¹ Laboratory of Strategy and Organization Management (LASMO), Hassan First University, BP 577, Settat, 26000, Morocco
² Independent Researcher, Casablanca, Morocco

Said Maizzou
Roles: Conceptualization, Data Curation, Formal Analysis, Methodology, Project Administration, Resources, Software, Writing – Original Draft Preparation

Abdelhak Anajar
Roles: Conceptualization, Formal Analysis, Investigation, Project Administration, Validation, Writing – Review & Editing

Mohamed Rahmo
Roles: Funding Acquisition, Investigation, Methodology, Resources, Supervision, Writing – Review & Editing

Africa is undergoing an unprecedented demographic transition. By 2026, the continent hosts approximately 1.5 billion people, of whom an estimated 545 million are young persons aged 15–35 the largest youth cohort in the world. This demographic trajectory presents a dual opportunity: if adequately absorbed into productive employment, Africa’s youth could generate a demographic dividend capable of sustaining economic growth for decades; conversely, failure to integrate young people into the labour market risks perpetuating cycles of poverty, social exclusion, and political instability.^1–5

Despite economic growth averaging 4–5% per annum across sub-Saharan Africa since 2000, employment generation has consistently lagged behind labour-force expansion. A structural disconnect persists between the sectors absorbing the largest share of workers predominantly subsistence agriculture and the informal economy and the productive, high-value activities needed to deliver sustainable income gains.^6,7 Simultaneous rapid expansion of tertiary enrolment without commensurate growth in high-skilled employment has generated widespread credential–employment mismatch.^8–11

Gender represents a critical axis of labour market disadvantage. Young women face lower participation rates, higher unemployment, and greater concentration in precarious informal work relative to male counterparts, reflecting entrenched social norms, discriminatory hiring, and disproportionate care burdens.^12–14 The NEET indicator has emerged as a more comprehensive proxy for youth disengagement than unemployment alone, capturing both active job-seekers and discouraged workers who have with-drawn from the labour market entirely.^15–18

This study is guided by four research questions (RQ):

Youth labour market outcomes in Africa are shaped by the interplay of structural economic transformation, human capital formation, demographic dynamics, and institutional constraints. Classical structural transformation theory posits that economic development entails a progressive reallocation of labour from low-productivity agriculture to higher-productivity manufacturing and services.⁶ In Africa, this process remains incomplete: the majority of young workers are absorbed into subsistence agriculture and low-productivity informal services rather than formal-sector manufacturing, perpetuating vulnerable employment.^7,19

Several strands of the labour market literature are directly relevant to this study. First, the Harris– Todaro model of segmented labour markets predicts that formal-sector wage premiums attract migrants and new entrants into urban job search, generating unemployment as an equilibrium outcome a mechanism consistent with the high measured YUR in North African and Southern African economies where formal sectors are large but constrained.^20–22

Second, signalling and screening theories of education predict that when educational credentials become widely accessible without a corresponding expansion of skilled employment, the informational content of degrees diminishes, leading to credential inflation and educated unemployment the “diploma disease” phenomenon.^9,23–25

Third, search-and-matching models emphasise that frictional and structural mismatches between worker skills and employer requirements generate equilibrium unemployment that persists even in growing economies.^8,26 Gender inequalities in African labour markets have been extensively documented. Women face lower participation rates, higher unemployment, and greater concentration in precarious informal work, reflecting entrenched social norms, discriminatory hiring practices, and disproportionate unpaid care burdens.^12,13 The NEET indicator has emerged as a more comprehensive proxy for youth disengagement than unemployment alone, capturing both active job-seekers and discouraged workers who have withdrawn from the labour market entirely.^15–18 Recent evidence indicates that motherhood is among the strongest predictors of NEETstatus across countries, amplifying gender disparities in youth labour market attachment.²⁷

The African youth demographic context adds further urgency. With the continent’s population projected to double by 2050, youth will constitute the largest cohort of both new labour-market entrants and potential emigrants, generating migratory pressures that interact with domestic labour market conditions.²⁸ The relationship between education and employment is particularly complex in this context: while higher education is widely perceived as a pathway to formal employment, rapid educational expansion has outpaced high-skilled job creation, generating the paradox of educated unemployment observed across the continent.^11,29 Income inequality has been identified as a key accelerator of youth unemployment across African countries, while political instability exerts additional upward pressure through its effects on investment and labour market flexibility.^4,5

Building on theory and recent empirical evidence,^30–32 this study formulates four testable propositions (H) grounded in descriptive comparisons:

Pan-African comparative analyses simultaneously examining YUR, NEET, LFPR, sectoral structure, and educational attainment with full sex disaggregation across all 53 African countries remain scarce.^1,30 This study fills that gap and provides an evidence base for policies aligned with the African Union’s Agenda 2063 and United Nations Sustainable Development Goal 8 (Decent Work and Economic Growth).

A cross-sectional, descriptive-correlational study design was employed. Analysis was conducted at country level across 54 African Union member states for the calendar year 2026. The study follows the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) guidelines, adapted for secondary aggregate data.³³ Geospatial analysis follows Bayesian approaches that account for spatial dependence and heterogeneity across countries and sub-regions.³⁴

All data derive from ILO harmonised modelled labour market estimates (ILOSTAT, 2024 release),³⁵ accessed via the Africa Youth Employment Clock platform (World Data Lab). These estimates are produced through a multi-step framework combining national household survey microdata, administrative records, and econometric imputation for countries with incomplete or outdated survey coverage, ensuring cross-national comparability.³⁶

Caveat on modelled estimates. Because the most recent observed data in the 2024 ILOSTAT release span 2022–2023 for most countries, the 2026 values reported in this study are modelled projections rather than directly observed labour market outcomes. All estimates therefore carry prediction uncertainty that is not captured in the point estimates reported below. Readers should interpret the findings as indicative projections subject to revision when observed survey data become available.

The target population is youth aged 15–35 years, consistent with the African Union’s definition of youth. Four labour market status variables were examined:

Three primary indicators were computed for each country:

where “Inactive” refers to economically inactive youth who are neither employed, unemployed, nor in education or training, consistent with the standard ILONEET definition. Students enrolled in education or training are excluded from both the numerator and the “Inactive” category. Definitions conform to ILO resolutions adopted at the 13th and 19th International Conferences of Labour Statisticians.³⁶

Sub-regional aggregates were computed for North Africa (Algeria, Egypt, Libya, Morocco, Sudan, Tunisia) and sub-Saharan Africa (the remaining 48 countries).

All analyses were performed in Python 3.12 using pandas v2.2, NumPy v1.26, statsmodels v0.14, and scipy v1.17; figures were produced with matplotlib v3.10 and seaborn v0.13 at a minimum resolution of 300 dpi to ensure legibility of all numerical labels and colour coding at print size. To test H₄, Pearson’s correlation coefficient r was computed between country-level YUR and NEET rate (n = 54; significance threshold α = 0.05). Cross-country inequality in YUR was quantified using the Gini coefficient.

To extend the analysis beyond bivariate associations, four nested ordinary least squares (OLS) regression models were estimated with YUR as the dependent variable. NEET rate and LFPR were excluded as predictors because NEET is a mathematical function of YUR and LFPR (NEET = (1 − LFPR) + (LFPR× YUR)); their inclusion would render the regression tautological. The models therefore use only conceptually independent predictors:

M₁ Sectoral model: YUR = β0+β1 AgricultureShare + ε .

M₂ Regional model: adds North Africa dummy to M₁.

M₃ Full model: adds gender gap in YUR and upper-secondary/tertiary share of unemployment to M₂.

M₄ Parsimonious model: retains only predictors significant at α = 0.05 in M₃.

Model assumptions were assessed via variance inflation factor (VIF, threshold <5) and residual diagnostics. All descriptive statistics are reported to one decimal place. Statistical significance is denoted as ^∗p < 0.05, ^∗∗p < 0.01, ^∗∗∗p < 0.001. Continental averages and the Gini coefficient are unweighted (each country contributes equally), which should be considered when interpreting aggregate figures.

This study uses exclusively publicly available, aggregate, anonymized ILO modelled estimates. No primary data collection involving human participants or animals was undertaken. Formal ethical approval was not required under applicable institutional guidelines. The dataset is freely accessible at https://africayouthjobs.io.

The total African youth population aged 15–35 comprised an estimated 545.1 million individuals across 53 countries in 2026 ( Figure 1). Of these, 311.5 million (57.1%) were employed, 23.1 million (4.2%) were unemployed, 81.7 million (15.0%) were economically inactive, and 128.8 million (23.6%) were in education or training. The continental YUR was 6.91% and the LFPR was 61.38%. The NEET rate reached 19.23%, representing approximately 104.8 million youth neither employed nor in education/training a population at elevated risk of long-term human-capital atrophy.^15,16

Figure 2 illustrates the pronounced cross-country heterogeneity in YUR. Rates range from 37.4% in Eswatini to 0.4% in Niger a 93-fold differential. The Gini coefficient for YUR across 53 countries equals 0.61, indicating very high cross-country inequality. The 15 highest- YUR countries include Eswatini (37.4%), South Africa (36.5%), Djibouti (33.6%), Botswana (29.2%), and the Republic of the Congo (25.1%), sharing characteristics of structurally constrained formal sectors relative to growing youth labour supply.^22,32

Consistent with H₁, North Africa recorded a substantially higher YUR(11.09%) than sub-Saharan Africa (6.40%). This descriptive comparison is consistent with H₁, although the small number of North African countries (n = 6) limits the statistical power of formal hypothesis tests. These patterns reflect the distinctive characteristics of North African labour markets: large but saturated public sectors, limited private-sector dynamism, and elevated rates of educated unemployment, especially among women.^21,31 Paradoxically, several sub-Saharan African countries record very low YUR (Niger 0.4%, Burundi 1.1%) yet high NEET rates (13.9% and 9.5%, respectively), reflecting necessity employment where the absence of social protection compels any form of work regardless of productivity.^2,37 Table 1 presents the full set of indicators for all 54 countries (selected, ranked by YUR).

Youth unemployment exhibits a non-linear age profile. Rates were highest in the 20–24 cohort (9.52%) and in the 15–19 cohort (9.41%), declining to 4.83% for ages 25–29 and 5.19% for ages 30–35. This pattern is broadly consistent with transitional unemployment among recent school-leavers.^1,8 However, the interpretation of these age-group differentials must account for the composition of the youth population. In particular, the unemployment rate may not be an informative indicator of labour market outcomes during the transition from education to work, because students can be simultaneously enrolled in education and actively seeking employment; their responses to survey questions on labour force participation are difficult to predict and may inflate measured unemployment in younger cohorts.³⁸ The relatively high YUR observed in the 15–19 group (9.41%) should therefore be interpreted with caution, as it partly reflects high labour force attachment among students rather than purely involuntary joblessness. The modest uptick for the 30–35 group is consistent with labour-market scarring, whereby prolonged early unemployment reduces long-run employability.¹⁵ Evidence from hysteresis tests confirms that youth unemployment persistence varies significantly across African countries and income levels.³⁹

Agriculture dominated youth employment at 45.9% (143.1 million), followed by services at 40.0% (124.6 million) and industry at 14.1% (43.8 million) ( Table 2). Agricultural dominance is most pronounced in Niger, Tanzania, and Ethiopia, where low measured YUR coexists with extensive subsistence farming. Countries with more diversified structures South Africa, North African states record higher industrial and services shares alongside higher formal unemployment.^6,7,19 The agriculture sector represents both a repository of last-resort employment and a potential engine for inclusive development if adequately modernised and linked to youth-inclusive value chains.^40–42

Supporting H₂, female YUR(7.56%) exceeded male YUR(6.40%), a gap of 1.16 percentage points at the continental level. This descriptive gap is consistent with H₂, although individual country-level patterns may vary. Gender disparities are further visible in the NEET rate, where female youth recorded substantially higher disengagement than male youth, reflecting the combined effects of lower participation, higher unemployment, and greater exclusion from education and training among young women. Motherhood has been identified as one of the strongest predictors of NEET status in cross-country analyses, suggesting that fertility-related barriers are a critical driver of gender disparities in youth labour market attachment.²⁷

Gender disparities are also evident in the sectoral distribution ( Figure 3; Table 2). Women were markedly under-represented in industry (10.1% of female employment vs. 17.1% of male employment), whilst services claimed a higher share among women (43.9%) than men (37.0%), reflecting the concentration of women in informal service activities, domestic work, and petty trade.^12,13 Female employment totaled 136.3 million versus 175.2 million for males.

Figure 4 and Table 3 are consistent with H₃. Upper secondary graduates constituted 32.9% of all unemployed youth (7.6 million) and tertiary graduates 19.8% (4.6 million); together, 52.7% of total unemployment. However, a rigorous test of credential– employment mismatch requires comparing these shares to the educational distribution of the labour force (rather than of the unemployed alone); this comparison is not possible with the aggregate data available. Furthermore, this pattern may be partly driven by higher labour force participation rates among more educated youth: because the unemployment rate is conditional on activity, higher search intensity and stronger formal-sector attachment among educated youth mechanically expand the pool of individuals who can be counted as unemployed.³⁸

By contrast, youth without formal education accounted for only 15.7% (3.6 million). This educated unemployment paradox reflects a combination of structural mismatches between curricula and private-sector demand and differential activity patterns across educational levels.^8–10,29,43 The persistent skills gap particularly in digital and soft skills constrains even educated youth from transitioning into productive employment.^44–46

By sex, women with upper secondary credentials constituted 35.3% of female unemployment (3.9 million) versus 30.7% for males (3.7 million), reinforcing the compounding role of gender discrimination in amplifying educational mismatch.^12,13

Pearson’s correlation between country-level YUR and NEET rate (n = 54) yielded r = 0.720 (p = < 0.001); Spearman’s rank correlation yielded ρ = 0.694 (p < 0.001), confirming that the association is robust to distributional assumptions ( Figure 5). A positive association between formal labour-market stress and broader youth disengagement is therefore evident. Notable deviations from the trend include Algeria (YUR 15.5%, NEET 35.0%) high on both dimensions and Chad (YUR 1.2%, NEET 31.3%) low unemployment yet extreme inactivity reflecting necessity employment in the absence of social protection.

Table 4 presents the results of four nested OLS models examining cross-country correlates of YUR. Because NEET rate and LFPR are mechanically related to YUR (see Discussion), these variables were excluded as predictors; only conceptually independent regressors are included.

In the sectoral model (M₁), agriculture employment share alone explained 33.1% of cross-country YUR variance (adjusted R² = 0.319). Agriculture share exerted a strong negative effect (β = −0.255, p < 0.001), confirming that countries with larger agricultural sectors exhibit systematically lower measured YUR consistent with the necessity-employment hypothesis (H₂).

Adding the North Africa dummy in M₂ did not significantly improve model fit (adjusted R² = 0.327; ∆R² = 0.008; North Africa β = −4.813, p = 0.201). This indicates that once sectoral structure is accounted for, North African countries do not significantly differ from sub-Saharan countries in YUR. This result qualifies H₁: the higher raw YUR in North Africa (11.09% vs. 6.40%) is attributable to differences in sectoral composition rather than region-specific labour market institutions per se. A similar pattern emerges when comparing the North Africa dummy across models: its coefficient becomes non-significant (p = 0.083) once agricultural share is controlled for, suggesting that the apparent regional effect is mediated by sectoral structure.

The full model (M₃; adjusted R² = 0.366) retained agriculture share as the dominant predictor (β = −0.299, p < 0.001).

The share of unemployed with upper-secondary or tertiary education was positively and significantly associated with YUR (β = 0.247, p = 0.043), providing partial multivariate support for the credential—employment mismatch (H₃). The gender gap in YUR was not significant (p = 0.238), and the North Africa dummy remained non-significant (p = 0.083). The log-transformed specification (M₅) confirmed the robustness of these findings: agriculture share (β = −0.030, p < 0.001) and upper-secondary education share (β = 0.028, p = 0.006) were both significant predictors of log- YUR, while gender gap (p = 0.181) and North Africa (p = 0.080) remained non-significant.

The parsimonious model (M₄) retained agriculture share (p < 0.001) and the education variable (marginally significant, p = 0.064; adjusted R² = 0.351). All VIF values were below 3.4, indicating no multicollinearity concerns. Residual diagnostics indicated non-normality in the untransformed models (Jarque–Bera p < 0.001 for M₁–M₄), but the log transformed specification (M₅) satisfied the normality assumption (Jarque–Bera p = 0.648). Together, these results demonstrate that agricultural employment share is the most robust correlate of cross-country YUR variation, while educational composition of unemployment shows a consistent positive association in better-specified models.

This study provides a harmonised, pan-African portrait of youth labour markets in 2026, with findings broadly consistent with all four propositions. The continental YUR of 6.91% (Gini coefficient 0.61) masks a NEET rate of 19.23%, a 93-fold country-level YUR range, a strong YUR–NEET correlation (r = 0.720), and persistent structural imbalances.^2,47 These findings are consistent with structural transformation theory, which predicts that economies with large agricultural sectors generate low measured unemployment because the absence of social protection compels necessity-driven employment in low-productivity activities even when productive, remunerative work is unavailable.^6,7,37 The regression analysis confirms this mechanism: agriculture share alone explains 33.1% of cross-country YUR variance (β = −0.255, p < 0.001), and the negative coefficient is robust across all specifications. This finding is consistent with the Harris–Todaro framework, wherein segmented labour markets produce divergent unemployment outcomes depending on the relative sizes of formal and informal sectors.²⁰

The share of unemployed with upper-secondary or tertiary education was positively associated with YUR in the full model (p = 0.043), providing partial multivariate evidence for credential employment mismatch (H₃). This pattern aligns with signalling and screening theories of education, which predict that credential inflation occurs when educational expansion outpaces the creation of high-skilled employment.^23,24 Notably, the North Africa sub-Saharan gap in YUR became non-significant once agriculture share was controlled for (p = 0.201), indicating that the higher raw YUR in North Africa is attributable to sectoral composition rather than region-specific institutions per se. These findings underscore the inadequacy of YUR alone as a policy metric and highlight the need for multi-dimensional frameworks.^15–17

Agriculture’s dominance (45.9%) reflects an unfinished structural transformation. Workers exiting agriculture are predominantly absorbed into low-productivity informal services rather than formal manufacturing, constraining wage growth and perpetuating vulnerable employment.^3,6,7,19 This phenomenon is most acute in East and West Africa, where very low YUR coexists with extensive subsistence employment and underemployment. Climate shocks compound these challenges, with rising temperatures reducing agricultural employment and accelerating labour reallocation towards manufacturing and services.⁴⁸ Addressing the agricultural trap requires strategies that modernise the sector while simultaneously creating alternatives in manufacturing and services for displaced agricultural workers.^41,42

The finding that upper secondary and tertiary graduates constitute 52.7% of unemployment constitutes compelling evidence of the educated unemployment paradox.^8,9,29 This pattern whereby higher educational attainment is associated with higher measured unemployment is characteristic of transition economies and reflects the interaction of several mechanisms. First, signalling and screening theories predict that when educational credentials become widely accessible without a corresponding expansion of skilled employment, the informational content of degrees diminishes, leading to credential inflation and educated unemployment the “diploma disease” phenomenon.^23–25 Second, higher educational attainment is typically associated with higher labour force participation rates, which mechanically increases the pool of individuals who can be counted as unemployed.³⁸ The observed pattern may therefore reflect, at least in part, higher search intensity and stronger formal-sector attachment among educated youth rather than or in addition to genuine skills mismatch. Third, the rapid expansion of tertiary enrolment in Africa has outpaced the growth of high-skilled employment, creating a structural surplus of credentialed workers.¹¹ A definitive test of the credential–employment mismatch hypothesis would require comparing the educational composition of the unemployed with that of the employed labour force; the aggregate data available do not permit this comparison. Nevertheless, the persistent skills gap in digital and soft skills constrains even educated youth from transitioning into productive employment, and the disproportionate impact on credentialled women underscores the compounding role of gender-based labour market discrimination.^{12,13,44–46}

The gender gap in YUR (7.56% female vs. 6.40% male) is consistent with recent evidence on structural inequalities in African labour markets.^12,13 However, attributing this gap solely to discrimination is insufficient. The labour market’s capacity to absorb workers varies according to the characteristics of the workforce, not only gender per se. Women’s concentration in services rather than industry partly reflects differential sectoral composition of female and male labour supply, as well as occupational segregation driven by social norms and care responsibilities. Recent cross-country evidence identifies motherhood as one of the strongest predictors of NEET status among young women,²⁷ suggesting that fertility transitions and childcare infrastructure are critical mediating factors. Women’s under-representation in industry (10.1% vs. 17.1% for males) limits access to productivity-enhancing formal employment, while the higher share of women in services (43.9% vs. 37.0%) reflects concentration in informal activities, domestic work, and petty trade.^12–14 Expanding women’s access to industrial and technology-intensive occupations requires both supply-side (education, skills) and demand-side (anti-discrimination legislation, childcare infrastructure) interventions.¹

The prevalence of low YUR alongside high NEET rates in many sub-Saharan African countries reflects widespread necessity-driven informal employment rather than genuine labour market inclusion. Young people engage in informal activities including artisanal small-scale mining, street trading, and agricultural subsistence as survival strategies in the absence of formal opportunities.^49,50 This pattern is consistent with the interpretation that low measured unemployment in agriculture-dominated economies reflects the absence of social protection rather than genuine labour market strength.^2,37

Africa’s rapid population growth will further intensify migratory pressures. Young people constitute the largest cohort of potential emigrants from the continent, and both internal rural–urban migration and international migration interact with domestic labour market conditions in complex ways. The Harris–Todaro model predicts that migration decisions are driven by expected income differentials between sectors and regions, generating urban unemployment as an equilibrium outcome.²⁰ In countries where formal sectors are saturated and informal employment is the norm, emigration may serve as a safety valve—but also as a brain-drain mechanism that depletes human capital.²⁸ The current analysis does not capture migration flows, representing an important limitation for future research.

Entrepreneurship programs show promise but require comprehensive support including financial literacy, mentorship, and access to credit.^51–53 Public-private partnerships, such as the Youth Employment Service in South Africa, represent innovative models but require stronger post-placement support mechanisms.^54,55

Several limitations must be acknowledged. First, ILO modelled estimates carry inherent uncertainty for countries with outdated survey coverage; formal confidence intervals are unavailable, precluding strict inferential statistical testing. Second, the dataset does not capture employment quality (formal vs. informal, wage vs. self-employment), which is critical for comprehensive policy diagnosis. Third, the cross-sectional design precludes temporal trend analysis and limits causal inference; the regression estimates identify associations, not causal effects, and may be biased by omitted variables (e.g., institutional quality, labour market regulations, or macroeconomic conditions) for which country-level proxies are not available in the ILO dataset. Fourth, the 15–35 age bracket, whilst consistent with African Union definitions, is broader than the conventional 15–24 ILO window, affecting comparability. Fifth, the aggregate data do not allow a rigorous test of credential employment mismatch, which requires comparing the educational distribution of the unemployed with that of the employed labour force. Sixth, the unemployment rate may not be an informative indicator of labour market outcomes for the 15–19 age group, where many individuals are simultaneously studying and searching for work; their survey responses regarding labour force participation are difficult to interpret.³⁸ Seventh, the analysis does not capture migration flows, which may significantly affect youth labour market dynamics in high-emigration countries.

Future research should employ longitudinal panel designs, harmonised household survey microdata, multi-dimensional employment quality indices, instrumental variable approaches, and structural equation modelling.^3,8

Evidence from this study, grounded in the preceding analysis and discussion, supports the following priority interventions^{1,2,29,47,52–54}:

The data underlying the results of this study were retrieved from the Africa Youth Employment Clock (World Data Lab), an open-access platform available at https://africayouthjobs.io, which draws on ILO harmonised modelled labour market estimates (ILOSTAT). The four underlying datasets (employed_2026.xlsx, unemployed_2026.xlsx, inactive_2026.xlsx, student_2026.xlsx) have been deposited on Zenodo: World Data Lab. Youth Labour Market Dataset Africa, 2026. https://doi.org/10.5281/zenodo.19322273.

Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0).

The authors are grateful to the International Labour Organisation for making its harmonised modelled labour market estimates freely available through the ILOSTAT database. The views expressed are those of the authors alone and do not represent the official position of any institution.

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Current Reviewer Status: ?

VERSION 2

PUBLISHED 03 Aug 2026

Revised

Reviewer Report 10 Aug 2026

Aryadimas Suprayitno, Faculty of Economics and Business, University of Sumatera Utara (Ringgold ID: 106100), Medan, North Sumatra, Indonesia 

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VERSION 1

PUBLISHED 22 Apr 2026

Reviewer Report 10 Jul 2026

Gabriele Marconi, Service Études et Statistiques, Agence pour le Développement de l'Emploi (ADEM), Luxembourg city, Luxembourg 

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Reviewer Report 08 Jul 2026

Włodzimierz Kołodziejczak, Poznan University of Life Sciences, Poznan, Poland 

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Reviewer Report 20 May 2026

Sudipa Sarkar, Institute of Public Policy, National Law School of India University, Bangalore, Karnataka, India 

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