Regional Analysis of Socio-Economic Development: The Case of Poland

Abstract

This article analyzes and evaluates the levels of socio-economic development of Polish voivodeships in two periods, 2010–2012 and 2020–2022. The purpose of this study was to outline regional disparities in development across the regions of Poland. To achieve the purpose of this study, 12 diagnostic characteristics were used, which were systematized by economic and social categories. Using the TOPSIS method, provinces were grouped, distinguishing between regions with the highest, high, low, and lowest levels of development. The approach adopted in this study identified changes in the processes of socio-economic development of Polish voivodeships in the analyzed years, highlighting clear spatial and structural differences. The findings can equip authorities in creating improved development plans and targeted responses to any shortcomings encountered.

1. Introduction

Promoting and sustaining development is an evolving discussion and remains one of the core objectives of any economy. Through the seventeen Sustainable Development Goals (SGDs), the United Nations has played a key role in shaping sustainable development in order to address global challenges ranging from poverty to climate change, education, underdevelopment, and sustainable resource use. It was forecasted that the world economy would grow by 2.7 percent in 2024, with a further slight increase projected at 2.8 percent in 2025. These changes are due to the increased performance of certain developed and emerging countries [1]. In addition to examining certain dynamics within countries in terms of household characteristics and behavioural responses to social–economic policy changes, environmental risks are also very important in modeling or analyzing the socio-economic development of countries [2]. The European Union’s regional policy was designed to foster solidarity and alleviate economic, social, and territorial disparities, to enable regions develop to their full potential.

The phenomenon of socio-economic development has received much attention. Research topics to date include how interdependencies and absorption of funds influence changes in socio-economic development [3], how the analysis of urban green spaces reflects varying socio-economic impacts and which mechanisms enable institutional capacity-raising policies [4], how cultural tourism is used as a strategy by EU countries to advance socio-economic development [5], and how environmental sustainability signals socio-economic development, which decreases the ecological footprint [6]. As this demonstrates, the concept is vast, and its commentaries have varied greatly in the socio-economic development indicators used and contexts studied. Sustainable socio-economic development of regions is an important policy objective oriented towards maintaining a country’s long-term internal stability and making efficient use of its resources [7]. In ensuring a livable and prosperous planet for future generations, sustainable development requires cooperation and synergy from governments, businesses, and individuals alike.

Development is not merely an economic process but a multifaceted transformation of society [8]. In the simplest terms, it can be understood as a process of positive changes comprising both quantitative growth and qualitative progress, where the quantitative aspect encompasses the concept of economic growth and the qualitative aspect covers the transformation of socio-economic structures, which acquire new characteristics as a result [9]. From the quantitative view point, economic development is a critical aspect of national progress, encompassing the processes through which a country improves the economic, political, and social well-being of its citizens. Furthermore, a country’s economic development is defined as a continuity of change in its socio-economic life that improves the living standards for its citizens as well as the organization of structures and ongoing processes in the country [10].

Social and economic development processes take place in a specific space and differ in their characters. The dynamics of development processes, at the national, regional, and local levels, are determined by the character of the space and the changes occurring over time [11]. Economic and social development is not limited to GDP per capita growth alone; it also concerns critical indicators of individuals’ well-being, such as improvements in the population’s health conditions, enhanced political and civil liberties, efficient legal and judicial systems, positive changes in education and employment, a robust cultural life, and an improved state of the natural environment. Factors that influence the level of socio-economic development can be classified into social, economic, infrastructural, and environmental/ecological factors. In addition, each group of general factors contains detailed characteristics, which should be considered when seeking to determine and measure social and economic development [12].

Differences in social and economic development at the regional level are increasing; hence, the continuous pursuit of developmental convergence [11,13].

Contemporary socio-economic conditions show, in the case of Poland, a deeply rooted dependence on historical factors. Nonetheless, Poland has, in the past 30 years, undergone a spectacular transformation from a backward post-communist country to one of the most dynamically growing members of the European Union [13]. In the 1990s, during the transformation of society and the economy, differences in the levels of development of the Polish regions began to increase [14,15].

The historical background is one of the main reasons for the differences among individual regions’ current development situations. Although the country now has different political, economic, and social conditions, its former political divisions and their consequences, in line with the principle of path dependence, form deep-seated determinants of its developmental processes. This scenario underscores the importance of history [9].

A nation’s history significantly shapes its socio-economic landscape. In the case of Poland, historical divisions and legacies of political and economic systems—particularly the post-communist transition—have contributed to regional disparities. Path dependence theory argues that historical events and decisions set regions on different development trajectories, which persist over time. Regions that were more industrialized or had greater access to foreign investments during the communist era, such as those in the west, continue to benefit from these advantages today. Geographic locations and natural resources also play critical roles in regional development. Western regions of Poland, closer to major European markets and historically more industrialized, have benefited from better infrastructure and easier access to investments. In contrast, regions in the east, which were more rural and less developed during the communist era, face challenges such as lower levels of industrialization, fewer investments, and slower economic growth. The role of governmental policies in shaping socio-economic outcomes cannot be overstated. Poland’s accession to the European Union in 2004 triggered significant economic and infrastructural improvements, especially in the western regions, which received substantial EU funding under cohesion policies. These funds were aimed at reducing disparities between the regions, but the effectiveness of such interventions has varied. Some studies suggest that while regions like Mazowieckie and Dolnośląskie have seen robust development, others, particularly in the east, have not fully capitalized on these opportunities.

In the context of Poland, the Mazowieckie region, which includes the capital city Warsaw, remains the most economically advanced region, benefiting from a concentration of financial services, education, and employment opportunities. On the other hand, regions like Lubusz and Warmian-Masurian in the east continue to face challenges in terms of industrial development and access to quality services.

Although a multidimensional issue due to its complex mix, socio-economic development is an essential phenomenon in terms of understanding the performance of regions [16]. This is because regions have varying socio-economic outcomes [17]. In the literature, disparities in terms of wealth between countries and regions have been widely discussed [18]. Hence, assessing the degree to which they vary in terms of the socio-economic indicators specific to that region provides a lens for further investigation, mobilization, and deployment of resources to underperforming regions, on the one hand, and better informs policy implementation and development in regions that are performing well, on the other hand. These analyses are fostering balanced growth, reducing inequality, and promoting sustainable development [19,20,21]. This aligns with the Regional Development Policy stressed by the Organisation for Economic Co-operation and Development (OECD) as an effort to decrease regional disparities by supporting business activities across all regions [22,23]. To this end, regional analysis provides a deeper insight into national development policies. According to [24], despite the evident periodically unstable conditions in highly developed EU and OECD countries, economic development in Poland between 1995 and 2015 was generally higher in comparison with those countries. This feat could be attributed to its ascension as an official EU member in 2004.

There are several debates on issues related to the development of regions in Poland [25,26,27]. The results of these studies provide varying insights ranging from disproportionate levels of information between regions, wherein Mazowieckie is the most developed region [25]; development in terms of EU cohesion policy, where regions in western Poland are the most developed [26]; and differentiation and classification of regions in terms of socio-economic development, with Dolnośląskie, Mazowieckie, and Pomorskie ranked the most developed regions, respectively. These results and the changing economic landscape not only serve as points for analysis but also show the strengths and weaknesses of the regions, which is crucial for authorities while creating developmental plans or strategies for the future. The purpose of this work was to identify all regional differences in levels of development in Poland, to enable decision-makers to adequately create development plans uniquely suited to the individual regions.

The following research questions were posed in the course of this work:

• What factors contributed to persistent significant regional disparities in socio-economic development among Polish voivodeships in 2010–2012 and 2020–2022?
• What are the key determinants of the low socio-economic development levels in voivodeships such as Lubelskie, Swietokrzyskie, and Podkarpackie?
• How did changes in household saving behavior during the COVID-19 pandemic differ across Polish voivodeships, and what were the socio-economic implications for each region?
• How did regional differences in the employment structure influence the socio-economic behavior of households during the COVID-19 pandemic?

This article addresses the extent to which emergency economic interventions should be deployed in an effort to reduce the disparities between and across regions, with a view to measuring up to the EU Cohesion Policy programs. The authors note that since Poland’s accession to the European Union in 2004, it has been a major beneficiary of EU cohesion funds aimed at reducing regional disparities and fostering socio-economic development. While there is evidence of overall economic growth, the extent to which these policies have achieved sustainable and inclusive regional development remains unclear. Adding to the existing research, our longitudinal approach provides valuable insights into the mechanisms driving regional disparities and assesses the effectiveness of targeted policies such as infrastructural investments in mitigating these disparities over time. Our study is timely since it assesses the periodic socio-economic development across the regions over a recent period of 10 years, with an emphasis on the current socio-economic situation. The rest of this paper is structured as follows: the Materials and Methods, Results, a Discussion, and the Conclusion.

2. Materials and Methods

In this study, the research material consisted mainly of information from the Central Statistical Office in Poland (Bank of Local Data). The analysis was focused on two periods, 2010–2012 and 2020–2022, within which the values of the adopted characteristics were averaged. We studied changes over a period of 10 years in Poland, with an emphasis on the current socio-economic situation. The choice of years was informed by the recovery from the financial crisis of 2008, the availability of the most recent data, and the COVID-19 to post-COVID-19 periods, which had several effects on the economy [28,29,30,31], as well as the current economic policies [32]. Moreover, these periods allowed this study to focus on transitions and turning points, offering insights into the effects of systemic shocks (financial crisis, pandemic) and subsequent recovery patterns. This enhances the relevance and timeliness of the research findings. By averaging data over three-year periods, this study mitigated short-term fluctuations, for more robust findings. This approach emphasized smoothing out year-on-year volatility. The periods selected reflect the implementation and outcomes of distinct policy environments, enabling this study to draw meaningful conclusions about the effectiveness of post-crisis and pandemic-era interventions. For the construction of the synthetic measure, 12 features corresponding to individual areas of socio-economic development were selected. The selection of simple features was based on a review of the literature focusing on both theoretical and empirical approaches to the economic development of territorial units, while analyzing their overall socio-economic situation. They were all intensity (or structure) indicators, which ensured the comparability of the data. In the social sphere, these were the following:

• x1: birth rate per 1000 of the population;
• x2: registered unemployment rate (%);
• x3: urban population in % of the total population;
• x4: share of population at the working age (%);
• x5: dependency ratio of the population (population of non-working age/100 of working-age population);
• x6: share of paid employees in agriculture (%).

The economic sphere of development was assigned the following features:

• x7: voivodeship’s income per capita (zloty/person);
• x8: total investment outlays per capita (zloty/person);
• x9: total entities of the national economy per 1000 of the population;
• x10: entities with a share of foreign capital per 10,000 of the population;
• x11: industrial output sold per inhabitant (zloty/person);
• x12: share of investment expenditures of local government units in total expenditures (%).

Each of the adopted features had a specific direction of preference in relation to the overall criterion. Depending on this direction, features were divided into stimulants (desired highest possible trait values) and destimulants (desired lowest possible trait values). In this case, nominates were not distinguished, i.e., traits behaving in a certain range as stimulants and in a certain range as destimulants. The following features were classified as stimulants: x1, x3, x4, x7, x8, x9, x10, x11, x12, while the following features were destimulants: x2, x5, x6.

The above features were then subjected to a two-stage verification process. The first stage consisted of substantive verification, i.e., determining whether these characteristics capture the sense of socio-economic development well. In the second stage, statistical analysis was carried out. This was aimed at eliminating characteristics with too high a correlation between them.

The inverse matrix method was used to analyze the correlation matrix between the characteristics under study. For both periods under study, one correlation matrix between the values of all 12 characteristics was determined. It was arbitrarily assumed that the elements lying on the main diagonal of the inverse matrix should not exceed a value of 15. When values above 15 occurred, the k-th feature was eliminated. In the course of this procedure, the fourth and seventh features (share of population at the working age (%) and revenue of the voivodeship per capita (zloty/person)) were excessively correlated, which led to their removal.

The TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) method [33] was used in this study. This is one of the most popular model methods for the linear ordering of objects. TOPSIS was chosen given the availability of precise data as it offers a level of simplicity in decision-making, providing a reliable ranking of alternatives. The method eliminates the need for fuzzy sets and simplifies implementation with the availability of precise, numerical values. The authors found it to be the most appropriate method to meet our goal of reducing complexities in computation while maintaining the highest level of accuracy. According to this method, the best object (voivodeship) is separated by the smallest distance from the ideal solution and, at the same time, by the largest distance from the negative-ideal solution.

As a first step, a normalized matrix of the values taken by the individual features was constructed so that the values were comparable with each other and ranged from 0 to 1. For the normalization, zero-based unitization was used according to the following formulae [34]:

$${\mathrm{F}\mathrm{o}\mathrm{r}\mathrm{s}\mathrm{t}\mathrm{i}\mathrm{m}\mathrm{u}\mathrm{l}\mathrm{a}\mathrm{n}\mathrm{t}\mathrm{s}:z}_{ik}={\displaystyle \frac{x_{ik}-\underset{i}{\mathit{min}}\left\{x_{ik}\right\}}{\underset{i}{\mathit{max}}\left\{x_{ik}\right\}-\underset{i}{\mathit{min}}\left\{x_{ik}\right\}}},(i=1,2,\dots ,m;k=1,2,\dots ,n),$$

$${\mathrm{F}\mathrm{o}\mathrm{r}\mathrm{d}\mathrm{e}\mathrm{s}\mathrm{t}\mathrm{i}\mathrm{m}\mathrm{u}\mathrm{l}\mathrm{a}\mathrm{n}\mathrm{t}\mathrm{s}:z}_{ik}={\displaystyle \frac{\underset{i}{\mathit{max}}\left\{x_{ik}\right\}-x_{ik}}{\underset{i}{\mathit{max}}\left\{x_{ik}\right\}-\underset{i}{\mathit{min}}\left\{x_{ik}\right\}}},(i=1,2,\dots ,m;k=1,2,\dots ,n)$$

where n is the number of objects (voivodeships), and m is the number of features.

The next step in the construction of the synthetic measure was to establish a weight system and create a weighted matrix of variables [33], but in the study under review, it was assumed that all features were equally important in determining the level of socio-economic development.

The ideal solution (A⁺) and the negative-ideal solution (A⁻) were then determined according to the formulas below:

$$A^{+}=\left\{\left(\underset{i}{\mathit{max}}{z}_{ik}|k\in K\right),k=\mathrm{1,2},\dots ,m|i=1,2,\dots ,n\right\}=\left\{z_1^{+},z_2^{+},\dots ,z_m^{+}\right\}$$

$$A^{-}=\left\{\left(\underset{i}{\mathit{min}}{z}_{ik}|k\in K\right),k=1,2,\dots ,m|i=1,2,\dots ,n\right\}=\left\{z_1^{-},z_2^{-},\dots ,z_m^{-}\right\}$$

The Euclidean distances of the objects from the ideal solution were calculated using the following formula:

$$d_{i^{+}}=\sqrt{{\sum }_{k=1}^m{\left(z_{ik}-z_k^{+}\right)}^2},i=1,2,\dots ,n$$

In turn, the Euclidean distances from the negative-ideal solution were calculated using the following formula:

$$d_{i^{-}}=\sqrt{{\sum }_{k=1}^m{\left(z_{ik}-z_k^{-}\right)}^2},i=1,2,\dots ,n$$

The final values of the synthetic measure were determined according to the formula as follows:

$$C_i=d_{i^{-}}/\left(d_{i^{+}}+d_{i^{-}}\right),0\le C_i\le 1,i=1,2,\dots ,n$$

This study used the division adopted by [35], which linearly ordered the values of the development index according to non-increasing values, and on this basis, typological groups of units were distinguished. The method of division into four groups was applied, which used the arithmetic mean and standard deviation calculated from the indicator values:

$$\begin{array}{ll}\mathrm{GROUP}\mathrm{I}:q_i^{~}\ge q^{-}+s_q,& \text{very high level of development}\\ \mathrm{GROUP}\mathrm{II}:q^{-}+s_Q>q_i^{~}\ge q^{-},& \text{high level of development}\\ \mathrm{GROUP}\mathrm{III}:q^{-}>q_i^{~}\ge q^{-}-s_q,& \text{medium level of development}\\ \mathrm{GROUP}\mathrm{IV}:q_i^{~}<q^{-}-s_q.& \text{low level of development}\end{array}$$

The approach adopted made it possible to conduct a comparative analysis and examine regional variations in Poland.

3. Results and Discussion

In economic science, determining the level of development of countries or regions is relatively common. Comparing different regions is not a simple and unambiguous process [36]. It requires verification and analysis of the phenomena occurring in individual economies on the basis of an appropriate selection of measures. The most popular and traditional measures of economic development in a country based on the System of National Accounts (SNA) are the Gross Domestic Product (GDP) and GDP per capita, the values of which in the regions of Poland have shown significant disparities. In 2022, GDP per capita reached the highest values in the case of the Mazowieckie, Dolnośląskie, and Wielkopolskie provinces, while the lowest values were in the Lubelskie, Podkarpackie, and Warmińsko-Mazurskie provinces. In the structure of Poland’s GDP by provinces, three provinces were responsible for 45% of GDP, which were the Mazowieckie, Ślaskie, and Wielkopolskie provinces, while another 10% was accounted for by the Warmińsko-Mazurskie, Świętokrzyskie, Podlaskie, Opolskie, and Lubuskie provinces (

Table 1. GDP and GDP per capita by voivodeship in Poland in 2022.

Region	GDP (mln zł)	Previous Year = 100	GDP Per Capita (zł)	Poland = 100
POLAND	3,074,798	116.9	81,286	100.0
Dolnośląskie	262,933	118.1	90,915	111.8
Kujawsko-Pomorskie	133,570	116.2	66,398	81.7
Lubelskie	111,576	114.4	54,950	67.6
Lubuskie	65,150	116.1	66,300	81.6
Łódzkie	183,413	114.9	76,883	94.6
Małopolskie	246,319	114.1	71,836	88.4
Mazowiecki regional	169,337	122.3	75,027	92.3
Opolskie	61,059	113.1	64 600	79.5
Podkarpackie	114,810	113.1	55,155	67.9
Podlaskie	68,369	117.1	59,667	73.4
Pomorskie	191,032	121.1	80,999	99.6
Śląskie	371,976	118.3	85,316	105.0
Świętokrzyskie	67,218	111.3	56,860	70.0
Warmińsko-Mazurskie	76,859	113.4	56,106	69.0
Warszawski Stołeczny	541,693	119.0	166,446	204.8
Wielkopolskie	300,126	115.5	85,841	105.6
Zachodniopomorskie	109,357	113.3	66,476	81.8

, Figure 1).

Nevertheless, as [37] points out, the more measures available, the better the situation can be described. This suggests that GDP and GDP per capita alone are not sufficient measures. Therefore, in this work, based on the available statistical material, an attempt was made to construct a synthetic indicator of the development of Poland’s provinces, capturing both the social and economic aspects.

In order to determine the level of development of Poland’s provinces in the periods 2010–2012 and 2020–2022, 12 diagnostic variables were used, whose basic characteristics are shown in

Table 2. Basic characteristics of diagnostic variables describing the levels of socio-economic development of voivodeships in 2010–2012 and 2020–2022.

Variable	Period	Max.	Min.	Arithmetic Mean	Range	Standard Deviation	Coefficient of Variation (%)
X1	2010–2012	9.6	4.4	7.3	5.3	1.4	19.0
	2020–2022	5.6	0.0	2.7	5.6	1.7	63.5
X2	2010–2012	20.5	9.4	14.0	11.1	3.0	21.2
	2020–2022	9.3	3.3	6.5	6.0	1.8	28.3
X3	2010–2012	77.7	41.3	58.9	36.4	9.7	16.4
	2020–2022	76.0	41.1	58.2	34.9	9.4	16.1
X4	2010–2012	65.4	63.1	64.3	2.3	0.8	1.3
	2020–2022	60.0	57.6	59.1	2.4	0.6	1.1
X5	2010–2012	58.4	52.9	55.6	5.5	2.0	3.6
	2020–2022	73.5	66.7	69.3	6.8	1.8	2.7
X6	2010–2012	38.6	6.2	18.8	32.4	9.7	51.9
	2020–2022	25.2	3.4	11.7	21.8	6.3	53.4
X7	2010–2012	329.7	82.1	140.6	247.5	62.2	44.3
	2020–2022	604.2	205.0	288.1	399.2	92.7	32.2
X8	2010–2012	8568.3	4364.3	5717.4	4204.0	1036.6	18.1
	2020–2022	14,203.7	5744.3	8377.8	8459.3	1948.8	23.3
X9	2010–2012	10.8	5.5	7.5	5.3	1.3	17.3
	2020–2022	9.7	5.1	6.7	4.6	1.1	16.0
X10	2010–2012	18.6	1.4	5.4	17.1	4.1	76.8
	2020–2022	17.7	1.3	4.9	16.4	3.8	76.2
X11	2010–2012	45,442.7	13,975.7	26,353.7	31,467.0	9569.3	36.3
	2020–2022	80,236.0	25,953.7	46,970.8	54,282.3	15,097.4	32.1
X12	2010–2012	23.2	15.3	20.2	7.9	2.3	11.1
	2020–2022	19.8	13.7	16.3	6.1	1.7	10.3

Source: Own study, based on data from the Bank of Local Data, Central Statistical Office, https://bdl.stat.gov.pl/bdl/ (accessed: 30 June 2024).

. All the adopted diagnostic variables showed sufficiently high variability (coefficient of variation (%) >10%), and therefore could differentiate the studied objects. Only in the case of the variables X₄—share of the population at working age (%) and X₅—dependency ratio of the population (population of non-working age/100 population of working-age) were the values of the coefficients in both studied periods less than 10%. However, these variables are important indicators of the level of social development of the area, so they were included in this study.

In the analyzed periods, the voivodeships were the most differentiated in terms of the variable X₁₀: entities with a share of foreign capital per 10,000 of the population. Both in 2010–2012 and 2020–2022, the most entities occurred in the Mazowieckie voivodeship, i.e., 18.6 (period I) and 17.7 (period II), respectively, and the least in the Świętokrzyskie voivodeship [1.4 (period I), 1.3 (period II)]. In the period 2020–2022, large differences also applied to the variables X₁: natural increase per 1000 population and X₆: share of paid employees in agriculture (%). In the period 2020–2022, there were favorable changes in the provinces in the adopted diagnostic characteristics. In all provinces, the value of total investment expenditures per capita (PLN/capita) increased significantly. In 2010–2012, the value averaged PLN 5717.4 per capita, and in the 2020–2022 period, it increased to PLN 8377.8 per capita. Industrial output sold per capita (PLN/capita) also increased on average in the years under review, from PLN 26,353.7 per capita in the first period to PLN 46,970.8 per capita in the second period. Again, in all provinces, the unemployment rate decreased on average from 14% to 6.5%. In 2020–2022, the lowest values were achieved in Wielkopolska (3.27%) and Śląskie (4.3%) provinces, while the highest values were achieved in Warmińsko-Mazurskie (9.3%) and Podkarpackie (9.27%) voivodeships (Table 2).

The analysis demonstrated moderate variation in the levels of socio-economic development among Poland’s provinces in the periods studied, as reflected in the coefficient of variation (17.21–20.52%) and the range of the synthetic metric (0.2943; 0.3404) (

Table 3. Selected descriptive statistics for the values of the synthetic measures for the voivodeships of Poland in 2010–2012 and 2020–2022.

	2010–2012	2020–2022
standard deviation	0.0833	0.0890
arithmetic mean	0.4839	0.4336
coefficient of variation (%)	17.2056	20.5178
range	0.2943	0.3404
med.	0.4589	0.4298
Q1	0.4158	0.3845
Q3	0.5595	0.4971
max.	0.6519	0.6225
min.	0.3575	0.2821
skewness	0.43	0.16

Source: Own study, based on data from the Bank of Local Data, Central Statistical Office, https://bdl.stat.gov.pl/bdl/ (accessed: 30 June 2024).

). There was a decline in the level of socio-economic development of the provinces, which suggests a decrease in the median value of the synthetic metric (from 0.4589 in 2010–2012 to 0.4298 in 2020–2022). The COVID-19 pandemic in 2020–2021 had an unfavorable development impact, as evidenced by the achievement of the minimum value of the synthetic metric during this period (falling to 0.2821). There was also moderate positive asymmetry in development levels, suggesting a preponderance of units with below-average values of the synthetic metric. This was particularly evident between 2010 and 2012 (As = 0.43) (Table 3).

This study showed the diversity of the levels of development of provinces in Poland. In 2010–2012, five provinces were included in the group of the most developed provinces, and in 2020–2022, only one (Mazowieckie). This province was the only one in this group to maintain its level of development. Group II (high level of development) in the first period included four provinces, i.e., Zachodniopomorskie, Lubuskie, Lesser Poland, and Kujawsko-Pomorskie, of which three were invariably included in the highly developed provinces of Poland.

In addition, when comparing the two periods, it should be noted that group II became the largest in the second period. In 2020–2022, the Kujawsko-Pomorskie voivodeship placed in group III, classified as medium developed, and the group of very highly developed to highly developed regions contained the Ślaskie, Pomorskie, Dolnoślaskie, and Wielkopolskie voivodeships. The group of medium-developed provinces changed most dynamically during the period under review. Their number fell from six to four counties in 2020–2022, where the medium level of development was maintained by the Łódzkie, Podlaskie, and Opolskie provinces. On the other hand, the number of regions in group IV increased over the periods studied. (See

Table 4. Development levels of voivodeships in Poland in 2010–2012 and 2020–2022.

Voivodeship	2010–2012			2020–2022			Difference in Metric Value
	Metric Value of Socio-Economic Development	Ranking	Typological Group	Metric Value of Socio-Economic Development	Ranking	Typological Group
Mazowieckie	0.652	1	I	0.623	1	I	−0.029
Śląskie	0.593	2	I	0.513	4	II	−0.080
Pomorskie	0.586	3	I	0.488	6	II	−0.097
Dolnośląskie	0.564	4	I	0.525	2	II	−0.039
Wielkopolskie	0.558	5	I	0.522	3	II	−0.036
Zachodniopomorskie	0.520	6	II	0.492	5	II	−0.028
Lubuskie	0.493	7	II	0.454	7	II	−0.039
Małopolskie	0.461	8	II	0.453	8	II	−0.008
Kujawsko-Pomorskie	0.456	9	II	0.393	12	III	−0.064
Łódzkie	0.446	10	III	0.406	9	III	−0.039
Warmińsko-Mazurskie	0.445	11	III	0.360	13	IV	−0.085
Podlaskie	0.416	12	III	0.394	11	III	−0.022
Opolskie	0.414	13	III	0.395	10	III	−0.018
Podkarpackie	0.397	14	III	0.342	14	IV	−0.055
Świętokrzyskie	0.385	15	III	0.282	16	IV	−0.102
Lubelskie	0.358	16	IV	0.296	15	IV	−0.062

Source: Own study, based on data from the Bank of Local Data, Central Statistical Office, https://bdl.stat.gov.pl/bdl/ (accessed: 30 June 2024).

).

In 2010–2012, only Lublin province showed a low level of development, and in 2020–2022, it was joined by Warmińsko-Mazurskie, Podkarpackie, and Świętokrzyskie voivodeships. The most favorable situation was observed in Mazowieckie voivodeship, where in both periods, it was among the most developed voivodeships in Poland. In 2010–2012, the least developed voivodeship was located in the eastern part of Poland (Lubelskie), and this was joined in 2020–2022 by other voivodeships also located in the eastern part of Poland (Figure 2).

The distances of the regions from the benchmark in 2010–2012 and 2020–2022 are shown in Figure 3 and Figure 4, respectively. The development distances separating Mazowieckie from the two runners-up increased. In the first period, the distances were 0.059 to the Śląskie voivodeship and 0.066 to the Pomorskie voivodeship. In turn, in the years 2020–2022, the gap between the leaders increased—the distance between Mazowieckie and Dolnośląskie was 0.098, and with Wielkopolskie it was 0.101. A definite deterioration in the socio-economic situation in the country can also be seen, where the values of the synthetic measure in the studied periods successively decreased. The largest decreases in the value of the development measure in 2020–2022 compared to 2010–2012 were for the Świętotrzyskie (decrease in the synthetic indicator of 0.102), Pomorskie (decrease in the synthetic indicator of 0.097), and Warmińsko-Mazurskie (decrease in the synthetic indicator of 0.085) voivodeships.

In summary, eight provinces did not see any improvement in their socio-economic situation (they were classified in the same development group in 2010–2012 and 2020–2022). However, this should not be interpreted as a lack of any effort to improve the quality of life of the population; instead, it reflects a less dynamic pace and scope of change compared to the other provinces. A decline in the classification of development groups was recorded in eight provinces. The study of socio-economic development in Poland’s voivodeships was expanded to include an assessment of the levels of development in two separate aspects of the analysis—i.e., economic and social terms.

The economic situation influenced the results of this study of the level of socio-economic development of Poland’s voivodeships. It was analyzed using six variables: a voivodeship’s income per capita (PLN/capita), total investment expenditures per capita (PLN/capita), total national economic entities per 1000 residents, entities with foreign capital per 10,000 residents, sold industrial output per capita (PLN/capita), and the share of investment expenditures of local government units in total expenditures (%).

The Mazowieckie voivodeship stood out significantly in the ranking compared to the rest of the area, especially in 2020–2022 (at that time, the value of the development measure was 0.649, which was higher than the second-ranked Wielkopolskie voivodeship by 0.175). This may mean that the provinces with large urban centers perform significantly better from the point of view of the economic situation. In six voivodeships (Pomorskie, Świętokrzyskie, Podkarpackie, Kujawsko-Pomorskie, Warmińsko-Mazurskie, and Lubelskie voivodeships), the economic potential in 2020–2022 was evaluated as worse than in 2010–2012. Lubuskie was the only voivodeship that advanced in the development classification from those with a low level of development of economic potential (group IV) to the group of voivodeships with a medium level of development (group III). In the analyzed years, in eight voivodeships, the economic potential did not change, and thus the typological groups did not change (

Table 5. Economic potential of voivodeships in Poland in 2010–2012 and 2020–2022.

PERIOD I				PERIOD II				Difference in Period I–Period II
2010–2012				2020–2022
Voivodeship	Metric Value	Ranking	Typological Group	Voivodeship	Metric Value	Ranking	Typological Group
Mazowieckie	0.618	1	I	Mazowieckie	0.649	1	I	0.031
Pomorskie	0.473	2	II	Pomorskie	0.343	6	III	−0.130
Wielkopolskie	0.446	3	II	Wielkopolskie	0.474	2	II	0.028
Śląskie	0.432	4	II	Śląskie	0.426	5	II	−0.007
Dolnośląskie	0.421	5	II	Dolnośląskie	0.465	3	II	0.045
Zachodniopomorskie	0.398	6	II	Zachodniopomorskie	0.434	4	II	0.036
Świętokrzyskie	0.368	7	III	Świętokrzyskie	0.210	14	IV	−0.158
Małopolskie	0.345	8	III	Małopolskie	0.338	7	III	−0.006
Podlaskie	0.343	9	III	Podlaskie	0.320	10	III	−0.023
Podkarpackie	0.338	10	III	Podkarpackie	0.250	13	IV	−0.088
Łódzkie	0.330	11	III	Łódzkie	0.326	8	III	−0.004
Kujawsko-Pomorskie	0.324	12	III	Kujawsko-Pomorskie	0.272	12	IV	−0.052
Warmińsko-Mazurskie	0.305	13	III	Warmińsko-Mazurskie	0.188	16	IV	−0.117
Lubelskie	0.300	14	III	Lubelskie	0.246	13	IV	−0.054
Lubuskie	0.293	15	IV	Lubuskie	0.326	9	III	0.034
Opolskie	0.203	16	IV	Opolskie	0.273	11	IV	0.070

Source: Own study, based on data from the Bank of Local Data, Central Statistical Office, https://bdl.stat.gov.pl/bdl/ (accessed: 30 June 2024).

).

Social potential also influenced the economic situation of Poland’s voivodeships, and in order to analyze it, six variables were used: population growth per 1000 people, registered unemployment rate (%), urban population in % of the total population, share of population of the working age (%), dependency ratio (people of non-working age/100 people of working age), and share of employed in agriculture (%). The Śląskie voivodeship stood out from the rest of the area in the ranking, especially in 2010–2012 (at that time, the value of the development indicator was 0.777, which was higher than that of the second-placed Pomorskie voivodeship by 0.053), and in turn, when comparing the two periods, it was the Śląskie voivodeship that recorded the most dramatic decrease in the value of the human development indicator, i.e., by 0.201. (See

Table 6. Social potential of voivodeships in Poland in 2010–2012 and 2020–2022.

PERIOD I				PERIOD II				Difference in Period I–Period II
2010–2012				2020–2022
Voivodeship	Metric Value	Ranking	Typological Group	Voivodeship	Metric Value	Ranking	Typological Group
Śląskie	0.777	1	I	Śląskie	0.576	4	II	−0.201
Pomorskie	0.724	2	I	Pomorskie	0.615	1	I	−0.109
Dolnośląskie	0.708	3	I	Dolnośląskie	0.577	3	II	−0.131
Mazowieckie	0.699	4	I	Mazowieckie	0.589	2	I	−0.110
Wielkopolskie	0.677	5	I	Wielkopolskie	0.563	5	II	−0.113
Lubuskie	0.657	6	I	Lubuskie	0.547	6	II	−0.110
Zachodniopomorskie	0.641	7	I	Zachodniopomorskie	0.537	8	III	−0.103
Kujawsko-Pomorskie	0.585	8	II	Kujawsko-Pomorskie	0.488	11	III	−0.097
Opolskie	0.574	9	II	Opolskie	0.490	9	III	−0.084
Małopolskie	0.571	10	II	Małopolskie	0.541	7	III	−0.031
Warmińsko-Mazurskie	0.563	11	II	Warmińsko-Mazurskie	0.489	10	III	−0.075
Łódzkie	0.560	12	III	Łódzkie	0.459	13	III	−0.100
Podlaskie	0.513	13	III	Podlaskie	0.468	12	III	−0.046
Podkarpackie	0.453	14	III	Podkarpackie	0.415	14	III	−0.038
Lubelskie	0.420	15	III	Lubelskie	0.340	15	IV	−0.080
Świętokrzyskie	0.405	16	IV	Świętokrzyskie	0.332	16	IV	−0.073

Source: Own study, based on data from the Bank of Local Data, Central Statistical Office, https://bdl.stat.gov.pl/bdl/ (accessed: 30 June 2024).

).

In ten voivodeships (Śląskie, Dolnośląskie, Wielkopolskie, Lubuskie, Zachodniopomorskie, Kujawsko-Pomorskie, Opolskie, Małopolskie, Warmińsko-Mazurskie, and Lubelskie), the social potential in 2020–2022 was assessed as worse than in 2010–2012, with the least favorable result in Zachodniopomorskie (a drop from group I to group III). Among the six voivodeships which maintained the same development group in both periods, the Pomorskie (group I), Mazowieckie (group I), Łódzkie (group III), Podlaskie (group III), Podkarpackie (group III), and Świętokrzyskie (group IV) voivodeships fared best.

The Polish labor market has been significantly affected by the influx of Ukrainian refugees caused by the war with Russia. These migrants have contributed significantly to economic growth, notably in the industrial and export sectors. However, with the prospect of calm in Ukraine, the expected repatriation of these workers may result in labor shortages, increasing current economic concerns such as excessive government debt and inflation [38].

Despite setbacks, Poland’s economy has proven resilient. The country’s GDP per capita increased drastically from USD 6200 in 1990 to USD 48,000 in 2023, making it one of Europe’s major success stories. This expansion is ascribed to integration into the European Union, which simplified access to the single market, boosted foreign direct investment, and generated an inflow of EU structural funds to support infrastructural development. Furthermore, the introduction of Special Economic Zones (SEZs) has attracted investment by offering favorable business conditions and tax breaks, thereby boosting economic development [39,40].

While the country has achieved notable economic growth and development, it faces ongoing challenges that require strategic planning and adaptation if it is to maintain stability and continue its progress.

4. Conclusions

The analysis of the levels of socio-economic development of voivodeships in 2010–2012 and 2020–2022 conducted on the basis of the value of a synthetic indicator, which included 12 diagnostic features, confirmed the persistence of significant disproportions between voivodeships. The highest place in the rankings in the years under study was consistently occupied by the Mazowieckie voivodeship, which from 2010 to 2012 significantly diverged in its level of socio-economic development from the others. The next places were occupied alternately by the following voivodeships: Śląskie, Dolnośląskie, Pomorskie, Wielkopolskie, and Zachodniopomorskie. The lowest levels of development were recorded in the following voivodeships: Lubelskie, Świętokrzyskie, and Podkarpackie.

In the period covered by the analysis, the socio-economic situation deteriorated in all voivodeships, which was reflected in a decrease in the value of the indicator. The scale of the decrease was uneven, which leads to the conclusion that there is a problem of increasing regional disproportions. The greatest decreases in the value of the development measure occurred in the voivodeships with the highest levels of development, and the smallest in the voivodeships at the last places in the ranking. This resulted in a widening gap between the lowest-ranked voivodeships and the most developed voivodeships. This is an unfavorable phenomenon in the context of the policy of leveling out regional development differences [41]. The demonstrated breakdown of the socio-economic situation may be closely linked to the crisis caused by the COVID-19 pandemic [23,42]. Similar conclusions were reached in [43]. In turn, there are indications that in Europe, Poland has become an exceptionally vulnerable country to the consequences of the crisis [44], and showed economic downturns in all sectors of the economy [45], the largest in services and industry and the smallest in construction. In addition, the coronavirus produced a significant slowdown in the economy, a decrease in GDP, and an increase in inflation. Poles started to save concertedly, making it difficult for the economy to return to its pre-pandemic state [46,47].

Based on the findings of this study, the authors recommend that future studies should incorporate additional indicators, such as the employment rate, investment per capita, and share of foreign capital entities, to provide a more holistic evaluation of regional development. In addition, tailored investment programs and infrastructural projects should be directed toward eastern provinces like Lubelskie, Podkarpackie, and Świętokrzyskie, which consistently lag behind in socio-economic development, in order to stimulate their growth and reduce regional disparities.

Provinces with a high dependency on limited economic sectors, such as agriculture, should diversify their economies. Initiatives to promote industrialization, technology adoption, and service sectors can help stabilize and improve their development metrics. While Mazowieckie has maintained a strong position, its growing disparity with other regions indicates the need for balanced development strategies. Policies that prioritize equitable resource distribution and interregional collaboration can reduce developmental gaps and ensure nationwide progress.

In conclusion, research on regional disparities in Poland is crucial, as it enables the identification of differences in the levels of socio-economic development between provinces. This makes it possible to identify regions in need of urgent financial and infrastructural support. With respect to the use of EU funds (2021–2027), Poland is one of the largest beneficiaries of EU funds, aimed at balancing regional differences, and accurate data on the level of development make it possible to effectively allocate funds for critical development sectors. In addition, the level of development of regions in changing political conditions is gaining importance, especially given the prospect of sanctions between the EU and the US. There is no denying that the international situation may directly affect individual provinces in different ways depending on their economic specificities. The results presented here have value as they provide data to support the creation of strategies or regional, national, or European policies aimed at equalizing development opportunities while reducing differences and increasing Poland’s nationwide cohesion.