Population Aging and Economic Competitiveness in Polish Small Towns

Abstract

The aim of this study is to identify relationships between population aging in small towns and the level of their economic competitiveness. These analyses are a continuation of research on the economic competitiveness of small town in Poland. The territorial scope comprised 110 small towns, while the timeframe covered the years 2004–2006, 2011–2013, 2017–2019, and 2020–2021. In this paper, the Perkal method was applied to construct a synthetic measure for the progression of the population aging process in the case of residents of small towns. In turn, the typology method was used to identify types of dependencies between the level of economic competitiveness and the degree of population aging for residents of small towns. In view of the advanced aging of inhabitants in the analyzed towns within the framework of the distinguished types, in the most recent investigated periods, most small towns were assigned to two types. They were the types comprising economically competitive towns, but with aging populations, and economically uncompetitive and aging towns, respectively.

1. Introduction

Population aging is a global phenomenon, systematically progressing since the early 1900s. Until the end of the 19th century, the process of passing through successive life phases was not accompanied by any significant changes in terms of the age structure of the population. The rate of changes in the process of population aging varies depending on the part of the world, countries, or regions. At present, Europe is considered to be the continent with the oldest population, with Poland leading among the European countries in terms of the most rapid aging of its population [1].

Population aging is a result of demographic changes related with a lower mortality rate and a decline in the rate of natural increase [2,3]. As it was reported by Maier [4], a change has occurred in Poland in the reproduction of the population, with a shift from the traditional, high rate of population replacement to one approaching a negative population growth. The process of population aging in Poland has been evident particularly since the 1990s, although the first symptoms (while still slight) were observed already in the interwar period, similarly as it was in other Western countries. In the 1960s, this process accelerated in rural areas, particularly due to the mass migration of the young generation. Starting from the late 1980s, among other things, as a result of the declining migration of young people and decreasing birth rates, the problem of population aging has also affected cities and towns [5]. In the early 21st century, individuals in the post-working age (aged 60 years and older in the case of women and aged 65 and older for men) accounted for slightly less than 15% of the total population, while according to the data for 2023, this percentage increased to 23% [6]. Forecasts indicate that by 2050, the share of the elderly in the population structure is to go up to as much as 34%. In terms of the place of residence, over 60% of elderly people live in cities and towns. As indicated by Janiszewska [7], in the case of urban areas, the largest Polish cities are characterized by the most advanced population aging process. However, small and medium-sized towns are also affected by this problem, particularly since they are more susceptible to changes and adverse phenomena in their socio-economic structure.

Demographic processes in Poland vary in their intensity depending on the local and regional conditions, or the urban–rural relationships. Observed demographic trends are connected with a decrease in the population size and the progressing process of population aging [8]. In the case of many areas, population aging is additionally exacerbated by depopulation (especially the decline in the number of young people). The primary factor leading to depopulation is connected with the lack of adaptation of the local economic base to the requirements of the free-market economy and competition from larger entities and industrial areas [9]. Among other things, this was a consequence of the fundamental changes in the political system, and the restructuring and transformation of all the sectors of the national economy in the 1990s. Those centers, which successfully underwent the transformation processes driving their development, have attracted new residents. These areas, to a lesser extent, suffer from population aging [10].

The aging of urban populations may be related with reduced resources of socio-economic services (urban shrinkage) or the decline in economic attractiveness [11]. This poses a threat to the availability of services for local residents and the city environs in the future, as well as generates challenges for urban development.

Demographic changes can present opportunities or barriers to achieving sustainability. The aging population of small towns is definitely a barrier and a challenge towards achieving sustainability. It leads to adverse consequences in social and economic aspects. Consequently, it may result in the loss of socio-economic functions, which is challenging to regain.

The presented study is a continuation of research conducted on small towns. The scope of earlier analyses comprised an evaluation of the level of economic competitiveness of small towns [12]. The next step, the results of which are presented in this paper, comprises the identification of relationships between the level of economic competitiveness and the progression of population aging in small towns, which is the aim of this study.

Such a formulated aim of this study constituted the basis for the verification of the following research hypothesis: small towns increase their economic competitiveness despite the progressing aging of their populations.

Within the above-mentioned aim of the presented investigations, two research tasks were distinguished:

−

Constructing a synthetic measure for the progression of aging processes among inhabitants of small towns;

−

Identifying types of relationships between the level of economic competitiveness and the measure of population aging for inhabitants of small towns.

Individual parts of this paper, following the introduction in Section 1, present a review of literature in the area of studies on population aging and the economic dimension of development, growth, and competitiveness. Section 3 discusses the materials and methods. Section 4 is devoted to the analysis of results, while Section 5 and Section 6 provide a discussion and concluding remarks.

2. Population Aging and the Economic Dimension of Competitiveness

It needs to be stressed that economic growth is a major factor determining demographic processes [3]. Thus, demography and economic growth are interdependent, being affected by complex feedback relationships, which are very difficult to quantify.

The scientific literature on the subject presents examples of studies investigating population aging as related to socio-economic growth. Maestas et al. [13] evaluated the flexibility in the increase in GDP per capita in relation to population aging. The analyses showed that the rapid aging of the US population will slow down economic growth (an increase in the percentage of individuals aged 60 years and older by 10% leads to a decrease in GDP per capita by 5.5%).

In turn, Gao et al. [14] investigated the effect of population aging on the economic development of 31 provinces in China. Empirical results showed that population aging has a significant effect (positive and negative) on the constructed index of economic development, and it shows regional heterogeneity. Similar conclusions were drawn in the study by Liang et al. [15], who also analyzed selected provinces of China in terms of the relationships between economic development and the aging of populations in the investigated regions.

Research conducted by Temsumrit [16] focused on the analysis of the effect of population aging on the allocation of government spending in various categories and changes in economic growth. Findings of Temsumrit concerning 89 countries suggest that an increase in the elderly population results in significantly higher total government spending, but only in developed countries, particularly in such categories as social and environmental protection.

Thanh Trong et al. [17] evaluated the impact of population aging on economic growth in selected ASEAN countries. Their results showed a definite negative dependence of the percentage of the elderly on an increase in GDP per capita, which is connected with the growing demand for expenditure on social insurance, social security, and healthcare.

An analysis of relationships between population aging and economic growth was also the aim of studies by Duc [18]. Results from 89 developing countries confirmed a negative effect on economic growth within a short-term perspective. However, in the long run, there is a positive relationship between the share of people aged 65 years and older and economic outcomes.

Investigations conducted by Bloom et al. [19] on OECD countries suggest that these countries probably record small—but not catastrophic—reductions in the rate of economic growth. Those authors indicated that behavioral responses (including a greater share of women in the workforce) and political reforms (including an increase in the legal retirement age) may mitigate the economic consequences resulting from the older population.

Lay and Yip [20] also investigated the problem of population aging and economic growth in the international context. Their analysis of results from 74 countries showed a negative relationship between population aging and economic growth. This dependence was mitigated by the share of older people in the workforce.

Buresch et al. [21] examined how population change is associated with changes in socio-demographics and economic outcomes across diverse geographic contexts in the United States. The results of this study indicated that areas experiencing population growth generally saw favorable increases in socio-economic indicators such as education levels and income, while areas with population decline tended to see increases in poverty rates and aging populations.

The problem of population aging is considered in the context of its effect on carbon dioxide emissions. Studies on this subject were conducted, e.g., by Wang and Wang [22] and Zhang et al. [23]. Among other things, population aging brought about changes in the structure of public consumption, also affecting the level of carbon dioxide emissions. Investigations by Zhang et al. [23] concerning 30 Chinese provinces and cities confirmed the hypothesis that an increase in the percentage of elderly people leads to growing CO₂ emissions.

The economic aspect of the development and competitiveness of cities (among other things) is connected with enhanced prosperity, an improved standard of living for their residents through more accessible technical and social infrastructure, attracting new investments, promoting social and economic activity of local residents, supporting housing development, as well as protecting the natural environment [24]. Thus, it comprises this aspect, which is reflected in other structures ensuring the proper functioning of cities. On the other hand, demographic processes influence the economic structure. They may affect labor resources, tax revenue, expenditure on social assistance, and innovativeness [25,26,27,28]. In general, demographic change, in its unfavorable scenario, leads to a decline in the productivity of the economy [29] and the competitiveness of areas [30].

3. Materials and Methods

This study focuses on small towns in Poland. Analyses were conducted on 110 small municipal centers, which at the earlier stage of research were evaluated in terms of their level of economic competitiveness. There were more than 700 small towns in Poland during the periods analyzed. Each of them constitutes an urban commune (municipality with the status of a city). The reason for selecting this group is that extending this study to towns which form part of an urban–rural commune would pose some restrictions and distort the analyses. To ensure an appropriate comparability of results, the same time frame was adopted for the conducted investigations, i.e., the years 2004–2021. Four time series objects were constructed, thus facilitating a comparison of temporal changes: 2004–2006, 2011–2013, 2017–2019, and 2020–2021. Due to the COVID-19 pandemic, which remodeled the socio-economic structure of small towns, the last of the above-mentioned study periods was distinguished as particularly interesting. The pandemic also disturbed the demographic structure as a result of numerous excess deaths. That period investigated in this study will provide data to verify whether the tragic consequences of the COVID-19 pandemic may have altered the rate of population aging in small towns.

Empirical data collected from the four adopted periods of analyses were averaged. Quantitative data used in this study were obtained from the Local Data Bank (Polish: Bank Danych Lokalnych) of Statistics Poland (formerly the Central Statistical Office of Poland, Polish: Główny Urząd Statystyczny, GUS; referred to as BDL, GUS).

The linear ordering of small towns in terms of the degree of population aging was based on the synthetic Perkal index [31]. This indicator is extensively used in research, as evidenced by studies of, e.g., Męczyński et al. [32], Konecka-Szydłowska [33], Uglis [34], Kruk and Waśniewska [35], Revko et al. [36], and Kulawiak [37].

In this study, the investigated characteristics proved to be measurable, comprehensive, and available (

Table 1. A list of variables proposed for the construction of a measure of population aging in small towns.

No.	Indicator	Definition	Source	Variable Nature
X1	old-age rate	The percentage of individuals of post-working age (60 years and older in the case of women and 65 years and older for men) in the total population (%)	[33,38,39,40]	D
X2	old-age dependency ratio	The ratio of individuals of post-working age per 100 individuals of working age (persons)	[8,10,15,34,41]	D
X3	parent support ratio	The number of individuals aged 85 and older per 100 individuals aged 50–64 years (persons)	[41]	D
X4	Potential support ratio (inverse old-age dependency ratio)	The ratio of working-age individuals to individuals of post-working age (persons)	[42]	S
X5	Dependency ratio	The ratio of the number of children (aged 0–14 years) and older people (aged 65 years and older) to the number of individuals aged 15–64 years (persons/100 working-age people)	[10,12,43]	D

S—stimulant, D—destimulant. Source: the author’s study.

); thus, the formal prerequisites for the selection of diagnostic variables were satisfied.

Considering statistical criteria, quasi-constant variables (the coefficient of variation ≤ 10%) and excessively correlated variables (values markedly exceeding 10) were excluded from the set of variables. In view of the above criteria, final diagnostic variables were selected, which were then used to construct the synthetic index for population aging in small towns. The set of characteristics did not contain variables exhibiting low variability. In turn, because of the high level of correlation with the other characteristics, X2 was excluded from further analyses.

The next step comprised the standardization of the characteristics, which is a normalization method. As a result of standardization, all variables attain dimensionless measures of variability. Moreover, following the standardization process, all variables are uniform in terms of the central tendency and exhibit the same degree of variation [44].

In view of the effect of the diagnostic variable on the investigated phenomenon, two types of variables were distinguished, i.e., stimulants and destimulants. A characteristic, in which greater values indicated a higher development level, was classified as a stimulant (S); in contrast, if a lower value of the variable is advantageous for the investigated phenomenon, the characteristic was termed a destimulant (D). In this study, characteristic X4 was found to be a stimulant, while the other characteristics were classified as destimulants.

In view of the above, characteristics being stimulants were standardized based on the following formula:

$${\mathit{y}}_{\mathit{i}\mathit{j}}={\displaystyle \frac{{\mathit{x}}_{\mathit{i}\mathit{j}}-\overline{\mathit{x}}}{{\mathit{S}}_{\mathit{j}}}}$$

(1)

In turn, for destimulants, values of the indicators were standardized by applying the following formula:

$${\mathit{y}}_{\mathit{i}\mathit{j}}={\displaystyle \frac{\overline{\mathit{x}}-{\mathit{x}}_{\mathit{i}\mathit{j}}}{{\mathit{S}}_{\mathit{j}}}}$$

(2)

where

y_ij—standardized value of the j-th characteristic for the i-th object;

x_ij—value of the j-th characteristic for the i-th object;

$\overline{\mathit{x}}$—arithmetic mean of values of the j-th characteristic;

S_j—standard deviation of values of the j-th characteristic.

The standardization of indicator values provided a matrix of standardized variables [37], which facilitated the ordering of small towns in terms of the degree of their population aging with the use of the following formula:

$${\mathit{W}}_{\mathit{P}\mathit{A}}={\displaystyle \frac{{\sum }_{\mathit{j}=1}^{\mathit{p}}{\mathit{y}}_{\mathit{i}\mathit{j}}}{\mathit{p}}}$$

(3)

where

W_PA—synthetic indicator;

j = 1,2 … p;

p—number of considered characteristics;

y_ij—standardized value of the j-th characteristic for the i-th object.

Values of the synthetic measure established applying the Perkal method assume values from −3 to 3 [36]. A higher value of the measure indicates a greater degree of the analyzed phenomenon. In the case of this study, a higher level of the measure denotes a lower progression of population aging in small towns; conversely, the lower the value of the measure, the greater the progression of population aging observed for small town residents.

Linearly ordered values of the synthetic measure of population aging in small towns were the basis for the ordering of small towns in relation to the level of their economic competitiveness. The synthetic measure for the level of economic competitiveness of small towns was constructed and described in a study by Józefowicz [12].

Coordinates for each investigated object—a small town, in relation to the values of the synthetic measure of the level of economic competitiveness (S_i^E) and the measure of population aging (W_PA), were calculated according to the formulas below:

• For economic competitiveness of small towns, the formula is as follows:

WE_i = S_i^E − IE

(4)

• For the measure of population aging in small towns, the formula is as follows:

WPA_i = W_PA − IPA

(5)

where

S_i^E—values of the synthetic measure for the level of economic competitiveness of small towns;

WPA—values of the synthetic measures for population aging in small towns;

IE—median of the synthetic measure for the level of economic competitiveness of small towns;

IPA—median of the synthetic measure for population aging in small towns.

Based on values of the coordinates, a specific type may be identified in small towns by ordering towns based on economic advantages and the measure of population aging in small towns.

As a result, four types of small towns were distinguished depending on the relationship observed between the economic competitiveness of small towns and the measure of population aging in those small towns (

Table 2. Types of small towns depending on the relationship between values of the measure of economic competitiveness and population aging.

Type	Aspect		Types of Small Towns
	E	PA
I	+	+	Economically competitive and relatively young	A high level of economic competitiveness and a low progression of population aging
II	+	−	Economically competitive and aging	A high level of economic competitiveness and a high progression of population aging
III	−	+	Economically uncompetitive and relatively young	A lower level of economic competitiveness and a low progression of population aging
IV	−	−	Economically uncompetitive and aging	A lower level of economic competitiveness and a high progression of population aging

Legend: E—economic competitiveness, PA—measure of population aging. Sign (+) denotes that the coordinate reached a positive value, sign (−) denotes that the coordinate reached a negative value. Source: the author’s study.

).

Type I: economically competitive and relatively young—represented by small towns with a high level of economic competitiveness and a lesser progression of population aging. This type comprises small towns for which a value greater than the median was observed for the synthetic measure of economic competitiveness, and the value of the synthetic measure of population aging in small towns exceeded the median.

Type II: economically competitive and aging—comprises small towns in which a high level of economic competitiveness is observed, while values of the measure of population aging was below the median (i.e., a high progression of population aging for their residents).

Type III: economically uncompetitive and relatively young—represented by investigated entities with the value of the economic competitiveness measure lower than the median, and a lesser rate in population aging progression (values of the measure above the median).

Type IV: economically uncompetitive and aging—refers to a group of small towns with a lower level of economic competitiveness and a strong progression of population aging. This type included small towns for which values of the measures for the level of economic competitiveness, and population aging in those towns was below the medians.

4. Results

4.1. Measure of Population Aging in Polish Small Towns

The investigated towns exhibited high diversity in terms of values of the measure of population aging progression (the coefficient of variation exceeded 50% in the analyzed periods). In the years 2004–2006, the median of values for this measure amounted to 1.075, while in the years 2017–2019, it was −0.536 (−0.972 in the period of 2020–2021). The synthetic indicator in the years of the study ranged from −2.692 to 2.361. Due to the high variability of this index, the small towns were classified into six types (

Table 3. Typological classes of aging measure of small towns in Poland in 2004–2006, 2011–2013, 2017–2019, and 2020–2021.

Type	Stage of Population Aging	Values of Metrics	2004–2006		2011–2013		2017–2019		2020–2021
			N	%	N	%	N	%	N	%
I	very low	above 2.000	2	1.8	0	0.0	0	0.0	0	0.0
II	low	1.001 to 2.000	58	52.7	11	10.0	0	0.0	0	0.0
III	medium-low	0 to 1.000	46	41.8	86	78.2	13	11.8	3	2.7
IV	medium-high	−0.999 to 0	4	3.6	13	11.8	82	74.5	54	49.1
V	high	−2.000 to −0.100	0	0.0	0	0.0	14	12.7	48	43.6
VI	very high	below −2.000	0	0.0	0	0.0	1	0.9	5	4.5

N—number of small towns. Source: the author’s study based on the Local Data Bank of the Central Statistical Office [6].

). Type I comprises towns with a very low rate of population aging, whereas type VI represented small towns with a very high progression of population aging.

In the years 2004–2006, most investigated towns were classified as types II and III, which means that the analyzed small towns were characterized by a medium-low and low progression of population aging. In the successive periods, the situation took a less advantageous turn, as the percentage of small towns classified as types IV, V, and VI was increasing markedly. In the years 2017–2019, over 80% small towns were assigned to the type representing a greater progression of population aging. Among the analyzed small towns, there was none in which a reduction in aging was observed. This was the reason why many towns in each successive period of the study were included in the type representing the greater progression of population aging among their residents. In the years 2011–2013, compared to the previous period (2004–2006), 55% of the investigated small towns were shifted in their ordering towards the type with a more advanced degree of population aging. In turn, in the years 2017–2019, in comparison to the period of 2011–2013, this percentage increased to 84% (93 small towns).

The years 2020–2021 covered the period of the COVID-19 pandemic, which is why the results concerning the analyzed phenomenon need to be interpreted with caution. Demographic consequences of the pandemic manifested in numerous excess deaths of individuals of various ages, resulting in a distortion of the results for that period. Thus, the considerable increase in the percentage and the resulting dominance of small towns classified to types IV and V was not solely the consequence of the demographic transition discussed in the literature on the subject.

The regional diversification in the measure of population aging does not provide definite observations concerning differences in the progression of population aging in small towns in individual parts of the country (Figure 1). Their location close to cities (urban centers over 100 thousand inhabitants) makes it difficult to draw conclusions about whether the vicinity of the largest cities may lead to the population rejuvenation in neighboring small towns. Communes located in the vicinity of cities frequently serve the role of commuter belts, as a result the percentage of working-age inhabitants is much higher.

A study conducted by Kamińska and Ossowski [45], who constructed an indicator of demographic aging in rural areas of Poland, showed the spatial diversification for this phenomenon. Rural areas in eastern Poland, as well as the Śląskie and Opolskie voivodeships, were distinguished as areas with high levels of population aging. Similar results were given in the study by Rosner [46] for the constructed metric of the demographic structure. In the investigations conducted on small towns, in the first study period (2004–2006), a numerous concentration of small towns from southern Poland (the Śląskie and Dolnośląskie voivodeships) was already observed within type III; these towns were then identified as towns with a greater progression of population aging processes. In the successive periods, a definite acceleration of population aging practically eliminated any marked regional differences.

As indicated by Sadowy [25], negative demographic changes have particularly affected medium and small towns. Among other things, these changes are typically connected with a decrease in population size, accompanied by migration (especially educated young people), or a change in the age structure of the population. The most important determinants for the diversification of the demographic situation include suburbanization processes, the economic profile, as well as functions served by individual small towns.

4.2. Population Aging of Small Towns and the Level of Economic Competitiveness

Small towns were classified into four types based on similar relationships between the level of economic competitiveness and the degree of their population aging (

Table 4. Classification of small towns depending on types of relationships between economic competitiveness and population aging.

Type	Aspect		Types of Small Towns	Years
	E	PA		2004–2006	2011–2013	2017–2019	2020–2021
I	+	+	Economically competitive and relatively young	31.8%	40.0%	6.4%	1.8%
II	+	−	Economically competitive and aging	1.8%	6.4%	52.7%	60.0%
III	−	+	Economically uncompetitive and relatively young	64.5%	48.2%	3.6%	0.9%
IV	−	−	Economically uncompetitive and aging	1.8%	5.5%	37.3%	37.3%

Legend: E—economic competitiveness, PA—measure of population aging. Sign (+) denotes that the coordinate reached a positive value, sign (−) denotes that the coordinate reached a negative value. Source: the author’s elaboration based on her studies.

).

Type I towns in the years 2004–2006 and 2011–2013 was found in over one-third of the small towns. In the successive periods, the percentage of economically competitive towns and a lesser degree of population aging dropped drastically. Only one town was classified as type I during all the periods of analyses. Over a half of type I small towns in the years 2004–2006 were again classified as such in the years 2011–2013. Type I towns comprised small towns in which a high level of economic competitiveness (classes I and II of economic competitiveness) was mainly observed [12]. In the years 2004–2006 and 2011–2013, this type included small towns with a less advanced rate of population aging. As a consequence of adverse demographic processes in the third and fourth periods of analyses, the share of type I towns dropped several percent.

Type II towns in the years 2004–2006 and 2011–2013 comprised less than 10% of the investigated towns. In the successive periods, in over a half of the analyzed towns, an advantageous situation was found in terms of economic competitiveness at values of the population aging metric above the median. Thus, in the years 2017–2019, the percentage of towns of such a type increased to over 50%. Type II towns consisted of economically competitive small towns (economic competitiveness classes I and II) [12], which at the same time are struggling with a strong progression of population aging processes (types IV, V, and VI). In the first two periods, 64.5% and 48.2% of the investigated small towns were classified as type III. A low progression of population aging and the level of economic competitiveness below the average were observed in those towns. In the next two periods (i.e., the years 2017–2019 and 2020–2021), their share dropped to 3.6% and 0.9%, respectively.

In the years 2004–2006 and 2011–2013, type III towns consisted of small towns with a lesser economic competitiveness (classes III and IV of economic competitiveness of small towns) [12]. In those towns, a low degree of population aging progression was recorded (types II and III). In the successive periods of analyses, the percentage of type III towns decreased considerably, since in many small towns an improved economic competitiveness was observed at the simultaneous progression of population aging in those towns. As a result, it was connected with the shift to type II towns in the years 2017–2019 and 2020–2021.

In the periods of 2017–2019 and 2020–2021, in over one-third of small towns, a lower level of competitiveness and a high progression of population aging were observed (type IV). In the periods of analyses, only one investigated town was consistently classified as type IV (Brańsk). The group of type IV towns expanded greatly in the third and fourth research periods, which was caused by the acceleration of population aging (types IV and V), rather than a deterioration in their economic competitiveness. This type consisted of small towns with a low level of economic competitiveness (classes III and IV) [12]. It needs to be stressed here that in those towns, the rate of changes manifested in the improved values of the synthetic measure of economic competitiveness was very low.

Two major directions of changes were observed within the distinguished types of relationships between the level of economic competitiveness and the index of population aging in small towns. One of these trends was connected with the shift between types I and II. This was mainly connected with the maintenance or improvement in the level of economic competitiveness at the simultaneous increasing progression of the population aging process. It seems crucial that towns classified as type II, despite adverse population aging processes, were able to maintain their high competitive position.

The other direction of changes, manifested within the distinguished groups, consisted in the shift between types III and IV. In this case, already in the first period of analyses, a weak competitive position of small towns was identified, and it did not change markedly within the successive period of over 15 years. Then, the situation for the measure of population aging in these towns was similar to that in towns with a better competitive position. In view of the conducted research, no definite direction of demographic changes could be considered as the primary cause for the weak competitive position. The attained level of economic competitiveness is influenced by several diverse factors, with the demographic situation being one of many determinants.

The regional diversification of small towns in terms of the types of relationships between economic competitiveness and the degree of population aging in those towns showed that in the years 2004–2006, most small towns in eastern Poland and in the Dolnośląskie voivodeship represented type III towns (Figure 2). This is understandable, since many of the investigated towns in that part of Poland have a weak competitive position [12]. In the successive period, those towns were transformed into types II and IV.

Generally, vast areas in eastern Poland are at risk of permanent marginalization [47], being of a lower level of socio-economic development [48]. In those regions, a slower rate of changes affects towns, also those medium-sized, which are identified as entities losing their socio-economic functions [47]. Moreover, regional demographic forecasts for Poland indicate that along with depopulation, the process of population aging will accelerate and will exhibit considerable spatial diversification. Areas in central and eastern Poland will have the oldest populations [49]. Thus, the importance of small towns in the context of competitive local centers in terms of their economic role will be particularly crucial in those areas. The conducted analysis divided the investigated towns in that part of Poland in the third and fourth periods of analyses into small towns of type II (economically competitive and aging) and type IV (economically uncompetitive and aging). This means that some towns, despite demographic barriers to their development, are capable of generating a relatively advantageous competitive position.

In turn, the investigated towns located in the Wielkopolskie and Zachodniopomorskie voivodeships, to a considerable extent, are the areas where the accelerated rate of population aging did not prevent them from improving their economic competitiveness (they reached type II in the last two investigated periods) (Figure 2). Small towns located in those voivodeships were characterized by a strong competitive position already in the initial periods of these analyses [12].

5. Discussion

Small towns located in the vicinity of cities were classified as the types exhibiting high economic competitiveness, despite adverse changes related to the aging of their populations (type II in the years 2017–2019 and 2020–2021). During the earlier periods, those towns typically represented type I towns. Cities are regional urban centers; consequently, surrounding areas also benefit from their development rate and structure. This is also the case with small towns. It may be assumed that their location in relation to cities provides them with a certain resilience against negative demographic changes, which as a consequence, will not greatly disturb their development processes. Despite the fact that demographic forecasts indicate that intensive population aging processes will also affect suburban areas and the vicinity of the largest cities, they will still be considered as having the relatively youngest population in comparison to the other parts of the country [49]. The greater capacity to generate potential demographic resilience to changes in areas located in the vicinity of cities has been reported, e.g., in Romania [27], in USA [50], or Italy [51], and in the opinion of those authors, this is consistent with the trends for the concentration of metropolitan populations.

A slightly different view in this area of the relation of the development of large cities to the surroundings is presented by Fothergill and Houston [52]. The causality in terms of economic growth does not necessarily run only from cities to their hinterlands. The prosperity and growth of the hinterland is itself likely to be a key driver of growth in the city. In turn, Jones et al. [53], based on empirical evidence from the UK, developed a typology of relationships between small towns and their larger city neighbours. This opens the discourse to in-depth analysis in this context. The city size–development relationship is a complex and ambiguous phenomenon [54]. Demographic changes, including, among other things, an aging population, reveal that demographic trends favor urban locations to varying degrees (Turok, Mykhnenko [55] elaborate on these issues). The achieved level of development competitiveness is the result of resilience to these changes. The conducted research represents a certain stage of analysis of this issue.

In the scientific literature, it is indicated that population growth stimulates the economic development and diversification of towns [56]. However, it is also essential to understand whether, despite population decline and population aging, it is possible to improve the level of competitiveness and development of countries, cities, and rural areas. Studies have shown that it is possible, although this is connected with a decrease in the growth rate and economic development. This is evidenced, e.g., by a study of Kotschy and Bloom [57]. Their results indicate that population aging in most countries worldwide will slow down their economic growth. In turn, research conducted by Liu et al. [58] pointed to the fact that population aging inhibits the economic development of Chinese provinces. Focusing on cities, Jedwab et al. [59] showed that mega-cities with higher dependency ratios, that is, with more children and/or seniors per working-age adult, grow significantly slower.

Analyses conducted in this paper provide an observation that most small towns are resistant to the impact of their population aging. So far, this phenomenon is connected with a slowed rate of improvement in the level of economic competitiveness. Similar conclusions were reported in studies on small towns in Romania [60]. Nevertheless, those authors stressed that it is an effect within a short timeframe, and it should not be underestimated.

Population aging in small towns constitutes a challenge in their attempts towards sustainable growth. As it was reported by Blaszke et al. [61], consequences of demographic changes are experienced by both big cities and medium-sized towns, which are growth poles thanks to their numerous education institutions and infrastructure, combined with a high standard of living. However, small towns should also be added to this group. Population aging, combined with the depopulation observed in Poland, may lead to the shrinking of small towns. This phenomenon slows down attempts to attain sustainable development. It also affects the need to remodel the development strategy, particularly in small towns, where progressing population aging accompanies a slow rate of competitiveness and economic growth.

In the functional hierarchy of towns and cities, small towns are first of all local and supralocal centers. They provide basic services including elementary (and sometimes secondary) education, basic healthcare, shops, local cultural institutions, etc. They frequently serve a central role for their immediate vicinity. Nevertheless, the improvement of competitiveness, as well as the analysis and diagnosis of demographic processes, may not be underestimated. Those towns, similar to cities, have to function and adapt to the growing progression of their population aging.

6. Conclusions

Analyses conducted within this study made it possible to verify the proposed research hypothesis, which states that small towns enhance their economic competitiveness despite the increased progression of their population aging.

In the course of the investigations a synthetic measure was constructed for the progression of population aging in small towns. Analyses showed that in the first investigated period (2004–2006), population aging was observed in the analyzed towns; however, the rate of aging was not highly evident. In the successive periods, a marked progression in population aging was shown in small towns. In the course of the realization of the next aim of this study, four types of relationships were observed between the level of economic competitiveness and the measure of population aging in small towns. In this part of the study, clustering was already observed in the first period, first of all in two out of the four proposed types. The type to which small towns were classified, in view of the relationships between the progression of population aging and competitiveness, was to a greater extent determined by the measure of population aging in those small towns. The constructed indicator in the investigated periods showed a more marked rate of change than was the case for changes in the level of economic competitiveness.

In view of the conducted investigations, it may be concluded that this study identified two groups of towns. One of them is the group in which adverse demographic processes did not hinder the improvement in their competitiveness. The other group comprised towns, in which disadvantageous demographic changes may have constituted an obstacle or a barrier to enhancing their economic competitiveness. Thus, population aging needs to be considered a barrier to an improvement in the competitiveness and development of towns (and other entities), which will result in socio-economic disparity. It may also be stated that in the future, the attained level of competitiveness in the case of territorial units will influence the degree of resilience and coping with the growing scale of population aging.

At present, population aging is a common phenomenon observed with varying intensity regardless of the location (city/town, countryside) or region of the world. In view of the global character of this phenomenon, it is essential to completely change our approach to this problem and instead treat population aging as a factor which may contribute to the development and improved competitiveness of towns. This shift in the perception of long-term trends in economic growth will address problems resulting from population aging, which will obviously accumulate in the future.

Population aging in small cities poses a challenge to sustainable development. Despite the observed advancement in the aging of small-town residents, the level of economic competitiveness has increased. Nevertheless, it is important to be aware that the resilience that has been observed in the economic dimension should also be present in the social and environmental dimensions.

Research indicates a negative dependency between population aging and the economic growth and regional development. In this respect, we need to focus on the slowing rate of economic growth or increased expenditure, e.g., on healthcare. In the context of inhibiting the adverse direction of the population aging processes, the importance of increasing professional activity and the labor force participation rate among older people is stressed. At present, the potential of the older people in this respect is not fully used, as indicated, e.g., by the PwC report entitled The Golden Age Index [62]. In that report, the potential labor force participation of individuals aged over 55 years in Poland was assessed as underutilized (Poland ranked 30 among the 35 analyzed OECD countries).

For this reason, it is crucial to appreciate and underline the potential for professional activity presented by older people. Thus, a discussion on the potential of older people for the development of towns was presented by Bartkowiak [39], while challenges for the labor market posed by active aging were reported by Flaszyńska and Męcina [63]. In turn, Richert-Kaźmierska [1] stressed the significance of population aging in regional strategies.

Conducted investigations fill the research gap in the area of relationships between the level of competitiveness of small towns and progression in population aging. The proposed approach is unitary in character and may be applied in analyses of other territorial units. The constructed measure may be modified and include quantitative data available for territorial units concerning population aging and economic competitiveness.

Obtained results may constitute a source of information for authorities of small towns when developing Local Development Strategies. In such strategies, these adverse demographic changes are included in the area of weaknesses and threats. The performed investigations show that this problem needs to be treated more comprehensively and consider negative demographic changes at the stage of planning actions aimed at improving competitiveness and development of small towns.

The conducted investigations have some limitations. They result from the availability of quantitative data in public databases, which in the next stages, facilitate the construction of a synthetic measure. In view of this barrier, the conducted studies need to be partly adapted to available quantitative data.