Active Labour Market Policies for Rural NEETs in Lithuania: A Case of Rural Municipalities

This article aims to analyse active labour market policy efficiency for rural young NEETs integration into the labour market in the socioeconomic context of rural municipalities in Lithuania. For the empirical analysis, the administrative data of the public employment service concerning active labour market policy measures, e.g., training and mobility support, subsidised employment, and support for establishing or adapting workplaces of 2018 and 2022, as well as Lithuanian statistics data of 2018 and 2020 are used. The socio‐economic environment of rural municipalities was analysed using the economic indicators (complex index), public transport accessibility, average wage, and free vacancies indicators. The recipient’s integration into employment after six months of participation in active labour market policy measures is analysed. The data revealed poor economic indicators, undeveloped public transport, lower average salaries, and a need for more vacancies in rural municipalities. The integration into employment fell significantly in two rural municipality clusters after the Covid‐19 pandemic.


Introduction
The active labour market policy (ALMP) for facilitating youth transition to the labour market is emphasised in the Youth Guarantee Initiative and other youth policy documents at the EU and national levels.
The European Pillar of Social Rights, among its 20 principles, includes important directions for NEET's (i.e., neither in education nor in employment or training) social inclusion, such as investing in skills and education to unlock new opportunities for all and active support for employment.The document emphasises special attention to young people, who are more vulnerable to fluctuations in the labour market.
The youth need additional support because they have fewer opportunities to enter the labour market for the first time.The reinforced Youth Guarantee guides a stable labour market integration focusing on quality employment.Traineeships or internships could facilitate young people's access to the labour market.In line with the Reinforced Youth Guarantee, the new Lithuanian Youth Guarantee action plan of 2021 aims to: (e) provide motivational services for inactive youth; (f) create favourable conditions for voluntary activities, and (g) implement measures of ALMP.
The political agenda proves the importance of ALMP measures in supporting youth transition to employment.
The effectiveness and impact of ALMP measures on youth employment are broadly discussed in the academic literature.Calmfors (1994) and Jackman (1994) defined the function of ALMPs as adjusting the structure of labour supply to demand.The limited impact of ALMP measures on youth employment was revealed by Alegre et al. (2015), Pohl and Walther (2007), and Simões et al. (2022).ALMP measures in Lithuania are analysed from a social investment perspective (Skučienė, 2021), and measure variety and development (Moskvina & Okunevičiūtė-Neverauskienė, 2011;Okunevičiūtė-Neverauskienė & Moskvina, 2010).
The Covid-19 pandemic has brought a new challenge for the Lithuanian labour market.The Lithuanian recovery and resilience plan analysis for the Lithuania pandemic has exacerbated Lithuania's labour market challenges even though specific measures were introduced during the Covid-19 pandemic in 2020 (Brazienė et al., 2022).During the second wave of the pandemic, the unemployment rate mainly increased, affecting the most vulnerable part of the population: youth, lower-educated, unskilled, and those living in rural areas (Brazienė et al., 2022).Therefore, the measures envisaged in the Lithuanian recovery and resilience plan are expected to have a lasting impact on the functioning of the labour market, as well as on poverty reduction and income equality.However, the coverage of active labour market policies supporting upskilling, reskilling, and mobility has decreased overall.
The Covid-19 pandemic has brought a new challenge to the Lithuanian labour market.As stated in the Lithuanian recovery and resilience plan analysis, the pandemic has exacerbated Lithuania's labour market challenges.This article aims to analyse the ALMP efficiency for rural NEET's integration into the labour market in the socioeconomic context of Lithuania's rural municipalities.There is an impressive amount of research on the role of ALMP measures in supporting youth employment in Western countries.However, the research focusing on rural NEETs in Europe's peripheries is very limited.Lithuania is an interesting case for a study because it represents a small country with huge regional disparities in terms of economic performance, infrastructure, and accessibility to services.There is also a need for a comprehensive analysis of the effectiveness of ALMP measures for rural NEETs.The research findings will contribute to the rural NEETs literature, particularly the type of literature analysing the ALMP measures for youth in the context of structural factors.The additional contribution will be the implications for the policy seeking to ensure the sustainability of ALMP in rural regions.
The article is organised as follows: Section 2 presents the conceptual framework; Section 3 describes the methodology; Section 4 presents data analysis; and the discussion and conclusions are presented in Section 5.

The Effectiveness of ALMP for Rural NEETs in Lithuania
ALMP measures intervene in the search-matching labour market process.Rogerson et al. (2005) define the matching of workers and firms getting together.Carlsson et al. (2006) characterised the matching similarly: how unemployed workers and job vacancies meet is a matching process.The ALMP functions in matching defined by Calmfors (1994) As with every state intervention, the effectiveness of ALMP concerns the policy implementers and researchers.There are negative findings concerning the efficacy of academic debates.Alegre et al. (2015) found the limitations of the vocational qualification programme depending on the shortage of jobs and the design or implementation of a programme.Cabasés and Úbeda (2021) have a similar finding, stating that training programmes of little value are designed to justify EU investments.Measures are based on acquiring competencies and individual skills, leaving young people responsible for their precarious situation (Cabasés & Úbeda, 2021).In Bulgaria, the analysis shows a minimal range of support programmes to tackle the diverse problems and difficulties young people face (Kovacheva & Hristozova, 2022).The vulnerability of NEETs is related to low education and poverty (Mussida & Sciulli, 2023;Nestić & Tomić, 2018;Papadakis et al., 2019;Vugt et al., 2022).Nestić and Tomić (2018) found that NEETs are slightly younger, less well-educated, and more often live in rural areas.Mussida and Sciulli (2023) discovered that poverty and the NEET status are interrelated.Vugt et al. (2022) emphasise that young people with low literacy skills are more likely to be long-term NEET.
EU public policies targeting youth need to recognise the intersectionality of youth, gender, country of origin, class, and other forms of vulnerability (Rodriguez-Modroño, 2019).Not all EU policy emphasises the quality of work, job security, and social security (Lahusen et al., 2013).The insufficient mediation of institutions solving issues of NEETs was revealed by Saczyńska-Sokół (2018), who states that difficulties also arise with reaching out to this category of young people.This situation is because young NEETs remain out of the registries of public employment services (PES).
A positive evaluation of ALMP outcomes was found by Vugt et al. (2022), as countries with high levels of ALMPs are more successful in keeping young people in education or training and preventing them from long NEET spells.Wapler et al. (2018) find that more successful completions of ALMP programmes increase the search effectiveness of the participants and the total number of matches in a region.Escudero (2018) stated that the start-up incentives and the cluster of policies (job rotation, supported employment, and direct job creation measures), aimed at the most vulnerable, show the most favourable results in reducing unemployment and increasing employment.
The impact of ALMP can be affected by various social and economic factors, including the overall economic structure and national institutional environment, domestic regulation, education system, labour market characteristics, and policy design.Despite mixed evidence on the effectiveness in different socioeconomic environments, policy measures can challenge unemployment and positively impact labour market performance (Brown & Koettl, 2012).
The measurement of the effectiveness of PES is part of the evaluation of ALMP.Cichowicz et al. (2021) stated that the effectiveness of PES is linked to the conditions in the country in which these units provide services.They used the financial and human resources of PES as inputs; the outputs are outflow from unemployment due to starting work and taking a job.Cichowicz et al. (2021) found that the employment offices included in the analysis had a significantly diversified efficiency level as they operated under certain environmental circumstances.Koning (2009) analysed the effectiveness of the Dutch PES workers using the job placement rates of benefit recipients, the impact of these workers on the benefit denial rate of unemployment insurance and social assistance schemes, and the role of the PES in attracting and registering vacancies and found that a higher worker/client ratio at offices increases the outflow rates for short-term unemployed.
The effectiveness of ALMP measures from a social investment perspective is discussed by Bonoli (2010), who classified those measures as "strong" and "weak" investments in human capital.This author believed that a substantial investment provides education or training, while a weak one creates new jobs, job subsidies, counselling, services for getting the job, job searching programmes, benefits, and tax allowances.
At the same time, Broka and Toots (2022) analysed youth welfare citizenship using selective/inclusive dichotomy.They found that all three Baltic countries represent the inclusive type.The inclusive type is characterised by a low level of school dropout, high shares of students in vocational training and education (VET); high school enrolment rate, low youth unemployment; low NEET rate; low work poverty; high participation at youth-oriented ALMP; high youth employment rate (Broka & Toots, 2022).As for the Baltic countries, Latvia can be clearly distinguished with its focus on training initiatives.In contrast, to some extent, Lithuania and Estonia focused on employment initiatives, e.g., the My First Job scheme with apprenticeship grants, voucher systems, and wage subsidies (Broka & Toots, 2022).Tosun et al. (2017) analysed youth-oriented ALMP in the Nordic and Baltic states in 2013-2014, where they distinguished Estonia's more significant focus on human capital investment, while Latvia adopts a broad policy approach to investment in human capital, and Lithuania focuses on wage subsidies.
In many rural and remote areas, the reforms in vocational education are needed.For example, as analysed by Bettencourt et al. (2023), the VETs needed reforms in the Azores.Their study shows the importance of improving the sector's social representation; the training and negotiation forums are needed to drive stakeholders to concrete actions aligned with the integrated governance views.
Regarding the social and territorial cohesion of the Lithuanian recovery and resilience plan (its fourth pillar), there are several measures to support this, such as the revision of the minimum income system and social benefits and additional ALMP measures-e.g., by enabling jobseekers to enhance their qualifications and competences through vocational training, higher education programmes, and providing an opportunity to learn by participating in apprenticeship programmes.
At a national level, in Lithuania, there is an increasing effort to support young people's integration into the labour market through the employment support system.The Parliament of the Republic of Lithuania (2020) aims to decrease unemployment and actively include different population groups in the labour market.
Among other groups, young people and long-term unemployed are prioritized.Through higher education institutions and the private sector, it is foreseen that: "social innovations will be expanded and ensure that

Methodology
The empirical part of this research focuses on rural municipalities in Lithuania, where there are 60 municipalities in total.There are five main clusters of municipalities: larger cities (Vilnius, Kaunas, Klaipeda, Siauliai, Panevezys, and Alytus), ring municipalities (Vilnius region, Kaunas region, Klaipeda region, Siauliai region, Panevezys region, and Alytus region), municipalities with the resort status (Neringa, Palanga, Birstonas, and Druskininkai), urbanised (Elektrenai, Sirvintai, Mazeikiai, etc.), and rural (Silute, Pagegiai, Lazdijai, Kalvarija, etc.).In total, there are 35 rural municipalities.All the municipalities are named according to their geographical location, indicating the degree of urbanisation (cities, urbanised, or rural) or special status (resort).Even though Lithuanian municipalities are among the largest in Europe and the OECD, and the average size of municipalities is approximately 47,000 inhabitants (vs.10,250 in the OECD and 5,960 in the EU), the size of the municipalities differs considerably.For example, as of 2023, Vilnius city municipality has 586,836 and Kaunas city has 302,875 inhabitants.For instance, some rural municipalities have very few inhabitants (Pagegiai municipality has 7,151 and Pasvalys 23,302 inhabitants).Although there are differences in the size of the municipalities, each municipality is responsible for the provision of services (education, health care, social protection, etc.).
The integration of ALMP measures recipients into the labour market is analysed in the socioeconomic context of rural municipalities.The socioeconomic environment of rural municipalities can characterise the capacities of labour market demand and mobility for searching for jobs.Two time periods, before and after pandemics (2018 and 2022), are selected for further analysis.For the evaluation of ALMP at the regional level, this research focuses only on rural municipalities.The selection of rural municipalities is based on their welfare index methodology (Vilnius Policy Analysis Institute, 2022).Out of 60, 35 municipalities were selected for further analysis.The secondary analysis of Lithuanian statistics for 2018 and 2020 was used to evaluate structural indicators, which include the average wage, the number of vacancies, and the number of unemployed youths in every municipality.These data are additionally interpreted using the meta-analysis of the municipalities index (Vilnius Policy Analysis Institute, 2022) and the data about public transport development in the municipalities (Ranceva & Ušpalytė-Vitkūnienė, 2022).
For the evaluation of ALMP effectiveness, administrative data of Lithuanian PES of 2018 and 2022 are used.
The data of PES includes the covers information on recipients' participation in the following measures: training support, mobility support, subsidised employment, and support for establishing or adapting workplaces.For this research, the age group of 18-29 was selected.The integration into the labour market indicator was used after six months of participation in measures.The PES data are analysed using descriptive statistics, hierarchical, and k-means cluster analysis.The hierarchical cluster analysis was used to explore the number of rural municipality clusters that have similar characteristics.The k-means cluster analysis allowed us to clarify the parameters of distinguished clusters.The clustering is based on these criteria in municipalities in 2018 and 2022: the number of young unemployed per vacancy; the number of provided ALMP measures provided per young unemployed; the average wage in the municipality ratio to the average wage in the country; and the capacity to get a job after the ALMP.The two structural indicators were selected for clustering (the number of young unemployed per vacancy and the average wage in the municipality ratio to the average wage in the country).The two PES-provided indicators (the number of ALMP measures provided per young unemployed and the capacity to get a job after the measures) are used.

Socio-Economic Characteristics of Rural Municipalities in Lithuania
The rural municipalities in Lithuania differ due to socio-economic characteristics: municipalities' welfare index, the number of inhabitants, and accessibility to services (health care, transportation, social services, etc.).The municipalities' welfare index was developed by a group of researchers from the Vilnius Institute for Policy Analysis in 2017 and has five dimensions: social security, physical security, economic performance, quality of education, and demography.The economic performance index is constructed out of five indicators (net monthly salary, number of operating small and medium-sized enterprises, the number of operating economic entities, foreign direct investment per capita, and employment level in the 16-65 age cohort).

Politics and Governance
In 2018, the economic performance index (the index scores vary in a 0-10 range), in rural municipalities, was the highest in Kretinga and Taurage.The index was lower in other rural municipalities, with Zarasi scoring the lowest value of 0.2.Similar trends were observed in 2022, after the pandemic.The highest value of the economic performance index was 2.9 in Kedainiai.At the same time, the lowest value was 0.2 in Zarasai.
The index value for Vilnius (the capital) was 9.6 in 2018 and 9.3 in 2022.A huge gap exists between index values in rural municipalities and the capital city.The low economic performance index in these municipalities, before and after the pandemic, shows limited public and business sector labour demand.
The public transport development level shows the possibilities for the mobility of the unemployed.
Accessibility of public transport in rural municipalities of Lithuania is less 20-40% (Ranceva & Ušpalytė-Vitkūnienė, 2022).According to Ranceva and Ušpalytė-Vitkūnienė (2022), the public transport stations in rural areas should be at most one kilometre from the urban territories.Particular attention should be paid to these municipalities with less than 20% accessibility.Among such are the Jurbarkas, Sakiai, Silute, Kazlu Ruda, and Rietavas municipalities.In rural municipalities, the public transport timetable frequency varies between 3-7 daily.Moletai, Ignalina, Zarasai, Kėdainiai, Jurbarkas, Lazdijai, Silale, Kretinga, Rietavas, Birzai, and Joniskis have each a frequency of 3.1-5 public transports per day (Ranceva & Ušpalytė-Vitkūnienė, 2022).The lowest-funded public transports are in Rietavas, Pagegiai, Pakruojis, Anyksciai, and Varena, having a funding indicator of 3 euros/ha (Ranceva & Ušpalytė-Vitkūnienė, 2022).All rural municipalities in our research are funded up to 7 euros/ha, which means the enterprises cannot cover transportation costs (Ranceva & Ušpalytė-Vitkūnienė, 2022).The accessibility limitation of public transport is another restriction for the mobility of the unemployed from rural municipalities to other locations.
The public transport loss-making does not have promise for the development of the accessibility of public transport without support.
The average wage in rural municipalities is lower than the average in the country.It comprises 79.5% in 2018and 79% in 2022(Lithuanian Portal of Official Statistics, 2023).The lowest wages were in Zarasai (71% in 2018 and 69% in 2022), Lazdijai (73% in 2018 and 72% in 2022), Skuodas (72% in 2018and 73% in 2022), and Varena (73% in 2018and 72% in 2022).The low-paid vacancies are not attractive to youth and, at the same time, can create a poverty trap between the social benefits and possible wages.Broka and Toots (2022) characterised the Baltic states with low youth unemployment; however, the picture seems different in the Lithuanian rural municipalities.Of all the unemployed in rural regions of Lithuania, about 20% are young people, which is a high rate considering young people's life-course perspective and the sustainability of integration in the labour market in these regions.

Youth Unemployment Characteristics in Lithuanian Rural Municipalities
Youth unemployment in rural municipalities ranged from 10% to 20% in 2018 and from 11% to 18% in 2022.
Investment in training is an important policy measure for inactive youth.It improves the human capital of inactive people and can ensure better prospects in the labour market over their life course.So, the reduced participation in training is a potential risk for sustainable integration in the labour market of youth in rural municipalities.The labour market needs more capacities to adapt to the changing labour demand.
The other measures that support mobility and establishing or adapting workplaces were smaller-scale measures provided for young unemployed.There was the most significant share of receivers for mobility The support for business start-ups received 23 young unemployed in 2018 and 19 in 2022.In 2018, the number of young people unemployed in business start-ups was: nine in Jonava, six in Birzai, four in Anyksciai, and four in Akmene.
Hierarchical cluster analysis shows seven clusters of municipalities using classifying criteria: structural indicators were selected for clustering (the number of young unemployed per job vacancy and the average wage in the municipality ratio to the average wage in the country) and the PES-provided services indicators (the number of provided ALMP measures provided per young unemployed and the capacity to get a job after the ALMP; see Table 1 and Table 2 in the Supplementary File).K-means cluster analysis discloses the indicator differences in clusters.
The employment rate of youth after ALMP in the third and seventh of rural municipalities was lower in 2022 when compared to 2018.There is a slight increase in the first and fifth clusters in 2022.The number of young unemployed per vacancy was the highest in the third, fifth, and seventh clusters.In the third cluster, low accessibility of public transport characterises Kazlu Ruda and Rietavas.The seventh cluster of municipalities has the highest average rate of young unemployed per vacancy.The significant lack of vacancies in these municipalities shows that each municipality had only one vacancy in 2018 and 2022.The Anyksciai's public transport was one of the lowest funded and the least accessible was Ignalina's (seventh cluster).Skuodas and Zarasai municipalities have the lowest part of the average wage compared to the country's average wage.Zarasai, Kalvarija, and Ignalina municipalities had the longest unemployment duration in 2016 (Daugirdas et al., 2019).Daugirdas et al.'s (2019) list of high territories excluded municipalities of Lithuania.Among them, Pagegiai was in the third cluster and Ignalina and Zarasai municipalities were in the seventh cluster.

Discussion and Conclusions
This research aimed to evaluate the ALMP measures for inactive youth to pursue integration into employment in the socioeconomic context of rural municipalities in Lithuania.The territorial labour market capacities should be considered by analysing rural youth unemployment.The analysis of socioeconomic  (2021), who emphasise the importance of labour market conditions for ALMP.Our contextual analysis additionally shows that labour market capacities should be analysed considering rural regions.
Broka and Toots (2022) attributed the Baltic countries (Lithuania included) as the inclusive type with low NEETs and investment in human capital.Our research on inactive rural youth shows slightly different situations regarding part of NEETs and investment in human capital.In Lithuania, the investment in the human capital of rural NEETs decreased after the pandemic, because the number of participants in training decreased.At the same time, in some municipalities, of the inactive youth, about 20% were unemployed.These findings follow Daugirdas et al. (2019) observations that, in rural municipalities, part of the population decreased during the last two decades, due to the reduced number of children and youth and their emigration.Rural municipalities are demographically vulnerable because the population of 65 years old and older is twice as high as the youth (Daugirdas et al., 2019).Such demographic trends influence business and labour market capacity.
Among the ALMP measures, training support and supported employment dominated.Training is a strong investment in human capital (Bonoli, 2010) and it is very important for rural NEETs seeking to improve their life course prospects.However, the inclusion in training decreased after the pandemic.The mobility support slightly increased after the pandemic, a sign of better inactive youth inclusion in employment from rural municipalities.
We clustered the rural municipalities of Lithuania using two structural indicators and two ALMP measures indicators.Although rural municipalities in Lithuania are quite similar in economic viability, public transport accessibility, and average wage, we observe differences in the number of unemployed youth per vacancy and the employment ratio after ALMP.In 2022, two clusters had lower rates of young unemployed, due to the capacity to get them employed after participating in ALMP measures.These clusters of rural municipalities also have lower transport accessibility or a higher number of young unemployed per vacancy and a small number of registered young unemployed.
These findings do not confirm the ineffectiveness of ALMP but rather offer some responses to the research of Lahusen et al. (2013), who criticised the focus of the youth employment strategy without considering the quality of work, job security, and social security.These findings align with Daugirdas et al.'s (2019), who classified the Lithuanian rural municipalities as having a high territorial exclusion, including Vilkaviskis, Lazdijai, Varena, Ignalina, Zarasai, Moletai, Anyksciai, Kupiskis, Pagegiai, Kelme, Radviliskis, Joniskis, Pasvalys, and Birzai as such regions.
Low regional labour market demand requires attention at the national level.The short-term solutions could be social innovations with government subsidies and more investment in the human capital of rural NEETs with adequate social security support.Long-term solutions could include developing a public transport network and attracting business investment in rural municipalities.
(a) ensure vocational counselling in every municipality; (b) increase the attractiveness of vocational counselling; (c) collect data about inactive youth; (d) provide social, educational and psychological services; : (a) it maintains the adequate aggregate supply of labour; (b) it can, through re-training, open new sectors; and (c) it can reduce the negative effect of the long-term unemployment benefits.Later, Wapler et al. (2018) list the functions of ALMP as follows: (a) it can help adapt the qualification of the jobseekers to the requirements of vacancies; (b) it can promote the search activity of jobseekers; (c) it can serve as a substitute for regular work experience; and (d) reduce the employer's uncertainty concerning the employability of job applicants.However, Escudero (2018) stated that ALMP: (a) facilitates the matching process between the supply and demand for labour so that a given number of job seekers will be associated with fewer vacancies; (b) maintains the level of adequate labour supply by keeping the long-term unemployed tight to the labour force; (c) affects the demand for labour; and (d) boosts the productivity of the labour force.
young people have the opportunity to work and earn and that working conditions are more flexible and responsive to individual needs" (Parliament of the Republic of Lithuania, 2020, p. 52).The strategic documents of Lithuania (specifically, the Income Inequality Reduction 2021-2030 Programme, the Strategy for Demography, Migration and Integration Policy 2018-2030, the 2014-2020 Programme for Employment, and the Inclusive Labour Market Development Programme 2021-2030) aim to improve the employability of the young unemployed and emphasise the measures of ALMP.The Lithuanian Youth Guarantee Implementation Plan (2014) defines the early intervention, activation, and motivation of youth and enhances youth integration in the labour market.The Republic of Lithuania (2021) intends "to ensure that all persons aged 15-29 who are not in employment, education or training receive an offer to work, continue learning (including professional training in the form of an apprenticeship), practice or an internship" (The Republic of Lithuania, 2021, p. 1).ALMP measures are defined in the Lithuanian Employment Law (2016).Every unemployed, despite their age, is entitled to these measures.Lithuanian ALMP includes (a) training support (vocational education, apprenticeship, internship, recognition of competencies acquired through non-formal education and self-education, informal adult education, acquisition of qualifications, and competencies that create high added value); (b) mobility support; (c) supported employment (subsidised employment and subsidy for the costs of a work assistant); (d) support for establishing or adapting workplaces (subsidising job adaptations, implementation of local employment initiative projects, and support for start-ups).The effectiveness of ALMP is defined in the methodology of the Lithuanian PES (2017).The integration into the labour market, registration, and direct benefit indicators measure the effectiveness of ALMP.
support in Pagegiai and Silute in 2022 and in Silale and Zarasai in 2018.Both in 2018 and 2022, the smallest part was in Plunge, Radviliskis, Akmene, and Kedainiai municipalities.There were only five cases of support for establishing or adapting workplaces in 2018.There were no subsidising job adaptation cases in rural municipalities in 2022.Implementing local employment initiatives was outside the ALMP measures in 2018.There were only eight cases in four rural municipalities (Ignalina, Kelme, Lazdijai, and Rokiskis) in 2022.

Table 1 .
Alegre et al. (2015)economic capacities of rural municipalities and that the average wage in rural municipalities is lower than in the rest of the country.The accessibility of public transport is very limited in most rural municipalities.These conditions define low labour demand and unattractive jobs and working conditions, due to low salaries.Therefore, the mediation of ALMP measures in the job search-matching process is limited.These findings align with the research ofAlegre et al. (2015)andCabasés and Úbeda