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Education system stimulates the development of human capital and provides informative signaling allowing to differentiate productivity of individuals. If education system is efficient then higher levels of education usually associated with greater returns on labor market. To evaluate the efficiency of Russian education system we aim to estimate the effect of vocational education and different levels of higher education on wages.

We use data on 8,764 individuals in the years 2019–2021. Our statistical approach addresses two critical issues: nonrandom selection into employment and the endogeneity of education choice. To tackle these problems, we employed Heckman’s method and its extension that is a structural model which addresses the issue of self-selection into different levels of education.

The results of the analysis suggest that there is a significant heterogeneity in the returns to different levels of education. First, higher education, in general, offers substantial wage premiums when compared to vocational education. Specifically, individuals with specialist’s and bachelor’s degrees enjoy higher wage premiums of approximately 23.59–24.04% and 16.43–16.49%, respectively, compared to those with vocational education. Furthermore, we observe a significant dis-parity in returns among the various levels of higher education. Master’s degree provides a substantial wage premium in comparison to both bachelor’s (19.79–20.96%) and specialist’s (12.64–13.41%) degrees. Moreover, specialist degree offers a 7.16–7.55% higher wage premium than bachelor’s degree.

We identify a hierarchical pattern in the returns associated with different levels of higher education in Russia, specifically “bachelor-specialist-master.” These findings indicate that each level of education in Russia serves as a distinct signal in the labor market, facilitating employers' ability to differentiate between workers. From a policy perspective, our results suggest the potential benefits of offering opportunities to transition from specialist’s to master’s degrees on a tuition-free basis. Such a policy may enhance access to advanced education and potentially lead to higher returns for individuals in the labor market.

There are many studies on returns to higher education in Russia. However, just few of them estimate the returns to different levels of higher education. Also, these studies usually do not address the issue of the endogeneity arising because of self-selection into different levels of education. Our structural econometric model allows addressing for this issue and provides consistent estimates of returns to different levels of education under the assumption that individuals with higher propensity to education obtain higher levels of education.

This systematic literature review aims to identify the pattern of data mining (DM) research by looking at the levels and aspects of education.

This paper reviews 113 conference and research papers from well-known publishers of educational data mining (EDM) and learning analytics-related research using a recognized literature review in computer science by Carrera-Rivera et al. (2022a). Two major stages, planning and conducting the review, were used. The databases of Elsevier, Springer, IEEE, SAI, Hindawi, MDPI, Wiley, Emerald and Sage were searched to retrieve EDM papers from the period 2017 to 2023. The papers retrieved were then filtered based on the application of DM to the three educational levels – basic, pre-tertiary and tertiary education.

EDM is concentrated on higher education. Basic education is not given the needed attention in EDM. This does not enhance inclusivity and equity. Learner performance is given much attention. Resource availability and teaching and learning are not given the needed attention.

This review is limited to only EDM. Literature from the year 2017 to 2023 is covered. Other aspects of DM and other relevant literature published in EDM outside the research period are not considered.

As the current trend of EDM shows an increase in zeal, future research in EDM should concentrate on the lower levels of education to identify the challenges of basic education which serves as the core of education. This will enable addressing the challenges of education at an early stage and facilitate getting a quality education at all levels of education. Appropriate EDM techniques for mining the data at this level should be the focus of the research. Specifically, techniques that can cater for the variation in learner abilities and the appropriate identification of learner needs should be considered.

Content sequencing is necessary in facilitating an easy understanding of concepts. Curriculum design from basic to higher education dwells much on this. Identifying the challenge of learning at the early stages will facilitate efficient learning. At the basic level of learning, data on learning should be collected by educational institutions just as it is done at the tertiary level. This will enable EDM to accurately identify the challenges and appropriate solutions to educational problems. Resource availability is a catalyst for effective teaching and learning. The attributes of a learner will enable knowing the true nature of the learner to determine the prospects of the learner.

This research has not been published in any journal. The information presented is the original knowledge of the authors. However, a pre-print of the work is in Research Square.

Literature on the board diversity of Islamic banks (IB) found limited knowledge of the “deep-level” attribute. This study aims to explain the impact of the board diversity attributes (education levels, educational backgrounds and the interactions between these two attributes of diversity) on profitability.

The research sample is 37 fully flagged IBs from five Southeast Asian countries, covering nine years (2010–2019). Data were analyzed using the two-step system generalized moment (2SYS-GMM) method.

We found that the cognitive conflict between the board of directors (BOD) and the Shariah Supervisory Board (SSB), which has heterogeneity in its education level and educational background, positively affects profitability. These results reinforce the resources dependence theory (RDT) approach that having boards with heterogeneous characteristics is beneficial for IB.

The findings of this study would offer useful information for Islamic banking authorities to revise or formulate rules and guidelines and make a greater effort to implement corporate governance (CG) reform measures by determining educational level and background as a requirement to become a member of a BOD or an SSB.

This paper contributes in three ways: (1) we use the “deep-level” diversity attributes of the BOD and the SSB, (2) it focuses on cognitive conflict in boards by presenting the expertise diversity of the BOD and SSB and (3) we interact with the level of education to evaluate the effect of a cognitive conflict.

This study examines the factors influencing educational aspirations among adolescents in secondary and high schools across Kazakhstan, Kyrgyzstan (officially Kyrgyz Republic), and Tajikistan, with a focus on their aspirations for higher and postgraduate education. Through an analysis of a sample comprising 1360 students from grades 7 to 11, the research explores the likelihood of these students pursuing tertiary education.

The ordered logit regression models were used to analyze the impact of variables related to family background, including parents’ education, family support and SES, and school grade. Additionally, the Exploratory Factor Analysis was utilized to distill a smaller set of underlying factors or constructs from the measured variables.

The findings indicate that the mother's education level is significantly more influential compared to the father's education in shaping adolescents' aspirations for higher and postgraduate education in the Central Asian context. Additionally, hypotheses concerning the influence of family socioeconomic status (SES), family support, and grade-level differences on adolescents' higher educational aspirations have only been partially confirmed.

To the best of the authors' knowledge, no prior research work has indicated a significant impact of a mother's education on the educational aspirations of her offspring in the Central Asian region. This trend is consistent with the concept of educational hypogamy, wherein mothers possess higher educational levels than fathers, a phenomenon that is gaining prominence in contemporary family dynamics.

This study investigates the differences in digitalization tendency levels among young adults (18–30) regarding their living environment, gender, and education.

Digital transformation (DT) refers to digital technologies becoming more prevalent in society. This process transforms lifestyles and business models using information and communication technologies. While digital transformation is on everyone’s agenda worldwide, it is still in its infancy. The data collected from 1,067 young adults show that all three control variables, living environment (CV1), gender (CV2), and education level (CV3), have a significant impact on digitalization tendency. Samples are selected using stratified sampling. Frequency distributions of quantitative data, descriptive statistics, ANOVA analysis, independent sample t-tests, reliability and validity analysis are interpreted in SPSS.

It is concluded from the study that the young adults living in Istanbul are more inclined to use digital technologies and to see the process from the standpoint of its positive results. It is observed that women have a higher digitalization tendency than men. Finally, the tendency to use digital technologies increases with education level, as does the likelihood of perceiving the outputs of these technologies as positive.

The study limited the sample to young adults aged 18–30.

Digitalization tendencies are positively influenced by factors such as living in Istanbul, being a woman, and having a high education level.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-01-2023-0020.

Introduction: The Republic of Moldova’s economy faces risks caused by the war in Ukraine and the economic crisis, proving that citizens’ prosperity is essential for national stability and that financial knowledge influences the standard of living. A minimum financial education provides information, knowledge, and tools to make correct decisions based on informed consent in an increasingly complex financial system. In the financial-banking and academic environment, in-depth research of consumers’ financial education level helps to optimise, streamline, and balance bank–client relations with fairness. This work is the consequence of studying the level of financial education among consumers of financial-banking services, with direct implications for their financial well-being.

Purpose: The main aim of this research is to measure the financial knowledge of consumers of financial-banking services, developing recommendations for measures to improve the situation.

Methodology: To explain the factors of influence, the following research techniques were used: analysis and synthesis of conceptual approaches to financial education; deduction and induction; analysis of the findings of sociological research on the level of financial education of users of financial-banking services; and recommendation synthesis.

Findings: The research validates that enhancing financial education has a positive effect on individuals and the economy, reinstates confidence in financial markets, makes an innovative contribution to accurately assessing consumers’ financial knowledge enabling the implementation of proactive measures.

Implications: This chapter provides insights into consumers’ financial education level, serving as a crucial indicator for institutions and public authorities in formulating and promoting effective educational initiatives to ensure minimal skill gaps.

Many studies have examined pre-diploma students' backgrounds and academic performance with results showing that some did not achieve the expected level of competence. This study aims to examine the relationship between students’ demographic characteristics and their academic achievement at the pre-diploma level using machine learning.

Secondary data analysis was used in this study, which involved collecting information about 1,052 pre-diploma students enrolled at Universiti Teknologi MARA (UiTM) Pahang Branch between 2017 and 2021. The research procedure was divided into two parts: data collecting and pre-processing, and building the machine learning algorithm, pre-training and testing.

Gender, family income, region and achievement in the national secondary school examination (Sijil Pelajaran Malaysia [SPM]) predict academic performance. Female students were 1.2 times more likely to succeed academically. Central region students performed better with a value of 1.26. M40-income students were more likely to excel with an odds ratio of 2.809. Students who excelled in SPM English and Mathematics had a better likelihood of succeeding in higher education.

This research was limited to pre-diploma students from UiTM Pahang Branch. For better generalizability of the results, future research should include pre-diploma students from other UiTM branches that offer this programme.

This study is expected to offer insights for policymakers, particularly, the Ministry of Higher Education, in developing a comprehensive policy to improve the tertiary education system by focusing on the fourth Sustainable Development Goal.

These pre-diploma students were found to originate mainly from low- or middle-income families; hence, the programme may help them acquire better jobs and improve their standard of living. Most students enrolling on the pre-diploma performed below excellent at the secondary school level and were therefore given the opportunity to continue studying at a higher level.

This predictive model contributes to guidelines on the minimum requirements for pre-diploma students to gain admission into higher education institutions by ensuring the efficient distribution of resources and equal access to higher education among all communities.

This study evaluates the economic effectiveness of higher education in Lithuania by measuring returns to investment in higher education for both individual university graduates and the state, particularly aiming to discover how higher education investments impact economic returns at both micro (individual) and macro (national) levels.

A dual methodological approach has been applied, utilizing both the Mincer earnings equation and the full discounting method, to draw a clear distinction between the returns enjoyed by individuals and those accrued to the country. Calculations for individual economic returns are done using the most recent available Lithuanian Department of Statistics data on the wage structure, while national return on education was based on the State Tax Inspectorate and Lithuanian Public Finance databases.

The research confirms that Lithuanian investments in education positively influence both individual earnings and society at large, mainly due to the low cost of education and the high returns. For individuals, net present value varies from €126,000 to €224,000, and the internal rate of return is from 7% to 46%, with the highest return being for males working in companies of 50–249 employees and holding a bachelor’s degree. It is also noteworthy that one additional year spent in education increases earnings on average by 4.1%. The financing of first cycle studies costs the state two times less than second and third cycle studies. For this reason, the net present value (NPV) and internal rate of return (IRR) of first cycle studies are higher than those of second and third cycle studies.

While higher education is generally and globally seen as a way to ensure financial stability and career advancement at the individual level and socioeconomic development at the national one, the question of cost versus benefits at both levels is principal and diachronic. Our research quantifies the NPV and IRR of education investments and highlights the differential economic returns of various education levels, where policymakers can utilize these insights to inform strategic decisions regarding education funding and resource allocation. This study, therefore, provides explicit quantitative answers and presents individuals and policymakers with tangible results and practicable direction in their decision-making. The findings are applicable to the specific country-focus, but also constitute an applicable case study in the international context, particularly for European and other countries of comparable economic structure and developmental stage.

Education had proven to be one of the main determinants of economic growth, and it is a reason of the variations in economic growth levels between developed and developing countries. One of the main dimensions in studding the relationship between economic growth and education is the gender dimension or the importance of gender equality or female education in achieving economic growth. This chapter aims to test the hypothesis of the existence of a positive relationship between female education and economic growth in Egypt since 1990.

To address this question, Auto Regression Distributed Lag (ARDL) Bound test approach is conducted to analyze the co-integration between female education and economic growth using Egyptian Data for the period 1990–2022. The Empirical analysis for Egypt suggests the existence of positive significant relationship both in the short run and long run and that the impact of female education on economic growth is larger than the impact of education in general on growth. This could be explained by the existence of gender gap in Egypt, labor market, and thus, more educated girls able to enter the labor market will affect the economic growth more than the education of both sexes, in other words, there is still a room for improvement in the female labor market opportunities than for both sexes. The chapter also confirms the existence of a direct link between education in general and economic growth and thus confirms the hypothesis of the positive impact of education economic growth.

Since smartphones became ubiquitous, online grocery and food purchases through take-away delivery platforms have steadily increased in China. Nevertheless, whether the development of take-away delivery can ameliorate urban–rural wage inequality still requires further analysis. The purpose of this paper is to clarify whether this positive effect exists.

This paper makes estimations based on city and individual levels combining the Chinese Household Income Project (CHIP) 2008, CHIP 2013, CHIP2018 survey data and the take-away delivery site data. At the city level, the Oaxaca-Blinder (O-B) decomposition method is employed to construct wage inequality index of urban and rural labors. At the individual level, this paper analyzes urban–rural wage differentials with high or low formal education level.

The rapid establishment of take-away delivery sites has resulted in an increase of 52.425 yuan on average in the annual wage of rural labors with low formal education level. When the cumulative number of sites increases by 1 unit, the annual wage inequality index decreases by 0.007 on average. Labors with the characteristics of rural household registration and low education can enjoy more dividends. Through inter-/within-industry decomposition, this paper elaborates formal education, age and cross-industry transfer as the main factors for the improvement of urban–rural wage inequality. Narrowing effect of wage differences between different groups in multiple sample slices also contributes to the mechanism analyses.

To the best of the author’s knowledge, this paper is the first to analyze the impact of take-away delivery development on the urban–rural wage inequality from the perspective of the establishment of take-away delivery sites. This empirical study will enrich the existing theoretical perspectives on urban–rural divide under the emergence of new forms of employment. The results indicate that new forms of employment represented by take-away delivery can not only promote economic growth, but also eliminate urban–rural inequality.