Keys to mitigating rural depopulation. The case of Castilla-La Mancha (Spain)

Keys to mitigating rural depopulation. The case of Castilla-La Mancha (Spain) | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Keys to mitigating rural depopulation. The case of Castilla-La Mancha (Spain) Germán Andrés Cevallos Meneses, María Carmen García Cortijo, Juan Sebastián Castillo Valero, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6972099/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Rural areas are undergoing a process of depopulation, especially in the smallest rural municipalities that are at great risk of population decline. In addition, they usually have very low population density. These factors determine their socio-economic development and amount to serious obstacles for entrepreneurial initiatives based on demand within the rural territory itself. This study therefore aims to find the main factors for preserving the rural population in Castilla-La Mancha, an eminently rural area. It specifies a Quantile Regression model using data from 2023. The results point to the need for more effective rural policies, for prioritising the role of women and for maintaining basic services. Figures Figure 1 Figure 2 Introduction Depopulation is a phenomenon that significantly affects a territory’s social, economic and environmental characteristics (Christiaanse, 2020; Selod & Shilpi, 2021). In spite of constant population growth, many rural areas are becoming depopulated (Derrible, 2019). Depopulation in rural areas creates a vicious circle that reduces regional vitality (Makkonen & Inkinen, 2023). As the population in such areas decreases, the provision of both public and private services is often cut back because they become more costly and less cost-effective (San-Martín González & Soler-Vaya, 2024). One of the adverse effects of depopulation is reduced well-being for inhabitants (OECD, 2020). This stems from lower income per capita, a worsening of living conditions because of limited access to basic services such as health care and education, and even a decrease in residents’ self-esteem (Poeta Fernandez, 2019). In farming areas, there is also a reduction in farmed land and agricultural production (Papadopoulos & Baltas, 2024). Historic and natural heritage also deteriorates and environmental impacts occur (Martínez-Abraín et al., 2020). Great challenges therefore arise for sustainability and equality in affected areas and for economic conditions, public facilities and services in the areas affected (Alamá-Sabater et al., 2021). Depopulation leads to disparity in rural areas (Pino & Camarero, 2017). On a global level between 2000 and 2020, population increased by 22% in urban areas and decreased by more than 18% in rural areas, leading to greater concentration in urban areas (Merino et al., 2024). It is expected that by 2050, 68% of the world’s population will live in urban areas (UN-Habitat, 2022). In Europe, this trend began in the second half of the 20th century and is expected to continue (Derrible, 2019). Migration from rural areas to urban areas, together with international emigration, exerted a significant influence during the last two centuries of industrialisation and economic development in Western European countries. This led to a negative demographic trend in rural areas (Collantes & Pinilla, (2020a). In the European Union (EU), rural areas account for 83% of the total territory and are home to 30.6% of the total population (Osorio et al. 2023), with the greatest depopulation occurring in the east and south (Eurostat, 2019; UN, 2019). All this implies that demographic challenges are at the epicentre of regional and rural policies in the EU (Johnson & Lichter, 2019; David Rodríguez-Rodríguez & Vargas, 2022). Spain is one of the countries that is most affected by demographic challenges (Gutiérrez et al., 2020). In the 1950s, industrialisation marked the start of a demographic decline (Rodríguez-Del Pino et al., 2021). This became more marked in the 1960s (García Coll & Stillwell, 1999), because of a great rural exodus generated by the low profitability of agricultural production based on self-sufficiency (Guillén-Gracia & Zúñiga-Antón, 2020). And in 2015, the population in rural areas continued to decline (González-Leonardo et al., 2023); 9% of municipalities with more than 10,000 inhabitants were home to 80% of the population (INE, 2023). Castilla-La Mancha, the region under study, is located in southern Europe and is characterised by its rurality, with 91% of its municipalities classified as rural (INE, 2023). Decree 108/2021 divides the region into different areas: sparsely populated, at risk of depopulation, intermediate and peri-urban. Since 2012, Castilla-La Mancha has seen a constant loss of population, especially from its smaller municipalities. The main purpose of this study is to analyse the needs of these rural municipalities, bearing in mind their characteristics and the factors that affect the phenomenon of depopulation. It is crucial to study economic, social and environmental factors because, in very small municipalities where there is an obvious lack of basic services such as schools, investment in them can make a significant difference. However, in larger municipalities, such actions often have little or no impact, which indicates that a different approach should be adopted for each territory. In particular, there are key sectors such as the agrifood industry and farming and key factors such as the role of women and subsidies which can be considered fundamental pillars for the development and sustainability of such areas. This article also fills an important gap in the literature by studying territories in line with their local needs, with a specific focus on the role of women in a rural context. The methodological tool used is quantile regression. The study comprises 4 sections. In the first, we present the framework and the area of study. We then describe the data base, and the methodology used. After presenting the results, these are discussed, and conclusions are drawn. Theoretical framework Over recent years, rural depopulation has become a problem for emerging economies and developed countries(Sheludkov et al., 2021) and many aspects are perceived in it – social, economic and territorial. According to authors such as Bellman et al. (2016); Oto-Peralías (2020), Reynaud et al. (2020) depopulation stems from the actual process of evolution, that is, it should be seen as a natural and inevitable demographic process (Franklin, 2020). The global trend towards ageing and low birth rates presents a challenge for the sustainability of rural communities (Hori et al., 2021). The lack of quality employment in rural areas has been identified as another of the main causes (Kvartiuk et al., 2020; Merino & Prats, 2020; O’Shea et al., 2019). Others include a lack of educational services (Guzman et al., 2020; Yu & Artz, 2019), health care (Ohashi et al., 2019), cultural services (Raymond et al., 2015), basic social services (Cox, 2023), and roads and digital communication infrastructure (Benedek, 2016). All these have intensified intra-national displacement towards urban areas (Alamá-Sabater et al., 2019; Fernando Collantes & Pinilla, 2020). Other authors find that a lack of territorial planning (Molina Ibáñez et al., 2022) and of a territorial cohesion policy (Alloza et al., 2017) aggravate the problem of depopulation. They point to the importance of an individualised territorial approach (Bjerke & Mellander, 2017; Di Figlia, 2016; Hansen & Aner, 2017) to stimulate integration between urban and rural areas (Comisión Europea, 2019); Viñas, (2019); Vaishar et al. (2020); Tenza-Peral et al. (2022)), and to offer employment opportunities while improving living conditions (Boarnet (1994)Vico Bosch & Rebollo Catalán, 2018; Ma et al. 2020; Pontones-Rosa et al. 2021). The closing of rural schools has a negative effect on the community (Lykke Sørensen et al., 2021a; Sageman, 2022) (Isaksson, 2023), which brings new challenges (Jiménez-Espada et al., 2022; Wimberley, 2019). It is linked to an increase in emigration of families with children(Elshof et al., 2015) and can therefore be seen as a threat for rural life (Amcoff, 2012). This link between the existence of primary schools in municipalities and the rural population has been extensively studied (Lykke Sørensen et al., 2021b).Cañal-Fernández & Álvarez (2022) consider that secondary schools can also help to fix the population. The closing of schools also has a negative impact on the region by speeding up emigration and affecting community development (Lehtonen, 2021; Lykke Sørensen et al., 2021c). Egorov(2023) proposes the reorganisation of rural schools and improved communications among territories as alternatives. Sánchez(2019) suggests the adoption of blended learning. Structural transformation of education(Kurashige, 2017) could therefore be an incentive for fixing population within a territory (Bjarnason, 2021; Chen et al., 2023; H. Zhang et al., 2023). Moreover, the adoption of entrepreneurial projects involving rural students offers opportunities for local development, holding back migration by young people (Amri et al., 2021). We therefore pose hypothesis 1: H1. Maintaining educational resources is key for maintaining population in rural areas. Regarding health-related public services, primary health care is fundamental in rural areas as they face unique challenges affecting health and access to medical care (Graham et al., 2018). In rural areas, it is often difficult to retain medical workers, which reduces primary care services even further (Weeks et al., 2008, (Ortiz et al., 2013). And the limited presence of medical specialists in rural areas may restrict access to specialist care, resulting in less complete and less specialised health care for their inhabitants (Ortiz et al., 2013). An & Zhang (2022) stress the importance of improving public health services as a strategy for combating depopulation. For these reasons, telemedicine is seen as a promising solution for improving health care in rural areas. It offers remote health care services, overcoming geographical barriers and allowing the elderly to access quality health care without having to travel (WHO, 2010). Hypothesis 2 is as follows: H2. The availability of primary health care centres is a factor that can maintain population in rural areas. Another aspect that can affect rural depopulation is technology (Rossouw & Garbutt, 2023). ICT can be key to economic growth and development (Yadav & R. Alfonso, 2022). However, the introduction of technologies such as Internet connections is still a challenge (Galperín, 2017), especially considering that the installation of 5G networks is very costly for telecommunications providers (Voinov et al., 2022). But the introduction of 5G in rural areas not only reduces the digital divide(Lappalainen & Rosenberg, 2022) but also connects such areas with a global community (Y. Zhang et al., 2021). Such access reduces migration(Najjuuko et al., 2021) and also creates new economic opportunities (Parker et al., 2021),(H. Zhang et al., 2023) while also helping to mitigate demographic decline and poverty. So, from the entrepreneurial point of view, the inclusion of such technology can be an important alternative for the demographic challenge (Jorge-Vázquez et al., 2023). This is in line with the findings of Han et al.(2023) and Liu et al. (2023). It is therefore important to invest in technology in order to revitalise rural communities (Domínguez, 2021). Hypothesis 3 is as follows: H3. Access to the Internet is important for maintaining the rural population. With regard to environmental considerations, protected areas can attract population if compensation is made for the negative externalities of ecological tourism (Kettunen & ten Brink, 2013; Sala et al., 2013). In Spain, several studies in municipalities with a population of less than 10,000 inhabitants (Labianca & Navarro, 2019; Molinero & Alario, 2019; Rodríguez-Rodríguez et al., 2021) have focused on the impact on the local population of protected areas (Dudley, 2008) One such municipality is Linares de la Sierra, where Cáceres-Feria et al. (2021) explored community tourism as a viable economic alternative for fixing the local population (Giampiccoli & Saayman, 2018; Salazar, 2012; Stone & Stone, 2011; Zielinski et al., 2020). Also, Ouchen & Montargot (2022) find that the arrival of tourists in a location can have a positive effect on surrounding locations. However, in spite of the advantages brought by ecological tourism to protected areas, Goodwin & Santilli (2009) find that this is not always an option because strong local collaboration is needed as well as a high level of qualified human capital (Baum, 2018). The environmental approach can therefore be seen as one possible solution to promote community resilience (Rapaport et al., 2018) and strengthen local society (Matarrita-Cascante et al., 2010, 2017) while dealing with demographic challenges (Lavalle, 2018; Urrecho & Fernández, 2017). Hypothesis 4 is as follows: H4. Positive environmental indicators affect the maintenance of population in rural areas. Another factor that can help mitigate depopulation is public aid. An example is the Leader programme (Dower, 2014), which was designed to support rural development projects at local level and to encourage experimentation with new approaches, with the aim of revitalising rural areas and generating employment (Knickel et al., 2018). The Leader approach is based on the principles of community development and accepts that local agents have in-depth knowledge of the needs and challenges of the territory in which they live. (Chmieliński et al., 2018; Masot & Alonso, 2017). Such aid is valuable for dealing with rural depopulation and contributing to local development. However, while it may have a positive impact, it is limited for guaranteeing demographic sustainability in the long term (Miranda García et al., 2020). A number of authors have studied the effects of such aid (Cañete et al., 2018; Chevalier et al., 2017; Dax et al., 2016; Navarro et al., 2016), butCollantes & Pinilla (2020b) indicate that if such policies are to be really effective, the approach adopted must take into account the specific needs of each community. Hypothesis 5 is as follows: H5. Leader aid is effective in the smallest municipalities. In addition, women play an important role in rural revitalisation (Garcia et al., 2004a; Nieto Figueras, 2006a), especially in the primary sector (Ball, 2020; Dunne et al., 2021a). Their active participation in economic and social life can significantly help mitigate the process of depopulation (Ebrahimi et al., 2022). Some studies(Bonafé, 2018; Sampedro & Camarero, 2007) stress that women are essential for the continuity and development of rural society and of the primary sector (Cavicchioli et al., 2018; Dunne et al., 2021a). In the Celtiberian Range, the massive rural exodus by women was caused by the lack of opportunities (Cobano-delgado & Llorent-bedmar, 2020). This was tested in other research (Fernández-Giménez et al., 2022; Johansson, 2016). As in Castilla-León, migration by young women reinforced the demographic and economic decline in marginalised areas (Pérez, 2018). In other areas, however, some women returned to their former communities to set up businesses, leading to a slight demographic recovery (Baylina et al., 2016). It is therefore essential to introduce policies that will encourage active participation by women in rural areas in order to improve sustainability in these territories (Rodero Sanz, 2022).Van der Ploeg & Renting (2004) state that sustainable rural development introduces new gender dynamics, working patterns and forms of cooperation that involve both the administration and businesses and society. This indicates that research should consider variables not only by gender but also by municipality (Camarero, 2020; Camarero & Oliva, 2019) Hypothesis 6 is as follows: H6. Women play a key role in maintaining and revitalising rural areas. Study area Castilla-La Mancha is an autonomous community in the south of Europe, with a population of around 2 million (INE, 2022). Most of the population is located in the central area of the region and in its corridors (Fig. 1). The community is made up of a total of 919 municipalities, of which approximately 90% (825) have less than 5,000 inhabitants. With regard to population evolution (Fig. 2 (a)), this follows the same trend as the rest of Spain: growth during the first decade of 2000, decrease as from 2011/2012 and a slight recovery as from 2018. Interannual variations are more marked in Castilla-La Mancha than in the rest of the country (Fig. 2 (b)). Decree 108/2021 classifies rural areas in four groups: 1) sparsely populated areas (extreme depopulation and intense depopulation), 2) areas at risk of depopulation, 3) intermediate areas (agricultural and diversified, and 4) peri-urban rural areas. Note that sparsely populated areas and those at risk of depopulation are predominant in the region, accounting for approximately 77% of the total area (JCCM, 2022) (see Table 1 ). Table 1 Rural areas in Castilla-La Mancha Classification % municipalities Number TV 2000–2012 TV 2012–2023 Sparsely populated areas Extreme depopulation 56.91% 523 -4.28 -12.76 Intense depopulation 18.82% 173 3.77 -11.15 Areas at risk of depopulation At risk 2.72% 25 3.52 -11.34 Intermediate areas Intermediate agricultural 6.75% 62 14.88 -7.10 Intermediate diversified 8.49% 78 37.10 3.82 Peri-urban areas Peri-urban 5.55% 51 119.5 17.34 Urban 0.76% 7 19.29 -1.28 Total 100% 919 Source : Drawn up by the authors based on data from JCCM (2022). Table 1 also shows the rate of population variation for the periods 2000–2012 and 2012–2023, and stresses that the loss of population has been concentrated in the smallest municipalities. Since these are located in areas with sparse population and at risk of depopulation, they have seen a demographic decrease that is significantly greater than that of larger municipalities, which have maintained greater population stability. This stresses the vulnerability to depopulation of the smallest rural territories and the importance of studying the idiosyncrasy of rural areas in Spain (Duranton & Venables, 2018). With regard to socio-demographic markers, it should be noted that the increased ageing of the population (16.52%), the falling birth rate (27.80%) and the decrease in young people (2.64%) lead to a 34% reduction in the labour replacement rate, that is, there are increasingly fewer people to occupy the positions of those taking retirement. Table 2 Socio-demographic markets 2023 2012 Rate of variation (%) Population 2.084.086 2.121.888 -1,78% Birth rate 7.01 9.71 -27,80% Death rate 9.92 9.08 9,25% Ageing rate 123.56 106.04 16,52% Level of young people 7.36 7.56 -2,64% Weight of women/total population 0.4976 0.4958 0.363% Weight of men/total population 0.5024 0.5041 -0.337% Labour replacement rate 52.65 79.81 -34.03% Source : Drawn up by the authors based on data from the CLM (2022). Data The data base used in this study is a cross-cutting one in which the units studied are municipalities. The year of analysis is 2022. The variables used in the study are presented in Table 3 . Table 3 Symbology and definition of variables Variable Description Population (POP) Total number of individuals in the municipality (INE, 2022). Education (primary and secondary) Presence of an infant and primary school or a secondary school (Instituto de Estadística de Castilla - La Mancha, 2022). Health care Presence of a health centre (Instituto de Estadística de Castilla - La Mancha, 2022). Technology (TEC) Percentage of 5G coverage over registered homes (Ministerio de Asuntos Económicos y Transformación Digital, 2022). SCA Presencia de Zonas de Especial Conservación (Instituto de Estadística de Castilla - La Mancha, 2022). LEADER Public aid (LEADER) granted to each municipality (JCCM, 2021). Women (WOM) Number of women in each municipality (INE, 2022). Open-ended contracts for women Index of open-ended contracts for women (SEPE, 2022). Temporary contracts for women Index of temporary contracts for women (SEPE, 2022). Notes : This table indicates the source and definition of each variable. Table 4 shows the descriptive statistics for each variable. Table 4 Descriptive analysis of the variables Variable Frequency Mean Std. Dev. Min. Max. 0 1 Population - - 5.893 1.856 1.099 12.057 Education 579 340 0.331 0.194 0 1 Health care 299 620 0.675 0.469 0 1 Technology - - 0.144 0.314 0 1 SCA 440 479 0.521 0.499 0 1 Leader - - 7.259 5.090 0 13.870 Women 794 125 0.136 0.343 0 1 Open-ended contracts for women - - 0.167 0.295 0 1 Temporary contracts for women - - 0.275 0.339 0 1 Notes : This table shows the frequency, mean and standard deviation and the minimum and maximum for each variable. Descriptive analysis of the variables reflects important aspects of the municipal context. The constant variables are transformed into logarithms to facilitate interpretation and reduce data dispersion. Methods According to Decree 108/21, it is important to consider the characteristics of each territory in Castilla–La Mancha. It classifies the municipalities as follows: 1) sparsely-populated; 2) at risk of depopulation; 3) intermediate agricultural, and 4) peri-urban. Quantile regression allows for models to be adjusted to different levels of conditional distribution (Lamarche, 2019), which is useful when distributions are asymmetrical, as is the case in the areas studied. Also, this technique captures heterogeneity, which is not visible in a conventional linear model (González-Val et al., 2017). Some studies on population have already used this methodology (Álvarez, 2018; Carson, 2018; Ogasawara & Matsushita, 2019). The quantile regression proposed by Koenker & Bassett (1978) extends the concept of linear regression by modelling the quantiles of the conditional distribution of the dependent variable \(\:\mathcal{y}\) given a set of independent variables X. Instead of modelling the conditional mean of \(\:\mathcal{y}\) , quantile regression aims to estimate any quantile \(\:\tau\:\) of the distribution of \(\:\mathcal{y}\) . The quantile \(\:\tau\:\) is defined as the value \(\:{y}_{t}\) so: $$\:P\left(Y\le\:{y}_{t}\:\right|\:X)=\:\tau\:\:$$ 1 For a quantile \(\:\tau\:\) , quantile regression minimizes a function of weighted loss, in which positive and negative results are treated asymmetrically, as follows: $$\:\text{min}\sum\:_{i=1}^{n}{p}_{\tau\:}({y}_{i}-{x}_{i}\beta\:)$$ 2 Where \(\:{p}_{\tau\:}\left(u\right)\) is the function of quantile loss defined as: $$\:{p}_{\tau\:}\left(u\right)=u(\tau\:-\parallel\:\left(u<0\right))$$ 3 Here, \(\:\parallel\:\) (u < 0) is an indicator function that takes the value of 1 if \(\:u<0\) , and 0 otherwise. This penalises the residuals differently below and above the quantile \(\:\tau\:\) , providing different fits depending on the value of the quantile. The quantile regression model is expressed as follows: $$\:{Q}_{y\tau\:}\left(X\right)=X{B}_{\tau\:}$$ 4 where \(\:{Q}_{y\tau\:}\left(X\right)\) represents the quantile \(\:\tau\:\) – nth of the distribution of \(\:y\) conditional to \(\:X\) , y \(\:{B}_{\tau\:}\) are the specific coefficients for each quantile. Independent models can be adjusted for the quantiles 0.76 (sparsely-populated), 0.79 (at risk of depopulation), 0.94 (intermediate agricultural) and 0.99 (peri-urban). This distribution allows for the analysis of the different characteristics of municipalities in relation to the behaviour of the explanatory variables in the model with the dependent variable, in this case, population. The equation that represents the model is the following: $$\:{Q}_{population}\left(\tau\:|X\right)={B}_{0}\left(\tau\:\right)+{B}_{1}\left(\tau\:\right)*Education+{B}_{2}\left(\tau\:\right)*Health\:care+{B}_{3}\left(\tau\:\right)*Technology+{B}_{4}\left(\tau\:\right)*ZEC+{B}_{5}\left(\tau\:\right)*LEADER+{B}_{6}\left(\tau\:\right)*Women+{B}_{7}\left(\tau\:\right)*Open-ended\:contract+{B}_{8}\left(\tau\:\right)*Temporary\:contract$$ 5 Results and discussion Table 5 shows the results of the regression at the different quantile levels − 0.76; 0.79; 0.94 and 0.99. Table 5 Results of the quantile regression Type/Variables Sparsely populated At risk Intermediate Peri-urban Education 1.087* (-0.782) 0.951 (-0.957) 2.389** (-1.195) 0.926 (-1.282) Health care 3.135*** (-0.182) 3.135*** (-0.166) 3.951*** (-0.382) 5.171*** (-0.414) Technology 2.942*** (-0.463) 2.898*** (-0.49) 4.271*** (-0.798) 5.553*** (-1.563) SCA 1.647*** (-0.183) 1.689*** (-0.177) 3.555*** (-0.27) 4.901*** (-0.182) Leader 0.261*** (-0.018) 0.272*** (-0.018) 0.375*** (-0.278) 0.498*** (-0.019) Women 1.831*** (-0.317) 1.898*** (-0.325) 2.543*** (-0.799) 4.673*** (-1.527) Open-ended contract 1.141*** (-0.328) 1.159*** (-0.326) 1.589 (-1.43) 4.912*** (-1.705) Temporary contract 2.267*** (-0.447) 2.82*** (-0.402) 4.615*** (-0.619) 8.495** (-3.291) Note. *** significance at 0.00; ** significance at 0.05; * significance at 0.1. Standard deviation(). In sparsely-populated areas, the variables related to education, health care, technology and SCA have a significant and positive impact on population retention, which suggests that improving these factors could encourage inhabitants to remain. It is clear that women play a key role in these areas as their presence has a highly positive effect and is essential for holding back depopulation. In addition, temporary contracts for women are seen to have a considerable impact, showing that, even if employment is of short duration, job opportunities for women are crucial for retaining the population. Leader aid programmes are also significant, although their effect is more moderate. This points to the relevance of rural development policies. In municipalities at risk of depopulation, education is seen to be less relevant than in sparsely-populated areas, but health care, technology and SCA continue to be key factors for attracting population. The role of women is still positive, and their importance is clear for demographic balance in rural areas. Employment contracts, whether temporary or open-ended, are still relevant, as are Leader aid programmes, although their impact is slightly smaller. In diversified intermediate agricultural areas, education recovers its importance while health care, technology and SCA are still determining factors. Employment and gender variables gain importance, with a more marked relevance for women, especially through temporary contracts which have a significant impact on fixing population. In contrast, open-ended contracts become less relevant and Leader aid programmes, while positive, have a moderate impact in comparison with other areas. In peri-urban and urban areas, education loses its significance, reflecting a lower impact in more densely-populated areas. However, health care, technology and SCA are still important factors, and job stability for women, through open-ended contracts, is becoming consolidated as a key to population growth. Temporary contracts also have a positive effect and although Leader aid programmes are still relevant, they have a smaller impact. The results reveal important nuances regarding how the key variables affect population retention in rural areas. Education shows significant variability depending on the type of territory. In agricultural areas, it is again relevant, while in peri-urban or urban areas its impact is much smaller. This result is in line with those obtained by Sanz-Magallón Rezusta et al. (2015), Yu & Artz (2019), Guzman et al. (2020), Alonso-Carrillo et al. (2023) who indicate that the initial educational level is important in rural contexts. For access to health care, the results are in line with those given by Graham et al. (2018), Ohashi et al. (2019) and An & Zhang (2022), who stress that better health care infrastructure is essential for maintaining the population in rural areas. Another determining factor is access to digital infrastrucutre, such as 5G coverage, which is still crucial for attracting population and for developing rural areas. These results are in line with studies by Pontones-Rosa et al. (2021) and Lappalainen & Rosenberg (2022), who stress that digital connectivity is a key pillar for modernising rural territories. Digitalisation is an essential requirement for attracting and retaining population, and the integration of digitalisation is a driver for economic and social development (Larraz & García-Gómez, 2020). This was also found by Merino & Prats (2020) and Jato-Espino & Mayor-Vitoria (2023), who stress that technology can facilitate employability and improve standard of living in rural territories. An important aspect in the analysis is the role of protected areas. Their existence promotes tourism and strengthens local economies, resulting in a direct benefit for retaining the population. This finding is supported by studies such as those by Labianca & Navarro (2019), Molinero & Alario (2019) and Rodríguez-Rodríguez et al. (2021), who argue that protected areas amount to a strategy for long-term sustainability. The Leader aid programmes have had a positive impact in all the contexts but were less effective in more urbanised rural areas, which suggests that rural development must be complemented by other policies in less densely-populated areas (González Fernández et al., 2023). This is in line with Alloza et al. (2017), Vico Bosch & Rebollo Catalán (2018), Molina et al. (2022), Ma et al. (2020) and Pontones-Rosa et al. (2021). Finally, this analysis stresses the importance of women as a factor for maintaining and increasing the rural population (Kumar et al., 2022). The presence and participation of women, through temporary contracts, is key for holding back population loss. This stresses the need for policies that promote gender equity as a key strategy in the demographic development of vulnerable areas (Bayisenge et al., 2019). These findings are in line with the observations of Johansson (2016), Rodríguez-Rodríguez et al. (2021), Alonso-Carrillo et al. (2023), Esparcia et al. (2020) and Fernández-Giménez et al. (2022) regarding the influence of women on rural migration dynamics. Conclusions This study offers a cross-cutting analysis of the population of Castilla-La Mancha, an eminently rural area in southern Europe. In spite of some signs of recovery over recent years, Castilla-La Mancha still suffers from a serious problem of rural depopulation. Its population is elderly, the young population has decreased, and it is increasingly difficult to fill the jobs of inhabitants reaching retirement age. These demographic dynamics create significant challenges. By applying the technique of quantile regression, it has been possible to identify some key factors that affect population retention and growth in different types of municipality, allowing for the adoption of a more profound approach that considers the specific needs of each territory. This methodology is able to capture any heterogeneity in the effects of the explanatory variables and amounts to a robust tool for guiding rural development policies based on the demands of each municipality. The analysis shows that access to essential services such as education, health care and technology, together with the existence of protected areas and public aid programmes, are determining factors for turning around depopulation. However, their impact varies depending on the size of the municipalities in question. In larger locations, an increase in service availability does not always lead to significant changes in the population dynamics. Conversely, in smaller municipalities, where the provision of basic services is usually limited, any improvement in the provision of such services has a considerable impact on population retention and on standard of living. This finding, which might seem obvious, stresses how important it is for strategic investments in infrastructure and services to take local needs into account. In Castilla La Mancha, in which 57% of municipalities are in a situation of extreme depopulation, the main actions to be taken should aim to provide services in the rural environment that will bring well-being and quality of life to local inhabitants. Internet access is essential for the professional and personal development of the local population and also helps to develop public and private services in such territories. Moreover, rural development programmes should continue to focus on fixing the population based on the principle of equal opportunities because women play a key role in the economic and social revitalisation of rural areas. Their active participation in rural life is fundamental for holding back depopulation and revitalising such areas. Temporary contracts play a key role, because they represent a significant source of employment for women in rural areas. Even if contracts are short, they facilitate women’s entry into the labour market, allowing them to become economically independent while also strengthening the social fabric. The inclusion of women in economic and social activities not only strengthens the local economy but also promotes social cohesion and sustainability in the long term. Public policies oriented towards gender equality, stronger labour rights and the creation of specific opportunities are essential for guaranteeing that women will remain and contribute to the overall development of rural areas. Finally, it is necessary for there to be dialogue and coordination among the various agents involved in the economic and social fabric in rural areas (Rural Development Groups, administration, businesses, the actual population), allowing for the design of strategies and measures that suit each territory. The key for fighting depopulation is to adopt an approach that distinguishes between the different territories, considering the particularities of each municipality and promoting collaboration among communities. Only by combining policies that take local specificities into account, with sustainable investments in services and infrastructure and measures to strengthen the social fabric, will it be possible to guarantee a viable and balanced future for rural communities. Declarations Author Contribution I.C, J.C and M.C review the paper after it was finished. G.C worked in the economic model Acknowledgements This article is financed by Project SBPLY/21/180501/000083 (Global and endogenous responses to the demographic challenge of ITI areas), funded by JCCM. There is not conflict of interests and the data is available within the project. Data Availability Data is provided within the manuscript or supplementary information files References Alamá-Sabater, L., Budí, V., García-Álvarez-Coque, J. M., & Roig-Tierno, N. (2019). Using mixed research approaches to understand rural depopulation. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6972099","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":506250347,"identity":"20be0a8b-3952-45e1-8a67-4870149d3dd5","order_by":0,"name":"Germán Andrés Cevallos Meneses","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABDklEQVRIiWNgGAWjYNCCAgsEmx9EJBQQ0mIggWBLNoC0GJCixeAAmMStWN798DGJHwYScvLTDh978OGPjb3x+dWJHx4YMMjzix3AqsXwTFqaZI+BhLHB7bR0w5ltaYnbbrzdLAF0mOHM2QnYtczgMTbgMZBI3CCdYybN23A4wezG2Q0gLQkGt3Fp4f9s+AeoZf7s/G/Sf/78tzeecXbzD3xa5CV4GB+DbGm4ncMmzcB2gHEDf+82vLYY8KQZPpaB+MVMsrctOXHGDd5tFgkGEjj9It9++MHBNxU2cvKzk59J/PhjZ8/ff3bzzR8VNvL80jhsOYAhJAFWKYEhDrelAUOIH9OUUTAKRsEoGNkAAEFvXWyo7y8NAAAAAElFTkSuQmCC","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Germán","middleName":"Andrés Cevallos","lastName":"Meneses","suffix":""},{"id":506250348,"identity":"562bfd1e-e0c1-4907-835c-994b75d5b563","order_by":1,"name":"María Carmen García Cortijo","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"María","middleName":"Carmen García","lastName":"Cortijo","suffix":""},{"id":506250349,"identity":"a18b9d9f-33e9-47ba-adda-02a12e98b992","order_by":2,"name":"Juan Sebastián Castillo Valero","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Juan","middleName":"Sebastián Castillo","lastName":"Valero","suffix":""},{"id":506250350,"identity":"7a06d188-fa0b-403b-ba0c-3aa60c14b9df","order_by":3,"name":"Inmaculada Carrasco Monteagudo","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Inmaculada","middleName":"Carrasco","lastName":"Monteagudo","suffix":""}],"badges":[],"createdAt":"2025-06-25 08:08:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6972099/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6972099/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":90542498,"identity":"dd6e0e4a-49d7-49b8-b8e3-3360b98f4c73","added_by":"auto","created_at":"2025-09-04 00:03:31","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":280232,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of population by municipalities in Castilla-La Mancha 2022\u003c/p\u003e\n\u003cp\u003eSource: Drawn up by the authors based on data from the Castilla–La Mancha Statistics Institute (2022). Realized in Arcgis. The map of Europe is from https://educativo.ign.es/atlas-didactico/relaciones-internacionales-bach/launineuropeaue.html\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6972099/v1/0759e3bc51e3175a82f08da2.jpeg"},{"id":90542499,"identity":"d6a3a365-7902-4522-a6f0-b2f8dba19297","added_by":"auto","created_at":"2025-09-04 00:03:31","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":123485,"visible":true,"origin":"","legend":"\u003cp\u003eComparison bteween Spain and CLM. (a) Population trend* 1998-2022. (b) Inter-annual variation (%) in population 1998-2022. Realized in Excel. Notes: (*) the depopulation series are in Neperian logarithms. Source: Drawn up by the authors based on data from the CLM Statistics Institute.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6972099/v1/3846033b4a6f78d191a7a3bd.png"},{"id":97328591,"identity":"1e5d1c2c-27e8-415f-81de-8593545c0c6b","added_by":"auto","created_at":"2025-12-03 08:55:18","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1271418,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6972099/v1/5f5109de-bd7b-46c6-a3ff-3cce54e4214d.pdf"},{"id":90542504,"identity":"e5621f5f-bd28-4382-a56b-ec8e72916a80","added_by":"auto","created_at":"2025-09-04 00:03:32","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":212368,"visible":true,"origin":"","legend":"","description":"","filename":"Femenino.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6972099/v1/9c4d762b0e10bec49db7a32f.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Keys to mitigating rural depopulation. The case of Castilla-La Mancha (Spain)","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDepopulation is a phenomenon that significantly affects a territory\u0026rsquo;s social, economic and environmental characteristics (Christiaanse, 2020; Selod \u0026amp; Shilpi, 2021). In spite of constant population growth, many rural areas are becoming depopulated (Derrible, 2019). Depopulation in rural areas creates a vicious circle that reduces regional vitality (Makkonen \u0026amp; Inkinen, 2023). As the population in such areas decreases, the provision of both public and private services is often cut back because they become more costly and less cost-effective (San-Mart\u0026iacute;n Gonz\u0026aacute;lez \u0026amp; Soler-Vaya, 2024). One of the adverse effects of depopulation is reduced well-being for inhabitants (OECD, 2020). This stems from lower income per capita, a worsening of living conditions because of limited access to basic services such as health care and education, and even a decrease in residents\u0026rsquo; self-esteem (Poeta Fernandez, 2019). In farming areas, there is also a reduction in farmed land and agricultural production (Papadopoulos \u0026amp; Baltas, 2024). Historic and natural heritage also deteriorates and environmental impacts occur (Mart\u0026iacute;nez-Abra\u0026iacute;n et al., 2020). Great challenges therefore arise for sustainability and equality in affected areas and for economic conditions, public facilities and services in the areas affected (Alam\u0026aacute;-Sabater et al., 2021).\u003c/p\u003e\u003cp\u003eDepopulation leads to disparity in rural areas (Pino \u0026amp; Camarero, 2017). On a global level between 2000 and 2020, population increased by 22% in urban areas and decreased by more than 18% in rural areas, leading to greater concentration in urban areas (Merino et al., 2024). It is expected that by 2050, 68% of the world\u0026rsquo;s population will live in urban areas (UN-Habitat, 2022). In Europe, this trend began in the second half of the 20th century and is expected to continue (Derrible, 2019). Migration from rural areas to urban areas, together with international emigration, exerted a significant influence during the last two centuries of industrialisation and economic development in Western European countries. This led to a negative demographic trend in rural areas (Collantes \u0026amp; Pinilla, (2020a).\u003c/p\u003e\u003cp\u003eIn the European Union (EU), rural areas account for 83% of the total territory and are home to 30.6% of the total population (Osorio et al. 2023), with the greatest depopulation occurring in the east and south (Eurostat, 2019; UN, 2019). All this implies that demographic challenges are at the epicentre of regional and rural policies in the EU (Johnson \u0026amp; Lichter, 2019; David Rodr\u0026iacute;guez-Rodr\u0026iacute;guez \u0026amp; Vargas, 2022). Spain is one of the countries that is most affected by demographic challenges (Guti\u0026eacute;rrez et al., 2020). In the 1950s, industrialisation marked the start of a demographic decline (Rodr\u0026iacute;guez-Del Pino et al., 2021). This became more marked in the 1960s (Garc\u0026iacute;a Coll \u0026amp; Stillwell, 1999), because of a great rural exodus generated by the low profitability of agricultural production based on self-sufficiency (Guill\u0026eacute;n-Gracia \u0026amp; Z\u0026uacute;\u0026ntilde;iga-Ant\u0026oacute;n, 2020). And in 2015, the population in rural areas continued to decline (Gonz\u0026aacute;lez-Leonardo et al., 2023); 9% of municipalities with more than 10,000 inhabitants were home to 80% of the population (INE, 2023).\u003c/p\u003e\u003cp\u003eCastilla-La Mancha, the region under study, is located in southern Europe and is characterised by its rurality, with 91% of its municipalities classified as rural (INE, 2023). Decree 108/2021 divides the region into different areas: sparsely populated, at risk of depopulation, intermediate and peri-urban. Since 2012, Castilla-La Mancha has seen a constant loss of population, especially from its smaller municipalities. The main purpose of this study is to analyse the needs of these rural municipalities, bearing in mind their characteristics and the factors that affect the phenomenon of depopulation. It is crucial to study economic, social and environmental factors because, in very small municipalities where there is an obvious lack of basic services such as schools, investment in them can make a significant difference. However, in larger municipalities, such actions often have little or no impact, which indicates that a different approach should be adopted for each territory. In particular, there are key sectors such as the agrifood industry and farming and key factors such as the role of women and subsidies which can be considered fundamental pillars for the development and sustainability of such areas. This article also fills an important gap in the literature by studying territories in line with their local needs, with a specific focus on the role of women in a rural context. The methodological tool used is quantile regression.\u003c/p\u003e\u003cp\u003eThe study comprises 4 sections. In the first, we present the framework and the area of study. We then describe the data base, and the methodology used. After presenting the results, these are discussed, and conclusions are drawn.\u003c/p\u003e"},{"header":"Theoretical framework","content":"\u003cp\u003eOver recent years, rural depopulation has become a problem for emerging economies and developed countries(Sheludkov et al., 2021) and many aspects are perceived in it \u0026ndash; social, economic and territorial. According to authors such as Bellman et al. (2016); Oto-Peral\u0026iacute;as (2020), Reynaud et al. (2020) depopulation stems from the actual process of evolution, that is, it should be seen as a natural and inevitable demographic process (Franklin, 2020). The global trend towards ageing and low birth rates presents a challenge for the sustainability of rural communities (Hori et al., 2021). The lack of quality employment in rural areas has been identified as another of the main causes (Kvartiuk et al., 2020; Merino \u0026amp; Prats, 2020; O\u0026rsquo;Shea et al., 2019). Others include a lack of educational services (Guzman et al., 2020; Yu \u0026amp; Artz, 2019), health care (Ohashi et al., 2019), cultural services (Raymond et al., 2015), basic social services (Cox, 2023), and roads and digital communication infrastructure (Benedek, 2016). All these have intensified intra-national displacement towards urban areas (Alam\u0026aacute;-Sabater et al., 2019; Fernando Collantes \u0026amp; Pinilla, 2020).\u003c/p\u003e\n\u003cp\u003eOther authors find that a lack of territorial planning (Molina Ib\u0026aacute;\u0026ntilde;ez et al., 2022) and of a territorial cohesion policy (Alloza et al., 2017) aggravate the problem of depopulation. They point to the importance of an individualised territorial approach (Bjerke \u0026amp; Mellander, 2017; Di Figlia, 2016; Hansen \u0026amp; Aner, 2017) to stimulate integration between urban and rural areas (Comisi\u0026oacute;n Europea, 2019); Vi\u0026ntilde;as, (2019); Vaishar et al. (2020); Tenza-Peral et al. (2022)), and to offer employment opportunities while improving living conditions (Boarnet (1994)Vico Bosch \u0026amp; Rebollo Catal\u0026aacute;n, 2018; Ma et al. 2020; Pontones-Rosa et al. 2021).\u003c/p\u003e\n\u003cp\u003eThe closing of rural schools has a negative effect on the community (Lykke S\u0026oslash;rensen et al., 2021a; Sageman, 2022) (Isaksson, 2023), which brings new challenges (Jim\u0026eacute;nez-Espada et al., 2022; Wimberley, 2019). It is linked to an increase in emigration of families with children(Elshof et al., 2015) and can therefore be seen as a threat for rural life (Amcoff, 2012). This link between the existence of primary schools in municipalities and the rural population has been extensively studied (Lykke S\u0026oslash;rensen et al., 2021b).Ca\u0026ntilde;al-Fern\u0026aacute;ndez \u0026amp; \u0026Aacute;lvarez (2022) consider that secondary schools can also help to fix the population. The closing of schools also has a negative impact on the region by speeding up emigration and affecting community development (Lehtonen, 2021; Lykke S\u0026oslash;rensen et al., 2021c). Egorov(2023) proposes the reorganisation of rural schools and improved communications among territories as alternatives. S\u0026aacute;nchez(2019) suggests the adoption of blended learning. Structural transformation of education(Kurashige, 2017) could therefore be an incentive for fixing population within a territory (Bjarnason, 2021; Chen et al., 2023; H. Zhang et al., 2023). Moreover, the adoption of entrepreneurial projects involving rural students offers opportunities for local development, holding back migration by young people (Amri et al., 2021).\u003c/p\u003e\n\u003cp\u003eWe therefore pose hypothesis 1:\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eH1.\u003c/em\u003e Maintaining educational resources is key for maintaining population in rural areas.\u003c/p\u003e\n\u003cp\u003eRegarding health-related public services, primary health care is fundamental in rural areas as they face unique challenges affecting health and access to medical care (Graham et al., 2018). In rural areas, it is often difficult to retain medical workers, which reduces primary care services even further (Weeks et al., 2008, (Ortiz et al., 2013). And the limited presence of medical specialists in rural areas may restrict access to specialist care, resulting in less complete and less specialised health care for their inhabitants (Ortiz et al., 2013). An \u0026amp; Zhang (2022) stress the importance of improving public health services as a strategy for combating depopulation. For these reasons, telemedicine is seen as a promising solution for improving health care in rural areas. It offers remote health care services, overcoming geographical barriers and allowing the elderly to access quality health care without having to travel (WHO, 2010).\u003c/p\u003e\n\u003cp\u003eHypothesis 2\u0026nbsp;is as follows:\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eH2.\u003c/em\u003e The availability of primary health care centres is a factor that can maintain population in rural areas.\u003c/p\u003e\n\u003cp\u003eAnother aspect that can affect rural depopulation is technology (Rossouw \u0026amp; Garbutt, 2023). ICT can be key to economic growth and development (Yadav \u0026amp; R. Alfonso, 2022). However, the introduction of technologies such as Internet connections is still a challenge (Galper\u0026iacute;n, 2017), especially considering that the installation of 5G networks is very costly for telecommunications providers (Voinov et al., 2022). But the introduction of 5G in rural areas not only reduces the digital divide(Lappalainen \u0026amp; Rosenberg, 2022) but also connects such areas with a global community (Y. Zhang et al., 2021). Such access reduces migration(Najjuuko et al., 2021) and also creates new economic opportunities (Parker et al., 2021),(H. Zhang et al., 2023) while also helping to mitigate demographic decline and poverty. So, from the entrepreneurial point of view, the inclusion of such technology can be an important alternative for the demographic challenge (Jorge-V\u0026aacute;zquez et al., 2023). This is in line with the findings of Han et al.(2023) and Liu et al. (2023). It is therefore important to invest in technology in order to revitalise rural communities (Dom\u0026iacute;nguez, 2021).\u003c/p\u003e\n\u003cp\u003eHypothesis 3\u0026nbsp;is as follows:\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eH3.\u003c/em\u003e Access to the Internet is important for maintaining the rural population.\u003c/p\u003e\n\u003cp\u003eWith regard to environmental considerations, protected areas can attract population if compensation is made for the negative externalities of ecological tourism (Kettunen \u0026amp; ten Brink, 2013; Sala et al., 2013). In Spain, several studies in municipalities with a population of less than 10,000 inhabitants (Labianca \u0026amp; Navarro, 2019; Molinero \u0026amp; Alario, 2019; Rodr\u0026iacute;guez-Rodr\u0026iacute;guez et al., 2021) have focused on the impact on the local population of protected areas (Dudley, 2008) One such municipality is Linares de la Sierra, where C\u0026aacute;ceres-Feria et al. (2021) explored community tourism as a viable economic alternative for fixing the local population (Giampiccoli \u0026amp; Saayman, 2018; Salazar, 2012; Stone \u0026amp; Stone, 2011; Zielinski et al., 2020). Also, Ouchen \u0026amp; Montargot (2022) find that the arrival of tourists in a location can have a positive effect on surrounding locations.\u003c/p\u003e\n\u003cp\u003eHowever, in spite of the advantages brought by ecological tourism to protected areas, Goodwin \u0026amp; Santilli (2009) find that this is not always an option because strong local collaboration is needed as well as a high level of qualified human capital (Baum, 2018). The environmental approach can therefore be seen as one possible solution to promote community resilience (Rapaport et al., 2018) and strengthen local society (Matarrita-Cascante et al., 2010, 2017) while dealing with demographic challenges (Lavalle, 2018; Urrecho \u0026amp; Fern\u0026aacute;ndez, 2017).\u003c/p\u003e\n\u003cp\u003eHypothesis 4 is as follows:\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eH4.\u003c/em\u003e Positive environmental indicators affect the maintenance of population in rural areas.\u003c/p\u003e\n\u003cp\u003eAnother factor that can help mitigate depopulation is public aid. An example is the Leader programme (Dower, 2014), which was designed to support rural development projects at local level and to encourage experimentation with new approaches, with the aim of revitalising rural areas and generating employment (Knickel et al., 2018). The Leader approach is based on the principles of community development and accepts that local agents have in-depth knowledge of the needs and challenges of the territory in which they live. (Chmieliński et al., 2018; Masot \u0026amp; Alonso, 2017). Such aid is valuable for dealing with rural depopulation and contributing to local development. However, while it may have a positive impact, it is limited for guaranteeing demographic sustainability in the long term (Miranda Garc\u0026iacute;a et al., 2020). A number of authors have studied the effects of such aid (Ca\u0026ntilde;ete et al., 2018; Chevalier et al., 2017; Dax et al., 2016; Navarro et al., 2016), butCollantes \u0026amp; Pinilla (2020b) indicate that if such policies are to be really effective, the approach adopted must take into account the specific needs of each community.\u003c/p\u003e\n\u003cp\u003eHypothesis 5\u0026nbsp;is as follows:\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eH5.\u003c/em\u003e Leader aid is effective in the smallest municipalities.\u003c/p\u003e\n\u003cp\u003eIn addition, women play an important role in rural revitalisation (Garcia et al., 2004a; Nieto Figueras, 2006a), especially in the primary sector (Ball, 2020; Dunne et al., 2021a). Their active participation in economic and social life can significantly help mitigate the process of depopulation (Ebrahimi et al., 2022). Some studies(Bonaf\u0026eacute;, 2018; Sampedro \u0026amp; Camarero, 2007) stress that women are essential for the continuity and development of rural society and of the primary sector (Cavicchioli et al., 2018; Dunne et al., 2021a). In the Celtiberian Range, the massive rural exodus by women was caused by the lack of opportunities (Cobano-delgado \u0026amp; Llorent-bedmar, 2020). This was tested in other research (Fern\u0026aacute;ndez-Gim\u0026eacute;nez et al., 2022; Johansson, 2016). As in Castilla-Le\u0026oacute;n, migration by young women reinforced the demographic and economic decline in marginalised areas (P\u0026eacute;rez, 2018). In other areas, however, some women returned to their former communities to set up businesses, leading to a slight demographic recovery (Baylina et al., 2016). It is therefore essential to introduce policies that will encourage active participation by women in rural areas in order to improve sustainability in these territories (Rodero Sanz, 2022).Van der Ploeg \u0026amp; Renting (2004) state that sustainable rural development introduces new gender dynamics, working patterns and forms of cooperation that involve both the administration and businesses and society. This indicates that research should consider variables not only by gender but also by municipality (Camarero, 2020; Camarero \u0026amp; Oliva, 2019)\u003c/p\u003e\n\u003cp\u003eHypothesis 6\u0026nbsp;is as follows:\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eH6.\u003c/em\u003e Women play a key role in maintaining and revitalising rural areas.\u003c/p\u003e\n\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n\u003ch2\u003eStudy area\u003c/h2\u003e\n\u003cp\u003eCastilla-La Mancha is an autonomous community in the south of Europe, with a population of around 2\u0026nbsp;million (INE, 2022). Most of the population is located in the central area of the region and in its corridors (Fig.\u0026nbsp;1). The community is made up of a total of 919 municipalities, of which approximately 90% (825) have less than 5,000 inhabitants.\u003c/p\u003e\n\u003cp\u003eWith regard to population evolution (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e(a)), this follows the same trend as the rest of Spain: growth during the first decade of 2000, decrease as from 2011/2012 and a slight recovery as from 2018. Interannual variations are more marked in Castilla-La Mancha than in the rest of the country (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e(b)).\u003c/p\u003e\n\u003cp\u003eDecree 108/2021 classifies rural areas in four groups: 1) sparsely populated areas (extreme depopulation and intense depopulation), 2) areas at risk of depopulation, 3) intermediate areas (agricultural and diversified, and 4) peri-urban rural areas. Note that sparsely populated areas and those at risk of depopulation are predominant in the region, accounting for approximately 77% of the total area (JCCM, 2022) (see Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"char\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eRural areas in Castilla-La Mancha\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eClassification\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e% municipalities\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eNumber\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eTV 2000\u0026ndash;2012\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eTV 2012\u0026ndash;2023\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eSparsely populated areas\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eExtreme depopulation\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e56.91%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e523\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-4.28\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-12.76\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIntense depopulation\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18.82%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e173\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3.77\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-11.15\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAreas at risk of depopulation\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAt risk\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.72%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e25\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3.52\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-11.34\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eIntermediate areas\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIntermediate agricultural\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.75%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e62\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e14.88\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-7.10\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIntermediate diversified\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8.49%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e78\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e37.10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3.82\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003ePeri-urban areas\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePeri-urban\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.55%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e51\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e119.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e17.34\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUrban\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.76%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e19.29\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-1.28\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eTotal\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e100%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e919\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cem\u003eSource\u003c/em\u003e: Drawn up by the authors based on data from JCCM (2022).\u003c/p\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e also shows the rate of population variation for the periods 2000\u0026ndash;2012 and 2012\u0026ndash;2023, and stresses that the loss of population has been concentrated in the smallest municipalities. Since these are located in areas with sparse population and at risk of depopulation, they have seen a demographic decrease that is significantly greater than that of larger municipalities, which have maintained greater population stability. This stresses the vulnerability to depopulation of the smallest rural territories and the importance of studying the idiosyncrasy of rural areas in Spain (Duranton \u0026amp; Venables, 2018).\u003c/p\u003e\n\u003cp\u003eWith regard to socio-demographic markers, it should be noted that the increased ageing of the population (16.52%), the falling birth rate (27.80%) and the decrease in young people (2.64%) lead to a 34% reduction in the labour replacement rate, that is, there are increasingly fewer people to occupy the positions of those taking retirement.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eSocio-demographic markets\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e2023\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e2012\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eRate of variation (%)\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePopulation\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.084.086\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.121.888\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-1,78%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBirth rate\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e7.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e9.71\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-27,80%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDeath rate\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e9.92\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e9.08\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9,25%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAgeing rate\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e123.56\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e106.04\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16,52%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLevel of young people\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e7.36\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e7.56\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-2,64%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWeight of women/total population\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.4976\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.4958\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.363%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWeight of men/total population\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.5024\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.5041\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.337%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLabour replacement rate\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e52.65\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e79.81\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-34.03%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cem\u003eSource\u003c/em\u003e: Drawn up by the authors based on data from the CLM (2022).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Data","content":"\u003cp\u003eThe data base used in this study is a cross-cutting one in which the units studied are municipalities. The year of analysis is 2022. The variables used in the study are presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSymbology and definition of variables\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDescription\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePopulation (POP)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal number of individuals in the municipality (INE, 2022).\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducation (primary and secondary)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePresence of an infant and primary school or a secondary school (Instituto de Estadística de Castilla - La Mancha, 2022).\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHealth care\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePresence of a health centre (Instituto de Estadística de Castilla - La Mancha, 2022).\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTechnology (TEC)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePercentage of 5G coverage over registered homes (Ministerio de Asuntos Económicos y Transformación Digital, 2022).\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSCA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePresencia de Zonas de Especial Conservación (Instituto de Estadística de Castilla - La Mancha, 2022).\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLEADER\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePublic aid (LEADER) granted to each municipality (JCCM, 2021).\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWomen (WOM)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNumber of women in each municipality (INE, 2022).\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOpen-ended contracts for women\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIndex of open-ended contracts for women (SEPE, 2022).\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTemporary contracts for women\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIndex of temporary contracts for women (SEPE, 2022).\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cem\u003eNotes\u003c/em\u003e: This table indicates the source and definition of each variable.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows the descriptive statistics for each variable.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDescriptive analysis of the variables\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"13\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u003cp\u003eFrequency\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eStd. Dev.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eMin.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e\u003cp\u003eMax.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePopulation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e5.893\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.856\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003e1.099\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u003cp\u003e12.057\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e579\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e340\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.331\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.194\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHealth care\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e299\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e620\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.675\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.469\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTechnology\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.144\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.314\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSCA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e440\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e479\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.521\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.499\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLeader\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e7.259\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e5.090\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u003cp\u003e13.870\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWomen\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e794\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e125\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.136\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.343\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOpen-ended contracts for women\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.167\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.295\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTemporary contracts for women\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.275\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.339\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"13\"\u003e\u003cem\u003eNotes\u003c/em\u003e: This table shows the frequency, mean and standard deviation and the minimum and maximum for each variable.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eDescriptive analysis of the variables reflects important aspects of the municipal context. The constant variables are transformed into logarithms to facilitate interpretation and reduce data dispersion.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eAccording to Decree 108/21, it is important to consider the characteristics of each territory in Castilla–La Mancha. It classifies the municipalities as follows: 1) sparsely-populated; 2) at risk of depopulation; 3) intermediate agricultural, and 4) peri-urban. Quantile regression allows for models to be adjusted to different levels of conditional distribution (Lamarche, 2019), which is useful when distributions are asymmetrical, as is the case in the areas studied. Also, this technique captures heterogeneity, which is not visible in a conventional linear model (González-Val et al., 2017). Some studies on population have already used this methodology (Álvarez, 2018; Carson, 2018; Ogasawara \u0026amp; Matsushita, 2019).\u003c/p\u003e\u003cp\u003eThe quantile regression proposed by Koenker \u0026amp; Bassett (1978) extends the concept of linear regression by modelling the quantiles of the conditional distribution of the dependent variable \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\mathcal{y}\\)\u003c/span\u003e\u003c/span\u003e given a set of independent variables X. Instead of modelling the conditional mean of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\mathcal{y}\\)\u003c/span\u003e\u003c/span\u003e, quantile regression aims to estimate any quantile \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\tau\\:\\)\u003c/span\u003e\u003c/span\u003e of the distribution of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\mathcal{y}\\)\u003c/span\u003e\u003c/span\u003e. The quantile \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\tau\\:\\)\u003c/span\u003e\u003c/span\u003e is defined as the value \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{y}_{t}\\)\u003c/span\u003e\u003c/span\u003e so:\u003c/p\u003e\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:P\\left(Y\\le\\:{y}_{t}\\:\\right|\\:X)=\\:\\tau\\:\\:$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFor a quantile \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\tau\\:\\)\u003c/span\u003e\u003c/span\u003e, quantile regression minimizes a function of weighted loss, in which positive and negative results are treated asymmetrically, as follows:\u003c/p\u003e\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\:\\text{min}\\sum\\:_{i=1}^{n}{p}_{\\tau\\:}({y}_{i}-{x}_{i}\\beta\\:)$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{p}_{\\tau\\:}\\left(u\\right)\\)\u003c/span\u003e\u003c/span\u003e is the function of quantile loss defined as:\u003c/p\u003e\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$$\\:{p}_{\\tau\\:}\\left(u\\right)=u(\\tau\\:-\\parallel\\:\\left(u\u0026lt;0\\right))$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eHere, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\parallel\\:\\)\u003c/span\u003e\u003c/span\u003e (u \u0026lt; 0) is an indicator function that takes the value of 1 if \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:u\u0026lt;0\\)\u003c/span\u003e\u003c/span\u003e, and 0 otherwise. This penalises the residuals differently below and above the quantile \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\tau\\:\\)\u003c/span\u003e\u003c/span\u003e, providing different fits depending on the value of the quantile.\u003c/p\u003e\u003cp\u003eThe quantile regression model is expressed as follows:\u003c/p\u003e\u003cdiv id=\"Equ4\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ4\" name=\"EquationSource\"\u003e\n$$\\:{Q}_{y\\tau\\:}\\left(X\\right)=X{B}_{\\tau\\:}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e4\u003c/div\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Q}_{y\\tau\\:}\\left(X\\right)\\)\u003c/span\u003e\u003c/span\u003e represents the quantile \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\tau\\:\\)\u003c/span\u003e\u003c/span\u003e – nth of the distribution of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:y\\)\u003c/span\u003e\u003c/span\u003e conditional to \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:X\\)\u003c/span\u003e\u003c/span\u003e, y \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{B}_{\\tau\\:}\\)\u003c/span\u003e\u003c/span\u003e are the specific coefficients for each quantile. Independent models can be adjusted for the quantiles 0.76 (sparsely-populated), 0.79 (at risk of depopulation), 0.94 (intermediate agricultural) and 0.99 (peri-urban). This distribution allows for the analysis of the different characteristics of municipalities in relation to the behaviour of the explanatory variables in the model with the dependent variable, in this case, population.\u003c/p\u003e\u003cp\u003eThe equation that represents the model is the following:\u003c/p\u003e\u003cdiv id=\"Equ5\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ5\" name=\"EquationSource\"\u003e\n$$\\:{Q}_{population}\\left(\\tau\\:|X\\right)={B}_{0}\\left(\\tau\\:\\right)+{B}_{1}\\left(\\tau\\:\\right)*Education+{B}_{2}\\left(\\tau\\:\\right)*Health\\:care+{B}_{3}\\left(\\tau\\:\\right)*Technology+{B}_{4}\\left(\\tau\\:\\right)*ZEC+{B}_{5}\\left(\\tau\\:\\right)*LEADER+{B}_{6}\\left(\\tau\\:\\right)*Women+{B}_{7}\\left(\\tau\\:\\right)*Open-ended\\:contract+{B}_{8}\\left(\\tau\\:\\right)*Temporary\\:contract$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e5\u003c/div\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Results and discussion","content":"\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e shows the results of the regression at the different quantile levels − 0.76; 0.79; 0.94 and 0.99.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eResults of the quantile regression\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eType/Variables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSparsely populated\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAt risk\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIntermediate\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePeri-urban\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.087* (-0.782)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.951 (-0.957)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.389** (-1.195)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.926 (-1.282)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHealth care\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.135*** (-0.182)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.135*** (-0.166)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.951*** (-0.382)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.171*** (-0.414)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTechnology\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.942*** (-0.463)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.898*** (-0.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.271*** (-0.798)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.553*** (-1.563)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSCA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.647*** (-0.183)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.689*** (-0.177)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.555*** (-0.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.901*** (-0.182)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLeader\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.261*** (-0.018)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.272*** (-0.018)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.375*** (-0.278)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.498*** (-0.019)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWomen\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.831*** (-0.317)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.898*** (-0.325)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.543*** (-0.799)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.673*** (-1.527)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOpen-ended contract\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.141*** (-0.328)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.159*** (-0.326)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.589 (-1.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.912*** (-1.705)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTemporary contract\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.267*** (-0.447)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.82*** (-0.402)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.615*** (-0.619)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8.495** (-3.291)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003eNote. *** significance at 0.00; ** significance at 0.05; * significance at 0.1. Standard deviation().\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eIn sparsely-populated areas, the variables related to education, health care, technology and SCA have a significant and positive impact on population retention, which suggests that improving these factors could encourage inhabitants to remain. It is clear that women play a key role in these areas as their presence has a highly positive effect and is essential for holding back depopulation. In addition, temporary contracts for women are seen to have a considerable impact, showing that, even if employment is of short duration, job opportunities for women are crucial for retaining the population. Leader aid programmes are also significant, although their effect is more moderate. This points to the relevance of rural development policies.\u003c/p\u003e\u003cp\u003eIn municipalities at risk of depopulation, education is seen to be less relevant than in sparsely-populated areas, but health care, technology and SCA continue to be key factors for attracting population. The role of women is still positive, and their importance is clear for demographic balance in rural areas. Employment contracts, whether temporary or open-ended, are still relevant, as are Leader aid programmes, although their impact is slightly smaller.\u003c/p\u003e\u003cp\u003eIn diversified intermediate agricultural areas, education recovers its importance while health care, technology and SCA are still determining factors. Employment and gender variables gain importance, with a more marked relevance for women, especially through temporary contracts which have a significant impact on fixing population. In contrast, open-ended contracts become less relevant and Leader aid programmes, while positive, have a moderate impact in comparison with other areas.\u003c/p\u003e\u003cp\u003eIn peri-urban and urban areas, education loses its significance, reflecting a lower impact in more densely-populated areas. However, health care, technology and SCA are still important factors, and job stability for women, through open-ended contracts, is becoming consolidated as a key to population growth. Temporary contracts also have a positive effect and although Leader aid programmes are still relevant, they have a smaller impact.\u003c/p\u003e\u003cp\u003eThe results reveal important nuances regarding how the key variables affect population retention in rural areas. Education shows significant variability depending on the type of territory. In agricultural areas, it is again relevant, while in peri-urban or urban areas its impact is much smaller. This result is in line with those obtained by Sanz-Magallón Rezusta et al. (2015), Yu \u0026amp; Artz (2019), Guzman et al. (2020), Alonso-Carrillo et al. (2023) who indicate that the initial educational level is important in rural contexts. For access to health care, the results are in line with those given by Graham et al. (2018), Ohashi et al. (2019) and An \u0026amp; Zhang (2022), who stress that better health care infrastructure is essential for maintaining the population in rural areas.\u003c/p\u003e\u003cp\u003eAnother determining factor is access to digital infrastrucutre, such as 5G coverage, which is still crucial for attracting population and for developing rural areas. These results are in line with studies by Pontones-Rosa et al. (2021) and Lappalainen \u0026amp; Rosenberg (2022), who stress that digital connectivity is a key pillar for modernising rural territories. Digitalisation is an essential requirement for attracting and retaining population, and the integration of digitalisation is a driver for economic and social development (Larraz \u0026amp; García-Gómez, 2020). This was also found by Merino \u0026amp; Prats (2020) and Jato-Espino \u0026amp; Mayor-Vitoria (2023), who stress that technology can facilitate employability and improve standard of living in rural territories.\u003c/p\u003e\u003cp\u003eAn important aspect in the analysis is the role of protected areas. Their existence promotes tourism and strengthens local economies, resulting in a direct benefit for retaining the population. This finding is supported by studies such as those by Labianca \u0026amp; Navarro (2019), Molinero \u0026amp; Alario (2019) and Rodríguez-Rodríguez et al. (2021), who argue that protected areas amount to a strategy for long-term sustainability.\u003c/p\u003e\u003cp\u003eThe Leader aid programmes have had a positive impact in all the contexts but were less effective in more urbanised rural areas, which suggests that rural development must be complemented by other policies in less densely-populated areas (González Fernández et al., 2023). This is in line with Alloza et al. (2017), Vico Bosch \u0026amp; Rebollo Catalán (2018), Molina et al. (2022), Ma et al. (2020) and Pontones-Rosa et al. (2021).\u003c/p\u003e\u003cp\u003eFinally, this analysis stresses the importance of women as a factor for maintaining and increasing the rural population (Kumar et al., 2022). The presence and participation of women, through temporary contracts, is key for holding back population loss. This stresses the need for policies that promote gender equity as a key strategy in the demographic development of vulnerable areas (Bayisenge et al., 2019). These findings are in line with the observations of Johansson (2016), Rodríguez-Rodríguez et al. (2021), Alonso-Carrillo et al. (2023), Esparcia et al. (2020) and Fernández-Giménez et al. (2022) regarding the influence of women on rural migration dynamics.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study offers a cross-cutting analysis of the population of Castilla-La Mancha, an eminently rural area in southern Europe. In spite of some signs of recovery over recent years, Castilla-La Mancha still suffers from a serious problem of rural depopulation. Its population is elderly, the young population has decreased, and it is increasingly difficult to fill the jobs of inhabitants reaching retirement age. These demographic dynamics create significant challenges. By applying the technique of quantile regression, it has been possible to identify some key factors that affect population retention and growth in different types of municipality, allowing for the adoption of a more profound approach that considers the specific needs of each territory. This methodology is able to capture any heterogeneity in the effects of the explanatory variables and amounts to a robust tool for guiding rural development policies based on the demands of each municipality.\u003c/p\u003e\u003cp\u003eThe analysis shows that access to essential services such as education, health care and technology, together with the existence of protected areas and public aid programmes, are determining factors for turning around depopulation. However, their impact varies depending on the size of the municipalities in question. In larger locations, an increase in service availability does not always lead to significant changes in the population dynamics. Conversely, in smaller municipalities, where the provision of basic services is usually limited, any improvement in the provision of such services has a considerable impact on population retention and on standard of living. This finding, which might seem obvious, stresses how important it is for strategic investments in infrastructure and services to take local needs into account.\u003c/p\u003e\u003cp\u003eIn Castilla La Mancha, in which 57% of municipalities are in a situation of extreme depopulation, the main actions to be taken should aim to provide services in the rural environment that will bring well-being and quality of life to local inhabitants. Internet access is essential for the professional and personal development of the local population and also helps to develop public and private services in such territories. Moreover, rural development programmes should continue to focus on fixing the population based on the principle of equal opportunities because women play a key role in the economic and social revitalisation of rural areas. Their active participation in rural life is fundamental for holding back depopulation and revitalising such areas. Temporary contracts play a key role, because they represent a significant source of employment for women in rural areas. Even if contracts are short, they facilitate women\u0026rsquo;s entry into the labour market, allowing them to become economically independent while also strengthening the social fabric. The inclusion of women in economic and social activities not only strengthens the local economy but also promotes social cohesion and sustainability in the long term. Public policies oriented towards gender equality, stronger labour rights and the creation of specific opportunities are essential for guaranteeing that women will remain and contribute to the overall development of rural areas.\u003c/p\u003e\u003cp\u003eFinally, it is necessary for there to be dialogue and coordination among the various agents involved in the economic and social fabric in rural areas (Rural Development Groups, administration, businesses, the actual population), allowing for the design of strategies and measures that suit each territory. The key for fighting depopulation is to adopt an approach that distinguishes between the different territories, considering the particularities of each municipality and promoting collaboration among communities. Only by combining policies that take local specificities into account, with sustainable investments in services and infrastructure and measures to strengthen the social fabric, will it be possible to guarantee a viable and balanced future for rural communities.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eI.C, J.C and M.C review the paper after it was finished. G.C worked in the economic model\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e\u003cp\u003eThis article is financed by Project SBPLY/21/180501/000083 (Global and endogenous responses to the demographic challenge of ITI areas), funded by JCCM. There is not conflict of interests and the data is available within the project.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData is provided within the manuscript or supplementary information files\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAlam\u0026aacute;-Sabater, L., Bud\u0026iacute;, V., Garc\u0026iacute;a-\u0026Aacute;lvarez-Coque, J. M., \u0026amp; Roig-Tierno, N. (2019). Using mixed research approaches to understand rural depopulation. \u003cem\u003eEconomia Agraria y Recursos Naturales\u003c/em\u003e, \u003cem\u003e19\u003c/em\u003e(1), 99\u0026ndash;120. https://doi.org/10.7201/earn.2019.01.06\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAlam\u0026aacute;-Sabater, L., Bud\u0026iacute;, V., Roig-Tierno, N., \u0026amp; Garc\u0026iacute;a-\u0026Aacute;lvarez-Coque, J. M. (2021). 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StoryMap\u003c/em\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-6972099/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6972099/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eRural areas are undergoing a process of depopulation, especially in the smallest rural municipalities that are at great risk of population decline. In addition, they usually have very low population density. These factors determine their socio-economic development and amount to serious obstacles for entrepreneurial initiatives based on demand within the rural territory itself. This study therefore aims to find the main factors for preserving the rural population in Castilla-La Mancha, an eminently rural area. It specifies a Quantile Regression model using data from 2023. The results point to the need for more effective rural policies, for prioritising the role of women and for maintaining basic services.\u003c/p\u003e","manuscriptTitle":"Keys to mitigating rural depopulation. 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