Local Development and LEADER Funding in Poland: Insights from the Wielkopolska Region

Abstract

LEADER is a European Union program designed to engage the energy and resources of people and local organizations in contributing to the development of their rural areas. Given the uniqueness of the program—particularly its bottom-up, incentive-based approach—it is of crucial importance to determine the local conditions affecting fund absorption. This study utilizes factor analysis and stepwise multiple regression to assess the influence of the local rural development conditions on the level of funding and rural residents’ participation in the LEADER program in Poland, using the example of the Wielkopolska region. This research spans two consecutive EU funding periods: 2007–2013 and 2014–2020. Our findings reveal that residents in areas with higher developmental needs and lower levels of socioeconomic development display a greater proclivity to access LEADER program funds. Consequently, the LEADER funds in the Wielkopolska region are directed towards areas in genuine need of reinforcement. These results contradict previous research on selected EU rural development measures under the second pillar of the CAP, as well as specific LEADER implementation areas. Furthermore, our findings indicate that entrepreneurship and cultural activities play a pivotal role in stimulating bottom-up initiatives within rural communities.

1. Introduction

The experiences of EU member countries in rural development policies have led to the adoption of the LEADER program. Its name is derived from the French phrase “Liaison Entre Actions de Développement de l’Économie Rurale”, which can be translated as “links between activities for the development of the rural economy”. The idea is to engage the energy and resources of people and local organizations as development actors rather than beneficiaries, empowering them to contribute to the future development of their rural areas by forming area-based local action group (LAG) partnerships between the public, private, and civil sectors [1]. These partnerships aim to integrate residents, enhance their opportunities to participate in local development, and access locally tailored European Union development funds.

The LEADER approach involves entrusting LAGs with the task of devising and directly implementing local development strategies. It aims to activate rural communities, facilitate local initiatives, and promote bottom-up rural area development [2]. This development is intended to be driven by local initiatives and the utilization of local potential. The LEADER program operates on the basis of a public–private partnership, wherein LAGs are formed collaboratively by three categories of partners: public, private, and social.

The multidimensionality of the concept of local development, including the specifics of bottom-up initiatives and the relatively novel experiences related to the LEADER program’s implementation within the EU, contribute to the broad scope of the related research. These studies encompass various aspects, such as social capital in local development and its accumulation through the implementation of the LEADER program [3,4,5,6,7], the effects of bottom-up initiatives [8,9,10,11,12], and the institutional barriers to the program’s implementation [13,14,15].

LEADER is an incentive-based and voluntary policy. These types of policies have evolved in the EU as alternative methods of achieving political goals and are used to support behaviors that are in line with achieving greater public benefits. According to the official statement of the European Parliament [16,17], the LEADER approach for local development has, over a number of years, proven its effectiveness in promoting the development of rural areas by fully taking into account the multi-sectoral needs for endogenous rural development through its bottom-up approach. However, due to its novelty, the policy needs constant monitoring.

Because of the voluntary and participative nature of the LEADER initiative, a highly significant factor revolves around the engagement of residents in areas covered by local development strategies in applying for its funds. The literature has extensively addressed the factors influencing the activity and absorption of the EU funds, especially coming from the second pillar of the Common Agricultural Policy [18]. According to Bielecka (2006) [19], the activity of individual entities in acquiring support from the EU significantly depends on their geographical location. Spatial disparities in the activity of obtaining funds from the EU in rural areas have also been pointed out by Rudnicki (2013) [20] and Kiryluk-Dryjska et al. (2020) [21]. Research into the engagement of residents in rural areas across EU countries indicates a negative correlation between the backwardness of rural areas and the level of support from the second pillar of the EU’s Common Agricultural Policy. This indicates an imbalance in the spatial distribution of funds. Some studies [22,23,24] demonstrate that the most disadvantaged EU regions exhibit a lower expenditure intensity in EU structural programs aimed at knowledge and innovation transfer in rural areas. Similar conclusions were drawn by Chaplin et al. (2004) [25] in their study of farm diversification. Additionally, Zasada et al. (2018) [26] showed the strong spatial dependence in the allocation of expenditure for rural development programs. Cañete et al. (2018) [11] examined the territorial effects of the LEADER program in Andalusia, Spain and concluded that projects were often concentrated in dynamic, densely populated areas with established business networks capable of accessing European funding. Consequently, these initiatives reinforce the economic prominence of these dynamic municipalities at the expense of less developed areas with limited social capital and fewer businesses. Similar results were presented by Masot and Alonso (2017) [27], who reported that LEADER investments have been executed in the most developed rural areas.

The aforementioned studies clearly illustrate that socioeconomic and structural factors influence the ability of specific areas to attract local funding from EU funds, including the LEADER program. However, due to the variation in outcomes, further research on fund allocation is warranted [24]. Given the uniqueness of the LEADER program—particularly its bottom-up, incentive-based approach—research on the local conditions affecting fund absorption holds particular significance in further policy planning. To the best of the authors’ knowledge, there is a lack of such studies for the Wielkopolska region. The results of this paper are intended to provide insights for agricultural and rural development policy planning at the regional level, but the methodology proposed can also be applied to future research in other regions.

The objective of this paper is to assess the influence of the local rural development conditions on the level of funding and rural residents’ activity in applying for LEADER program funds in Poland, via the example of the Wielkopolska region.

Although Wielkopolska ranks among the better-developed regions in terms of rural living conditions, it displays internal variations. This makes it a suitable subject for the study of the alignment of the LEADER program expenditures with the local development conditions. The following questions were posed: (1) Is there a correlation between the level of local development and the value of projects undertaken within the LEADER program, as well as the activity in seeking funds? (2) What characteristics of rural areas impact the activity of seeking funds within the LEADER program?

The structure of this paper is as follows. First, we introduce our research methods. Next, we apply these methods to study the impact of local rural development determinants on the value of projects undertaken within the LEADER program, as well as the activity in seeking funds. We also analyze the characteristics of rural areas that impact the activity of seeking funds within the LEADER program in Wielkopolska. Finally, we conclude with a discussion.

2. Materials and Methods

2.1. Study Area

The Wielkopolska region (NUTS 2), in the system of territorial units according to the Polish Central Statistical Office, belongs to one of the seven macroregions in Poland—specifically, the North-Western Macroregion (NUTS 1). The Wielkopolska region is divided into six subregions (NUTS 3) and consists of 35 counties (NUTS 4) (including four cities with county rights), which are composed of municipalities (NUTS 5): 19 urban, 94 semi-rural, and 113 rural municipalities.

Wielkopolska is one of the most economically developed regions in Poland. Its area is nearly 30,000 square kilometers, and its population is close to 3.5 million people. The region’s strengths remain the size and quality of its labor market resources, its market absorption, its transport accessibility, its developed economic infrastructure, and the overall level of economic development.

Studies on the regional differentiation of development in Poland emphasize that this phenomenon has historical roots, dating back to the 19th century, when the territory of present-day Poland was divided among three partitioning powers (Russia, Germany, and Austria) and was subject to different agricultural and economic policies. In the German partition area (including Wielkopolska), agriculture was expected to serve as a food base for the rapidly developing industry in the western part of the country. As a result, agriculture in this region developed rapidly. As noted by various studies, the territorial units located in the former German partition (including the Wielkopolska region) are generally more developed than the rest of the country. In rural areas of this region, relatively large, family-run farms are predominant. Research on rural development differentiation [21,28,29,30] highlights that the north-western regions of Poland have higher agricultural potential compared to the national average. Despite the fact that Wielkopolska does not have large areas of high-class soils and is located in a zone with less favorable water conditions, two-thirds of its area, or over 1.8 million hectares, is agricultural land. More than 123,000 farms actively operate here, producing the largest global agricultural output in Poland. Moreover, these regions lead in applications for EU pro-investment funds [30].

This study encompassed rural and semi-rural municipalities (NUTS 5 regions)) within the Wielkopolska region (207 units) and the local action groups (LAGs) established in this area, operating during two funding periods: 2007–2013 (31 LAGs) and 2014–2020 (29 LAGs).

2.2. Material

For all municipalities in Wielkopolska where LAGs operated, 30 initial variables were selected to characterize their social and economic situations. The variables characterized fundamental aspects of development, such as entrepreneurship, social and technical infrastructure, rural residents’ activity, their propensity for association, and natural landscape attributes. Statistical data from the Polish Central Statistical Office (Statistical Office’s Local Data Bank) for the years 2007 and 2015 were used for this purpose. The determination of the rural development status in 2007 aimed to illustrate the initial development levels of the municipalities within the Wielkopolska region belonging to specific LAGs at the beginning of the 2007–2013 LEADER implementation period. Similarly, data from 2015 aimed to demonstrate the state at the outset of the 2014–2020 implementation period.

Additionally, data on the LEADER program expenses for the years 2007–2013 and 2014–2020 came from the Marshal’s Office of the Wielkopolska region, the Agency for Restructuring and Modernization of Agriculture, and the public information bulletins of individual local government units (municipalities).

2.3. Method

This study was conducted in three stages. In the first stage, based on initial, observed variables (computed from the data for 2007), the main factors of rural area development in the Wielkopolska region were identified at the commencement of the EU 2007–2013 financial period. Factor analysis was used for this purpose. Its primary goal was to reduce the number of observed variables to a smaller set of factors, from which the observed initial variables were linearly dependent and which best explained the correlations among them. In factor analysis, the observed variables are expressed as linear combinations of underlying latent factors and unique errors. This can be represented by the following matrix equation:

X = ΛF + ϵ

(1)

where

• X is the p × n matrix of observed variables (with p observed variables and n observations);
• Λ is the p × m matrix of factor loadings (with p observed variables and m underlying factors);
• F is the m × n matrix of common factors (with m factors and n observations);
• ϵ is the p × n matrix of unique errors (specific to each observed variable).

Common factors (F) are unobserved latent variables that explain the common variance shared between the observed variables, while factor loadings (Λ) are coefficients that show how much each observed variable is influenced by the underlying latent factors.

The use of factor analysis is considered valid only if there are sufficiently large values for the correlation coefficients between the examined observed variables. It is assumed that all coefficients should be above 0.3. Therefore, if the set of observed variables has very few or no correlations with the other variables, they should be removed from further analysis. Otherwise, unreliable and difficult-to-interpret results may be obtained at the final stage of the study [31]. Thus, variables that did not correlate with others were excluded, resulting in 26 indices to extract the main factors of rural area development in Wielkopolska.

For each municipality involved in the study (207 units), the factor scores were calculated. The factor score is a composite measure created for each NUTS 5 region of each factor extracted in the factor analysis and is standardized using a z-score method. Thus, the factor scores for the Wielkopolska sum up to zero, while the factor scores for individual NUTS 5 regions reflect their relative positions with regard to the level of development of the main features of agriculture and rural areas in Poland.

In our approach, the factor scores of the municipalities belonging to the LAG’s area were averaged at the LAG level and compared with those of the Wielkopolska region. Thus, the factor scores for individual LAGs differ, reflecting the relative differences in the main features of agriculture and rural areas. Briefly, negative values for the average LAG scores for a given factor suggest that this factor (feature) is underdeveloped in the LAG. Conversely, positive values indicate that the factor (feature) is better developed than the average in the Wielkopolska region. To summarize, it was assumed that lower factor scores for municipalities belonging to specific LAGs in relation to the regional score (Wielkopolska) indicated the relative lag of the LAG, which could be associated with developmental barriers, while higher scores suggested a relative developmental advantage. This approach enabled the determination of the local development conditions of the LAGs in relation to the Wielkopolska region.

In the second stage, the average value of the factor scores for all determined factors in the given LAG was calculated to obtain a relative index of rural development in the LAG’s area. Based on its value, five typological groups of LAGs were identified, ranging from 1 (the least developed) to 5 (the most developed).

Typologies of rural areas are often performed to compare their spatial development patterns. The types of areas that exhibit similarities in their development structures—meaning that they show a similar combination of development components—can be used to draw conclusions and recommendations for economic policies and practices. Different methods can be used for this purpose, such as the NUTS 2 typology performed by Rosner and Stanny (2017) [32], which identified synthetic development factors using the dynamic cloud method. In our approach, we also use the synthetic features of the municipalities, but these are extracted from the factor analysis. This methodological approach has been successfully used by Kiryluk-Dryjska and Beba (2018) [33] to determine groups of regions with similar development characteristics.

In the third and central stage of the study, the impact of the rural development level on the activity in applying for LEADER program funds was determined. Moreover, to investigate the influence of the specific local developmental conditions on rural residents’ activity in applying for LEADER program funds, a stepwise multiple regression method was employed [34]. The multiple linear regression formula can be expressed as follows:

Y = β₀ + β₁X₁ + β₂X₂ +⋯+ β_kX_k + ε

(2)

where

• Y is the dependent variable;
• X₁, X₂, …, X_k are the independent variables;
• β₀ is the intercept (the value of Y when all X_is equal 0);
• β₁, β₂, …, β_k are the coefficients that represent the change in Y for a one-unit change in each corresponding X;
• ε is the error term (residual) that accounts for the variability in Y that cannot be explained by the predictors.

In our case, the dependent variable is the number of applications submitted within the LEADER program per 1000 residents of the LAG. The independent variables in the regression model are the values of the indicators previously obtained from the factor analysis (factor scores).

The coefficient of determination (R²) indicates the percentage of the variance in the dependent variable that is explained by the regression function. It takes values in the range of (0;1), where the closer R² is to 1, the better the regression model describes the behavior of the dependent variable being studied. The essence of forward stepwise multiple regression is the sequential addition of variables to the model, starting with the variable most correlated with it. The process of creating the model ends when all independent variables that have a significant impact on the dependent variable are added to the model. The significance of each variable is evaluated using F-Snedecor statistics. The verification of the regression model includes the analysis of residuals. A small and symmetrical distribution of residuals around the line indicates that the model meets the normality condition as regards the residuals, which confirms the significance of the model parameters [34].

The regression was performed for all municipalities in the Wielkopolska region that belonged to LAGs. Two separate models were built for the years 2007–2013 and 2014–2020.

The level of funding for actions within the LEADER program was analyzed within individual typological groups of LAGs, verifying the relationship between the funding magnitude and the development level of LAG groups.

In order to clarify the procedure, the flowchart of the applied method is presented in Figure 1. The same procedure was conducted for the 2014–2020 period, based on data from 2015.

3. Results

3.1. Determination of Main Development Factors

As previously mentioned, the variables used in this analysis were designed to capture key aspects of rural development, such as entrepreneurship, social and technical infrastructure, residents’ engagement, their association tendencies, and natural landscape attributes.

Table 1. Average values of variables used in this analysis across all municipalities studied.

Variable	2007		2015
	Average	Standard Deviation	Average	Standard Deviation
Share of permanent grassland within agricultural land (%)	14.24	4.77	13.74	2.69
Relation of forested land to agricultural land	5.23	2.65	4.64	1.01
Population connected to water supply network as % of population	88.34	6.92	92.42	6.22
Percentage of population using sewerage system %	34.74	18.98	58.73	10.66
Population connected to gas supply network as % of population	18.94	23.87	33.99	7.49
Length of sewerage network in relation to water supply network	23.33	18.23	36.86	26.02
Libraries per 100 km2	2.15	1.16	2.20	0.61
Members of sports and religious clubs in total population (%)	23.57	5.83	34.68	1.65
Artistic groups (per 1000 people)	0.52	0.56	0.56	0.57
Cultural centers, clubs, and community centers (per 1000 people)	0.21	0.41	0.15	0.25
Entities of the national economy per 1000 people	74.47	9.46	78.83	8.97
Persons engaged in economic activity (per 1000 people)	60.34	19.98	66.05	20.61
Newly built residential buildings (per 1000 people)	4.00	3.19	4.57	1.43
Share of forests in total area (%)	22.68	4.64	29.03	5.04
Unemployed per 1000 people of productive age	6.25	3.13	5.22	2.26
Share of parks and green areas in total area (%)	0.12	0.19	0.12	0.20
Share of recreational parks in total area (%)	0.08	0.16	0.04	0.11
Primary schools (per 100 km2)	3.79	1.71	3.42	1.56
Middle schools (per 100 km2)	1.53	0.86	0.23	0.16
Clubs and organizations (per 1000 people)	0.43	0.26	0.48	0.17
Foundations, associations, and social organizations (per 1000 people)	2.25	0.72	3.00	0.14
Entities newly engaged in economic activity (per 1000 people)	7.61	2.58	7.22	1.29
Equipment of tourist accommodation facilities (per 100 km2)	2.21	5.00	3.24	5.51
Agricultural production space valorization index	63.57	11.52	63.27	8.13
Number of tractors per farm	1.05	0.33	1.11	0.34
Consumption of NPK fertilizers per 1 ha of agricultural land (in dt)	26.78	12.86	29.45	10.02

Source: own calculations based on Polish Statistical Office’s Local Data Bank.

presents the average values of these variables across all municipalities studied.

Between 2007 and 2015, most rural development indicators showed improvements. Technical infrastructure, reflected by variables like access to gas, sewage, and water supply networks, advanced. Rural residents demonstrated greater involvement in economic activities, as indicated by an increase in the number of businesses and a higher employment index. The density of entities of the national economy increased, meaning that more working places emerged in rural areas coming from Polish and foreign companies. Additionally, there was a notable rise in participation in sports, religious, and artistic activities, as well as an increase in the number of foundations, associations, and social organizations, signaling growth in social and human capital. Storberg (2002) [35] points out that theories of social capital remain underdeveloped and fragmented in specific disciplines (e.g., sociology, psychology, social psychology, political economy). However, as underlined by Mesut (2005) [36], there is a growing consensus that social capital reflects the ability of actors to secure benefits by virtue of membership in social networks or other social structures. Human capital can be defined as the stock of skills that the labor force possesses [37]. Both social and human capital are critical for bottom-up initiatives.

The agricultural intensity also rose, as evidenced by the higher number of tractors per farm and the increased use of NPK fertilizers. However, the number of schools decreased, likely due to demographic shifts in rural Poland.

Table 2. Eigenvalues and percentages of explained variance for factors (data for the year 2007).

Factor	Eigenvalue	Percentage of Explained Variance	Cumulative Eigenvalue	Cumulative Percentage of Explained Variance
Factor 1	5.6	21.64	5.63	21.6
Factor 2	3.3	12.55	8.89	34.2
Factor 3	2.3	9.03	11.24	43.2
Factor 4	1.7	6.41	12.9	49.6
Factor 5	1.7	6.39	14.56	56.0
Factor 6	1.3	5.02	15.87	61.0
Factor 7	1.2	4.58	17.06	65.6
Factor 8	1.0	3.98	18.09	69.6

Source: own calculations.

presents the eigenvalues and the percentages of explained variance for the factors obtained through the factor analysis conducted for the year 2007. Following Kaiser’s criterion, the number of factors was extracted in such a way that none of their eigenvalues were less than 1. This condition was met for eight factors. The total cumulative percentage of explained variance by the derived factors was nearly 70%. The first extracted factor explained 21.6% of the variance, while the last one accounted for approximately 4%.

Table 3. Factor loadings (data for the year 2007).

Variable	Factor 1	Factor 2	Factor 3	Factor 4	Factor 5	Factor 6	Factor 7	Factor 8
Share of permanent grassland within agricultural land (%)	−0.093686	−0.757220	−0.137811	−0.023578	0.004044	−0.010787	−0.056574	0.006731
Relation of forested land to agricultural land	0.022459	−0.596246	−0.089477	0.048176	−0.096304	0.187361	−0.053586	−0.003517
Population connected to water supply network as % of population	0.009411	0.487266	0.226105	0.126653	0.044183	0.183936	0.175050	0.169818
Percentage of population using sewerage system %	0.159101	0.079158	0.231753	0.175846	−0.027304	0.016965	0.875618	−0.040430
Population connected to gas supply network as % of population	0.305703	0.115950	0.218409	0.175123	−0.071206	0.033496	0.764549	−0.087563
Length of sewerage network in relation to water supply network	0.192055	0.012145	−0.045960	0.037125	0.003108	−0.174717	0.693634	0.261235
Libraries per 100 km2	0.266832	0.084588	0.666825	0.196573	−0.062425	−0.016552	0.242909	0.014219
Members of sports and religious clubs in total population (%)	0.064650	−0.020382	−0.011069	0.033655	0.223887	0.819404	0.018417	−0.167028
Artistic groups (per 1000 people)	−0.062375	−0.001148	0.002476	−0.046990	0.816753	0.080996	−0.006537	0.070712
Cultural centers, clubs, and community centers (per 1000 people)	0.013614	0.153481	−0.167524	−0.036159	0.176998	0.103459	−0.051322	0.694771
Entities of the national economy per 1000 people	0.779030	0.096587	0.082712	0.085121	0.012802	0.056971	0.472147	0.101268
Persons engaged in economic activity (per 1000 people)	0.780959	0.066593	0.100143	0.059206	0.008913	0.040805	0.444616	0.105996
Newly built residential buildings (per 1000 people)	0.771337	−0.045304	0.110079	−0.020883	0.117694	−0.076979	−0.121980	−0.011580
Share of forests in total area (%)	0.096942	−0.631854	−0.499728	−0.128090	−0.012991	0.004174	0.291554	−0.074681
Unemployed per 1000 people	−0.595925	−0.020919	0.017391	−0.146195	0.362655	0.027786	−0.089211	−0.078292
Share of parks and green areas in total area (%)	0.083004	0.090446	0.145128	0.932426	−0.036578	0.018526	0.263110	−0.014967
Share of recreational parks in total area (%)	0.052804	0.060390	0.068098	0.976377	−0.010530	0.015374	0.059776	−0.002102
Primary schools (per 100 km2)	0.062254	−0.033974	0.840914	0.034218	−0.085111	−0.025390	0.000199	−0.098210
Middle schools (per 100 km2)	0.161918	0.105188	0.642471	0.030270	−0.018094	0.055513	0.332542	−0.074966
Clubs and organizations (per 1000 people)	0.080308	0.042190	−0.134490	0.029106	0.760508	−0.041056	−0.041251	0.191171
Foundations, associations, and social organizations (per 1000 people)	−0.186193	−0.067538	0.020280	−0.010036	−0.242169	0.687794	−0.140257	0.319806
Entities newly engaged in economic activity (per 1000 people)	0.793086	0.083838	0.141734	−0.010019	0.048011	−0.037518	0.305161	0.085375
Equipment of tourist accommodation facilities (per 100 km2)	0.221945	−0.110239	0.049647	0.019042	0.096943	−0.081314	0.169331	0.652725
Agricultural production space valorization index	−0.062188	0.846499	0.047475	0.025629	0.017267	−0.059136	0.078395	0.040581
Number of tractors per farm	0.090660	0.769686	−0.332009	0.088555	−0.151205	0.114708	−0.034925	−0.040176
Consumption of NPK fertilizers per 1 ha of agricultural land (in dt)	0.217098	0.691382	−0.419636	0.013809	0.005644	−0.013054	0.068330	−0.148443

Source: own calculations.

presents the factor loading values obtained from the calculations. The factor loading value determined the strength of the relationship between the extracted factors and each of the considered statistical features. To consider these features as correlated with the factor, it was assumed that the factor loading value had to be greater than 0.6. Such a level is often suggested in the literature [38].

The first factor included features such as entities of the national economy per 1000 people, with newly engaged persons engaged in economic activity (per 1000 people) and newly built residential buildings (per 1000 people). These features characterize the activity of municipality residents in creating new jobs and improving the housing conditions. This factor was labeled “entrepreneurship”.

The next factor was the result of the agricultural production space valorization index, the number of tractors per farm, the consumption of NPK fertilizers per 1 ha, the proportion of permanent grassland in the total agricultural land, and the forestation index. Because this set of features describes the situation of the agricultural sector, it was named “agriculture”. High values of this factor indicate a favorable agricultural structure in the area. It should be noted that both the proportion of permanent grassland among the total agricultural land and the forestation index are negatively correlated with the agricultural factor, meaning that their increase has a negative impact on the factor value. The agricultural factor explains 12.6% of the variance.

The third factor (explaining 9% of the variance) contains features describing the network of schools and libraries; therefore, it was named “social infrastructure”. The proportion of parks, green spaces, and local green areas in the total area, as well as the proportion of recreational parks in the total area, formed the “natural landscape factor”, explaining 6.41% of the variance. Artistic groups per 1000 residents and associations and clubs per 1000 residents constituted “cultural activity”. The factor analysis also revealed correlations between indicators characterizing residents’ membership in sports and religious clubs and the number of foundations, associations, and social organizations. It was deemed that the factor comprising these features characterized residents’ social activity. The technical infrastructure factor consisted of indicators providing information about the municipalities’ connections to sewage, gas, and water networks. This factor explained approximately 5% of the variance. The final factor, named “tourism and recreation”, explained less than 5% of the variance, yet it still met the Kaiser criterion. It encompassed the equipment of tourist accommodations per 100 km² and the number of cultural centers, clubs, and community centers.

An analogical procedure to determine the main rural development factors in Wielkopolska at the beginning of the 2014–2020 funding period was conducted based on data from 2015. The results are presented in Appendix A.

Table A1. Eigenvalues and percentages of explained variance by factor (data for the year 2015).

Factor	Eigenvalue	Percentage of Explained Variance	Cumulative Eigenvalue	Cumulative Percentage of Explained Variance
Factor 1	5.5	21.06	5.48	21.1
Factor 2	3.1	11.84	8.56	32.9
Factor 3	2.4	9.34	10.98	42.2
Factor 4	1.7	6.7	12.73	48.9
Factor 5	1.6	6.24	14.35	55.2
Factor 6	1.4	5.21	15.7	60.4
Factor 7	1.1	4.3	16.82	64.7
Factor 8	1.1	4.06	17.88	68.8

Source: own calculations.

demonstrates the eigenvalues and percentages of explained variance for the factors, while

Table A2. Factor loadings (data for the year 2015).

Variable	Factor 1	Factor 2	Factor 3	Factor 4	Factor 5	Factor 6	Factor 7	Factor 8
Share of permanent grassland within agricultural land (%)	−0.099922	−0.754354	0.003683	−0.236395	0.009443	−0.009667	−0.067089	0.153377
Relation of forested land to agricultural land	−0.042432	−0.538154	−0.193049	−0.278886	0.083256	−0.305036	−0.040234	−0.125364
Population connected to water supply network as % of population	0.342297	0.067495	0.060925	0.027747	0.149930	−0.390861	0.045308	0.724052
Percentage of population using sewerage system %	0.440083	0.254369	0.000061	−0.171132	0.268112	0.222503	0.622834	0.159641
Population connected to gas supply network as % of population	0.613813	0.267123	−0.022309	0.073262	0.236189	0.178542	0.220351	−0.103398
Length of sewerage network in relation to water supply network	0.440073	0.152165	0.019776	−0.053731	0.180107	0.289208	0.529303	−0.044838
Libraries per 100 km2	−0.049930	0.071910	−0.102416	0.426231	−0.381777	−0.414241	0.145325	−0.080331
Members of sports and religious clubs in total population (%)	0.017097	−0.083051	0.048526	0.058706	−0.007065	−0.338572	0.670313	0.067919
Artistic groups (per 1000 people)	−0.091869	−0.077670	0.782620	0.005144	−0.099583	−0.048219	0.212581	0.039863
Cultural centers, clubs, and community centers (per 1000 people)	0.046629	0.008238	0.709148	−0.165928	0.047148	−0.033355	−0.147591	−0.001555
Entities of the national economy per 1000 people	0.935282	0.067393	0.005401	0.082055	0.167155	0.003149	0.083672	−0.046741
Persons engaged in economic activity (per 1000 people)	0.948458	0.025335	−0.003427	0.128974	0.114837	0.008193	0.032654	−0.041432
Newly built residential buildings (per 1000 people)	0.662250	−0.034664	0.008842	0.088721	−0.221044	−0.088770	0.001470	0.006384
Share of forests in total area (%)	0.045532	−0.567630	0.001133	−0.489841	−0.068403	−0.031466	0.313479	−0.252997
Unemployed per 1000 people	−0.408983	−0.138793	0.197389	0.010040	−0.207541	0.178280	0.155621	0.651686
Share of parks and green areas in total area (%)	0.145146	0.123052	−0.090688	0.221673	0.818690	−0.030812	0.127772	−0.059739
Share of recreational parks in total area (%)	0.010765	−0.065651	−0.034970	0.137015	0.840643	−0.032072	0.054695	0.006771
Primary schools (per 100 km2)	0.227517	−0.053331	−0.244127	0.722481	0.188111	0.112630	−0.063581	0.027585
Middle schools (per 100 km2)	0.209425	0.061146	−0.051642	0.655361	0.281740	0.009424	0.058001	−0.057700
Clubs and organizations (per 1000 people)	0.023980	0.089341	0.796965	−0.052040	−0.053105	0.003663	0.033958	0.057209
Foundations, associations, and social organizations (per 1000 people)	−0.031602	−0.105670	0.098322	−0.090573	0.056732	0.783034	0.040864	0.086356
Entities newly engaged in economic activity (per 1000 people)	0.888335	0.063996	−0.037909	0.042034	0.009969	0.024496	0.085202	0.098225
Equipment of tourist accommodation facilities (per 100 km2)	0.373229	−0.091163	0.215153	0.100291	0.095696	0.007636	0.332260	−0.348879
Agricultural production space valorization index	0.002721	0.840634	0.044861	0.107929	0.043307	0.081002	0.065142	0.071171
Number of tractors per farm	0.044014	0.825925	−0.077521	−0.266190	0.061552	−0.124208	−0.106976	−0.044759
Consumption of NPK fertilizers per 1 ha of agricultural land (in dt)	0.161533	0.736792	−0.048884	−0.382468	−0.018441	0.070630	0.086766	−0.033060

Source: own calculations.

contains the factor loadings, and

Table A3. Factor scores for LAGs, relative indices of rural development, and typological groups of LAGs in Wielkopolska region (data for the year 2015).

Name of LAG	Entrepreneurship	Agriculture	Cultural Activity	Social Infrastructure	Nature and Landscape	Social Activity	Tourism–Recreation	Technical Infrastructure	Relative Index of Rural Development of LAG	Group
Ostrzeszowska LAG	0.3344	−1.1290	−0.6956	−0.5743	−0.2288	−1.2213	−0.3320	−0.2141	−0.5076	I
Czarnkowsko-Trzcianecka LAG	−0.3009	−1.6168	−0.4192	−1.7229	0.1769	0.4177	−0.0049	0.0514	−0.4273	I
Wspólnie dla Przyszłości	−1.3477	0.0789	0.1161	0.6298	0.3421	0.5312	−0.4184	−2.5677	−0.3295	II
Długosz Królewski	−0.4430	−0.5104	−0.0367	1.1675	−0.6456	−0.8655	−0.5579	−0.5769	−0.3086	II
Puszcza Notecka	−0.2946	0.2877	0.2915	−1.0465	−0.2806	−1.2639	0.9550	−0.9677	−0.2899	II
Turkowska Unia Rozwoju—T.U.R.	−0.6896	−1.0997	−0.1965	0.1381	0.8091	−0.8218	−0.7474	0.3537	−0.2818	II
Solidarni w Partnerstwie	−0.1649	−1.1385	−0.4998	0.6809	−0.3932	−0.1681	−0.7179	0.7304	−0.2089	II
Wrota Wielkopolski	0.2719	−0.0510	−0.8619	0.2292	0.0095	−0.6995	−0.1076	−0.2953	−0.1881	II
LGD7—Kraina Nocy i Dni	−0.2192	−0.3847	−0.3765	0.7011	0.0245	−0.3498	−0.6086	−0.1966	−0.1762	II
Unia Nadwarciańska	−0.0583	−0.2307	0.1746	0.0470	−0.7082	−0.4560	0.1292	0.7603	−0.0428	III
KOLD	0.2163	0.0337	0.0774	−0.3602	0.8813	−0.5903	0.0708	−0.6613	−0.0415	III
Solna Dolina	−1.3302	0.1538	−0.1657	0.9201	−0.3409	0.3489	−0.3984	0.7266	−0.0107	III
Ziemi Grodziskiej LEADER	0.1362	0.2595	−0.4134	−0.2375	0.2689	0.6819	−0.3551	−0.1096	0.0289	IV
Krajna nad Notecią	−0.4705	0.1053	0.1863	−0.5411	−0.3947	0.3591	0.8083	0.3086	0.0451	IV
Dolina Wełny	−0.1275	0.7659	0.3827	−0.8220	−0.1695	0.3415	−0.2892	0.4827	0.0706	IV
Krajna Złotowska	−0.9011	−0.0528	0.6812	−1.3202	−0.4389	1.1699	1.1508	0.3552	0.0805	IV
Z nami warto	−0.0838	0.1579	−0.1555	−0.0134	0.6518	0.0224	−0.2338	0.3602	0.0882	IV
Światowid	−0.3470	0.9755	−0.1157	−0.1266	−0.1701	−0.3111	0.6930	0.1540	0.0940	IV
Kraina Trzech Rzek	1.3733	0.0935	0.0310	−0.6629	0.0497	0.4140	−0.3000	−0.1664	0.1040	IV
Wielkopolska Wschodnia	−0.6530	−1.0091	0.3020	1.0651	−0.5523	0.8431	−0.4063	1.2864	0.1095	IV
Okno Południowej Wielkopolski	−0.2187	0.3077	−0.1729	0.8107	0.1537	−0.3658	0.3096	0.1130	0.1172	IV
Gościnna Wielkopolska w Pępowie	0.1046	0.6734	0.0232	0.0760	0.1418	−0.0943	−0.1490	0.1684	0.1180	IV
Kraina lasów i jezior	0.6722	0.0346	0.5264	−0.1236	−0.0176	0.1268	0.3230	−0.4984	0.1304	IV
Wielkopolska z Wyobraźnią	−0.4295	1.5487	0.6558	0.3941	0.2385	−0.4745	−0.4935	0.2037	0.2054	V
Lider Zielonej Wielkopolski	0.2930	0.6861	0.0181	0.3935	0.2951	0.0839	0.2012	−0.1552	0.2270	V
Trakt Piastów	1.0986	0.9618	−0.4479	0.0399	0.2392	0.3989	−0.0456	−0.2621	0.2479	V
Dolina Noteci	0.1538	−0.8514	1.7374	−1.0984	−0.0919	1.2388	0.0207	0.9720	0.2601	V
Między Ludźmi i Jeziorami	−0.6447	−0.3173	0.8848	1.1526	−0.8319	0.7446	1.0484	0.9046	0.3676	V
Źródło	2.0274	0.8928	−0.4776	0.0848	−0.0952	0.7016	−0.0314	−0.1096	0.3741	V

Source: own calculations.

contains the factor scores for the LAGs, the relative indices of rural development, and the typological groups of the LAGs in the Wielkopolska region.

3.2. Relative Indices of Rural Development in LAGs in Wielkopolska Region and Typological Groups of LAGs with Similar Development Levels

Table 4. Factor scores for LAGs, relative indices of rural development, and typological groups of LAGs in Wielkopolska region (data for the year 2007).

Name of LAG	Entrepreneurship	Agriculture	Social Infrastructure	Nature and Landscape	Cultural Activity	Social Activity	Technical Infrastructure	Tourism–Recreation	Relative Index of Rural Development of LAG	Group
Czarnkowsko-Trzcianecka LAG	−0.3070	−1.3543	−1.4824	−0.1945	−0.0828	−0.7599	0.2351	−0.5006	−0.5558	I
Solidarni w Partnerstwie	−0.3033	−1.0889	0.9129	−0.1895	−0.2062	−0.0139	−0.6836	−0.4713	−0.2555	II
Wielkopolska Wschodnia	−0.8866	−0.5187	0.9020	−0.3100	0.4893	−0.3799	−0.6037	−0.5750	−0.2353	II
Puszcza Notecka	−0.0601	0.0632	−1.3548	−0.4625	−0.7767	0.1088	0.5516	0.1106	−0.2275	II
LAG7—Kraina Nocy i Dni	−0.2831	−0.4455	0.8256	−0.0806	−0.5313	−0.5236	−0.6506	−0.0245	−0.2142	II
Krajna nad Notecią	−0.5922	0.0549	−0.5221	−0.4590	0.2228	0.2182	−0.0132	−0.5034	−0.1993	II
Turkowska Unia Rozwoju—T.U.R.	−0.4919	−1.0952	0.3206	1.0588	−0.4754	0.1114	−0.9556	0.0247	−0.1878	II
Ziemi Grodziskiej LEADER	0.2557	0.2510	−0.3457	−0.0273	−0.6923	−1.1888	0.0752	0.1822	−0.1862	II
Długosz Królewski	−0.7658	−0.6143	1.0535	−0.4351	0.6208	−0.2059	−0.5463	−0.2284	−0.1402	II
Ostrzeszowska LAG	0.4075	−1.2530	−0.2237	−0.2502	−0.8550	1.4341	−0.3374	0.0224	−0.1319	II
Kraina Trzech Rzek	0.6320	0.1957	−0.9306	−0.4767	−0.1942	−0.4510	1.0462	−0.7887	−0.1209	II
Solna Dolina	−1.2258	0.7529	0.8222	−0.4345	0.5980	−0.3107	−0.3024	−0.4883	−0.0736	III
Dolina Noteci	−0.3537	−0.5817	−0.7952	−0.4551	0.9968	−0.6051	0.6284	0.7116	−0.0567	III
Zaścianek	−0.7083	0.5584	−0.2413	−0.0205	0.1364	−0.6550	0.3613	0.2718	−0.0372	III
Wrota Wielkopolski	0.3087	−0.1124	0.4854	0.2445	−0.6861	−0.2142	−0.2267	0.0232	−0.0222	III
Dolina Wełny	−0.2293	0.8767	−0.7509	−0.4729	0.1624	0.4180	0.0114	0.0796	0.0119	III
Unia Nadwarciańska	0.1511	−0.1671	−0.0722	−0.2261	0.9011	0.2400	−0.5355	−0.0508	0.0301	III
Gościnna Wielkopolska w Pępowie	−0.2020	0.8919	−0.0787	−0.1098	−0.6445	0.2902	−0.1524	0.3942	0.0486	III
Między Ludźmi i Jeziorami	−1.0733	0.1509	0.6842	−0.4363	1.4724	0.0970	−0.1947	−0.1372	0.0704	III
Wielkopolska z Wyobraźnią	−0.5869	1.4514	0.1312	0.2449	−0.1550	0.4370	−0.4501	−0.1477	0.1156	IV
KOLD	0.3924	−0.1691	−0.6328	1.0092	−0.0944	0.2812	0.0415	0.1242	0.1190	IV
Okno Południowej Wielkopolski	−0.2599	0.3301	1.0253	−0.2842	−0.1461	0.2809	0.4808	−0.4087	0.1273	IV
Wspólnie dla Przyszłości	−0.4404	−0.0441	0.6171	1.1929	−0.0898	0.5647	−0.5754	0.2311	0.1820	IV
Światowid	0.6788	0.9359	−0.3479	0.2769	0.0038	0.2115	−0.1791	−0.0594	0.1901	IV
Lider Zielonej Wielkopolski	0.1065	0.5922	0.1616	0.2417	0.1331	−0.2506	0.8039	−0.1777	0.2013	IV
Źródło	2.3502	0.9844	0.5277	−0.4649	−0.3325	−0.6622	−0.4434	−0.2443	0.2144	IV
Z nami warto	−0.1151	0.0783	−0.0104	1.2512	0.2180	0.7251	0.1393	−0.3010	0.2482	IV
Kraina lasów i jezior	0.6057	0.0844	−0.0583	−0.2728	0.1201	0.3835	0.1524	1.2143	0.2787	IV
Dwa Mosty	−0.5557	−0.7113	1.2216	−0.3943	0.2592	−1.8161	0.4030	0.4014	0.3510	IV
Dolina Samy	1.7149	0.6275	0.3149	0.2402	0.2559	−0.6771	0.4600	0.6322	0.4461	V
LEADER Suchy Las	3.8501	−0.2017	0.7359	−1.0852	0.4990	0.5962	0.5623	−0.5514	0.5507	V

Source: own calculations.

presents the factor scores obtained for the LAGs (calculated as the average of the factor scores of the municipalities belonging to the LAG area) compared with those of the Wielkopolska region. A negative value for a factor for a given LAG indicates a lower level than the average in the Wielkopolska region. On the other hand, a value greater than zero indicates that a particular factor is more developed in the LAG than the average in the region. The table also contains the average values of the eight factor scores for each LAG, which were adopted as relative indices of the rural development of the LAGs. These measures for the analyzed LAGs ranged from −0.56 to 0.55, where a higher value indicated a relatively higher level of development. Based on the adopted relative indices of rural development, the LAGs in Wielkopolska were divided into five typological groups. This division was performed using an equal interval of the index (0.22).

The obtained typological groups of LAGs, differing in terms of their rural development levels, were used in further analyses. The results for the funding period 2014–2020 are presented in Appendix A (Table A3). The subsequent part of the work compares the research results for the two funding periods.

3.3. Impact of Rural Development Level on Activity in Applying for LEADER Program

Figure 2 demonstrates the LEADER program expenditures per capita in the analyzed typological groups of LAGs in the years 2007–2013 (it includes both the costs of the LAGs’ organizational activities and those of the actions for which the beneficiaries directly applied).

In the years 2007–2013, a total of PLN 145 (EUR 35) was spent per capita on the LEADER program, with the highest amount (almost PLN 200 or EUR 48) in the first typological LAG group. As the socioeconomic development index increases (moving to subsequent typological groups), there is a decrease in program funding. The lowest value of PLN 120 (EUR 29) was observed in the fifth group, which had the highest development index value. An exception is the third group, where the funding amount is higher than in the second group; however, it is still lower than in the first group. The reduction in LEADER program expenditures as the relative indices reflecting the rural development of the LAGs increase demonstrates a statistically significant linear relationship. The observed trend is statistically significant (R² = 0.87, at p < 0.05).

In Figure 3, the total expenditures per capita for LEADER program activities in the typological LAG groups for the years 2014–2020 are presented. In this period, the average amount of funding per capita is PLN 93 (EUR 22). It can be observed that, similarly to the 2007–2013 perspective, there is a decrease in funding as the relative indices reflecting the rural development of the LAGs increase. This trend is also statistically significant at the significance level of p < 0.1.

Figure 4 and Figure 5 demonstrate the relationship between the activity of rural area residents, measured by the number of applications submitted for the LEADER program per 1000 inhabitants, and the level of development of municipalities belonging to the typological LAG groups. The results indicate that, in the years 2007–2013, there was a downward trend in interest in the LEADER program among rural area residents as the relative indices reflecting rural development in the LAGs increased. In other words, the lower the value of the relative indices of rural development, the higher the residents’ activity indicator. This implies that residents of areas with relatively larger development needs were more willing to use LEADER funds, which is a positive outcome.

In the years 2014–2020, it is difficult to identify a clear relationship between the level of socioeconomic rural development of municipalities belonging to the LAG and their activity in applying for funds from the LEADER program. Nevertheless, residents of the least developed typological group still exhibit the highest activity. During the period of 2014–2020, the application frequency is noticeably lower than in 2007–2013 (the average number of applications per 1000 inhabitants during this programming period was 0.36, compared to 1.1 in the previous years). However, it is important to point out that the process of spending funds in 2014–2020 was hindered by the COVID-19 pandemic. Because of the significantly lower economic activity in this period, the program’s operation was extended until 2025. Thus, the final results may still undergo some changes.

Table 5. Summary of stepwise regression for the activity of rural area residents in applying for the LEADER program in years 2007–2013.

Factor	R = 0.83 R2 = 0.70
	BETA	Standard Error BETA	B	Standard Error B	p
Constant term			3.18	0.11	0.00
Social infrastructure	−0.63	0.13	−0.88	0.19	0.00
Agriculture	−0.49	0.12	−0.69	0.17	0.00
Technical infrastructure	−0.34	0.13	−0.68	0.28	0.02

Source: own calculations.

and

Table 6. Summary of stepwise regression for the activity of rural area residents in applying for the LEADER program in the years 2014–2020.

Factor	R= 0.89 R2= 0.78
	BETA	Standard Error BETA	B	Standard Error B	p
Constant term			1.39	0.09	0.00
Agriculture	−0.56	0.13	−0.57	0.13	0.00
Cultural activity	0.65	0.12	1.01	0.19	0.00
Entrepreneurship	0.34	0.13	0.39	0.15	0.00

Source: own calculations.

present a summary of the stepwise regression of the dependent variable of activity in applying for the LEADER program for 2007–2013 and 2014–2020. The independent variables in the regression model were the values of the development factors (factor scores).

During the years 2007–2013, the variable most correlated with activity in applying for the LEADER program, which was added to the model in the first step, was the factor of social infrastructure. In the following two steps, the factors of agriculture and technical infrastructure were added. Further steps were not included in the model due to the lack of statistically significant impacts (at a significance level of p < 0.05). As a result, the model contains three development factors whose influence is statistically significant: social infrastructure, agriculture, and technical infrastructure. The model explains 70% of the variability in the dependent variable. Distributional analyses of the residuals from multivariate regression models confirmed the validity of our assumptions of normality and homoscedasticity.

The greatest impact on the dependent variable is shown by the social infrastructure factor. The negative sign indicates that the frequency of applying for LEADER program in Wielkopolska was lower in areas characterized by better-developed social infrastructure. Similarly, albeit to a lesser extent, the agriculture and technical infrastructure indices also negatively influenced the explained variable. This means that the more challenging the socioeconomic conditions in the municipalities covered by the LAG actions, the higher the activity of the residents of these municipalities in applying for funding.

The model for the years 2014–2020 includes three development factors, whose effects are statistically significant: agriculture, cultural activity, and the entrepreneurial factor. The model explains 78% of the variability in the dependent variable. The cultural activity factor has the greatest positive impact on the dependent variable. Moreover, the entrepreneurial factor positively influences the activity in applying for the LEADER program. The negative impact of the agricultural index indicates that, in LAGs where the agricultural structures were better developed, the activity in applying for the LEADER program was lower.

4. Discussion

In the years 2007–2013, residents from areas with relatively higher developmental needs (belonging to LAG groups with lower levels of socioeconomic development) showed a greater inclination to utilize LEADER funds in the Wielkopolska region. This trend was less prominent from the financial perspective of 2014–2020, although the highest frequency of applications still came from the typological group with the lowest development level.

Prior research [22,23,24] focusing on selected rural development measures within the second pillar of the CAP and specific LEADER implementation areas [11,27] indicated a contradictory relationship. In more remote locations with lower incomes, where rural area development programs appear to be most necessary, their effectiveness was relatively low. Rosner and Stanny (2016) [39] pointed out that positive developmental processes in rural areas are mainly visible in regions that are already characterized by more favorable parameters. This might lead to territorial polarization in terms of rural and agricultural development. However, our study demonstrates that, in the case of the LEADER program in Wielkopolska, this negative trend cannot be observed. In contrast, relatively more funds were allocated to areas needing reinforcement. The pattern highlighted in this study might be associated with the tendency to favor the equitable allocation of LEADER funding across territories in Poland, demonstrated by Furmankiewicz et al. (2021) [40].

It should be mentioned that the engagement of residents in areas covered by local development strategies in applying for funds might depend on many factors besides the level of development. Kozera (2011) [18] categorizes these influences into two groups: external and internal. External factors encompass historical, natural, urban, and social conditions (including regional institutional development). Internal factors, on the other hand, depend on the beneficiary and their individual decisions. This differentiation influences the choice of research methods in analyzing the absorption of CAP funds. In the case of internal factors, the most commonly used method is behavioral economics, which indicates the factors that motivate beneficiaries or discourage them from undertaking activities. Bielecka (2006) [19], analyzing internal conditions, concludes that effective hindrances in applying for support include changes in documentation and the complexity of the application process. The analysis of the external conditions of resource absorption requires the determination of the impact of the broader environment on the actions of potential beneficiaries. Most commonly, this environment is described by adequately selected statistical data at different levels of aggregation [41]. This method has been applied in this study.

The great advantage of the LEADER program, in terms of local development, stems from the fact that it is the LAG that selects the projects, the implementation of which will contribute to accomplishing the objectives of a common local development strategy. However, as pointed out by Chmieliński [14], the substantive assessment of the applications submitted is conducted at the regional level by the employees of the Agency for Restructuring and Modernisation of Agriculture or voivodeships’ local governments. Thus, the LAGs are responsible for announcing the call for proposals, for the eligibility assessment in line with the local development strategy, and for the technical organization of the beneficiary acquisition process, but the final decision regarding project acceptance depends on external institutions. The engagement of residents might also be hampered by the domination of the public sector in the LAG structure, which, according to Biczkowski (2020) [8], constitutes the greatest weakness of the LEADER program in Poland.

Our results also demonstrate that, in both financing periods, the activity of applying for LEADER funding was negatively correlated with the agricultural factor. This suggests that the better the agricultural sector’s development in a given LAG, the less interest rural area residents had in applying for LEADER funding. This trend contrasts with other CAP programs. As demonstrated in previous studies [21,42,43,44,45], in regions with better-developed agricultural structures, the frequency of applying for funding under the second pillar of the CAP was higher compared to regions with weaker agricultural development. This could be attributed to the unique nature of the LEADER program, which primarily aims to activate rural communities and promote rural development, rather than agriculture. The initially weaker position of agriculture in rural areas could motivate individuals to engage in non-agricultural activities funded by the LEADER program. In the financial period of 2007–2013, the frequency of applying was also negatively correlated with infrastructural factors (both social and technical infrastructure). This might indicate that, in areas with more underdeveloped infrastructure, often with a lower population density, residents of rural areas show more interest in bottom-up initiatives. This phenomenon should be seen positively, as it contributes to mitigating territorial disparities in rural development.

During the financing period of 2014–2020, the positive influence of the cultural activity and the entrepreneurship factor on the activity of applying for funds was demonstrated. The higher the density of cultural centers in rural areas, the higher the frequency of applying for LEADER initiatives. It can be presumed that increased opportunities for social interaction brought communities together and motivated collective actions. This aligns with the findings of [3], which indicate that many projects under the LEADER program aim to strengthen the attraction of communities by promoting a local identity, including broad participation in collective events, such as enjoying nature, cultural events, sports, and other social activities. Many of these activities have the potential to spill over into other types of social innovation, such as entrepreneurship and new solutions to social needs.

Between 2014 and 2020, more applications were submitted by LAGs with higher levels of entrepreneurship. This demonstrates the positive influence of the dynamic environment on decisions to apply for program funding. As indicated by Angelucci et al., 2015 [46], “rural development programs often result in positive horizontal side effects, as they encourage participants to adopt a certain course of action and lead to faster adaptation of local communities to new conditions”. The findings are also in line with other studies showing the positive impacts of LEADER on local communities. Farrell and Thirion (2005) [10] report that the LEADER program has influenced the forms and structuring of institutional relationships by garnering support for local and collective developmental processes. Nordberg et al. (2020) [3] highlight that LEADER, as a bottom-up and partnership-based territorial development approach, fosters the creation of new social innovation networks and revitalizes existing ones. These networks foster collaborative efforts and new social relationships to address local needs. Bosworth et al. (2016) [9] conclude that, in England, LEADER has demonstrated the effectiveness of neo-endogenous development in leveraging local assets and promoting local cooperation and innovation. Chmieliński (2009) [6] emphasizes the significance of social capital accumulation through the implementation of the LEADER program in Poland.

However, several limitations of the procedure should also be acknowledged.

First, this research was conducted in only one region, so its results cannot be generalized to the entirety of Poland. As mentioned earlier, the rural areas in the Wielkopolska region are better developed compared to other regions of Poland. More favorable agrarian structures, along with the presence of relatively large farms, may result in rural residents being more involved in farming and more interested in pro-investment CAP measures than in local development programs for rural areas. The verification of the presented relationships for other regions requires further study, which could be conducted using the methodology presented in this article.

Second, the observed trend of decreasing LEADER program funding with the increasing socioeconomic development level of LAG municipalities for the years 2007–2013 was not fully confirmed in the period of 2014–2020. However, as previously mentioned, due to the COVID-19 pandemic, the spending period was extended until 2025; thus, the data concerning activity and expenditure from the perspective of 2014–2020 are not complete. The presented trends for the 2014–2020 period are preliminary and require verification after the spending process is concluded.

Finally, we are also aware that the degree of analysis was limited by the availability of complete data used for factor analysis. The choice of development variables always involves a subjective element. When making choices, one must consider the diversity of the economic and social characteristics, and their significance may vary regionally [47,48,49]. Similar variables to those used in this study to assess the level of rural development in Wielkopolska were employed by [50,51]. The variables adopted in this study pertained to the economic conditions, cultural potential, agricultural development, technical and social infrastructure, and natural environment. Variables describing the role of social capital [4,5,6,7] were also included. However, it was not possible to capture all elements of local development in the form of statistical data.

5. Conclusions

In conclusion, the results of this study confirm the significant influence of the local development conditions on the activity of potential beneficiaries in applying for EU funds, as highlighted in the literature. However, the observed trends in the Wielkopolska region differ from those found in analyses of other rural development programs, excluding LEADER. This indicates the need for an individualized approach when analyzing the effects of each EU program.

The results show that the LEADER program supports relatively less developed areas with underdeveloped infrastructure and weaker agricultural potential. This finding is crucial in planning agricultural and rural development policy in Poland. As stated by Rosner and Stanny (2017) [32], the structural problems of agriculture (land fragmentation, low labor efficiency) should be addressed outside of agriculture by increasing non-agricultural employment, which will, in turn, facilitate a reduction in agricultural employment. Thus, the results suggest that LEADER not only stimulates positive changes in rural areas but can also help to address the weaknesses in agricultural structures, which are one of the key challenges faced by rural Poland. Moreover, it contributes to reducing territorial disparities in rural development, which should be positively assessed. These findings provide guidance on the allocation of rural development funds, supporting the strengthening of LEADER financing in regions with weaker agricultural potential. This research also highlights the role of entrepreneurship and cultural activity in local development, which fosters unity and stimulates bottom-up initiatives.

The success of local development policies depends, to a large extent, on the level of local community participation in socioeconomic life, which entails the necessity to build social capital [14]. Biczkowski (2020) [8] demonstrated that structural funds are effectively implemented in municipalities where investments align with their genuine needs. Thus, to bolster local economies, it is crucial for the implemented projects to be aligned with the resources of individual regions and their local potential. The identification of the local potential and the factors influencing the pursuit of support, as performed in this study, can help in taking appropriate actions to mobilize potential beneficiaries to submit applications.