Search results

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.

In this study, the authors explore the association between employee education level and the efficiency of corporate investment using data from a sample of Chinese listed firms during the period from 2011 to 2018. By examining the impact of education on investment efficiency, the authors' study provides valuable insights that contribute to a deeper understanding of the underlying economic mechanisms related to education.

The authors conduct multivariate regression analyses to examine the relationship between investment efficiency (following Richardson, 2006) and the level of employee education, along with a series of control variables. To ensure the reliability of the authors' findings, the authors subject the their results to a comprehensive set of robustness tests, such as a staggered difference-in-difference (DiD) regression approach, an instrumental variable (IV) method and the use of alternative employee education level and investment efficiency measurements.

The findings offer compelling evidence that higher levels of education have a positive impact on firms' investment efficiency, and this effect remains robust across various model specifications and endogeneity considerations. Moreover, the influence of education is more pronounced in firms that prioritize employee training, maintain effective internal communication and offer attractive financial rewards. Furthermore, the results suggest that the relationship between education and investment efficiency is influenced by the firms' business nature and competitive environment. Factors such as business complexity, labor intensity and business location also play a role in shaping the impact of education on investment outcomes.

The study emphasizes the crucial role of education in influencing investment decisions and performance within firms. By delving into this previously unexplored area, the authors' research contributes to the existing literature, establishing that the level of employee education is a significant determinant of corporate investment efficiency. This valuable insight has substantial implications for firms aiming to enhance their investment decision-making processes and overall performance. Understanding the positive impact of education on investment efficiency can empower organizations to leverage their human capital effectively and achieve better investment outcomes, ultimately contributing to long-term success and competitiveness in the market.

Using survey data from Shandong, Henan and Guizhou provinces of China, the purpose of this paper is to accurately measure the impact of rural residents’ financial education on financial literacy.

This paper chooses one province from the Eastern, Central and Western Regions of China, namely, Shandong, Henan and Guizhou, respectively, and 1,565 samples are obtained through a questionnaire survey. First, the paper constructs a financial literacy assessment framework and, then, scores the financial literacy of the respondents. Second, using ordinary least squares, feasible generalized least squares method and forward search method, the paper estimates the impact factors of financial literacy level. To avoid sample selection errors and endogeneity problems, the authors divide the respondents into treatment group (participated in financial education) and control group (non-participating in financial education) and, then, adopt propensity score matching (PSM) to analyze the impact of rural residents’ financial education on financial literacy.

The results show that education level and risk level have significant impact on rural residents’ participation in financial education, and some unobservable abilities and qualities also affect their participation. Therefore, the process of rural residents’ participation in financial education exists, which gives rise to self-selection and endogeneity problems; financial education is promoting rural residents’ financial literacy, but the effect of promotion becomes smaller after taking into account sample self-selection and endogenous problems. Rural residents of female, higher age, single, higher education level, higher parental education level, agricultural type, higher family annual per capita income and lower risk level show stronger effects on their financial literacy level, if they participate in financial education.

The survey sample was drawn from three provinces randomly but the site selection was not random. The implication is in rural China, financial education has positive effect on residents’ financial literacy level but considering the sample self- selection and endogenous nature, its impact becomes smaller.

The government should encourage rural residents to participate fully in financial education activities, especially those with a low educational level, low risk preference and mainly engaged in agricultural production.

The effect of financial education on financial literacy has not reached a consistent conclusion, and there is fewer quantitative discussion about this issue. The originality of this paper is based on the Organization for Economic Co-operation and Development evaluation index system; this paper constructs the evaluation index system of rural residents’ financial literacy in China and uses the PSM method to accurately measure the effect of financial education on financial literacy.

The ecosystem view is a fascinating perspective which provides management scholars with innovative conceptual tools to investigate the functioning of complex service systems. The purpose of this paper is to focus on the “mega” level of the education service ecosystem in an attempt to explain the relationships between education attainments and income disparities across Europe.

Data were collected from the European Union Statistics on Income and Living Conditions. Data trends over the time period (2007-2010) were investigated, involving 27 European countries. Unobserved time-invariant heterogeneity was controlled and dynamics over time were investigated. A random effects model was estimated for each country. The semi-log functional form is informed by Mincer’s (1974) human capital models.

Education levels were found to be a predictor of income inequality in all the countries included in this research, i.e. higher education level leads to higher income and vice versa. However, the effect of education attainments on individual earnings was irregular. Eastern European countries, inter alia, revealed a strong relationship between education attainments and individual earnings, whereas Scandinavian countries showed a weak link between education levels and income.

Education has the potential to affect income inequalities in Europe. Policy makers should develop tailored strategies to deal with the consequences of education levels on individual earnings. Both education services’ quality and the interaction between education and moderating socio-demographic variables may influence income inequality in European countries.

This is one of the first attempts to investigate the relationship between education and income inequalities drawing on the service ecosystem perspective. Further conceptual and practical developments are needed to better explain the effects of education attainments on income inequality.

The purpose of this paper is to provide a country‐level consideration of the relationship between entrepreneurship, immigration and education. In contrast to studies that report on immigration and entrepreneurship in a region or single country, the authors seek to determine whether levels of immigration, and the level of education obtained by the immigrants, are predictive of levels of entrepreneurship activity. A common set of variables and data from developed countries are used to test the hypothesized relationships.

Using data on 21 OECD countries and five measures of entrepreneurship from the Global Entrepreneurship Monitor project, the authors assess the significance of immigration and education level on entrepreneurial activity using regression analysis.

The stock of immigrants in a country was found to be predictive of the proportion of that country's population involved in starting and managing a new business (early stage entrepreneurship), as well as the growth expectations held by those early‐stage entrepreneurs. Also, levels of high growth and high growth expectation entrepreneurship were predicted by the proportion of more highly‐educated immigrants.

This study provides national‐level comparative evidence linking entrepreneurial activity to immigration and to the level of education obtained by those immigrants, thereby adding to our understanding of immigration, education and entrepreneurship. The results have implications for the immigration policies of countries seeking to add to their economic base by encouraging entrepreneurship and job creation.

There is plenty of individual-level evidence, based on the estimation of Mincerian equations, showing that better-educated individuals earn more. This is usually interpreted as a proof that education raises labour productivity. Some macroeconomists, analysing cross-country time series, also support the idea that the continuous expansion of education has contributed positively to growth. Surprisingly, most economists with an interest in human capital have neglected the level of the firm to study the education-productivity-wage nexus. And the few published works considering firm-level evidence are lacking a proper strategy to cope with the endogeneity problem inherent to the estimation production and wage functions. The purpose of this paper is to aim at providing estimates of the causal effect of education on productivity and wage labour costs.

This paper taps into a rich, firm-level, Belgian panel database that contains information on productivity, labour cost and the workforce’s educational attainment to deliver estimates of the causal effect of education on productivity and wage/labour costs. Therefore, it exclusively resorts to within firm changes to deal with time-invariant heterogeneity bias. What is more, it addresses the risk of simultaneity bias (endogeneity of firms’ education-mix choices in the short run) using the structural approach suggested by Ackerberg et al. (2006), alongside more traditional system-GMM methods (Blundell and Bond, 1998) where lagged values of labour inputs are used as instruments.

Results suggest that human capital, in particular larger shares of university-educated workers inside firms, translate into significantly higher firm-level labour productivity, and that labour costs are relatively well aligned on education-driven labour productivity differences. In other words, the authors find evidence that the Mincerian relationship between education and individual wages is driven by a strong positive link between education and firm-level productivity.

Surprisingly, most economists with an interest in human capital have neglected the level of the firm to study the education-productivity-pay nexus. Other characteristics of the workforce, like gender or age have been much more investigated at the level of the firm by industrial or labour economists (Hellerstein et al., 1999; Aubert and Crépon, 2003; Hellerstein and Neumark, 2007; Vandenberghe, 2011a, b, 2012; Rigo et al., 2012; Dostie, 2011; van Ours and Stoeldraijer, 2011). At present, the small literature based on firm-level evidence provides some suggestive evidence of the link between education, productivity and pay at the level of firms. Examples are Hægeland and Klette (1999); Haltiwanger et al. (1999). Other notable papers examining a similar question are Galindo-Rueda and Haskel (2005), Prskawetz et al. (2007) and Turcotte and Whewell Rennison (2004). But, despite offering plausible and intuitive results, many of the above studies essentially rely on cross-sectional evidence and most of them do not tackle the two crucial aspects of the endogeneity problem affecting the estimation of production and wage functions (Griliches and Mairesse, 1995): first, heterogeneity bias (unobserved time-invariant determinants of firms’ productivity that may be correlated to the workforce structure) and second, simultaneity bias (endogeneity in input choice, in the short-run, that includes the workforce mix of the firm). While the authors know that labour productivity is highly heterogeneous across firms (Syverson, 2011), only Haltiwanger et al. (1999) control for firm level-unobservables using firm-fixed effects. The problem of simultaneity has also generally been overlooked. Certain short-term productivity shocks affecting the choice of labour inputs, can be anticipated by the firms and influence their employment decision and thus the workforce mix. Yet these shocks and the resulting decisions by firms’ manager are unobservable by the econometrician. Hægeland and Klette (1999) try to solve this problem by proxying productivity shocks with intermediate goods, but their methodology inspired by Levinsohn and Petrin (2003) suffers from serious identification issues due to collinearity between labour and intermediate goods (Ackerberg et al., 2006).

Measurement of returns to vocational education has always remained a matter of controversy (Psacharopoulos, 1994; Bennell and Segerstrom, 1998; Ziderman, 1997). Based upon the return evidence many World Bank projects were scrapped (Middleton and Ziderman, 1997, Bennell and Segerstrom, 1998). However, there is again a growing interest for Vocational Education in different countries as well as in international body like UNESCO (Debroy, 2009; King, 2009; McGrath, 2012). Unfortunately there is little justification for this growth from the returns to Vocational Education literature. India is one among the countries where fervent around vocational education and training (henceforth VET) is quite discernible at present. The paper aims to discuss these issues.

Standard Minerian, extended Mincerian and Heckman two-stage methods are used to measure returns to VET vis-à-vis returns to general education in Indian context while taking care of selection bias problem. It is measured at different levels of education since the characteristics of VET changes significantly at different levels of education. This work also considers different classifications of VET (Formal, Hereditary, On the Job). National Sample Survey (NSS) data which recently has included data on VET is used for this measurement.

It shows that the returns to formal VET and On the Job training (OJT) are quite high in the primary level. There is a gradual decline in these returns when compared with general education at higher levels, namely, secondary and tertiary level. At tertiary level effect of formal VET or OJT on income becomes almost insignificant. On the other hand, hereditary training and other category of VET have significantly negative impact and insignificant impact on income, respectively.

This work suggests that the investment in VET in present context is justified however there is a requirement to focus on specific area within the VET for this investment. Investment in higher level of formal VET as well as in the OJT set up can provide better return to the individuals. However, NSS data does not provide income data for the self-employed persons. Hence, self-employed category remains outside the purview of this study.

In general, formal VET and OJT are profitable for individual in wage employment. However, this profitability declines when the effect of the training is measured at a higher level of general education. Formal vocational training/OJT for the primary and secondary school dropouts are clearly profitable. However, OTJ seems to be providing a return similar to what is provided by formal training. A poor performance of formal VET indicates an institutional gap and reflects present cry from industry for “quality” manpower.

To author’s knowledge there is no study on return evidence from various types of VET in Indian context. This study is a deviance from the existing literature on VET in following ways. First, this study suggests that investment in VET can be profitable. Second, many earlier works missed out to consider different types of VET and compare them with general education at different levels. Putting all types of VET into one basket and then analyzing returns has the risk to misleading the policies. Different VETs have completely different dynamics and hence it is required to treat them separately.

This account aims to introduce contrasting perspectives on teaching and learning methods, and to detail the growth of new forms and vocabularies of access to learning. As we move towards the new millennium, the development of national, yet diversified, credit frameworks and systems will provide an essential underpinning for the organisational culture that will be needed to sustain the wellbeing and growth of the educational system. These new systems are already being incorporated into the practice of ‘virtual’ education. Lifelong learning has widespread support across the social and political spectrum and its importance can hardly be over‐stated as we seek to maintain competitiveness in a changing world. Increasing knowledge and understanding to serve both the needs of the economy and of individuals to play a major role in democratic life has become an agenda of necessity as well as desire. An open society requires open systems of knowledge. A prognosis for the future is submitted where the significance of part‐time modular and open flexible learning is evaluated in terms of a curriculum rooted in useful knowledge and competences, acquired at different sites of learning, including the workplace. It is argued that modular structures, using the potential offered by credit accumulation and transfer to different institutions with different missions, can transcend and transform the learning opportunities for students in a mass system of higher education which is rapidly becoming part of a global market economy and society. Continuous lifelong learning involving its key features of open access, recognition of learning wherever it takes place and the growth of new learning networks and partnerships, is at the conceptual heart of the development of the virtual university.

Identifies an error in the measurement of private rates of return to aggregate levels of education. When education is viewed as a continuous variable, the estimated rate of return is to an incremental year of schooling. However, rates of return are often estimated for aggregate levels of education such as the secondary and university levels. When an aggregate level has sub‐levels, such as bachelors′ and masters′ for the university level, the conventional procedure underestimates the rate of return to the aggregate level due to the dominance of up‐front costs in the discounting procedure used to compute rates of return.

The International Standard Classification of Education (ISCED) is a tool for harmonising education-related information. It covers almost all countries in the world and is centrally maintained and documented by UNESCO Institute for Statistics. ISCED is commonly used in official statistics and surveys (e.g. by OECD and Eurostat), but it is also increasingly used for the measurement of educational attainment in academic cross-national surveys. ISCED has been revised between 2008 and 2011, and the new version was adopted by the UNESCO General Conference in November 2011. This research note describes ISCED 2011 and the most important changes as compared to the previous version, ISCED 1997, with a special focus on educational attainment. A brief discussion of strengths and weaknesses of the classification as well as future challenges conclude the note.