Is It Possible to Tackle Youth Needs with Agricultural and Rural Development Policies?

Abstract

Rural youth are influenced by a wide range of uncertainties regarding their personal and professional development. Rural youth and in particular rural NEETs (Not in Employment, Education or Training) are especially vulnerable and face higher risks of labour market, social and economic exclusion. This paper aims to analyse the determinants of the dynamics of rural NEETs in three post-transitional countries (Bosnia and Herzegovina, North Macedonia and Serbia) compared to the EU-28 average and to Ireland as an example of an established EU member state with over 50% of its population living in rural areas that can act as a benchmark for effective policy implementation to address challenges of marginalized youth, during the 2009–2019 period. The dynamics of rural NEET status and the efficiency/adaptability of regional development policies are revealed through analysis of macro and socioeconomic factors as well as specific employment-related indicators disaggregated by gender and degree of urbanisation. The comparative analysis indicates deficiencies in regional development policies among post-transitional countries and the potential to adapt modern European practices and policies for improving the rural NEETs’ position.

1. Introduction

Youth citizenship is continually faced with various uncertainties regarding their general and professional development and career/job opportunities [1]. In many countries across Europe, young people (aged 15–24 as defined by International Labour Organization) suffer significant disadvantages compared to their peers, by being detached from both the labour market and the education and training systems [2]. Central to this is the cohort of young people characterised as ‘Not in Employment, Education and Training’ (NEET youths) [3]. Although especially vulnerable, this societal faction contains unexploited potential in terms of their capacity to engage in the labour force and contribute to local economies. The NEET concept encapsulates a heterogeneous group [4,5], represented by (i) long-term and short-term unemployed NEETs, (ii) unavailable NEETs due to family duties, (iii) unoccupied NEETs, i.e., those not actively seeking a job, but also not restricted due to other obligations, (iv) NEETs seeking opportunities, including youth actively trying to find a job or education and training but who are still waiting for offers that match their competencies and status, and (v) voluntary NEETs, i.e., those who choose to be in such a situation [6].

The importance of this concept has been recognised by policy makers and researchers in many countries, as isolated attention is focused on the NEET indicator as a supplement for youth unemployment rates [7]. This focus has drawn uniform attention across and beyond Europe, evidenced with the reduction of NEETs population being highlighted in the Sustainable Development Goals 2030 (specifically goal 8.6), and thus adopted by all United Nations Members in 2015 [8]. Consequently, the rate of NEETs (aged 15–24), has significantly decreased for most European countries, reaching its lowest level in 2019. The average NEET rate for the EU-28 countries in this year was 10.10%, the highest rate evidenced by Italy at 18.10% and the lowest by the Netherlands at 4.30% [9]. However, this is still an emerging public policy issue for the post-transitional economies where relatively higher rates of NEETs were evidenced in North Macedonia (18.10%), Bosnia and Herzegovina (21.28%), and Serbia (15.30%) in 2019 [9,10].

The situation is particularly concerning in terms of the increased distribution of NEETs in rural areas, where the likelihood of falling into a NEET category is more prominent in most European countries, including those aspiring to become EU members. In 2019 the EU-28 countries demonstrated a higher NEET rate in rural regions (10.60%) compared to their urban counterparts (10.10%). These numbers were higher still in the rural NEET population of the post-transitional countries of North Macedonia (R:17.60%, U:18.10%), Serbia (R:18.70%, U:15.30%) and Bosnia and Herzegovina (R:24.12%, U:21.28%) [9]. The rationale for this numerical NEET surge is due to lower socioeconomic status, poor educational achievements [11] and limited opportunities for professional development of the rural population [6,12]. Furthermore, NEETs in rural areas experience higher risk regarding their economic and career advancement, are more often exposed to unemployment and poverty [13] and also develop longer dependence on parental support [14]. The level of economic development and the rural–urban population distribution are closely related, but post-transition countries are challenged with more acute demographic changes and internal and external migration. These trends therefore limit the capacity of rural NEETs to avail of positive mobility opportunities for education, employment and training.

In the context of post-transitional economies, numerous factors discourage and demotivate rural youth to secure an enhanced future in rural areas or indeed to mobilise and return (e.g., lack of access to services and technology and the remoteness—geography of the locality, social setting, role of family, friends and social networks, etc. [15,16]). Their primary challenge is seen in the restricted access to education and information, which is largely limiting their personal development, expertise and innovative thinking [17]. Limited access to financial resources, underdeveloped infrastructure and communication systems are some of the other factors which contribute to the exclusion of rural youth in the labour market [17]. Moreover, agriculture as the main source of income in rural areas, remains to be an unattractive profession for young people. This is mostly due to the low average wages in the sector, the lack of large capital investments to support modern technology advancement in the area emanating from a risk of uncertain markets and low prices of agricultural products, intensive physical work and many other unfavourable aspects [18,19].

The dyadic impact of migrated youths not seeing value in a return to agriculture in rural locations, coupled with unsupported youths in situ raises a particular concern for youth NEETs left behind and the agricultural industry in these areas. These factors are constantly contributing to labour force shortages in agriculture. This is not only directly damaging agricultural production but also influencing rural and regional development and the agricultural labour markets. It has been acknowledged that long-term or permanent migration can be reduced if the infrastructure, technology and information channels in rural areas are initiated or improved [17,20] or through the initiation of new business in rural areas with better paid positions for their employees [21]

With an ageing population and a lack of investment in the education, training and labour opportunities that support a financially receptive workforce in rural areas, and the potential negative impact on agriculture within and beyond rural communities, it is imperative that policy makers pause for thought and reassess current responses to challenges faced in rural areas. This paper aims to provide support in this regard by addressing the following research questions:

RQ1: What are the determinants of rural NEETs in three post-transitional countries, Bosnia and Herzegovina, North Macedonia and Serbia, in comparison to those of select European countries?

RQ2: What deficiencies are evident in the regional development policies among post-transitional countries, and in contrast, what successful practices and policies have been evidenced in EU-28 countries, with specific attention to Ireland, in an effort to identify barriers and solutions towards enhanced rural NEET positive mobility?

2. Materials and Methods

To ascertain the determinants of rural NEETs in the identified countries (post-transitional countries: Bosnia and Herzegovina, North Macedonia and Serbia; and EU-28 countries, Ireland given specific attention), EU-28 countries were chosen as a benchmark comparison as the literature evidences their particular success in terms of declining numbers of rural youth NEETs. Ireland was chosen in particular, as an example of an established EU member state with over 50% of its population living in rural areas. Ireland scores above the EU-28 average across a number of factors that influence the prevalence of rural NEETs. Between 2009 and 2019 Ireland has introduced a number of macro to micro level actions that have had a positive impact on unemployment figures with the target group and that establish this country as a benchmark standard for tackling challenges associated with rural disadvantage, in particular, challenges associated with being a rural NEET.

Secondary data was collected through different official data sources (Eurostat, ILOstat, State Statistical Office of each country, as well as national programmes and reports). The dynamics were observed within a time range of a 10-year period (2010–2019), as it is considered a recovery decade from the 2008 economic crisis (the Western Balkan region and transitional economies in general were directly hit mostly in 2009 and 2010). The analysis began with descriptive statistics in order to understand the macroeconomic context for being NEET in the observed countries [2]. Thus, the following indicators were included: youth population (aged 15–24) as a share of the total population, labour productivity (based on the Gross Value Added (GVA)), as a volume measure of the output, and the number of employed people, as a measure of the input use [22]. The influence of the general economic development on youth unemployment and NEETs was also assessed through the countries’ level of Gross Domestic Product (GDP) and, where available, through the risk of poverty rate. Additionally, an overview of the agricultural and rural development policy measures which directly or indirectly shape the rural environment and possibilities for young people was also included.

In addition to the international literature in which NEETs and rural youth issues are addressed [23], this issue has not attracted the attention of researchers or policy makers in the Western Balkan post-transitional countries. Therefore, a lack of youth policies is evident. As a consequence, there have been very few attempts (other than ad-hoc actions) to craft policies or programs to assist rural youth (including the more general NEET population) in capacity building to support and enable their transition to the labour market, thus improving their quality of life. Therefore, agriculture and rural development policies are seen as a major policy support to the rural areas of post-transitional countries. The analysis of the agricultural and rural development budget transfers focus on direct support measures [24], which is not an efficient way of dealing with youth or rural–urban migration challenges [21]. However, in situations where NEET-targeted policies do not exist, the set of existing agriculture and rural development policies will be presented and compared across the countries during the period from 2010–2019 (APM database for BA, MK and RS and the DAFM database for Ireland/EU-28). Furthermore, it is important to point out successfully applied public policies which contribute to the decrease of NEET share in total population, such as policies in Ireland, which has similar political, economic and social problems. Such analysis, together with a socioeconomic analysis, highlights the need to apply modern policies or those modelled on EU best practice. Specifically, analysis focused on the determinants of employment-related indicators (employment rate, unemployment rate and NEET rate) at the national level, and specifically for rural areas, to help identify priorities in the policy decision-making process. A linear regression model is used to determine whether the rural NEET indicator depends on other factors (rural unemployment, rural employment, gross domestic product and the youth population aged 15 to 24). However, because this model does not provide information on how strongly the variables are related, the Pearson correlation coefficient, calculated for each country, is employed for analysis of the relationship among the employment-related and the macroeconomic indicators [3].

3. Results

In most European countries, urban areas dominate, encompassing around 75% of the territory. In contrast, the portion of rural areas in the observed post-transitional countries dominate. The rural areas in North Macedonia cover around 88.70% of the territory and 47.50% of the total population. In Bosnia and Herzegovina it is even higher, with 91.94% of the territory being rural areas and 67.05% of the total population living there, while the rural population living in the rural areas of Serbia comprise 39% of the total population [25]. Heightened societal challenges in these rural regions include poverty or social exclusion (23.90% in rural areas versus 21% in urban areas), and in terms of academic prowess, levels of tertiary educational attainment are reduced (most of the rural regions in the EU present levels of tertiary educational attainment below 40%, while in the most urbanised regions, it is 60–80%), with lower levels of digital skills among the rural population (49% of the population have relatively basic skills compared to 63% for those living in the urban parts) [7,26]. In recent years, the rural areas in post-transitional countries have faced notable challenges relating to negative mobility, such as immigration, and have a small number of inhabitants. Furthermore, considering the age structure of people living in these areas, there is a high risk of regional depopulation through abandonment in the relatively near future. This trend is also evident in European countries, thus becoming a major concern for agricultural and rural development and sustainability [27,28].

The macroeconomic context of the observed countries reveals a modest but positive trend in all countries (

Table 1. Socioeconomic context indicators, 2010 and 2019.

	BA		MK		RS		IE
	2010	2019	2010	2019	2010	2019	2010	2019
Population (000)	3843	3488	2055	2076	7291	6964	4549	4904
Youth 15–24, as share of total population (%)	13.8	11.2	15.5	12.4	12.3	10.5	13.7	12.6
GDP at current prices (in mil. EUR)	12,570	18,012	7109	11,340	31,546	45,912	186,937	356,051
GDP per capita (EUR per capita)	3270	5164	3459	5462	4326	6592	36,770	60,170
Total employment (000 persons)	843	830	637	797	2396	2901	1882	2275
Unemployment rate (%)	27.2	15.7	32.1	17.3	19.2	10.9	14.6	5
Labour productivity (EUR/person)	12.3	18.5	9.6	12.4	11.0		66.51	147.1
At risk of poverty rate (%)			27.0		9.2	23.2	15.2	13.1

Legend: BA—Bosnia and Herzegovina; MK—North Macedonia; RS—Serbia; IE—Ireland. Source: Eurostat 2021 and National State Statistical Offices 2021.

). The GDP per capita in 2019 varies from EUR 5.1 thousand in Bosnia and Herzegovina, to EUR 6.5 thousand in Serbia. Similar positive trends are observed in European countries where the GDP per capita is significantly higher, that is, the EU-28 average reveals an increase of about 22% between 2010 and 2019, resulting in a GDP per capita of around EUR 32 thousand in 2019, and even higher in Ireland with around EUR 60.2 thousand per capita.

In contrast, all countries show a negative mobility depopulation trend when it comes to youth aged between 15 and 24. The lowest share of this category of youth is evidenced in Serbia (10.50% of the total population in 2019), while in Bosnia and Herzegovina, this youth category captures a relatively higher share of 11.20% of the total population. The share of this youth category is almost the same in North Macedonia and Ireland with 12.40% and 12.60% of the total population, respectively. The declining trend compared to 2010 is more prominent for Western Balkan countries, ranging from 14.63% in Serbia to 20% in North Macedonia. The youth population aged between 15 and 24 is also declining in Ireland, but at a relatively lower rate compared to the post-transitional countries.

The correlation between general economic development and unemployment revealed some interesting findings across countries. Total employment from EU-28 countries from 2010 to 2019 exposed a relatively small drop, whereas in the other observed countries, a significant growth in employment was recorded, except in Bosnia and Herzegovina where the total employment remained relatively stable during the analysed period. Consequently, the unemployment rate dropped markedly in all countries. The lowest unemployment rate is evidenced in Ireland (5%), while the unemployment is very similar within the Western Balkan countries, among which the lowest rate is determined for Serbia (14.60%) and the highest for North Macedonia (17.30%) in 2019. Considering the dynamic economic development, labour productivity is one of the key economic indicators offering a dynamic analysis of the economic growth, competitiveness and the living standards within an economy, as it depends on both labour availability and quality [24]. Calculated as the total Gross Value Added per employed person, the labour productivity levels have risen in all observed countries.

Consequently, these results are reflected in the youth unemployment in all countries. Considering youth aged 15–24, the highest drop is noted for Ireland (approx. 59%), and the lowest for the EU-28 countries (approx. 32%). Regarding the post-transitional economies, a significant decrease of about 43% is marked for Bosnia and Herzegovina and Serbia, while for North Macedonia was around 38% in 2019 compared to 2010. Such dynamics are also in line with the literature, whereby there is a strong positive correlation between the positive economic development trends and the general unemployment rate, including the youth unemployment and consequently the share of NEETs in one country [29,30].

Regarding the determinants of youth unemployment in the analysed countries, the results show quite different levels. The evidence presented on Figure 1 confirms the disparities among all observed countries on a national level and, in particular, for localised rural areas. Focusing on youth aged 15–24, all post-transitional countries reveal an unemployment rate of approximately 30%, with highest value marked for North Macedonia (35.60%) and the lowest rate determined for Serbia (27.50%). The youth unemployment in the post-transitional countries is almost comparable to the European countries; the youth unemployment rate of Ireland was 12.50% in 2019, while at the level of the EU-28 it amounts 14.40%. Very high youth unemployment rates and the relative disadvantage of young people compared to adults was also noted for the Mediterranean countries, which has arisen mostly due to the inflexible and sequential educational system enforced in those countries [2].

The situation deteriorates for those in rural settings. The combination of unsuccessful academic advancements and a limited labour market results in a pressure to accept less qualified jobs [6], the limited labour market in particular yielding significantly higher unemployment in rural regions in comparison to the national average. In this respect, the results for 2019 reveal the highest unemployment rates of rural youth (aged 15–24) in Bosnia and Herzegovina (34.10%), followed by the unemployed rural youth population in North Macedonia (32.94%). The unemployment of the rural youth in the observed European countries is significantly lower, i.e., for the EU-28 it is 13% and for Ireland in particular, 12.10%. Nevertheless, the economic growth evidenced for all analysed countries positively correlates with the employment possibilities for the rural population, leading to a significant drop of the unemployment rates when compared to 2010.

Subsequently, employment has increased during the analysed period for all countries. With regard to the post-transitional Western Balkan countries, the highest employment rates are suggested for Serbia (41.10%), followed by Bosnia and Herzegovina (23.10%) and North Macedonia (20.70%). Nevertheless, the employment in these countries is still lagging behind the EU countries, where the employment rate is much higher. These trends are also reflected for the rural population in all countries. Compared to 2010, the highest increase is found for Bosnia and Herzegovina (+38%), followed by Serbia (+33%) and North Macedonia (+31%). In the context of the observed EU countries, a steady growth of the employment in the rural regions is evident.

The unemployment rate is often correlated with the share of youth in the NEET category. In most of the European countries, a relatively high share of NEETs are registered, mostly due to the fact that this group of young people are in a detrimental position in the labour market compared to their peers [2]. In this context, the most adequate indicator of this detrimental position derives from the comparison between the youth unemployment rate and the corresponding rate for the rest of the population. In this respect, Figure 2 reveals the determined NEET rates for the observed post-transitional countries in comparison with the EU-28 average and Ireland. As we can see, the share of NEET has significantly decreased in all countries during the 2010–2019 period. The reduction of the NEET rate primarily relates to the proactive inclusion of young people in the job market and/or investment in youth education and training, thus determining their position in the labour market and improving their participation in positive mobility actions. The highest share of the NEET population is registered in Bosnia and Herzegovina (21.28%) and the lowest is in Serbia (15.30%). However, this is considerably higher than the share of the NEET population in the EU countries, with 10.1% for both the EU-28 and Ireland in particular. Considering that the highest NEET rates are not necessarily associated with the highest unemployment rates (for instance, the highest NEET rate is registered for Bosnia and Herzegovina, while the highest youth unemployment rate is evidenced for North Macedonia) leads our research to reveal that the NEET status should not be directly explained in reference to youth unemployment rates. This is not only a case within the observed countries, a similar situation is also evident for other European countries [2].

Moreover, the population living in the countryside face different, although related, barriers with respect to labour opportunities or education and training attainment, often forcing them to abandon rural communities in search of academic and professional opportunities in urban areas [31]. This induced migration contributes to degradation of rural communities, leading to limited job and education opportunities for rural youth. Consequently, the share of rural NEETs is higher compared to the share of this category of youth for the total country. In the context of the analysed post-transitional economies, it is even worse. It is more prominent for Bosnia and Herzegovina, with a share of 24.12% of the total rural population aged 15–24, followed by Serbia with 18.70%. Although the share of the NEET population in North Macedonia was higher at the national level, when examined for rural areas, the results show a decreasing trend, thus leading to lowest rate in 2019 of 17.60%. However, the lower rates for North Macedonia might arise from the fact that there is a significant migration from the rural regions on one side but also a notable decrease in the population aged 15–24 in the country. The share of rural NEETs at the European level is significantly lower in comparison to the Western Balkan countries.

In particular, the rural NEET indicator in all four countries is strongly influenced by the studied independent variables, such as employment and unemployment rates on one hand, and the gross domestic product and the share of youth aged 15–24 on the other, with an R² of 0.781 in Bosnia and Herzegovina to R² of 0.994 in Ireland (

Table 2. Relationship among the observed variables, per country.

BA	Pearson Correlation Coefficients				Regression Coefficients	p-Value
	rur_NEET	rur_empl.	rur_unempl.	GDP
Intercept					17.029	0.785
rur_NEET	1.000
rur_empl	−0.826	1.000			−1.52	0.090
rur_unempl	0.767	−0.982	1.000		−0.321	0.291
GDP	−0.681	0.897	−0.887	1.000	0.002	0.376
Youth 15–24	0.605	−0.786	0.787	−0.958	2.368	0.447
R Square	0.781
Significance F	0.066
MK	Pearson Correlation Coefficients				Regression Coefficients	p-Value
	rur_NEET	rur_empl.	rur_unempl.	GDP
Intercept					376.763	0.017
rur_NEET	1.000
rur_empl	−0.805	1.000			−1.592	0.168
rur_unempl	0.843	−0.982	1.000		−0.352	0.444
GDP	−0.623	0.626	−0.682	1.000	−0.011	0.012
Youth 15–24	0.569	−0.620	0.666	−0.995	−14.516	0.012
R Square	0.93
Significance F	0.004
RS	Pearson Correlation Coefficients				Regression Coefficients	p-Value
	rur_NEET	rur_empl.	rur_unempl.	GDP
Intercept					47.564	0.077
rur_NEET	1.000
rur_empl	−0.970	1.000			−0.228	0.465
rur_unempl	0.964	−0.994	1.000		0.128	0.618
GDP	−0.822	0.831	−0.827	1.000	0.000	0.263
Youth 15–24	0.766	−0.848	0.859	−0.860	−1.341	0.134
R Square	0.964
Significance F	0.001
IE	Pearson Correlation Coefficients				Regression Coefficients	p-Value
	rur_NEET	rur_empl.	rur_unempl.	GDP
Intercept					−13.773	0.152
rur_NEET	1.000
rur_empl	−0.695	1.000			0.186	0.281
rur_unempl	0.989	−0.775	1.000		0.547	0.004
GDP	−0.954	0.883	−0.969	1.000	0.000	0.966
Youth 15–24	0.307	0.287	0.188	0.193	0.828	0.187
R Square	0.994
Significance F	0.000

). Furthermore, the Pearson Correlation Coefficient confirms the strong positive relationship between the rural NEET indicator and each country’s unemployment rate on one side, as well as the negative correlation between the rural NEETs and the general macroeconomic conditions in terms of GDP and rural employment in the observed countries on the other.

All this highlights the low efficiency and efficacy of the applied public development policies, particularly in rural areas, i.e., most of the observed indicators clearly indicate that the rural areas are at high risk of being depopulated in the relatively near future, and therefore, prompt action should be focused there. This is partly a result of the negative image that rural regions have, but also due to ongoing processes of globalisation such as growing services and industry sectors that attract a more low-skilled, more youthful population. Bearing this in mind, tailor-made instruments and mechanisms should be developed and applied through national and European funds [28].

As evidenced in the available literature, in the post-transitional countries, public policies targeting young or NEET populations in rural areas are very rare and, in some instances, non-existent. Thus, agricultural and rural development policies are the only permanent source of support to the rural communities including youth. Those policies promote very traditional approaches to development in rural areas, focusing on the primary sector and mostly providing direct support to farmers. Only a small share of total budgetary support to agriculture is dedicated to structural and rural development measures (in BA 6.66%, MK 26.38%, in 2019), except in Serbia, where 62.24% of the budget is dedicated to the structural and rural development support in 2019. In addition, the subsidies from agriculture, are not policies that enable connections between markets, information and social networks and therefore do not contribute efficiently to rural development [21]. Despite limited success stories, there are few alignments between policy implementation and addressing the needs of the rural population (including youth, NEETs and producers) and where the discretion to make decisions is in the hands of those who are most connected to the context of influence [32].

Table 3. Types and levels of implementation of rural development measures, 2010–2019.

Measures	BA	MK	RS	IE	EU-28
STRUCTURAL AND RURAL DEVELOPMENT MEASURES	+ (whole period)	+ (whole period)	+ (whole period)	+ (whole period)	+ (whole period)
Improving the competitiveness of agro-food sector	+ (whole period)	+ (whole period)	+ (whole period)	+ (whole period)	+ (whole period)
On-farm investment and restructuring support	+ (whole period)	+ (whole period)	+ (whole period)	+ (whole period)	+ (whole period)
Agricultural infrastructure	+ (whole period—except 2013/14/15)	+ (whole period—except 2010)	+ (whole period—except 2016/17/18)	+ (whole period)	+ (whole period)
Off-farm storage, processing, marketing and promotion	+ (whole period)	+ (whole period)	negligible	+ (whole period)	+ (whole period)
Providing environmental and societal benefits	negligible	+ (whole period)	+ (whole period)	+ (whole period)	+ (whole period)
Payments to farmers in areas with natural and environmental constraints	− (no records)	+ (whole period)	− (no records)	+ (whole period)	+ (whole period)
Agro-environment, organic and animal welfare payments to farmers	negligible	+ (whole period)	+ (whole period—very low)	+ (whole period)	+ (whole period)
Other ecosystem related payments	− (no records)	− (no records)	− (no records)	negligible	+ (whole period)
Supporting rural economy and population	+ (whole period—very low)	+ (whole period, spec.2016/17)	− (no records)	+ (whole period)	+ (whole period)
Creation and development of non-agricultural activities in rural areas	2010/11/13/14 (very low)	+ (2015,2016,2019)	− (no records)	+ (whole period, decreasing trend)	+ (whole period)
Rural infrastructure, basic services and village development	+ (very low for the whole period, except 2014 and 2015)	+ (whole period, decreasing trend)	+ 2010/13/17/18/19	+ (whole period, decreasing trend)	+ (whole period)
Building local capacity (LEADER)	− (no records)	− (no records)	− (no records)	+ (whole period)	+ (whole period)
Miscellaneous rural development measures	− (no records)	− (no records)	negligible	+ (whole period)	+ (whole period)
Miscellaneous—support to producers	− (no records)	− (no records)	2010/2011	+ (whole period)	+ (whole period)
Miscellaneous—other support	− (no records)	− (no records)	2010/2011	+ (whole period)	+ (whole period)
OTHER MEASURES RELATED TO AGRICULTURE	+ (whole period)	+ (whole period, increasing trend)	+ (whole period, increasing trend)	+ (whole period)	+ (whole period)
Research, development, advisory and expert services	+ (whole period)	+ (whole period)	+ (whole period, increasing trend)	+ (whole period, increasing trend)	+ (whole period)
Food safety and quality control	+ (whole period)	+ (whole period, increasing trend)	+ (whole period)	+ (whole period)	+ (whole period)
Other general support measures	+ 2018 and 2019	+ (whole period)	− (no records)	+ (whole period)	+ (whole period)

Legend: BA—Bosnia and Herzegovina; MK—North Macedonia; RS—Serbia; IE—Ireland. Source: APM database; DAFM database.

sums up different measures implemented under the rural development programmes within the observed countries, for the 2010–2019 period.

Within the agricultural and rural development measures, there are a limited number of measures that support the youth population in BA, MK and RS. In the case of BA, measures targeting youth are still not attractive to government representatives and policymakers. Typically, such measures include building housing units for young married couples and single parents in rural areas, support for young agronomists who graduate from the Faculties of Agriculture and return to the countryside, and measures that support co-financing up to 35% of mechanisation purchase. Lack of consistency within these measures remains a concern. For example, it can be the case that when funds from specific measures are allocated under the budget, individual farmers who are eligible for the support do not have necessary skills to apply for specific funds either through low awareness or low education. One of the factors associated with this challenge is that specific measures that deal with information exchange and knowledge transfer do not exist to support such individuals in rural settings. Indeed, modern, non-traditional measures have not adapted to the knowledge level and capabilities of agricultural producers and/or youth population—the election is the other way around. Consequently, most of the “modern”, sophisticated public policy measures, focused on social capital development, remain unused resulting in such measures being identified as ineffective.

It is clear that change is necessary in order to support the EU youth citizenship. They play a crucial role in societal development, and they are drivers of modern rural development in era of rapid digitalisation caused by the on-going COVID-19 pandemic. In particular, young farmers tend to be more productive [33] as a result of their openness to new ideas and are more open to informed risk taking [34]. It is vital that this demographic is supported as youth farmers play an important role in meeting the challenges of food security and global warming [35]. In the case of the observed post-transitional countries, the role of rural youth is crucial considering the ageing profile of the rural population, the inheritance of the agricultural production and also the effects of the agricultural and rural development policies in strengthening the agricultural sector and the rural areas. In addition, such change must come with a recognition that challenges being faced by rural youth are also related to underdeveloped infrastructure, high land prices, difficulty in accessing credit and policy support as well as problems with farm succession [36]. The issues of farm succession are of particular importance in the case of older farmers who do not have formal retirement plans or are concerned about the future, as they are usually not willing to transfer their farms or they want to maintain social currency, lifestyle, identity and status [37,38]. If older farmers do not pass farms to the younger generation, as part of a structured process of transition, there is a risk that rural youth may become disillusioned, further compounding the issue [39,40].

In contrast, and with these issues that face young people in rural areas, Ireland has taken a position to proactively support youth NEETs, which may be considered by other European partners. A Progress Report [41] reveals several measures targeting youth in the Ireland Rural Development Policy and programs of works are broken down on a region by region basis. In addition, government initiatives include a targeted youth scheme that supports young people impacted by a range of factors including rural isolation. In addition, ongoing measures or programmes include: the engage youth in new activities in local communities (Pillar 4 Fostering Culture and Creativity in Rural Communities); support for local community groups, including low income workers and youth NEETs and support for lifelong and community education opportunities for disadvantaged communities and individuals (completed activity 2017–2019); the participation of young people in rural Ireland in Science, Technology, Engineering and Maths through the SFI Smart Futures Programme; public engagement activities through the support of regional science festivals in rural Ireland; and supporting the inclusion of the voices of children and young people in the development of tourism and recreation opportunities through the Children and Young People’s Participation Hub. Such a comprehensive approach to the NEET, rural youth population and its challenges underpins the success that Irelands is experiencing in decreasing the NEET population and increasing the quality of life in rural areas.

4. Conclusions

Several research findings indicate the importance of new programmes that focus on youth as drivers of inclusive rural transformation. As stated above, this vulnerable group of people is affected by many factors, and one of the indicators that captures the vulnerability of youth are NEET (Not in Education, Employment or Training) statistics. While it is true that that the NEET demographic is decreasing worldwide, it is still a significant challenge for developing countries, especially in rural areas. This is also reflected in high unemployment rates within youth populations and a low level of employment in observed countries.

Applied public policies in BA, MK and RS were focused on the first pillar measures based mainly on direct support to agricultural producers to generate additional income and to make their workplace more attractive so as to sustain production and, in addition, develop their business. However, it is evident that this approach decreases farmers’ autonomy on what and how to produce because the policies are oriented towards the generation of additional income and not towards the efficiency and efficacy of the use of available resources. The results are an ageing demographic of producers who are not oriented towards the market demands, nor to the optimisation of business processes, nor to the sustainable use of resources. Indeed, efforts to innovate are often frustrated by a dearth of educational programmes for older farmers or rural youth who are open to lifelong learning opportunities once there is a sustainable pathways for succession in place.

This issue has been addressed, in part, by Ireland and the EU-28. In these instances, specific production areas are not targeted; rather, there is a focus on general weaknesses within the sector. Policy users, in this sense, agricultural producers and indeed all other stakeholders e.g., tourism workers, hiking, etc., are considered in order to shape and implement interventions/projects to overcome specific problem or problems within the sector. Such approaches are more holistic in nature and require multi-stakeholder buy-in for successful implementation. In such contexts, rural youth play a critical role in this process.

In recent years, the launch of non-agricultural activities on the family farm has become topical (investment in tourism in rural areas, direct sale of own products, investment in crafts and trades related to agriculture, forestry, tradition, souvenirs, processing of domestic products). For example, the organising of various cultural programs, art colonies, art exhibitions, production and tasting of cakes, cheeses and wines (gastro program), an exhibition of old folk costumes, sightseeing of ethno-museums with old tools and old furniture. All this promotes the holistic involvement of rural communities as well as attracting visitors. The goal of such initiatives is to keep and revive the tradition of old knowledge and crafts. Providing the opportunities to youth to express themselves in a way that would lead to the improvement of engagement with the agricultural sector, as young farmers tend to be more productive than older generations when provided with opportunities to do so. Young farmers are more open to new ideas and are eager to take a greater risk that consequently leads to higher innovation capacities within the rural regions, resulting not only in agricultural development but also in the development of complementary businesses. Is it possible to tackle youth needs with agricultural and rural development policies? The authors believe that it is. However a more concerted approach is required along with a clear and unambiguous recognition of the relationship between lifelong learning specific to rural settings, the availability of targeted policies and the energy of young rural citizens that is required to perpetuate rural dwelling. Therefore, future research activities should be focused on education and lifelong learning system impact on rural NEET. In addition, future research should emphasize the “greying of Europe” (and observed countries). It is important to outline the main limitations of this study such as the use of secondary data and the weakness of statistical systems in transitional countries (BA, MK, RS).