Abstract
Utilizing the Survey of Adult Skills from the Program for the International Assessment of Adult Competencies (PIAAC) and the OECD database, this study investigates the role of gender inequality and public social spending in the gender gap in Not in Employment, Education, or Training (NEET) risk across various age cohorts. The research identifies a concentration of female NEETs aged 25 to 34 in nations with more pronounced gender employment gaps, whereas such disparities were not evident among the 15 to 24 age groups. In exploring the role of public social spending as a mitigating factor for the gender-based NEET rate gap, the study underscores that investments in education, labor market activation, and early childhood education serve as key factors in reducing gender discrepancies in the likelihood of NEET status.
Introduction
Despite advancements in gender equity, social exclusion remains a pressing issue, particularly visible in the disproportionate number of women falling into the Not in Employment, Education, or Training (NEET) category—a concern that stands at the forefront of policy discourse (Chamie, 2018; Fremstad et al., 2021). According to the OECD (2016), women within its member states have a 1.4 times higher probability of being NEET compared to men. This discrepancy is more acute among “inactive NEETs,” women disengaged from both the labor market and educational opportunities, outnumbering men by a ratio of three to one (OECD, 2019). A similar trend is observed globally, with countries outside the OECD like India, Rwanda, and Saudi Arabia reporting even higher female NEET rates, with ratios reaching 2.86, signifying a 25.8% NEET rate for women (ILO, 2019). Age exacerbates this gender gap; in the United States, data from 2020 indicated a modest gender NEET rate disparity of 1.4% points among the 20 to 24 age bracket, escalating to 6.4% points in the 25 to 29 cohort and further to 13.3% points among those aged 30-34 (Fremstad et al., 2021). The profound social exclusion of women from economic activities might not only pose a significant threat to the vitality of overall economic growth but also undermine the potential contributions of women to the socioeconomic sphere (Klasen & Lamanna, 2009).
The persistent challenge of female NEETs worldwide is met with a noticeable deficiency in empirical investigation into the structural impediments to women’s full engagement in education and employment, as well as the targeted policy measures required to ameliorate women’s NEET risks. This study endeavors to close this research gap by harnessing detailed data from the Programme for the International Assessment of Adult Competencies (PIAAC) combined with macro-level information from the OECD’s Social and Welfare statistics. Our aim is to unravel the complex contributors to the high incidence of NEET status among women and to scrutinize the role of social welfare policies in redressing these gender disparities.
The surge in the global NEET population has prompted an increase in empirical studies dedicated to uncovering the intrinsic risk factors associated with this demographic (Carcillo et al., 2015; Pitkänen et al., 2021; Youn & Kang, 2023). Nonetheless, these investigations have overlooked to address the unique challenges faced by female, a result of the diversity and systemic inequalities within the NEET demographic. While policy dialogues have theoretically proposed reducing unpaid caregiving responsibilities to lower the rates of female NEETs (Eurofound, 2012), a robust empirical foundation for such strategies, especially considering age-related variations, remains lacking. Acknowledging the critical role of age, particularly in relation to educational engagement and societal participation, is essential in understanding the dynamics of NEET occurrence among women. Our study aims to fill this research void by examining the critical role welfare state policies play in facilitating women’s integration into the labor market and educational settings. By analyzing how these institutional arrangements can address gender-specific disparities within NEET statistics, we endeavor to provide an empirical basis for developing and implementing targeted policy interventions.
In pursuit of this objective, our research articulates inquiries to assess the correlation between gender inequality and the incidence of female NEETs, as well as the moderating impact of welfare state mechanisms on the gender divide among those disengaged from work and learning; (a) does the likelihood of being NEET differ significantly between females and males, after adjusting for both individual and institutional characteristics? (b) Is there a discernible variation in the gender disparity of NEET rates among distinct age groups? (c) To what extent does gender inequality in the labor market, along with public spending on education, childcare, family assistance, active labor market initiatives, and employment protection laws, mitigate the likelihood of NEET status across various age cohorts of females?
Literature Review
Individual and Institutional Characteristics on NEET
The escalating prevalence of NEET individuals has emerged as a focal point of modern research, endeavoring to unravel the structural impediments that precipitate the socio-economic detachment of young people from established educational and occupational avenues. This academic endeavor is dichotomized into two principal investigative trajectories: the scrutiny of individual risk factors influencing youth disengagement and the assessment of institutional variables affecting NEET demographics across diverse geopolitical landscapes.
A substantial segment of NEET research is allocated to dissecting the socio-demographic adversities that predispose youth toward disengagement. Identified critical risk factors include coming from families with low educational attainment, immigrant status, or residing in single-parent households. Longitudinal and cross-national studies have consistently demonstrated that youths from these underprivileged backgrounds possess a significantly higher propensity to enter NEET status (Kevelson et al., 2020; Pemberton, 2008). This susceptibility is indicative of an intergenerational perpetuation of socio-economic hardships, frequently leading to premature cessation of educational endeavors and a resultant absence of qualifications. For example, empirical findings from Europe countries indicate an elevated inclination among youth from less educated familial backgrounds to disengage early from the educational system, markedly amplifying their risk of sustained NEET status (Bynner & Parsons, 2006; Rennison et al., 2005).
Conversely, a distinct line of NEET research emphasizes the critical role of institutional factors, particularly the quality of educational systems and the effectiveness of pathways from education to employment. This research advocates for targeted investments in education, especially those providing substantial subsidies, as key to reducing early school dropout rates. Such targeted funding is essential in equipping young people with the necessary skills for a seamless entry into the workforce (Caroleo et al., 2020; Quintano et al., 2018). Furthermore, the importance of active labor market policies cannot be overstated. The link between labor market dynamics and NEET incidence, notably during economic recessions such as Ireland’s Great Recession, demonstrates how economic contractions can significantly elevate unemployment and NEET rates (Kelly & McGuinness, 2015). Comprehensive initiatives, including job search assistance, training, and employment subsidies, have been shown to reduce NEET rates through increased public investment in these programs (Eurofound, 2012). This evidence highlights the importance of these policies, particularly for disadvantaged youth who often lack the necessary resources or information for successfully transitioning from education to the labor market (Assmann & Broschinski, 2021).
Expanding upon this dialogue, Youn and Kang (2023) elucidate the profound impact of augmented public social expenditure in mitigating NEET risks amongst socioeconomically disadvantaged youth. Their investigations reveal that increased social welfare investments significantly diminish the likelihood of NEET status, underscoring the pivotal role of welfare state apparatuses in bolstering prospects for individuals with inferior academic achievements or from educationally marginalized backgrounds. Moreover, discussions on labor market flexibility and its ramifications for youth employment pathways yield a nuanced consensus: whereas strict protections for temporary employment may unintentionally elevate NEET probabilities, the liberalization of standard employment norms exerts minimal influence on these risks. This perspective aligns with broader economic hypotheses suggesting that a rigid labor market may obstruct the genesis of new employment opportunities, thereby stalling the timely assimilation of youth into professional realms (Eurofound, 2012). A critical extension of this discourse is the nuanced examination of public social spending’s role in facilitating transitions of youth from academia to the workforce, particularly for those from socially vulnerable sectors. It underscores the imperative for more in-depth investigation into the distinct factors contributing to women’s heightened susceptibility to persistent NEET risks and examines the effectiveness of existing social safety nets in addressing gender-specific disparities in NEET occurrences.
Labor Market, Education Condition and Female NEET
The scholarly dialogue on female NEETs and gender disparities within the labor market calls for an intensified examination of the structural and societal barriers that impede women’s economic engagement. While research from Austria has highlighted that women, who exit the educational system prematurely or who are of an older age group are more susceptible to becoming NEET compared to their male counterparts (Tamesberger et al., 2014), there remains a paucity of investigations into how national characteristics, particularly structural determinants like gender inequality in the labor market, contribute to the gender gap in NEET rates. The direct influence of labor market gender inequalities on women’s likelihood of falling into NEET status constitutes a relatively unexplored domain, with a notable deficiency of empirical research addressing this issue. This lacuna in academic inquiry is especially pronounced in the context of examining the relationship between women’s labor force participation and various determinants including the quality of education, childcare provisions, family support systems, and policies facilitating the transition from school to work.
In the exploration of gender norms and their impact on labor market participation, a substantial body of research has illuminated the profound influence of societal expectations and entrenched traditional roles on women’s economic activities. This dynamic is particularly evident in studies like those conducted by Benotsmane and Stoeffler (2022), which have revealed significant gender disparities in NEET rates in regions where traditional gender norms are deeply rooted, such as in certain areas of Turkey. They demonstrate that in environments where conventional gender roles are strongly upheld, even women with advanced education and qualifications are often inclined to prioritize domestic responsibilities over professional opportunities. This societal framework not only limits women’s workforce participation but also suggests a broader cultural and policy challenge.
On the institutional front, the availability and affordability of childcare emerge as decisive factors influencing women’s decisions regarding employment. The OECD’s 2016 report (2016) underscores the critical role of childcare availability in determining women’s participation in the labor force. The lack of affordable childcare options is identified as a primary barrier to women’s employment, leading to their increased disengagement from the workforce. This finding is supported by research from Assman and Broschinski (2021), which points out that regions with accessible and affordable childcare facilities, such as in certain Northern European countries, tend to exhibit lower female NEET rates. These studies highlight the pivotal role that state-supported childcare systems can play in facilitating women’s economic participation. By providing reliable and affordable childcare solutions, the governments and institutions could alleviate one of the key barriers to women’s employment, thereby contributing to a reduction in gender disparities in the labor market and promoting greater gender equality in economic participation.
In scholarly discussions surrounding the NEET among women, the significance of public investment in family support policies has been increasingly emphasized. These policies are particularly relevant in societies where traditional gender roles are deeply rooted, as they have the potential to facilitate women’s entry into the labor market. However, the impact of such public spending on reducing NEET rates among women is a subject of varied findings in the research landscape. On one side, the increased allocation of funds to family-oriented policies does not necessarily correlate with a decline in NEET rates (Kevelson et al., 2020). This outcome implies that financial resources alone may not adequately address the complex barriers faced by women, which are often intertwined with societal norms and expectations related to caregiving roles. Conversely, other research presents a different narrative, proposing that comprehensive family support systems can significantly contribute to defeminizing welfare states and, consequently, reducing the likelihood of women falling into NEET status (Van Vugt et al., 2022).
These contrasting findings highlight that the effectiveness of public spending on family support policies in diminishing female NEET rates varies across different contexts. As indicated in welfare studies by Zwiers and Koster (2015), increased social expenditure does not guarantee equal benefits for all segments of the youth population. This disparity highlights the need for a more detailed understanding of the NEET population, considering the diverse life stages and circumstances that shape their specific risks and needs (Carole et al., 2020). Expanding on this notion, it becomes clear that age plays a pivotal role in determining the efficacy of such policies. Older individuals may have different barriers to employment or education compared to their younger counterparts, who might face challenges related to longer periods of unemployment or caregiving responsibilities.
This study leverages data from the PIAAC along with other OECD database sources to explore the institutional obstacles hindering women’s access to employment and education, and to evaluate how social safety nets can minimize the gender disparities in NEET rates among different age cohorts. This methodology seeks to offer a thorough insight into the determinants influencing women’s engagement in the workforce and educational systems, aiming to inform the creation of more effective policies tailored to address these challenges. Further, the age-sensitive approach could provide a more strategic pathway to reducing NEET rates and fostering gender equality in participation within the labor market and educational settings.
Data and Method
Data
PIAAC offers extensive insights into adult competencies, including literacy and numeracy, across individuals aged 16 to 65. This data, collected between 2011 to 2012, through a meticulous four-stage stratified random sampling technique, ensures national representation and comparability across 24 participating countries (Kirsch & Thorn, 2013). This initiative marked the first iteration of the survey, which is intended to be repeated every ten years. PIAAC’s comprehensive nature, detailing respondents’ demographic, educational, and employment backgrounds, allows for in-depth analysis of factors influencing NEET status. For this analysis, we concentrated on data from young adults aged 15 to 34, specifically from countries offering open-access data with reliable national attributes, resulting in a sample of 30,983 individuals across 16 countries. Notably, eight country data were excluded due to accessibility issues. Furthermore, this research was enriched by incorporating country-level indicators from OECD statistics, merging individual data with broader socio-economic contexts. This integration enables a more nuanced understanding of the influences on NEET status, recognizing the interplay between individual circumstances and larger national policy frameworks.
Measure
Outcome Variable
The primary outcome variable under consideration is the NEET status, identifying individuals aged 15 to 29 who were disengaged from education, employment, or training activities over the previous year. Employment is specifically defined as engaging in at least 1 hr of remunerated work in the past year, while participation in education or training encompasses even brief engagements. Our stringent criteria for NEET classification aim to exclude individuals temporarily disengaged from work or educational pursuits, focusing instead on those genuinely disassociated from these activities, thereby enhancing the reliability and accuracy of the NEET identification (Kirsch & Thorn, 2013). For analytical clarity, individuals identified as NEETs are coded as “1,” and their counterparts as “0.”
Exploratory Variables
The study meticulously incorporates a comprehensive suite of both individual and institutional determinants, unearthed through a thorough literature review and preliminary analyses. At the institutional level, the variables introduced include: (a) GDP from the World Bank, utilized as a barometer for economic scale and disparity; (b) the nation’s populace, reflective of the competitive nature of the labor market; To enhance interpretability in the analytical process, GDP, and the population are subjected to logarithmic transformation. (c) the total unemployment rate, an indicator of labor market fluidity; the degree of employment protection pertaining to (d) regular contracts concerning individual and collective dismissals, and (e) temporary contracts, as categorized by the Employment Protection Legislation (EPL) within the OECD’s employment database; (f) the GII from WHO, a metric of gender disparity across health, empowerment, and job market engagement; (g) the gender wage gap, measuring the median salary differences by gender; (h) the gender employment gap, comparing the employment rates across genders; (i) public expenditure on education relative to GDP, signifying investments into educational entities and familial subsidies; (j) public investment in active labor market programs, representing the GDP’s portion allocated to employment facilitation strategies; (k) public spending on family aid, which includes healthcare, housing, childcare, and parental leave, compiled as a GDP ratio within the OECD’s social statistics; (l) public spending on early education, evaluating GDP proportion spent on structured daycare services and pre-primary educational offerings.
At the individual level, our study meticulously includes a varied array of personal variables: age, gender, educational achievements of both the subjects and their parents, immigration status, household book volume, marriage status, and the count of children. Age is introduced as a continuous variable, yet further categorized into quintets: 15 to 19, 20 to 24, 25 to 29, and 30 to 34 years. Gender is dichotomized, adopting “male” as the reference category. Individual education year is continuous variable. Parental educational attainments are rigorously classified in line with the International Standard Classification of Education (ISCED), distinguishing between lower-secondary, upper-secondary, and tertiary education or above. Immigration status differentiates between first- and second-generation immigrants compared to native residents. Book volume is converted from categorical to a continuous scale, ranging from 1 to 6 points. This scale is anchored on the quantity of books present in the home environment, providing a nuanced measure of the household’s cultural and economic capital. anchored on the quantity of books in the home environment. Marriage status reflects cohabitation with a spouse or significant other. The number of children is stratified into none, one, two, three, or more, with childlessness serving as the baseline. Descriptive statistics for these variables by country are compiled in Table A1.
Analytic Plan
The following model building was carried out consistently with research questions to address whether gender inequality and public expenditure are associated with the gender gap in the NEET rate across age cohorts. Initially, logistic regression analyses were conducted to explore the relationship between gender and the risk of being NEET, with adjustments for both individual and institutional characteristics. Subsequently, subgroup analyses were carried out separately for males and females to assess how the considered factors differentially predict the likelihood of NEET status across genders. To address the second research question regarding age-related variations in NEET risk, the study segmented the analysis into four distinct age cohorts (15–19, 20–24, 25–29, 30–34 years old) to pinpoint the ages at which women are particularly susceptible to becoming NEET. For the third research question, focusing on cross-national comparisons and within-country variations, we developed interaction models for each age group, incorporating variables such as the overall gender inequality index, gender employment gap, gender wage gap, public spending on education, public expenditure on active labor markets, enrollment rates of 0 to 2-year-olds in public early education, public spending on early childcare, public expenditure on family aid, and public labor market flexibility, all in relation to females. These interaction models are devised to elucidate the impact of gender inequality and public social spending on the gender gap in NEET rates among different age groups.
To ensure robust statistical inferences that account for potential clustering errors due to the hierarchical structure of the data, non-responses, and various weighting adjustments like post-stratification and raking, we employed the repest module in STATA, utilizing the Balanced Repeated Replication (BRR) method with 80 replicate weights (Kirsch & Thorn, 2013). This replication technique generates multiple subsamples that mirror the original sample’s design, allowing for the computation of estimates for each subsample. The overall estimate is then derived from the aggregation of squared deviations between each subsample estimate and the aggregate sample, ensuring the reliability and validity of our findings.
Results
First, we report the NEET gender gap over country before but also after controlling for individual characteristics. The mean value of the observed NEET gender gap across 16 countries is 6.6%, from the lowest 0.6% in Spain to the highest at 16.4% in Japan. In all countries in our sample, females show higher NEET rate than males, the degree of the gender gap vary across countries. For Slovak Republic and Italy, the NEET ratio was 19% and 17%, respectively, ranking first and second among all countries, respectively (see Table A1), and the NEET ratio gap was also the first and fourth highest, respectively. Meanwhile, Japan and Korea show an average NEET ratio of about 10%, but the NEET ratio gap between men and women was found to be the first and third largest among all countries, respectively. In the case of Spain, the NEET ratio was the third highest among all countries, but the gap between men and women was not confirmed to be large. As Figure 1 shows, comparing the actual NEET gender gap with the predicted NEET gender gap after controlling for all individual level factors, including individual education year, age, parental education level, marriage status, etc., except Spain there are large portion of the gender gap remains unexplained and crucially, that the role of background characteristics could differ across country.
Actual and predicted gender gaps in NEET rate, by country.
Table 1 presents the parameter estimates predicting the association between gender and NEET risk for total, male, and female samples, after controlling for individual and institutional characteristics at constant. First, we found that female show 2.69 times (Exp(.99) = 2.69, p < .001) higher probability to be NEET than male after controlling institutional and individual factors. Consistent with previous studies, the results indicate that individual and parental higher education level, and the number of books at home are negatively related to the probability of being NEET, while Age, and number of children are positively associated with being NEET. Among institutional factors, public spending on education, and active labor are negatively related to the probability of being NEET. Public spending on family aid, and employment protection for temporary contract positvely predict NEET risk.
Predicting of Being NEET as a Function of Institutional and Individual Factors by Gender.
Note. Standard errors are reported in parentheses. Unstandardized coefficients and standard errors are adjusted by Balanced Repeated Replication (BRR) method.
p < .05. **p < .01. ***p < .001.
In the subgroup analysis conducted, it was discerned that the effects of public expenditure on education and active labor market initiatives do not significantly mitigate NEET risk among males, whereas for females, these factors exhibit a significant protective impact. The gender inequality index is observed to have a positive correlation with an increased NEET risk among males but not females. Contrarily, public spending on family aid is found to be positively correlated with NEET status exclusively for females. On the level of individual variables, an interesting pattern emerges with marital status: married males display a reduced propensity toward NEET status in comparison to their unmarried counterparts, whereas married females are more likely to experience NEET risk than unmarried females.
Second, to furnish empirical support for the second research question regarding the potential variation in the gender gap in NEET risk across different age groups, Model 2 in Table 2 incorporates the additional variables of gender wage gap and employment gap, building upon the foundational Model 1. Model 3 was further refined to assess the distinct effect of government expenditure on early childcare on NEET risk. This necessitated the exclusion of certain covariates that exhibited partial overlap or high correlation, such as expenditure on early childcare and the number of children enrolled in early childhood centers, as well as the overall gender inequality index and labor market inequality.
The Parameter Estimates From Logistic Regression Predicting the Gender Gap on the Odds of Becoming NEET After Controlling for Individual and Institutional Characteristics on the Risk of Being NEET Across Four Age Cohorts.
Note., Standard errors are reported in parentheses. Unstandardized coefficients and standard errors are adjusted by Balanced Repeated Replication (BRR) method.
p < .05. **p < .01. ***p < .001.
Model 1 in Table 2 reveals that, after adjusting for both individual and national characteristics, there is no significant difference in NEET risk between genders within the younger age cohorts of 15 to 19 and 20 to 24 years. However, a noticeable gender disparity in NEET risk emerges in the 25 to 29 age group, with females being 3.29 times more likely (Exp(1.19) = 3.29, p < .001) to be NEET compared to males. This gender gap further expands in the 30 to 34 age group, where females are found to be 5.21 times (Exp(1.65) = 5.21, p < .001) more likely to be NEET than their male counterparts.
Finally, we introduced a series of interaction terms combining the female variable with various institutional variables related to gender inequality and public social expenditure. These included the gender inequality index score, gender wage gap, gender employment gap, public expenditure on education, expenditure on early education, active labor market policies, family aid, and the enrollment rate of children aged 0 to 2 years in public early educational institutions. Finally, to understand the results of the interaction intuitively, we visualized the results of the predicted probability of being NEET as a function of gender by country level factors over age group while holding the covariates constant. The analysis of interaction terms, as detailed in Table A2, has been graphically represented to highlight the most significant interaction effects. This analysis demonstrates that the differences in the gender employment gap, public investment in education, activation measures in the labor market, early education programs, and protections for temporary contract employment are more significantly associated with females than males, particularly within older age groups. Figure 2 delineates how the anticipated NEET incidence rates for both sexes alter in light of changes in country-level variables, under the assumption that both independent and institutional factors are held at their mean levels. Excluding the aforementioned variables, a consistent interaction effect was not detected across the cohort or within specific age groups for variables such as the gender inequality index, gender wage gap, public spending on family aid, and protections for regular employment contracts.
Predicted probability of being NEET as a function of gender by gender employment gap, public spending on education, active labor, early education, and employment protection for temporary contract over ag group holding covariates constant.
Column A of the analysis intimates that an expanding gender gap in employment corresponds with a tendency toward a higher NEET risk for women, a relationship that does not appear to alter significantly for men. This pattern is particularly notable in the 25 to 29 and 30 to 34 age cohorts, whereas it is less pronounced among the younger age groups, where the NEET risk does not exhibit a notable variance in conjunction with the gender gap in employment for either gender. In Column B, there is an observable association where an increase in public expenditure on education is coincident with a trend toward lower NEET rates for females, a trend which does not equally apply to males. This association seems to gain prominence in the older age groups. Column C suggests a correspondence between increased public spending on active labor and a trend toward a narrowing of the gender gap in NEET rates, with this trend being particularly observable in the 20 to 24 and 30 to 34 age groups. Column D illustrates a concurrent trend between elevated public spending on early education and a decline in NEET risk for women, more so in the 25 to 29 and 30 to 34 age brackets, suggesting a trend toward diminishing the gender gap in NEET risk. Finally, Column E indicates a concurrent trend where greater employment protection for temporary contracts aligns with a rise in NEET risk for males, while the corresponding trend for females is less discernible, implying a trend toward a reduced gender disparity in NEET risk, notably in the 25 to 29 and 30 to 34 age groups.
Discussion
The outcomes of the investigation on the overall gender gap in NEET risk reveal a pronounced disparity in the likelihood of attaining such status between genders, with our analyses indicating that women are approximately twice as likely to be NEET compared to men. Subsequent subgroup analysis elucidates that women who are married or have children tend to exhibit an increased propensity toward NEET status. Conversely, for married men, the likelihood of being NEET is lower, with no significant association detected between the number of children and NEET risk for males. The greater impact of marital status and parenthood on women’s NEET status, compared to men’s, can be attributed to societal gender roles that assign women more domestic and caregiving duties, limiting their engagement in work or education (Corrigall & Konrad, 2006). Additionally, consistent with previous studies (Bynner & Parsons, 2006; Kevelson et al., 2020; Pemberton, 2008; Rennison et al., 2005), socio-economic determinants, including individual and parental educational levels and the presence of books in the household, emerge as critical factors associated with the probability of being NEET for both sexes. These findings underscore the necessity for gender-sensitive approaches in the development of employment and education policies. Policymakers are urged to recognize the unique barriers encountered by women, particularly those who are married or with children, in accessing employment or educational opportunities. The implementation of targeted support measures, such as flexible working arrangements, childcare provisions, and educational initiatives, could significantly mitigate the gender gap in NEET rates. Moreover, enhancing overall educational outreach and resources within communities could further reduce NEET probabilities by addressing key socio-economic vulnerabilities.
The analysis, which segmented the population by age, revealed that the disparity in NEET status between men and women predominantly manifests within the 25 to 34 age group. For younger individuals aged 15 to 24, the gap in NEET rates between genders was not statistically significant, with both men and women exhibiting a low predicted likelihood of being NEET. This pattern suggests that the risk of becoming NEET is intricately linked with the transition phases from educational environments to the long-term labor market. Notably, this indicates that women might be more vulnerable to the dynamics of the education and labor market during these transition periods compared to men (Carole et al., 2020). The implications of these findings are significant for policy development and intervention strategies. They underscore the critical need for targeted support measures during key transitional stages for young adults, especially for women, to mitigate the heightened risk of NEET status. This could involve enhancing career guidance and support services within educational institutions, developing bridging programs that facilitate smoother transitions into the workforce, and implementing policies that encourage gender equality in employment opportunities.
Our research indicates that a higher incidence of NEET status among females compared to males is linked to gender disparities within employment opportunities, with this gap notably wider in the 25 to 34 age group. Despite a reduction in statistical significance in age-specific analysis, the gender disparity in NEET risk between genders is accentuated by a gender wage gap as well. The data suggests that increased NEET risk among women may result from gender-based asymmetries in the labor market, including systemic employment disparities and inflexible work conditions, further intensified by societal norms that disproportionately burden women with domestic and caregiving duties (OECD, 2017). These structural obstacles might be pronounced as women age and undergo key life transitions, such as marriage or becoming parents, limiting their participation in education and employment (UN Women, 2015). This evidence underlines the critical need for policymakers to devise comprehensive strategies to dismantle these systemic inequalities to mitigate the gender gap in NEET risk. Such strategies should include the enactment and enforcement of gender equality legislation in hiring practices, ensuring wage equity, promoting flexible work arrangements, and embedding gender perspectives into all stages of policy development.
Our research provides robust evidence supporting the advocacy for increased public expenditure on education, aligning with welfare research that highlights the correlation between educational spending as a percentage of GDP and the reduction of NEET risks among socio-economically disadvantaged population (Youn & Kang, 2023). Enhanced educational investment is instrumental in prolonging female students’ engagement in schooling, mitigating early school departures, and bolstering labor market readiness. Specifically, our analysis underscores the significance of public spending on early education in diminishing the NEET risk for women. Social investment in early childcare stands out as a decisive element in narrowing the gender gap in NEET rates, particularly within the 25 to 34 age demographic. The provision of affordable early childcare services plays a vital role in lowering the reservation wages for women, thereby facilitating their engagement or re-engagement in the labor force following childbirth.
This analysis suggests the imperative for policy reformation, particularly focusing on the 25 to 34 age group, moving away from the conventional emphasis on the younger cohort of 15 to 24 year-olds. Such a strategic pivot is essential to tackle the significant gender disparities observed in NEET rates, underlining the critical role of public investment in education and early childcare for this mature age category. The data indicate that providing specialized support for women within the 25 to 34 age range, by augmenting funding for lifelong learning and ensuring the availability of accessible childcare services, can markedly diminish the obstacles to re-engagement in employment and education. From a policy perspective, this calls for a thorough reassessment of existing welfare policies to verify their effectiveness in facilitating women’s labor market participation during and after pivotal life transitions, such as childbirth. Concentrating policy efforts on this older demographic enables government bodies to actively work toward eradicating deep-rooted gender imbalances in labor market engagement, thereby creating an economic landscape that is more inclusive and responsive to the changing needs of women throughout their career and life stages.
Moreover, directing public funds toward educational initiatives is anticipated to generate a synergistic impact when integrated with expenditures on active labor market policies, thereby enhancing the transition from educational settings to the workforce, especially for women. As the analysis shown, investment in active labor market strategies, such as job search support, training initiatives, and skill enhancement coaching, is particularly beneficial for women in the 25 to 34 age bracket, who may face barriers to accessing employment and training opportunities due to marriage and childcare responsibilities. These comprehensive interventions are crucial for facilitating women’s smooth re-entry into the labor market, simultaneously addressing the intertwined challenges of educational attainment and employment access.
Another important institutional precondition that may motivate social integration among women appeared to be the deregulation of part-time employment. Although there is a controversy regarding the flexibility of the labor market and NEET rate, our findings are consistent with NEET literature that discussed that the deregulation of part-time jobs might lower the barrier, especially for young people from a socially disadvantaged background who pursue labor market entry (Assmann & Broschinski, 2021; Caroleo et al., 2020). In particular, women challenged to balance work and family may benefit from part-time jobs with flexible working schedules. Nevertheless, the flexibility of the labor market may need to be supplemented by regulation to prevent employers from exploiting young women for a cost reduction.
While the present study has enriched the body of NEET research by focusing on the structural barriers faced by women, it is necessary to recognize a limitation stemming from its exclusive concentration on OECD countries. This research offers critical insights into the gender gap in NEET rates among OECD member states; however, the issue of gender disparity in NEET status is also prevalent in non-OECD countries (ILO, 2019), highlighting the need for comparative research across a more extensive array of nations. Expanding the research to include both OECD and non-OECD countries would facilitate a deeper understanding of the diverse global contexts and cultural factors that influence the gender gap in NEET rates. Undertaking such a comparative analysis would not only broaden our comprehension of the underlying causes of gender disparities in NEET status but also contribute to the development of more nuanced and effective policy interventions tailored to the specific needs of different regions.
Footnotes
Appendix
Parameter Estimates From Logistic Regression Predicting the Varying Impact of Institutional Characteristics on NEET rate Between Gender Across Four Age Cohorts After Controlling for Individual and Institutional Characteristics.
| Variable | Full cohort | Age group | |||
|---|---|---|---|---|---|
| Age 15-19 | Age 20-24 | Age 25-29. | Age 30-34 | ||
| Interaction terms | |||||
| Female × Gender inequality index(A) | −.88 | 11.38 | −.05 | 2.27 | −4.48 |
| (1.84) | (6.32) | (3.34) | (3.77) | (3.70) | |
| Female × Gender wage gap(B) | .03** | .02 | .04 | .03 | .02 |
| (.01) | (.08) | (.02) | (.02) | (.02) | |
| Female × Gender employment gap(B) | .05** | .01 | −.00 | .07* | .08** |
| (.02) | (.06) | (.03) | (.03) | (.03) | |
| Female × Public spending on education(A) | −.53*** | .16 | −.16 | −.64** | −.68*** |
| (.11) | (.53) | (.27) | (.23) | (.17) | |
| Female × Public spending on active labor(A) | −.62*** | −.60 | .01 | −.74* | −.78*** |
| (.17) | (.95) | (.40) | (.30) | (.23) | |
| Female × Public spending on family aid(C) | −.17* | .48 | −.05 | −.21 | −.26 |
| (.07) | (.30) | (.16) | (.14) | (.14) | |
| Female × Public spending on early education(C) | −1.57*** | −1.77 | −.49 | −1.76** | −1.96*** |
| (.29) | (1.29) | (.67) | (.60) | (.48) | |
| Female × Employment protection for regular contract(A) | .10 | .87 | −.35 | .34 | .07 |
| (.20) | (.87) | (.37) | (.38) | (.27) | |
| Female × Employment protection for temporary contract(A) | −.46*** | −.84 | −.07 | −.67** | −.36 |
| (.11) | (.49) | (.19) | (.23) | (.19) | |
Note. (A), (B), and (C) each correspond to a specified interaction model retained as Model 1, Model 2, and Model 3 in Table 2, respectively. Standard errors are reported in parentheses. Unstandardized coefficients and standard errors are adjusted by Balanced Repeated Replication (BRR) method.
p < .05. **p < .01. ***p < .001.
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding
The author(s) received no financial support for the research, authorship, and/or publication of this article.
Ethical approval
This study has been conducted according to international and local guidelines ensuring ethically conducted research.
