Demographic Trends and Regional Disparities in the EU

Abstract

This paper investigates the impact of demographic changes on regional disparities within the European Union. The analysis focuses on key demographic trends—population ageing, low fertility rates, and migration flows—and their long-term implications for labor markets, economic growth, and the sustainability of social systems across EU regions. Using Eurostat data for 169 NUTS2 regions for 2022 and 2023, the study applies descriptive statistics, correlation analysis, and multiple regression models to quantify the effects of life expectancy, fertility rates, and educational attainment on GDP growth and employment in knowledge-intensive and high-tech sectors. Findings indicate that regions with higher shares of highly educated labor and favorable demographic profiles tend to achieve greater economic resilience and competitiveness. In contrast, demographic decline and outmigration continue to deepen structural disparities, particularly in rural and less developed regions. The results underscore the importance of integrated EU policies aimed at addressing demographic challenges while promoting regional cohesion through investment in human capital, innovation, and infrastructure. By highlighting the critical role of demographic factors in shaping regional development, this study contributes to the discourse on designing effective policies to foster sustainable and inclusive growth within the European Union.

1. Introduction

In recent decades, EU countries have undergone significant demographic changes, including population aging, declining fertility rates, and migration flows. These processes have long-term consequences for the labor market, economic growth, social systems, and regional development. According to the European Commission (2023), demographic changes are shaping Europe’s economic future by impacting labor markets, social protection systems, and public services. One of the primary demographic trends in Europe is the accelerated aging of the population. Eurostat (2022) data project that by 2050 nearly one-third of the EU population will be over 65 years old. This trend poses a significant challenge for pension systems, healthcare provision, and the workforce. The decline in the working-age population leads to fewer taxpayers and increased pressure on public finances, potentially threatening economic growth and the sustainability of social policies.

While empirical studies often document associations between life expectancy and economic growth, it is important to note that such patterns are typically correlational rather than causal (Preston 1975; Lee 2003). Regions with higher life expectancy may experience slower growth due to convergence effects and demographic aging, rather than life expectancy directly reducing growth.

Additionally, fertility rates in many EU countries remain below the level required for natural population replacement. Eurostat (2022) reports an average fertility rate of approximately 1.5 children per woman in the EU, well below the threshold of 2.1 needed for demographic stability. Low fertility has long-term implications for labor force sustainability, even if short-term economic effects are limited. Causes for low fertility include economic insecurity, difficulties balancing work and family life, as well as changes in social norms and life priorities.

Migration within and into the EU further shapes demographic and regional patterns. High labor mobility within the EU helps alleviate labor shortages in certain sectors and regions, while emigration from less-developed regions to economic centers often results in rural depopulation and exacerbates regional inequalities. External migration pressures, particularly from Africa and the Middle East, also influence labor markets and population structures (European Commission 2021a). Although migration is not explicitly included in the empirical model used in this study, it is an important determinant that can modify demographic and economic outcomes.

Uneven economic development within the EU generates significant regional disparities. Some regions, especially in Western and Northern Europe, experience high economic growth and attract investments, while others—particularly rural and less-developed areas in Southern and Eastern Europe—face stagnation and depopulation. The European Parliament (2021) highlights that demographic trends impact EU regions differently, creating both opportunities and challenges regarding labor and infrastructure. The urban-rural divide is also pronounced: major cities and industrial centers attract young and educated workers, whereas rural regions experience population decline, school and healthcare facility closures, and reduced economic opportunities. These inequalities affect political stability and social cohesion within the EU, as marginalized regions often express dissatisfaction with central and EU policies. Understanding demographic and regional trends is vital for shaping public policies in the EU. The European Commission and other EU institutions seek to mitigate the effects of demographic change and reduce regional disparities through strategies such as the Cohesion Policy and the European Social Fund. Eurostat (2022) stresses that a strategic response to demographic challenges requires integrated policies supporting innovation, employment, and regional cohesion. Without adequate measures, demographic trends may deepen economic divides between regions, threaten public finance sustainability, and weaken the EU’s global competitiveness. Accordingly, demographic factors and regional disparities play a crucial role in shaping Europe’s economic and social future. Sustainable and inclusive policies addressing these challenges are essential to preserve the stability and prosperity of the European Union. EU demographic and regional challenges require models that boost social and ecological resilience. Čegar et al. (2024) note, “The regenerative economy seeks to create a system where human activities enhance rather than deplete natural and social capital.” This supports sustainable and cohesive regional growth.

Regional disparities within the EU are shaped by the interaction of economic, demographic, and institutional factors, where population aging, migration, and low fertility rates have long-term effects on labor markets, innovation, and social system sustainability. These processes contribute to the shrinking working-age population, reduced taxpayer base, and increased pressure on pension systems, complicating regional capacities to ensure stable growth and social cohesion (Eurostat 2023; European Commission 2019, 2021b, 2024; OECD 2024; Lee 2003; Rowland 2009; International Monetary Fund (IMF) 2021).

Neoclassical and endogenous growth models emphasize the importance of capital, infrastructure investment, human capital, and innovation as key factors in reducing regional disparities, while new economic geography theories highlight the role of agglomeration effects in generating competitive advantages for certain regions (Solow 1956; Swan 1956; Romer 1990; Lucas 1988; Barro and Sala-i-Martin 1992; Howitt 2010; Meliciani et al. 2022; Krugman 1991; Fujita et al. 2001; Markovic et al. 2022). Empirical studies show that regions with developed infrastructure, educational institutions, and innovation capacities achieve higher growth rates and resilience, while regions facing demographic decline and migration often have limited ability to attract investments and retain young populations (Crescenzi and Rodríguez-Pose 2011; Faggian and McCann 2009).

Population aging affects productivity and labor structure, while simultaneously increasing demand for healthcare and social protection services, placing additional strain on public finances of member states (Bloom et al. 2010). Migration and urbanization within the EU may reduce labor shortages in some regions but can also contribute to depopulation of rural and peripheral areas, thereby increasing regional inequality (Harris and Todaro 1970; Rodríguez-Pose and Wilkie 2017; Rodríguez-Pose and Ketterer 2020). Smart specialization and digitalization policies, alongside innovation strategies, offer opportunities for sustainable growth and reduction in regional disparities through enhancing competitiveness and linking local economies to global value chains (McCann and Ortega-Argilés 2011; OECD 2020; European Commission 2021c, 2023).

Urbanization and the role of cities are crucial for addressing demographic challenges and reducing regional disparities in the EU. As centers of population and economic activity, cities drive sustainability and innovation agendas, supporting climate-friendly and resilient development. As Kourtit et al. (2023) note, “Since cities are seedbeds of innovative sustainability in geographical space, it is important to make a systematic typology of distinct environmental or climatological roles to be played by cities.” This underscores the potential of cities to foster demographic renewal, technological progress, and environmental sustainability, contributing to balanced regional development in the EU.

Investments in education, digital skills, and infrastructure contribute to increased employability and strengthened resilience of regional economies against demographic challenges (Becker 2009; Barro and Lee 2013; Meliciani et al. 2022), while the challenges of demographic aging require adaptation of labor and pension policies to preserve system sustainability (Bloom et al. 2010; Eurostat 2024). The EU’s Cohesion Policy, through strategic investments and support to less developed regions, remains a vital mechanism for reducing inequalities and promoting balanced development within the Union (European Commission 2024).

Based on this introduction, it can be concluded that demographic and regional disparities remain key challenges for the European Union, requiring integrated policies that account for aging, fertility trends, migration, and economic convergence. While this study uses cross-sectional data (2022–2023) to analyze associations, results should be interpreted as correlations rather than causal effects. Sustainable and inclusive policies targeting active aging, human capital development, and regional cohesion are essential for preserving the stability and prosperity of the European Union.

2. Materials and Methods

The study utilizes data from Eurostat for 169 NUTS2 regions in the European Union for the years 2022 and 2023. The analysis uses cross-sectional data for 2022–2023. As such, the results should be interpreted as associations rather than causal effects. The following variables were used: gross domestic product (GDP) in millions of euros, GDP growth rate, employment of highly educated individuals in the science and technology sector expressed as a percentage of the workforce, employment in the high-tech sector expressed as a percentage of total employment, life expectancy, fertility rate, and the percentage of the highly educated population expressed as the share of individuals aged 25–64 who have successfully completed higher education. Abbreviations used in the models are as follows: ST refers to the science and technology sector, while HT refers to the high-tech sector.

The following models were analyzed:

$${\mathit{G}\mathit{D}\mathit{P}}_{\mathit{t}}={\mathit{b}}_1\ast {\mathit{l}\mathit{i}\mathit{f}\mathit{e} \mathit{e}\mathit{x}\mathit{p}\mathit{e}\mathit{c}\mathit{t}\mathit{a}\mathit{n}\mathit{c}\mathit{y}}_{\mathit{t}-1}+{\mathit{b}}_2\ast {\mathit{f}\mathit{e}\mathit{r}\mathit{t}\mathit{i}\mathit{l}\mathit{i}\mathit{t}\mathit{y} \mathit{r}\mathit{a}\mathit{t}\mathit{e}}_{\mathit{t}-1}+{\mathit{b}}_0\ast {\mathit{h}\mathit{i}\mathit{g}\mathit{h}\mathit{l}\mathit{y} \mathit{e}\mathit{d}\mathit{u}\mathit{c}\mathit{a}\mathit{t}\mathit{e}\mathit{d} \mathit{l}\mathit{a}\mathit{b}\mathit{o}\mathit{u}\mathit{r}}_{\mathit{t}-1}+\mathit{c}+\mathit{\epsilon }$$

(1a)

GDP_t = b₁ ∗ male life expectancy_t−1 + b₂ ∗ fertility rate_t−1 + b₀ ∗ highly educated labour_t−1 + c + ε

(2a)

BDP_t = b₁ ∗ female life expectancy_t−1 + b₂ ∗ fertility rate_t−1 + b₀ ∗ highly educated labour_t + c + ε

(3a)

employment in ST_t = b₁ ∗ life expectancy_t−1 + b₂ ∗ fertility rate_t−1 + b₀ ∗ highly educated labour_t−1 + c + ε

(1b)

employment in ST_t = b₁ ∗ male life expectancy_t−1 + b₂ ∗ fertility rate_t−1 + b₀ ∗ highly educated labour_t−1 + c + ε

(2b)

employment in ST_t = b₁ ∗ female life expectancy_t−1 + b₂ ∗ fertility rate_t−1 + b₀ ∗ highly educated labour_t−1 + c + ε

(3b)

employment in HT_t = b₁ ∗ life expectancy_t−1 + b₂ ∗ fertility rate_t−1 + b₀ ∗ highly educated labour_t−1 + c + ε

(1c)

employment in HT_t = b₁ ∗ male life expectancy_t−1 + b₂ ∗ fertility rate_t−1 + b₀ ∗ highly educated labour_t−1 + c + ε

(2c)

$${\mathit{e}\mathit{m}\mathit{p}\mathit{l}\mathit{o}\mathit{y}\mathit{m}\mathit{e}\mathit{n}\mathit{t} \mathit{i}\mathit{n} \mathit{S}\mathit{T}}_{\mathit{t}}={\mathit{b}}_1\ast {\mathit{f}\mathit{e}\mathit{m}\mathit{a}\mathit{l}\mathit{e} \mathit{l}\mathit{i}\mathit{f}\mathit{e} \mathit{e}\mathit{x}\mathit{p}\mathit{e}\mathit{c}\mathit{t}\mathit{a}\mathit{n}\mathit{c}\mathit{y}}_{\mathit{t}-1}+{\mathit{b}}_2\ast {\mathit{f}\mathit{e}\mathit{r}\mathit{t}\mathit{i}\mathit{l}\mathit{i}\mathit{t}\mathit{y} \mathit{r}\mathit{a}\mathit{t}\mathit{e}}_{\mathit{t}-1}+{\mathit{b}}_0\ast {\mathit{h}\mathit{i}\mathit{g}\mathit{h}\mathit{l}\mathit{y} \mathit{e}\mathit{d}\mathit{u}\mathit{c}\mathit{a}\mathit{t}\mathit{e}\mathit{d} \mathit{l}\mathit{a}\mathit{b}\mathit{o}\mathit{u}\mathit{r}}_{\mathit{t}-1}+\mathit{c}+\mathit{\epsilon }$$

(3c)

Although the models estimate the association between demographic variables and economic outcomes, no causal inference is implied due to the cross-sectional nature of the data. The data analysis includes descriptive statistics, correlation analysis, and regression models to quantify the impact of demographic changes on economic and regional disparities within the EU. IBM SPSS Statistics v.22 was used for data analysis.

The analysis is based on publicly available aggregated regional data obtained from Eurostat (https://ec.europa.eu/eurostat accessed on 24 May 2025). The dataset covers 169 NUTS2 regions in the European Union for the years 2022 and 2023. Variables were used without transformation unless otherwise specified, and no imputation or data exclusion methods were applied beyond standard data cleaning procedures. The study did not involve human or animal subjects; therefore, ethical approval was not required. All materials and data used in this study are accessible to readers upon request or can be directly downloaded from the Eurostat database. Statistical analyses were conducted using IBM SPSS Statistics version 22. Generative AI tools were used solely for superficial text editing and formatting, and not for data analysis, study design, or interpretation. Potential limitations include endogeneity between economic and demographic variables, as well as the omission of migration flows, which could affect outcomes. Future research could use panel data or simultaneous equation models to address these issues more robustly.

3. Results

Table 1. Descriptive statistics.

Variables	Number of the Region	Average Value	Standard Deviation	Minimum	Maximum
GDP	169	71,337.93	91,073.99	6256.93	860,067.30
GDP (%)	169	116.61	13.93	86.50	188.50
Employees with Higher Education in the Science and Technology Sector 1	169	46.50	10.87	21.60	72.90
Employment in the High-Technology Sector 2	169	4.48	2.51	0.90	12.70
Life expectancy	169	80.83	2.86	72.30	85.20
Male life expectancy	169	77.84	3.45	68.70	82.40
Female life expectancy	169	83.21	2.33	76.40	87.70
Fertility rate	169	1.47	0.23	0.86	2.37
Highly Educated Population 3	169	35.06	11.35	12.60	62.00

Notes: ¹ Expressed as a labor percentage, ² Expressed as a total employment percentage, ³ Expressed as the percentage of the population aged 25–64 who have completed higher education.

presents descriptive statistics for the observed variables, including GDP, GDP growth rate, the percentage of highly educated employment in the science and technology sector, employment in the high-technology sector, life expectancy, fertility rate, and the percentage of highly educated population across 169 NUTS2 regions in the European Union.

The average GDP is 71,337.93 million euros, with substantial variability (SD = 91,073.99), indicating large differences among regions. The lowest recorded GDP is 6256.93 million euros, while the highest is 860,067.30 million euros, reflecting the presence of highly developed and economically dominant regions alongside those with significantly lower economic activity. Lower GDP values in some regions suggest a relatively weak economic base, likely characterized by smaller populations, weaker industrial sectors, lower investment levels, and limited technological development capacity. These regions may face structural challenges such as low productivity, high unemployment, or unfavorable demographic trends. Conversely, regions with high GDP levels likely have a high concentration of economic activity, developed industrial, service, and technology sectors, and strong global market connections. Their GDP may result from substantial investments, high productivity, the presence of multinational companies, and innovative industries. The range between the minimum and maximum GDP clearly demonstrates significant economic disparities among regions, which may be due to factors such as geographic location, resource availability, urbanization, infrastructure, population education structure, and applied economic policies.

Before interpreting the empirical results, it is important to consider the underlying theoretical mechanisms. Life expectancy may influence economic growth through its effects on health, human capital accumulation, and labor productivity. Conversely, economic development itself can influence life expectancy, as emphasized by the Preston curve and convergence literature. Therefore, any empirical analysis of demographic factors must control for initial levels of economic development to separate genuine causal effects from convergence dynamics.

In addition to absolute GDP levels, it is important to consider relative economic performance through GDP growth rates. The average GDP growth rate is 116.61, ranging from 86.50 to 188.50. These values indicate that some regions are economically much stronger, while others fall below the average, highlighting the need for additional economic incentives and development measures to reduce regional disparities.

On average, 35.06% of the population aged 25–64 in the regions have completed higher education, with a minimum of 12.60% and a maximum of 62.00%, indicating an uneven distribution of the highly educated workforce, with some regions having significantly higher levels of educated citizens. Differences in educational structures may result from various factors, including the availability of universities and higher education institutions, migration flows, and the economic specializations of regions. Regions with higher percentages of highly educated citizens are likely centers of knowledge and innovation, while those with lower education levels may have economies based on more traditional sectors with lower demand for highly skilled labor.

The average percentage of highly educated employment in the science and technology sector is 46.50%, with values ranging from 21.60% to 72.90%. This variability indicates that some regions are significantly more developed in high-tech occupations and research sectors. A high share of highly educated individuals in science and technology may indicate a strong university system, the presence of research institutes, and innovative companies engaged in developing new technologies. In contrast, regions with lower percentages in this sector may have less developed technological industries or limited opportunities for research and innovation.

Employment in the high-technology sector averages 4.48%, ranging from 0.90% to 12.70%, suggesting that certain regions have a significantly higher share of employment in this sector, while others remain economically focused on more traditional activities. Regions with high shares of employment in high-tech sectors likely have strong innovation ecosystems, the presence of technology parks, startup ecosystems, and investments in research and development. Conversely, regions with lower employment shares in these sectors may rely on traditional economic sectors such as agriculture, mining, or manufacturing, which may limit their potential for technological advancement and economic diversification.

The average life expectancy in the analyzed regions is 80.83 years, ranging from 72.30 to 85.20 years. The differences between the minimum and maximum values indicate significant heterogeneity among regions, which may be due to various socioeconomic factors, quality of healthcare, living standards, nutrition, degree of urbanization, pollution levels, and access to education and preventive medicine. Regions with lower life expectancy likely face unfavorable living conditions, higher levels of poverty, limited access to quality healthcare services, or higher rates of chronic diseases. Conversely, regions with higher life expectancy probably include areas with high living standards, well-developed healthcare infrastructure, healthier lifestyles, and better socioeconomic opportunities. Men live on average 77.84 years, while the average life expectancy for women is higher at 83.21 years. These differences are consistent with global trends, where women generally live longer than men. The fertility rate, or the average number of children per woman, is 1.47, which is below the natural population replacement rate (around 2.10). The lowest recorded fertility rate is 0.86, while the highest is 2.37, suggesting that some regions have significantly lower birth rates, while others are closer to the level required for stable population replacement.

The Pearson correlation coefficient was calculated to determine the relationship between GDP growth rates, the percentage of employees with higher education in the science and technology sector, employment in the high-tech sector, life expectancy, fertility rates, and the percentage of highly educated individuals (

Table 2. Correlation.

Variables	1	2	3	4	5	6	7	8
1—GDP (%)	1
2—Employees with higher education in the science and technology sector	0.22 ***	1
3—Employment in the high-technology sector	0.47 ***	0.71 ***	1
4—Life expectancy	−0.25 ***	0.43 ***	0.09	1
5—Male life expectancy	−0.24 ***	0.42 ***	0.12	0.99 ***	1
6—Female life expectancy	−0.26 ***	0.39 ***	0.03	0.97 ***	0.93 ***	1
7—Fertility rate	−0.01	−0.06	−0.06	−0.31 ***	−0.29 ***	−0.33 ***	1
8—Highly Educated Population	0.29	0.95 ***	0.67 ***	0.37 ***	0.36 ***	0.36 ***	−0.10	1

Note: *** p < 0.01.

). The results indicate that an increase in employment of highly educated individuals in the science and technology sector, as well as an increase in employment within the high-tech sector, is statistically significantly associated with higher GDP growth rates. These findings are consistent with economic theory. Regions with a higher share of highly educated workers in science and technology typically exhibit higher levels of innovation, research, and development (R&D), which contribute to productivity and long-term economic growth. High-tech sectors, such as information technology, biotechnology, and advanced manufacturing, often generate high added value and demonstrate greater productivity compared to traditional sectors. These industries attract investment, stimulate entrepreneurship, and enhance the competitiveness of the economy at the global level. Such results suggest that policies aimed at promoting education in STEM fields (science, technology, engineering, mathematics), investing in research and development, and fostering the growth of technology sectors could significantly contribute to the economic expansion of regions.

Furthermore, the results of the Pearson correlation coefficient indicate that higher life expectancy, whether overall, male, or female, is statistically significantly associated with lower GDP growth rates. The negative coefficient of life expectancy should not be interpreted as evidence that longer life expectancy directly slows regional growth. It likely reflects two intertwined mechanisms: convergence, where regions with higher initial GDP and life expectancy naturally grow more slowly, and demographic ageing, since longer life expectancy correlates with an older population and lower labor force participation. Without controlling for initial GDP per capita and dependency ratios, the estimated effect of life expectancy captures broader demographic and economic dynamics rather than a direct causal relationship. While this finding may appear counterintuitive, several explanations are possible. Regions with higher life expectancy are often economically more developed and have already achieved a higher level of GDP per capita, which implies lower GDP growth rates compared to faster-growing but less developed regions (“growth convergence”). Population aging may play an important role in this process, as regions with higher life expectancy often have an older population, which may reduce GDP growth rates due to a smaller working-age population and increased healthcare and social protection costs. Lower labor force participation among older populations can result in fewer individuals in their productive years, which affects economic output and growth dynamics. These findings indicate a potential challenge for long-term economic development, particularly for regions with low fertility rates and an aging population. Another important factor not included in the current specification is migration. Both internal and international migration flows strongly influence regional growth by affecting the size and age composition of the labor force. Regions with net out-migration, especially of younger cohorts, may experience slower growth and accelerated ageing. Metropolitan centers with net in-migration can sustain high growth despite low fertility and high life expectancy. Omitting migration may bias the estimated effects of demographic variables and overstate the negative impact of ageing. Policies promoting active aging, extending the working life, and enhancing productivity through technological innovation and automation may help mitigate the negative effects of aging on economic growth.

The results of the correlation analysis also show that the relationship between fertility rates and GDP growth rates is not statistically significant. In some regions, higher fertility rates may stimulate long-term growth by increasing the labor force, while in others, it may have a neutral or even negative effect if a higher number of children strain economic resources and public services. Other mediating factors may exist in the relationship between fertility and GDP growth, such as education levels, human capital investments, and labor force structure. Modern economies increasingly depend on the quality rather than the quantity of the labor force, so an increase in fertility does not automatically guarantee faster economic growth. These results suggest that economic policy should not focus solely on increasing fertility rates but also on enhancing productivity through investments in education, technology, and innovation.

Table 2 also shows a statistically significant positive relationship between the percentage of employees with higher education in the science and technology sector and life expectancy, while the relationship with fertility rates is not statistically significant. The science and technology sector includes areas such as medical technology, pharmaceuticals, biotechnology, and public health, which directly contribute to improving the quality and accessibility of healthcare. A higher number of educated professionals in these sectors facilitates faster advancements in the development of new drugs, medical treatments, and diagnostic methods, resulting in reduced mortality and increased life expectancy. Regions with a higher share of highly educated individuals in the science and technology sector often exhibit higher average incomes, better social status, and improved living conditions, all contributing to longer life expectancy. Higher incomes enable better nutrition, higher quality healthcare, and greater investments in preventive measures, collectively reducing the risk of disease and extending life. Higher levels of education are associated with greater awareness of healthy lifestyles, including healthier diets, regular physical activity, and a more responsible approach to preventive medicine. Individuals in more educated environments tend to utilize healthcare services more frequently, undergo regular check-ups, and adhere to medical advice, all of which contribute to increased life expectancy.

Fertility rates depend on numerous economic, cultural, and social factors, including family policies, the cost of raising children, value orientations toward parenthood, and employment opportunities for women. Although the science and technology sector contribute to economic development, it is not necessarily the main determinant of fertility decisions. Highly educated workers in the science and technology sector often have demanding jobs that require many years of education, research work, and career ambitions, which may delay family formation decisions. Women employed in these sectors may face challenges in balancing family and professional obligations; however, this does not necessarily imply lower fertility rates. In some regions, a higher presence of highly educated individuals in the technology sector may coincide with better social policies (e.g., childcare subsidies, flexible working conditions), which may neutralize the impact on fertility rates. In other regions, a lack of support for parenthood and family life may limit the number of children employees wish to have.

The absence of a statistically significant effect of fertility on short-term economic growth should not be interpreted as fertility being irrelevant for regional dynamics. Fertility rates are often more a reflection of economic development rather than a driver of growth. While periods of rapid growth may coincide with higher fertility due to improved living standards and family policies, higher fertility itself does not directly stimulate economic expansion. In some cases, higher fertility may even exert downward pressure on growth in the short term, for example, through reduced female labor force participation. Therefore, interpretations of fertility’s role should distinguish between its long-term demographic implications and its limited immediate impact on growth.

Table 3. Regression analysis—GDP growth rate.

Variable/Model	Model 1	Model 2	Model 3
Life expectancy	−2.17 *** (0.37)
Male life expectancy		−1.69 *** (0.31)
Female life expectancy			−2.70 *** (0.46)
Fertility rate	−6.91 (4.32)	−5.91 (4.32)	−7.49 * (4.33)
Highly Educated Population	0.54 *** (0.09)	0.52 *** (0.09)	0.53 *** (0.09)
C	283.02 *** (31.75)	239.49 *** (25.76)	334.19 *** (39.33)
R2	0.2221	0.2083	0.2273
F	16.99 ***	15.73 ***	17.47 ***

Note: *** p < 0.01; * p < 0.10. Independent variables were used for the period t − 1.

presents the results of the regression analysis, specifically the effects of life expectancy and fertility rates on GDP growth rates in the NUTS2 regions of the EU, using the percentage of highly educated individuals as a control variable. The results show that life expectancy is a statistically significant negative predictor of GDP growth rates, with male life expectancy having a smaller statistically negative effect on GDP growth rates (Model 2a) compared to overall life expectancy (Model 1a) and female life expectancy (Model 3a). Fertility rates are not statistically significant predictors of GDP growth rates. The percentage of highly educated individuals is a statistically significant positive predictor of GDP growth rates. These findings align with previous research indicating the potential negative effects of population aging on economic dynamics (Bloom et al. 2004; Acemoglu and Restrepo 2017). Additionally, these results may be explained by different patterns of economic activity and labor market participation between genders, where women, due to longer life expectancy, more frequently experience the economic consequences of prolonged retirement periods and reduced labor force participation (Lee and Mason 2010). Given that the percentage of highly educated individuals is used as a control variable, these findings imply that even when accounting for education levels, life expectancy remains a significant factor influencing economic growth in the analyzed regions. These results indicate the need for policies that promote active aging and extended labor force participation among older workers to mitigate the negative economic effects of increased life expectancy (OECD 2019).

Table 4. Regression Analysis: Highly Educated Employment in Science and Technology.

Variable/Model	Model 1	Model 2	Model 3
Life expectancy	−0.17 *** (0.06)
Male life expectancy		−0.11 ** (0.05)
Female life expectancy			−0.28 *** (0.07)
Fertility rate	−0.73 (0.64)	−0.55 (0.64)	−0.99 (0.63)
Highly Educated Population	0.16 *** (0.01)	0.16 *** (0.01)	0.17 *** (0.01)
C	13.69 *** (4.74)	8.33 ** (3.86)	23.78 *** (5.80)
R2	0.4724	0.4608	0.4973
F	49.24 ***	47.00 ***	54.41 ***

Note: *** p < 0.01; ** p < 0.05. Independent variables were used for the period t − 1.

presents the results of the regression analysis, specifically the effects of life expectancy and fertility rates on the employment of highly educated individuals in the science and technology sector across EU NUTS2 regions, with the percentage of highly educated population used as a control variable. The results indicate that life expectancy is a statistically significant predictor of employment of highly educated individuals in the science and technology sector, with male life expectancy showing a smaller statistically negative effect (Model 2b) compared to overall life expectancy (Model 1b) and female life expectancy (Model 3b). These findings suggest that increasing life expectancy may reduce opportunities for the employment of highly educated personnel in the science and technology sector, which may be explained by demographic changes and labor market rigidities (Lutz et al. 2019). Notably, female life expectancy exhibits the strongest negative effect. This finding may indicate greater barriers to career advancement for women in science and technology sectors, particularly in later stages of their careers, due to factors such as gender differences in retirement patterns and reemployment opportunities (Goldin 2014). Additionally, it is possible that longer life expectancy implies extended working lives, which may reduce opportunities for younger generations to enter highly skilled sectors, particularly in labor markets characterized by long-term contracts and stable employment positions (Lutz et al. 2019).

Extended life expectancy may lead to longer working lives, which, in closed and stable industries, can reduce opportunities for the entry of younger talent and diminish innovation dynamics (Aghion et al. 2017). This issue is particularly pronounced in academic and research sectors, where entrenched hierarchies and limited mobility hinder the flow of new researchers into key positions. Given that female life expectancy has the strongest negative effect on employment in the science and technology sector, it is possible that women, despite living longer, encounter more barriers in advancing and maintaining careers within innovative industries (Goldin 2014). Gender differences in work patterns, career interruptions, and lower rates of reintegration after extended breaks may contribute to this effect.

Population aging can negatively affect regional innovativeness due to a decline in entrepreneurial activity and a lower propensity among older workers to adopt new technologies and working methods (Bloom et al. 2010). This trend may contribute to reduced research productivity and slower technological progress in certain regions. The results highlight the need for policies aimed at mitigating the negative effects of demographic changes on innovation. Such policies should include: fostering intergenerational mobility through mentorship and knowledge transfer programs between older and younger researchers (OECD 2020); creating more flexible labor markets in the science and technology sector to enhance adaptability in employment and facilitate the creation of new jobs (Lutz et al. 2019); and supporting women in STEM sectors, including tailored career pathways and return-to-work initiatives after extended career breaks (Goldin 2014).

These findings confirm the importance of demographic factors in shaping the innovative potential of regions and suggest that economic policies must consider the long-term implications of extended life expectancy and changes in labor force structure within the science and technology sectors.

Table 5. Regression Analysis: High-Technology Sector Employment.

Variable/Model	Model 1	Model 2	Model 3
Life expectancy	1.73 *** (0.28)
Male life expectancy		1.39 *** (0.23)
Female life expectancy			1.95 *** (0.35)
Fertility rate	3.68 (3.45)	3.04 (3.44)	3.48 (3.53)
C	−97.89 ** (24.55)	−66.66 *** (20.04)	−121.16 *** (31.27)
R2	0.1907	0.1841	0.1612
F	19.56 ***	18.73 ***	15.95 ***

Note: *** p < 0.01; ** p < 0.05. Independent variables were used for the period t − 1.

presents the results of the regression analysis, specifically the effects of life expectancy and fertility rates on employment in the high-technology sector across EU NUTS2 regions. The results show that life expectancy is a statistically significant positive predictor of employment in the high-technology sector, with male life expectancy exhibiting a smaller statistically positive effect (Model 2c) compared to overall life expectancy (Model 1c) and female life expectancy (Model 3c). Fertility rates are not a statistically significant predictor of employment in the high-technology sector.

This suggests that longer life expectancy may contribute to higher employment in the high-technology sector, particularly through the accumulation of knowledge and experience among older workers, thereby increasing productivity and innovativeness (Bloom et al. 2010). Extended life expectancy enables workers to remain in highly skilled occupations for longer periods, which may enhance employment levels in the high-technology sector. Longer working lives allow for the gradual accumulation of skills and knowledge, which is crucial for technological innovation (Goldin 2014). Female life expectancy has a stronger positive effect compared to male life expectancy, which may be attributed to the increased participation of women in highly educated sectors and their growing presence in STEM professions (Goldin 2014). This trend may contribute to the diversification and strengthening of innovation capacities within high-technology industries.

These findings indicate that demographic changes do not necessarily present a barrier to innovation and economic growth but may serve as an advantage if accompanied by appropriate education and employment policies. Life expectancy, particularly among women, has a positive impact on employment in high-technology sectors, while fertility rates have no significant effect, suggesting that the key factors shaping innovative industries are related to human capital and technological development.

The empirical analysis indicates a significant impact of demographic factors on regional economic disparities within the European Union. The research findings show that the educational structure of the population, particularly within the science and technology sectors, has a positive effect on regional economic growth and innovativeness. Regions with a higher share of highly educated employees in the science and technology sector record higher GDP levels and faster economic growth, confirming the crucial role of human capital in fostering productivity and competitiveness.

On the other hand, life expectancy has a negative effect on GDP growth rates, which may be attributed to demographic ageing and a reduction in the working-age population. Extended life expectancy can lead to higher social and healthcare costs while simultaneously reducing the number of young individuals entering the labor market, potentially slowing economic growth. However, the analysis of employment in the high-technology sector shows that longer life expectancy, particularly among women, may contribute to higher employment in this sector, indicating the potential to utilize the experience and accumulated knowledge of older workers.

Although fertility rates did not have a statistically significant effect on economic growth, they remain an important factor in the long-term maintenance of the labor force. Their effects depend on additional factors such as education policies, labor market dynamics, and conditions for balancing work and family life. These results imply that policies aimed at promoting education in STEM fields, extending the working activity of older workers, and increasing labor market flexibility can contribute to reducing regional disparities and supporting long-term economic development within the EU.

Additionally, investments in innovation and technological development, as well as improving employment conditions for women in science and technology sectors, may enhance the economic resilience of regions in the face of demographic challenges.

4. Discussion

The empirical analysis conducted in this study underscores the significant impact of demographic factors on regional economic disparities within the European Union. The findings reveal that the educational structure of the population, particularly in science and technology sectors, positively influences economic growth and regional innovativeness. Regions with a higher share of highly educated employees in science and technology sectors exhibit higher GDP and faster economic growth, confirming the critical role of human capital in fostering productivity and competitiveness.

These results align with the theoretical expectations regarding the role of human capital in promoting regional development, where a skilled workforce in STEM fields contributes to innovation, technological advancement, and economic dynamism. However, the findings also indicate that life expectancy has a negative effect on GDP growth rates across regions, which may be explained by population ageing and the resulting reduction in the working-age population. Extended life expectancy can increase social and healthcare costs while reducing the inflow of young workers into the labor market, potentially slowing economic growth. The negative association between life expectancy and economic growth should be interpreted cautiously, as it may partly reflect endogeneity. More developed regions tend to have both higher life expectancy and lower growth rates due to convergence dynamics, rather than life expectancy directly slowing growth. Future studies could address such endogeneity using methods like simultaneous equations or instrumental variables. For example, countries with lower life expectancy, younger populations, and lower GDP per capita, such as many in Africa, often experience higher growth rates despite poorer health outcomes. This illustrates that faster growth in regions with lower life expectancy does not imply better wellbeing, highlighting the need for cautious interpretation. This finding is consistent with the notion that demographic ageing can impose structural challenges on economic systems, particularly in regions already facing low fertility rates and a shrinking labor force.

It should be noted that the negative association between life expectancy and GDP growth likely reflects convergence dynamics, where more prosperous regions with higher life expectancy naturally grow more slowly, as well as demographic ageing, which reduces labor force participation. Life expectancy itself is not a barrier to growth; rather, it is the age structure and workforce composition that pose challenges. Policies focusing on active ageing, extending working lives, and adapting labor markets are essential to mitigate these effects.

In contrast, the analysis of employment in high-technology sectors suggests that longer life expectancy, particularly among women, may contribute to higher employment in these sectors. This indicates that extended working lives can enable the utilization of accumulated knowledge and experience of older workers, which can positively affect productivity and innovation within high-technology industries. This finding highlights the potential benefits of leveraging demographic trends through active ageing policies and lifelong learning initiatives to sustain and enhance regional competitiveness.

Another important factor not included in the current specification is migration. Both internal and international migration flows strongly influence regional growth by affecting the size and age composition of the labor force. Regions with net out-migration, especially of younger cohorts, may experience slower growth and accelerated ageing. Metropolitan centers with net in-migration can sustain high growth despite low fertility and high life expectancy. Omitting migration may bias the estimated effects of demographic variables and overstate the negative impact of ageing.

Although fertility rates did not demonstrate a statistically significant impact on economic growth within the analysis, fertility remains a relevant factor for maintaining the labor force in the long term. The effects of fertility on economic outcomes are likely mediated by additional factors such as educational policies, labor market structures, and the availability of work–life balance measures, which can influence the extent to which higher fertility translates into increased economic activity.

While fertility rates did not show a statistically significant effect on short-term economic growth, they remain important for the long-term sustainability of the labor force. Fertility outcomes are largely influenced by economic development, educational policies, labor market conditions, and family support measures, which in turn shape their potential impact on growth over time.

These results suggest that policies aimed at promoting education in STEM fields, extending the working activity of older workers, and increasing labor market flexibility can contribute to reducing regional disparities and fostering long-term economic development within the EU. Furthermore, investments in innovation and technological development, alongside improving conditions for women’s employment in science and technology sectors, may strengthen regional resilience to demographic challenges and support sustainable growth.

Future research could expand on these findings by exploring the dynamics of these relationships using panel data analyses to capture temporal changes and regional heterogeneity within the EU more effectively. Additionally, examining the interplay between migration flows, demographic structures, and regional innovation capacities would provide a more comprehensive understanding of the drivers of regional economic convergence and divergence. Considering the ongoing digital transformation, further investigation into how digital skills and technological readiness interact with demographic factors to shape regional economic performance would also represent a valuable avenue for future studies.

5. Conclusions

This study highlights the significant influence of demographic factors on regional economic disparities within the European Union. The results clearly show that regions with a higher share of highly educated workers and stronger employment in high-tech sectors tend to experience faster economic growth and greater competitiveness. This confirms the vital role that human capital plays in driving innovation and productivity. Longer life expectancy, while reflecting improved health and wellbeing, is associated with demographic ageing and a reduction in labor force participation, which can slow economic growth in regions with declining working-age populations. Life expectancy itself is not a barrier to growth; policies should focus on active ageing and extending working lives. This underlines the challenges faced by social protection systems and the economy as a whole in adapting to an aging population. Policies aimed at encouraging active aging, extending working life, and creating more flexible labor markets are therefore crucial.

Fertility rates did not have a statistically significant impact on short-term growth. Although fertility rates do not significantly affect short-term growth, their long-term implications for maintaining the labor force are important. Supportive family policies, childcare, and flexible working conditions remain crucial for sustaining future skilled labor supply.

The study points to the concentration of economic activity and innovation in more developed urban regions, which deepens the divide with rural and less developed areas. To address this, targeted investments in infrastructure, digitalization, and regional development are needed to foster more balanced growth and reduce inequalities.

The findings suggest that demographic trends present both challenges and opportunities. A coordinated policy approach that integrates education, innovation, labor market reforms, and social support is key to enhancing regional resilience and fostering sustainable economic development across the EU. Further research exploring the interplay between demographic changes, labor market dynamics, and technological progress would help to refine these strategies.

Overall, coordinated policies integrating education, innovation, labor market flexibility, active ageing, and family support are essential to enhance regional resilience and promote sustainable economic development across the EU.