive, equitable, and sustainable development across the European Union. By addressing regional disparities, integrating sustainability, and prioritizing long-term and inclusive outcomes, the ESF can be a powerful driver of transformative change.

This research has several limitations that should be acknowledged. First, the study relies on secondary data from publicly available sources, which may contain measurement inconsistencies or gaps. Additionally, the analysis focuses on a specific time range (2015–2023), limiting the ability to assess the longer-term impacts of ESF interventions. Another limitation is the clustering of Member States based on GNI per capita, which may oversimplify the diverse economic and social contexts within each group.

Future studies could expand the temporal scope to include additional years, enabling a more comprehensive analysis of trends and long-term impacts. Exploring the interactions between ESF funding and other EU financial instruments, such as the European Regional Development Fund (ERDF) or Cohesion Fund (CF), could provide deeper insights into how different funding mechanisms collectively influence the SDGs. Furthermore, qualitative research, such as case studies or stakeholder interviews, would complement quantitative analyses by capturing the nuanced effects of ESF interventions at the local level. Finally, future research could investigate how ESF funding strategies might be optimized to address environmental sustainability goals, aligning with the EU’s broader green transition agenda. By addressing these limitations and exploring these future directions, subsequent research can build on the findings of this study to further understand and enhance the role of the ESF in sustainable development.