vocational education research paper

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• Published: 15 December 2023

Knowledge mapping of vocational education and training research (2004–2020): a visual analysis based on CiteSpace

• Yumi Tian 1 ,
• Jiayun Liu 1 ,
• Xin Xu 1 &
• Xueshi Wu 1  

Scientific Reports volume  13 , Article number:  22348 ( 2023 ) Cite this article

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The study aims to analyze the leading researchers of vocational education and training from dimensions of individuals, institutions and countries. This article utilises the scientific information measurement software—CiteSpace—to conduct a scientometric analysis of 2,024 articles on vocational education and training from the Web of Science (W.o.S.). According to the research results, some useful conclusions can be drawn as follows: (1) vocational education and training research has become interdisciplinary and subject involved are “psychology”, “sociology”, “economics” and “pedagogy”; (2) the United States, the Netherlands and Australia make the majority of contributions and there are numerous collaborations among countries; (3) Univ Amsterdam, Univ Utrecht and Univ Melbourne were the main research institutions; (4) J Vocat Educ Train, Rev Educ Res, Thesis Elev, Econ Educ Rev and J Educ Work were the top 5 highly cited journals; (5) “Engagement”, “Program”, “Self-efficacy”, “High school”, “Predictor” and “Labor market” have become major research hotspots currently.

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Introduction.

Nowadays the importance of vocational education and training has been highlighted by the rapid economic and social development with relatively mature vocational education and training systems established in countries like the United States, Germany, Australia, the United Kingdom and the Netherlands. Subsequently, researchers with academic backgrounds in economics 1 , 2 , 3 , management 4 , 5 , 6 , and information technology 7 , 8 , 9 around the world have paid close attention to the courses 10 , 11 , professions 12 , entrepreneurship 13 , 14 , skills 15 , 16 and evaluations 17 of this field.

Social changes are manifested in greater mobility of workers, shifting labor markets, frequent changes of professions, the disappearance of several professions and the emergence of new ones 18 , 19 . The emergence of artificial intelligence tools, which are transforming the entire landscape of the labor market, is becoming a significant risk and challenge. The need to learn constantly and throughout life and the instability of professional development make vocational education and training the most important and defining sector of education 20 , 21 . Many researchers agree that its role will constantly grow and cover all sectors of professional implementation 22 , 23 . Bibliographic analysis of the field of research in the field of vocational education is rapidly evolving, but there are fewer review works on this sector than it requires 24 .

Vocational education and training are studied from the point of view of assistance from the state administration or municipalities in the retraining of employees 6 , 16 ; many country case studies provide insight into the differences in the educational context of individual countries 2 , 3 , 11 , 14 . Experimental studies on combining vocational education and work or other types of employment make it possible to assess the potential of problems that require solutions 25 , 26 , 27 . The use of technical and digital tools within vocational education is also being devoted to more and more research 9 , 28 , 29 .

The existing research results have laid an important foundation for the reform and development of vocational education and training. However, vocational education and training still have a series of problems that need to be solved, such as the large gap between the skill supply of vocational education and the skill demand of the labor market, and the low enthusiasm of enterprises to participate in vocational education and training 4 , 9 . In addition, although many scholars use a variety of research methods from different dimensions to explore related issues of vocational education and training, few studies have investigated vocational education and training comprehensively and systematically 23 , 30 . There is an urgent need for bibliometric analysis to identify areas of development, areas of greatest interest among researchers, and stratification of research by country, institution, and area. This will allow the efforts of new researchers to be more targeted and their quality improved.

The objectives of this study are as follows:

Analyze the leading researchers of vocational education and training from dimensions of individuals, institutions and countries;

Figure out the distribution of journals related to vocational education and training;

Delve into the main research topics and knowledge structure in this field;

Aggregate the research hotspots and frontiers in this field.

The data used in the study were obtained through advanced retrieval from the Web of Science Core Collection (WOSCC): "Science Citation Index Expanded (SCI-E) (2004–2020)"; Social Science Citation Index (SSCI) (2004–2020)"; "Conference Proceedings Citation Index-Science (CPCI-S) (2004–2020)". These data are intended to provide comprehensive, scientific and systematical research on the existing literature related to vocational education and training. The retrieval strategy was as follows: TS = ("Vocational Education" or "Technical and Vocational Education and Training" or "Technical Education" or "Technical and Further Education" or "Technical and Further Education" or ((VET) or (TVET) and (education))), and with language options of "English" and literature type selection "Article". Here VET is the "Vocational Education and Training" abbreviation and TVET "Technical and Vocational Education and Training" abbreviation.

Indeed, 644 data sources were retrieved from SCI-E, 697 from SSCI, and 693 from CPCI-S. A total of 118 duplicate articles were identified. The cleansing of a sample of data sources was carried out manually by carefully studying the content of abstracts and excerpts from the sources or full versions if they were available. Finally, a total of 2,024 bibliographic citations were obtained (Fig.  1 ).

Literature related to vocational education and training from 2004 to 2020.

Research tools

Information visualization is the process of representing and visualizing abstract data with the help of computer software, which can enhance researchers' perception of abstract information 31 . Based on the existing literature, visualization analysis can adopt the method of dynamic graphic visualization to reveal the trends, hotspots and frontiers of scientific research. Therefore, information visualization can facilitate researchers to understand and predict the frontiers and trends of scientific research opportunely and break new grounds for new ideas amid complex information.

In this study, the analysis software CiteSpace 5.7 was used to conduct research cooperation, cited references and co-occurrence analysis of keywords in literature pertinent to vocational education and training. CiteSpace is an important software in bibliometrics 32 . CiteSpace visualized knowledge maps can be used to identify, display and predict research trends and elucidate knowledge structure and development 33 . Therefore, CiteSpace's visualized knowledge map was adopted in this study to analyze literature in the field of vocational education and training. The analysis elements included Author, Institution, Country, Cited authors, Cited journals, Cited journals, and Cited references.

A network of cited references, co-authors and keywords co-occurrence can represent the scientific knowledge domains 34 . The network provides a systematic and scientific description of the evolving field of scientific knowledge through knowledge mapping, a novel method of literature analysis, enabling researchers to better understand knowledge structures, research collaborations and the hotspots and trends of research 35 .

Research process

In this study, a visual analysis of the bibliography of vocational education and training was conducted through CiteSpace. The research process was as follows: Firstly, the basic knowledge cluster of vocational education and training was constructed according to the reference literature of the field. This cluster is necessary for the next steps to identify the main clusters in the knowledge graph and highlight the most influential literature in this field of knowledge. Also, the basic knowledge cluster will help to study the evolution of each cluster, and future trends and identify key literature from a timeline perspective.

Secondly, the hotspots and frontiers of vocational education and training can be identified based on the frequency of the keywords in the related literature. Meanwhile, keyword bursts can also reveal the evolution of vocational education and training and determine the latest research trends. Burst refers to the significant change in the value of a variable over a relatively short period, which is adopted by Citespace to identify research frontiers.

Finally, the researchers, research institutions and countries were visualized to identify the major contributors to the evolution of knowledge in vocational education and training.

Research limitations

The results obtained may be limited to searching only the Web of Science Core Collection (WOSCC) database, without considering Scopus or other relevant data sources. Also, the sample may not contain sources that directly relate to the topic under study but did not use the corresponding keywords in the article description or other identifiers, and this happens.

Knowledge clustering of vocational education and training research

The emergence and development of any new knowledge are based on existing research and findings, and so are vocational education and training. In general, the frontiers of research in a particular discipline can be represented by journal papers to a certain degree, and the cited references form the knowledge base for the journal paper. The important references can be clustered and the co-cited clustering can be determined with the help of specialized computer software, an important step in figuring out the knowledge base for vocational education and training.

The distribution of selected bibliographic citations by year is presented in Fig.  1 and Table 1 . First, one should evaluate the relatively uniform growth in the number of works devoted to the topic under study throughout the entire period under study. The only exception is the sharp increase in the number of studies in 2012.

In Eq.  1 \({c}_{ij}\) represents the number of co-occurrences of i and j, \({s}_{i}\) is the frequency of occurrence of i, \({s}_{j}\) is the frequency of occurrence of j.

After running CiteSpace, the knowledge mapping was obtained.

Cluster names related to the field of vocational education and training were extracted with the application of MI (Mutual Information). The formula is calculated as Eq.  2 , where \({g}_{st}\) is the number of shortest paths from node s to node t; \({n}_{st}^{i}\) is the number of shortest paths through node i among the \({g}_{st}\) shortest paths from node s to node t. 166 clusters were generated based on the co-primer clustering information with 10 main clusters.

Modularity Q is a measure of visual networks ranging from 0 to 1. The formula is calculated as Eq.  3 ; \(P(w|{c}_{i})\) is the co-occurrence probability of w and c, \(P(w)P({c}_{i})\) is the frequency of occurrence of w, \(P({c}_{i})\) is the frequency of occurrence of i-type values). The higher the value, the better the network clustering. In general, Modularity Q ranging from 0.3 to 0.8 indicates that network clustering is acceptable. Weighted Mean Silhouette S is a homogeneous indicator of network clustering ranging from − 1 to 1. The larger the Weighted Mean Silhouette S, the higher the clustering homogeneity. In general, Weighted Mean Silhouette S below 0.5 means that the clustering results are acceptable, and above 0.7 means that the clustering results are more reliable 36 . Figure  2 shows the Modularity Q value of 0.392 and the Weighted Mean Silhouette S value of 0.9641 for the visual network in the field of vocational education and training. Weighted Mean Silhouette S values of all 10 major clusters are above 0.8. The above data demonstrate that knowledge mapping is a high-quality clustering of the knowledge domain of vocational education and training.

Co-cited literature clusters.

Table 2 further provides a more detailed description of each of the knowledge clusters depicted.

It can be concluded from Table 2 that Vocational Interests (#0) ranked first in the knowledge cluster that includes job quality, linkage, occupation, completion, ring-biased Technological change, Labour market entry and dual training system, including 62 literature, most of which were published around 2014. The Weighted Mean Silhouette S value of the cluster is 0.966, indicating the high homogeneity of the 62 literature in the cluster. Among them, General Education, Vocational Education, and Labor-Market Outcomes over the Life-Circle, by Hanushek et al. 37 on Journal of Human Resources , are the articles with the highest citation (60% of the articles in this cluster cited this article); Vocational Education and Employment over the Life Cycle by Forster et al. 38 on Sociological Science , ranks the second (34% of the studies in the cluster cited this article); Educational Systems and the Trade-Off between Labor Market Allocation and Equality of Educational Opportunity by Bol and Van de Werfhorst 18 on Comparative Education Review ranks the third (27% of the studies in the cluster cited this article).

The second clustering is Workplace Simulation (#1) with 55 articles, and the Weighted Mean Silhouette S value of the cluster is 0.879. The most cited article was Students' Learning Processes during School-based Learning and Workplace Learning in Vocational Education: A Review by Schaap et al. 40 on Vocations and Learning . Forty percent of the studies in the cluster cited this article. The third cluster, Task Shifting (#2), consisted of 42 articles. The Weighted Mean Silhouette S value of the cluster is 0.967, a high homogeneity. The detailed information of each major cluster is shown in Table 3 .

In addition, widely recognized studies can be identified based on the number of citations. The top 3 studies with the highest citations (over 20 citations) are as follows: General Education, Vocational Education, and Labor-Market Outcomes over the Life-Cycle published by Hanushek et al. 37 on Journal of Human Resources (37 citations); Students' Learning Processes during School-based Learning and Workplace Learning in Vocational Education: A Review by Schaap et al. 40 (22 citations) on Vocations and Learning ; Vocational Education and Employment over the Life Cycle by Forster et al. 38 on Sociological Science (20 citations).

Given the lack of expansion of the thematic field of research after 2013, as shown further in tables, it can be assumed that the Vocational Interests knowledge cluster has focused the most interest of researchers and perhaps provided the most topics for further in-depth research. It is the state of the labor market and the relationship with employment that has received the most attention. researchers in connection with vocational education.

Distribution of countries

The number of papers published by different countries and their academic influence can be elaborated on in Table 4 . It can be inferred that the United States was the most productive among the top 10 countries followed by the Netherlands and Australia in the field of vocational education and training with 260 papers published from 2004 to 2020, accounting for about 14% of all literature. However, China ranked sixth with 88 papers, accounting for 4.8% of all papers, far lower than that of the United States. In terms of betweenness centrality value, the USA (0.67), England (0.36) and Germany (0.27) ranked in the top three, indicating a significant academic influence on the field of vocational education and training. Betweenness centrality indicates the strength of a node's influence on the flow of information in the graph. This is a measure of the influence of a separate node in a whole network 33 . The United States ranked first regarding the academic influence in the field of vocational education and training research while China is still in a relatively disadvantaged position. A more complete visual representation of the distribution of academic influence by country is presented in Fig.  3 .

Network of countries distribution for VET.

Burstiness (Table 4 the last section) is an increase and decrease in activity or frequency of publications that disrupts the continuity or pattern of distribution. In this case, the higher this indicator, the more uneven the participation of publications from this university in the research field being studied. The Burstiness results of countries more active in the field of vocational education and training (Table 4 , Burstiness section) showed that: Turkey ranked first with a value of 12.38, followed by England which has been more active in this field. However, although Sweden and New Zealand are not very active in this field, academic attention has been drawn to them.

Distribution of research institutions

In terms of the production of research institutions in the field of vocational education and training, Univ Amsterdam ranked first with 30 articles, followed by Univ Utrecht (28 articles) and Univ Melbourne (22 articles) (Fig.  4 ).

Network of institutions for VET research.

However, the ranking of research institutions based on betweenness centrality demonstrated significant influence from other research centers. The top three universities with betweenness centrality were IZA (0.09), Univ Turku (0.09) and Univ Helsinki (0.07), indicating the importance and influence of these three universities in the field of vocational education and training. Regarding the post surge capacity, Gazi Univ was in the lead with a surge of 5.52, followed by Leiden Univ (5.02) and Univ Utrecht (4.24). See Table 5 for details.

Cited journals

In the citation network of journals, the larger the circle, the higher the citation frequency (Fig.  5 ).

Cited journals network.

Totally 233 pieces of literature on J Vocat Educ Train were cited; 208 on Rev Educ Res ; 181 on Thesis Elev ; and 156 on Econ Educ Rev (Table 6 ).

However, from 2004 to 2020, Thesis Elev ranked first in burst detection with a burst value of 31.74. Other journals with relatively high emergent detection values include SOC SCI Res, Comp Educ, Teach Teach, Econ J, J Labor Econ, etc. These journals mainly come from the fields of psychology, sociology, economics and pedagogy, the source of knowledge in the field of vocational education and training.

Hotspots and trends of the research on vocational education and training

The research hotspot is the focus of researchers' attention shared by a group of interrelated papers in a relatively short period. Keywords are the gist and soul of an academic paper, a highly summarized and refined research problem, and an important index of research hotspots. Therefore, the research hotspots and main characteristics of a certain field can be abstracted from the change in keyword frequency. In this study, "Keyword" was selected from the CiteSpace node types for Keyword co-occurrence network analysis. The larger the node, the more important the node.

In terms of keywords frequency (Table 6 ), related research mainly focused on vocational education and training in vocational education, the transition, inequality, gender, perception, attitude, and the program, work, school, and skill, among which, the keyword "vocational education" ranked first for appearing 399 times, followed by education (234 times) and vocational education and training (181 times). Betweenness centrality higher topics include health, adolescent, perspective, gender, employment, model, etc.

Meanwhile, keyword selection was carried out to clearly show the research hotspots in different years and their interrelation and evolution. Since none of the keywords identified during the study were localized for the period after 2013, we can conclude that the thematic field of research after this time developed almost exclusively intensively, and not extensively, that is, the research hotspots that had already been emphasized earlier were explored (Fig.  6 ).

Timeline of co-citation clusters from 2004 to 2020.

Keywords that appeared more than 25 times were selected and checked for betweenness centrality, as shown in Table 7 .

Compared with previous research 23 , 24 , this research uses CiteSpace V to analyze the research hotspots and research frontiers of vocational education and training from 2004 to 2020, and finds that:

First, the annual volume of research literature is steadily increasing, but the growth rate is relatively low. This is the same as the result of Hui's research 23 . The reasons for this result are as follows: The first is that the academic level and subject status of vocational and technical education are not yet mature, and its knowledge fields and subject boundaries are not clear enough, which causes the subject of vocational and technical education to face multiple identity crises 27 , 28 . The second is that the interdisciplinary nature of vocational education makes its research power scattered in many disciplines such as pedagogy, economics, management, and sociology, while there are fewer academic groups specializing in vocational and technical education 3 , 7 .

Second, from the perspectives of research countries, institutions, authors and journals, the main drivers of research in the field of vocational education and training come from the United States, the Netherlands and Australia, with Univ Amsterdam, Univ Utrecht and Univ Melbourne as the leading institutions. De Bruijn from Utrecht University, Christopher Winch from University of Westminster Univ Westminster, Pietty Runhaar from Deakin University, Martin Mulder from King's College, and Derek G Shendell from Rutgers State University, were the major contributors to vocational education and training. Literature on J VOCAT Educ Train was the most highly cited (233 times), followed by the top five journals including Rev Educ Res (208 times), Thesis Elev (181 times), Econ Educ Rev (156 times) and J Educ Work (150 times). This is different from Yu and Zhou's research results 24 . Through analysis of 719 literature titles, Li proposed that the main research countries for vocational education and training are European countries and the United States 12 . The reasons for the difference between the two may be: The first is the sample size. This study uses 3844 literature titles in the Web of Science database, which has a larger sample size coverage and more effective results; while Li's research has only 719 literature titles and a smaller sample range. The second is the time frame. This research uses 15 years of literature from 2004 to 2020, which represents the latest research characteristics in the field of vocational education and training; while Li uses literature from 2000 to 2009, which can only represent the characteristics of previous research.

Third, in terms of the most popular research topics, growth, vocational education and training, politics, university, secondary education, the environment, China, and other aspects of inequality took the lead from 2004 to 2015, and after 2015 20 , 24 . Other researchers agree with the results obtained in that study showing that the field started to focus on inequality, the teacher, professional development, engagement, program, self-efficacy, high school, the predictor and labor market, among which, the fields of engagement, program, self-efficacy, high school, predictor and labor market are still active and may become future research directions 16 , 24 . This is consistent with Hui's research results 23 . Technological changes and socio-economic development require vocational education and training to gradually shift the focus to students’ cross-industry abilities, and to pay close attention to the dynamic needs of the labor market. In addition, this has a certain relationship with the gradual change of vocational education research from macro to meso and micro.

Although an effective visual analysis of the relevant studies in the field of international vocational education and training from 2004 to 2020 was conducted, the obtained data cannot fully represent the overall picture of the development of international vocational education and training. Limited by research conditions, the related studies of international vocational education and training from the Web of Science were downloaded from 2004 to 2020. Significant potential for future research is to explain the observed spillovers in the influence and contributions of different countries and institutions over significant periods and how they change due to market influences, changes in technology, and other possible factors. Future researchers are encouraged to use a wider range of journals over a longer period.

Conclusions

By drawing the scientific knowledge map of international Vocational Education and Training from 2004 to 2020, this paper intuitively demonstrates the growth law of papers, knowledge sources, author contributions, institutional cooperation and national cooperation in this research field. It also analyzes the research hotspots in the field of vocational education and training, and draws the following conclusions from a comprehensive perspective:

Paper growth law. From 2004 to 2009, the development of Vocational Education and Training research was relatively slow. Since 2010, new Vocational Education and Training research has shown a vigorous development trend. The amount of new media research will reach its peak in 2020. The author predicts that in the future, Vocational Education and Training research will continue to show a trend of vigorous development.

Knowledge source. In the field of Vocational Education and Training, 12 journals have been cited more than 120 times. These journals mainly focus on psychology, sociology, economics and pedagogy. This shows that the knowledge in the field of Vocational Education and Training mainly comes from the above four disciplines.

Author contribution. Hanushek Ea, Forster AG, Bol T, Schaap, Akkerman, McGrath, Brockmann, Kuijpers, Nylund and other highly cited authors have provided high-quality papers and belong to high-impact authors.

Institutional cooperation. The most researched institution in the field of Vocational Education and Training is Univ Amsterdam (30 articles), followed by Univ Utrecht (28) and Univ Melbourne (22 articles), Maastricht Univ (22 articles), and Univ Helsinki (22 articles). On the whole, there is a lack of cooperation and exchanges between institutions, and no large-scale cooperation network has been formed.

Country cooperation. The country with the most research in the field of Vocational Education and Training is the USA (260 articles), followed by NETHERLANDS (251) and AUSTRALIA (217). Although there are many research results in the field of Vocational Education and Training in various countries, the cooperation network between countries needs to be strengthened urgently.

Research hotspots. The relatively high intermediary centrality in the field of Vocational Education and Training is health (0.13), adolescent (0.13), gender (0.1), employment (0.1), and model (0.1). This shows that the above content is a research hotspot in this field.

Data availability

Data will be available from the corresponding author (Xueshi Wu) on request.

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Tian, Y., Liu, J., Xu, X. et al. Knowledge mapping of vocational education and training research (2004–2020): a visual analysis based on CiteSpace. Sci Rep 13 , 22348 (2023). https://doi.org/10.1038/s41598-023-49636-7

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Vocational Education Research: Research on Vocational Pedagogy, Vocational Discipline and Vocational Didactics

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The chapter introduces the occupation and vocational education as a subject of research and their research framework, which include the emergence and origins of didactic approaches and concepts of vocational teaching and learning. The author reviews the history of research on vocational disciplines and vocational didactics at universities and the relation between research on occupation and vocational education and its methods in contemporary historical retrospect. Areas, bounds, and working fields of vocational pedagogy, vocational didactics, and vocational disciplines are also analyzed. The author further predicts the development opportunities and prospects for the subjects of vocational didactics and occupation research on vocational education.

The article refers to the research situation and its genesis in Germany.

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Pahl, JP. (2014). Vocational Education Research: Research on Vocational Pedagogy, Vocational Discipline and Vocational Didactics. In: Zhao, Z., Rauner, F. (eds) Areas of Vocational Education Research. New Frontiers of Educational Research. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-54224-4_2

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General Education, Vocational Education, and Labor-Market Outcomes over the Life-Cycle

Policy debates about the balance of vocational and general education programs focus on the school-to-work transition. But with rapid technological change, gains in youth employment from vocational education may be offset by less adaptability and thus diminished employment later in life. To test our main hypothesis that any relative labor-market advantage of vocational education decreases with age, we employ a difference-in-differences approach that compares employment rates across different ages for people with general and vocational education. Using micro data for 18 countries from the International Adult Literacy Survey, we find strong support for the existence of such a trade-off, which is most pronounced in countries emphasizing apprenticeship programs. Results are robust to accounting for ability patterns and to propensity-score matching.

We thank participants at the CESifo area meeting in the Economics of Education in Munich, in particular Stefan Wolter, Sue Dynarski, Lance Lochner, and Holger Sieg, for valuable discussion and comments. Hanushek was supported by the Packard Humanity Institute. Woessmann gratefully acknowledges support from the Pact for Research and Innovation of the Leibniz Association. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.

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Vocational training in India: determinants of participation and effect on wages

• Rishi Kumar   ORCID: orcid.org/0000-0001-7520-0187 1 ,
• Shravanth Mandava 1 &
• Venkata Sandeep Gopanapalli 2  

Empirical Research in Vocational Education and Training volume  11 , Article number:  3 ( 2019 ) Cite this article

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India is at the cusp of a population change and is currently undergoing the phase of ‘demographic dividend.’ This has thrown a challenge towards the policymakers to ensure that there are enough employment opportunities for the ever-increasing labour force. One of the areas where improvement is urgently required in India is skill development. This paper attempts to identify the factors, which affect an individual’s participation in vocational training using nationally representative National Sample Survey Office (NSSO) data. Further, we investigate the impact of vocational training on the wages of an individual at overall and sectoral levels.

For addressing the first question of factors that are associated with participation in vocational training programmes, we will be applying logit and multi-nominal logit models. The explanatory variables will broadly be social and economic indicators of the individual as well as the individual’s household characteristics. The second issue of identifying the effect formal vocational training on wages will be analysed through a multiple regression model.

We found that being an urban dweller increases the odds of participating in formal vocational training. Further, being male increases the odds of receiving formal vocational training. We found that having formal training increases the wage by 4.7% in the overall economy as compared to a person without any training. The effect is highest in the primary sector, where the individuals with vocational training had a wage increase of 36.9%. Workers with formal vocational training in the secondary sector had an increase in wages by 17.6%.

Conclusions

The analysis done in the paper reveals that formal vocational training is associated with higher wages with the effect being the highest in the primary sector. Being male and urban dweller improved the odds of participating in formal vocational training. The model suggests that there exist good economic returns, which are associated with formal vocational training, and hence, it makes sense to invest resources in vocational training.

Introduction

India’s economic growth has received appreciation and recognition globally. Over the last 3 decades, the transformation of the Indian economy has been quite remarkable and conspicuous. The growth of per capita income, which was a little over 1% prior to 1980 jumped to over 4% per annum in years afterward. India’s economic growth rate in the beginning of the 21st century was among the ten highest in the developing world. The country achieved an average growth rate of around 8% in the period from 2001–2002 to 2005–2006 (Krueger 2013 ). However, this growth has attracted a lot of criticism as well. The ‘inclusivity’ of this growth has always been a point of contention with the growth of India often being labelled as ‘jobless growth’. The population growth of India has been declining over many years, yet the labour force is projected to grow by close to 2% or some 7 million or more per year over next few years. Modernisation and social processes have led to more women entering the work force, lowering the dependency ratio from 0.8 in 1991 to 0.73 in 2001 and is expected to further decline to 0.59 by 2011 (Goel 2017 ). The dependency ratio has indeed declined to 0.55 in 2011 and further reduced to 0.51 as of 2016. Simply put, that in the coming times, there would be more number of people who would be looking for a job in India every year.

India is currently in a unique position where both the employers requiring skilled workers and the employment seeking population are facing issues. It is expected that the Indian manufacturing sector would require labour force of 20 million, which in turn would necessitate the training of 1.5 million technicians every year (McKinsey 2004 ). A survey on ‘labour/skill shortage for industry’ of over 100 companies by the Federation of Indian Chambers of Commerce and Industry (FICCI 2011 ) found that 90% of respondents (companies) were facing a shortage of labour. About 89% of the respondents said that they have been unable to meet the potential demand for their products in the market due to labour shortage. One the other hand, as India never saw an economic boom in the manufacturing sector it has become very difficult to provide jobs to the unskilled and semi-skilled population. This problem is further aggravated by the fact that the country does not have enough extensive Skill Development programs to bridge the gap and make people employable. Vocational education and training is defined as the educational and training programs designed for achieving a particular job or type of job in the labour market (OECD 2010 ). Even after almost 50 years of independence, Industrial Training Institutes (ITIs), which are important government run centres for imparting vocational training in India, were able cater to only 660,000 youth in the year of 2002. During the 10th Five Year Plan period of 2002–2007, the number rose to only 750,000. Hence, the skilling of the labour force has been insufficient in the country further worsening the employability of the masses, especially the youth. Briefly, there is a mismatch between the demand and supply of labour (Agrawal 2012 ). This paper is an attempt to see which factors determine the participation of an individual in vocational training. Further, we will try to assess the impact of the skill training received through vocational courses on the wages an individual receives.

Literature review

Based on 61st round of NSSO survey (2004–2005), only 4% of the population between 15 and 29 years had received (or was receiving at the time of survey) formal vocational education & training (VET) and 8% of that age group had received non-formal VET. A vast majority of the population (89%) in that age group do not have any sort of VET. A high unemployment rate for vocational graduates was found but the rate was slightly lower than the unemployment rate of general secondary graduates in the age group. Daily wages of both casual and regular/salaried workers of people with VET were found to be higher than general secondary graduates in males as well as females (Agrawal 2012 ). From 61st round of NSSO survey (2004–2005) to 66th round of NSSO survey (2009–2010), the population in the age group of 15–29 years who had received formal vocational education reduced from 2.37 to 1.96% and those who had received non-formal vocational education declined from 7.74 to 4.80%. Although unemployment rate among the formal VET graduates has decreased by 2 percentage points from 2004 to 2009, still around one-fourth of formally trained labour force remained unemployed (Agrawal 2014 ). Returns to vocational education were found to be significantly higher than returns to general secondary general education using Mincerian wage function approach on data from 68th round of NSSO survey (2011–2012) of those people who have received vocational education in the age group of 15–59 years. Average daily wage of formal trainees was found to be much higher than non-formal trainees suggesting that VET was indeed helpful from an earnings point of view. The unemployment rate of formal trainees who mainly worked in the tertiary sector though, was found to be much higher than rate of unemployment of informal trainees who mostly worked in the primary and secondary sectors (Agrawal and Agrawal 2017 ).

Manufacturing sector can provide meaningful employment on a large scale. In a study by Banerjee ( 2016 ), participation in the manufacturing sector was found to increase across all social groups with VET. However, the participation as wage workers increased to lesser extent in socially backward castes (OBCs, SCs, and STs) than General category workers. VET was also found to enhance wages in the manufacturing sector overall. The wage differential among manufacturing workers with VET was found to be lesser between General category and all socially backward workers than the wage differential between them without VET. VET had improved the conditions of all social groups in the manufacturing sector but it does not do much to remove the social inequality existing in the Indian manufacturing sector.

Previous studies have also reported the same positive effects of vocational education with the same caveats, in other developing countries. Technical and Vocational Education and Training (TVET) in several African countries was found to start at the secondary school level and taken up by students belonging to families with a financial burden preventing them from accessing general secondary education. TVET was seen to occupy a small and marginal position in the schooling system of Africa, mostly taken up with the hope of joining the labour market and early income (Oketch 2007 ). When allowed for selectivity, a much higher return for vocational education was found over and above general education at the same level in Thailand (Moenjak and Worswick 2003 ). Vocational stream graduates employed in occupations related to their field of study had significantly higher earnings than regular academic stream graduates in Brazil (Arriagada and Ziderman 1992 ). Men in Egypt with vocational education had a 29% higher rate of return over men having education till the general secondary level. This rate however, was found to be only 2% higher in women (El-Hamidi 2006 ). Vocational high school education was found to be a low risk investment strategy in Turkey. Children with humble backgrounds were found more likely to be enrolled in a vocational school compared to children whose parents held a managerial position. Women who received vocational education were found to have a higher probability of employment (Tunali 2002 ). Salaried earnings in women were found to rise substantially due to increased employment, better access to jobs and increase in productivity when unemployed youth belonging to the lowest two strata in population were randomly provided training in Columbia (Attanasio et al. 2011 ).

Contrasting sets of evidence also exist which find academic education to be better than vocational education as per the labour market outcomes. Returns to vocational training were found to be higher than academic education at the lower levels of education and lower than academic education at the higher levels of education (Kahyarara and Teal 2008 ). The returns to general or academic education in Suriname for both the genders were found to be exceeding the returns to vocational or technical education from both private as well as social perspective (Horowitz and Schenzler 1999 ). Employment prospects and earnings were found to be lower for vocational school graduates in newly industrialized countries of Taiwan, Singapore, South Korea and Malaysia than academic graduates (Tzannatos and Johnes 1997 ). Very little difference was found between the returns of technical and formal education in Singapore. Returns to technical education were found to be higher among women at the secondary level whereas the returns to formal education were found to be higher among men at the same level (Sakellariou 2003 ). Most students in Australia were found not to complete their VET and discontinue their studies as soon as they have gained employment or after the obtainment of the specific skills they were seeking (Fieger 2015 ).

Numerous studies have shown the benefits of vocational education in developed countries too. Though vocational education hinders students’ chances of moving onto higher education and thus hindering their chance of entering highly prestigious occupations, it provides a safety net for students who are unlikely to receive further education. Higher education dropouts in Germany without pre-tertiary vocational qualifications were found to have greater difficulties in finding a job compared to such dropouts with pre-tertiary vocational qualifications indicating that VET does serve as a safety net in case of dropouts (Scholten and Tieben 2017 ). Vocational education increased chances of employment for secondary graduates in the US and helped them in becoming skilled workers rather than unskilled workers (Arum and Shavit 1995 ). Earning gains of 8 percent were found for vocational school graduates working in their training related jobs over workers who followed normal US high school curriculum (Campbell et al. 1987 ). Students who take up vocational education tended to be of lesser academic ability and came from a lower socio-economic background and their parents were less educationally qualified. For such students who do not take up higher education in Israel, vocational schooling was found to be more cost effective and graduates with vocational training earned more than general secondary school graduates if they worked in closely related occupations to their field of vocational study (Neuman and Ziderman 1991 , 1999 ). Vocational qualifications have a high value for individuals of low ability and those who work in low skill jobs. However, wage premiums were found to be higher from academic qualifications than vocational qualifications in Britain in absolute terms. But the wage gap reduced to a large extent when the time taken for acquiring academic degrees was controlled for as vocational degrees take much shorter time to complete (Dearden et al. 2002 ). Governments of industrialized countries provide VET through the educational system to improve the job opportunities of youth who do not possess the skills demanded of them in the labor market and the ability, funding or motivation to pursue higher education (Eichhorst et al. 2015 ). Workers in United States were found to reap substantial benefits from training provided both inside the firm and outside the firm. Wages of workers who received training outside the firm were found to be ten percent higher while the wages of workers who received training inside the house were found to be eight percent higher (Lengermann 1996 ). No difference in long-term wages was observed for individuals of basic vocational programs who were additionally provided with an extra year of general education in Netherlands (Oosterbeek and Webbink 2007 ). Positive labour market outcomes were observed for individuals in Australia who had not completed high school at all levels of VET qualifications. However, similar outcomes were observed for individuals who had completed high school at only diploma or higher levels of VET qualifications (Lee and Coelli 2010 ).

A systematic review of 26 studies found the impact of TVET interventions on monthly earnings, overall paid employment and formal employment to be positive and significant albeit the effects being small, in youth from middle to low-income countries (Tripney and Hombrados 2013 ). Overall, a large number of empirical studies, both in developing and developed countries setting, showed that vocational education was found to increase participation, employability, probability of employment and wages of the workers who received it in all kinds of countries.

In Indian context, there have been few studies, which have used the dataset used in the present paper. Banerjee ( 2016 ) in her analysis found that participation in the manufacturing sector was found to increase across all social groups with VET. Agrawal and Agrawal ( 2017 ) found evidence of higher returns to VET as compared to general education. They further found that there exists mismatch between skills attained through VET and the field of employment in which these people worked. Our paper makes important contribution to the existing literature on the vocational training in India. The research objective of our paper is twofold. Firstly, we identify the factors, which are associated with an individual’s participation in vocational training. Secondly, we investigate the impact of formal vocational training on the wages of an individual at overall and sectoral levels. To the best of our knowledge, there is no econometrics-based studies, which tried to find out the factors associated with individuals’ decision to participate in vocational training in India. The returns to vocational training at three major sectoral level is also a significant contribution to literature as the authors were not able to identify any such study for India.

Skill development in India: a discussion

Usually, a growing economy banks on agriculture in its initial phases, which is followed by a high growth phase in its manufacturing sector and finally leading to a strong service sector growth. In the case of India, the ‘high growth phase of the manufacturing sector’ was more or less absent in its growth take-off. As Table  1 suggests, the growth in Agricultural and industrial sectors was never able to match the consistent and strong growth of the Services sector after the 80s. The share of services increased from 30% of gross domestic product (GDP) in 1950–1951 to a mammoth 57% in 2008–2009. On the other hand, agriculture’s share has declined from 55% to less than 17% during the same period (Eichengreen and Gupta 2011 ). Owing to consistent GDP growth and a strong service sector, job opportunities for the highly skilled are relatively easily available. However, in a developing country only limited population has access to quality higher education. The situation in India is no different. Hence, a large section of population has found it difficult to be employed in productive activities. In fact, it is feared that if the issue of the employment is not addressed soon enough, India may not be able to fully capitalize on the demographic dividend that it is currently experiencing.

The system of skilling of India in the form of vocational training is facing a multitude of problems. The ecosystem of skill training is unable to cope with demands in terms of quantity, quality and relevance as expected from the industry. A survey of ITIs found shortage of trained teachers/instructors. Moreover, many instructors were not at par with the new technologies and needs of the modern industry. Many ITIs had actual staff strength much lesser than the sanctioned posts. Apart from this, the apprenticeship system of India is also very weak. India has 270,000 apprentices in an economy that has 460 million persons of working age. Since the Indian economy is very dynamic, the training imparted in ITIs alone may not be sufficient for acquiring a skill. Training in the actual work environment by the experts is necessary. Although Apprentices Act of 1961 has been implemented to promote apprenticeship, private enterprises have not shown much enthusiasm for offering apprenticeships. This may be because there is no incentive for them to do so and because of the fact that they have to bear the costs for it.

Lately many initiatives have been launched to improve the situation. The government has realised the importance and need of reaping maximum benefits from its demographic dividend. National Democratic Alliance government has set up a new ministry of Skill Development and Entrepreneurship. The National Youth Policy (2014) and the proposed National Employment Policy will prioritize skill development and tackle the problems identified with the VET. It has also taken other important steps like redesigning the curriculum of around 10,000 vocational schools, expanding women ITIs, setting up of National Career Service portal, implementing the Standard Training Assessment and Reward scheme and maintaining a central repository, namely Skill Development Management System, which can be utilised by various stakeholders. Apart from these, there are various reforms, which are being carried out in Craftsman Training Scheme of the ITIs and Apprentices Act of 1961. Further, the initiatives like National Skills Qualification Framework facilitating mobility from vocational to general education and vice versa, Labour Market Information Systems to plan the skill training more efficiently, National Employability Enhancement Mission focussing on the job practical training, among others have been undertaken.

A third of India’s population was below 15 years of age in 2000 and close to 20 per cent were young people in the 15–24 age group. The population in the 15–24 age group grew from around 175 million in 1995 to 190 million in 2000 and 210 million in 2005, increasing by an average of 3.1 million a year between 1995 and 2000 and 5 million between 2000 and 2005. In 2020, the average Indian will be only 29 years old, compared with the average age of 37 years in China and the US, 45 in west Europe and 48 in Japan. The responsibility of using this to our advantage lies in the hands of the Government. This has potential to be a crucial resource for a country but also can turn into a liability if not handled well (Chandrasekhar et al. 2006 ). There is a need to develop an effective and innovative pro-poor and gender aware informal economy strategy to achieve decent and productive outcomes from skill development programmes (Agrawal 2014 ).

Skill training in India—current status

The official sample survey on employment showed that the proportion of age group 15–29 years population that had received/were receiving formal training increased by a meagre 0.1% from 2004–2005 to 2011–2012. Between 2004–2005 and 2011–2012, the skilled working population almost remained stagnant, in spite of decent economic growth registered by the country in the same period (Table  2 ). Another important finding was that majority of the skilled population acquire skills through non-formal training, which includes hereditary passing of skills, self-learning, or on-job acquisition of skills etc.

The data further showed that out of those who rely on non-formal vocational training for upgradation of their skills, the reliance on learning on the job was more in urban areas and total population (Table  3 ), which show the skill levels of those in job force are clearly below the required levels. The other methods of non-formal vocational training included hereditary and self-learning, which are clearly not the optimal way for acquiring the skills. Further, the non-formal vocational training also means that labour skilled through these methods do not have certification to back their skills, which in-turn may lead to lower employability and low wages from point of view of labour. From the employer’s perspective, hiring the labour without any formal training may be detrimental to productivity and efficiency.

As far as the employment status of those who received or were receiving formal vocational training is concerned, more males were found to be employed as compared to their female counterparts. Further, the employment of vocationally trained or being trained persons was higher in the urban areas rather than rural areas, may be indicating better absorption of skilled labour in these areas (Table  4 ).

Data and methodology

In the present study, we have used the 68th round of National Sample Survey Office (NSSO) data (2011–2012) to investigate and empirically analyse the issues related to the skill development in India and 61st round of NSSO has been used in the preceding section for understanding the broad changes in the situation. The sample survey is carried out throughout India and is nationally representative in nature. The 61st round of NSSO pertaining to year 2004–2005 was the first time that data related to vocational training was collected by this government agency. The survey was designed in a way that it captured the factors affecting people’s enrollment and participation in the vocational training programmes apart from various others things. Thus, it acted as a good starting point for comparison and keeping track of advancements in this area over the period. The 68th round of NSSO was conducted between the years 2011–2012. The survey was expanded over 12,737 first stage units (7469 villages and 5268 urban blocks) covering 1,01,724 households (59,700 in rural areas and 42,024 in urban areas) and surveying 4,56,999 persons (2,80,763 in rural areas and 1,76,236 in urban areas). As far as source of earning is concerned, the households deriving their major income from regular income was much higher in urban areas compared to rural areas (Table  5 ).

The questionnaire included the questions of socio-economic background of the people including educational background, their employment status, whether they availed vocational training and other related issues. To maintain the brevity of the paper, the other finer details have been skipped like exact sample selection methodology, definitional aspects etc. However, the details regarding the same can be found in the official reports accompanying NSSO data viz. NSSO ( 2015 ).

The study aims to make inquiry into two main aspects. First, we will try to find out the relevant factors, which determine the participation of the households in the vocational training programmes. However, in India many people rely on informal training for acquiring skills. Hence, our study will identify the factors responsible for their participation in non-formal training as well as formal training. This analysis will give us a clear picture about the background of the households who are undertaking the vocational training. The second part will attempt to identify the effect of formal training on income of the respondents. Further, these questions are investigated for primary, secondary and tertiary sectors separately.

For addressing the first question of factors that determine participation in vocational training programmes, we are applying logit and multi-nominal logit models. While in logit model, the dependent variable is whether an individual has attended formal training or not, the multi-nominal logit model has three categories, which are ‘received/receiving formal training’, ‘received/receiving informal training’ and ‘no training’. The explanatory variables will broadly be variables related to social and economic status of the individual as well as his/her household characteristics.

The logit model is given by:

where, L is the log of the odds ratio, P is the probability of the event happening, X i 's are the independent variables included in the study.

In our case,

P i  = Probability of getting formal vocational training.

Therefore, P i /1 − P i denotes the odds of getting formal vocational training.

X i ’s, the independent variables consist of variables associated with Household characteristics and Individual characteristics.

Similarly, in order to determine the participation in formal as well as informal vocational training, we have used the multinomial logistic regression. The multinomial model is given by:

P is the probability of the event happening, X i ’s are the independent variables included in the study (discussed above).

Finally, to determine the effect of vocational training on wage, we have used the multiple regression model given below:

The main independent variable used in our analysis is regarding the vocational training status (dummy variable), where 1 denotes that the respondent has received formal vocational training and 0 denotes otherwise and X i s are the other independent variables related to household and individual characteristics. It is important to mention that the results from logit, multinomial and multiple regressions should not be interpreted as causal relation (Cox 1992 ).

We start with the discussion of the factors that show association with the participation of an individual in a formal vocational training programme. As discussed earlier, we have employed a logit model to ascertain the significant factors for an individual’s participation. The results are presented in Table  6 followed by an interpretation of the results.

From the above results, we observed that being in an urban area as compared to a rural area as well as being a male as compared to a female increased the odds of receiving formal training. Similarly, increased education level as compared to no education and having received technical education as compared to not receiving any technical education increases the odds ratio of receiving formal training. Further, an ever-married person has a lesser chance of getting formal training as compared to a single person.

In the case of religion, individuals belonging to Christianity and other religions have higher odds ratio of receiving formal training as compared to the individuals belonging to the Hindu religion. Among different social groups, Scheduled Tribe (STs) individuals have lower odds ratio than all other types of social groups in being enrolled for formal training. Individuals working in manufacturing (secondary) sector and service (tertiary) sector have got higher odds ratio of getting formal training as compared to the individuals working in the agriculture & allied activities (primary) sector.

Focussing on marginal effects, we observe that individuals from urban area have higher probability of getting formal training as compared to the individuals from rural area. Similarly, male and single individuals have higher probability of getting formal training as compared to females and ever-married individuals, respectively. Individuals with any formal education have higher probability of getting formal training as compared to the illiterate individuals. Individuals with any technical education have higher probability of getting formal training as compared to those who do not have any education. In the case of religion, individuals from Christianity and other religions have higher probability than individuals following Hinduism. As far as social groups are concerned, individuals from Scheduled Castes (SCs), Other Backward Castes (OBCs) and general households have higher probability of getting formal training as compared to the individuals from STs households. Individuals working in secondary and tertiary sector have higher probability of having received formal training as compared to those working in primary sector.

In the second leg of identification of factors behind participation in formal training vis-à-vis informal training programmes, we have used the multinominal Logit Model. The results of the model are tabulated in Table  7 .

The relative log odds of receiving informal training as well as formal training will increase if the respondent belongs to an urban area as compared to a rural area. With the increase in age, the relative log odds of receiving formal or informal training will decrease. In the case of gender, the relative log odds of receiving informal training increase for females as compared to their male counterparts while it decrease for the females in the case of formal training. In the case for formal training, relative log odds increase with increase in level of education while in the case of informal training, there was no clear trend but it decreases at higher levels of education. The relative log odds will decrease for an ever-married person as compared to a single person but the difference is significant only for formal training.

The relative log odds will increase for the individuals having technical education for formal training as compared to the individuals without technical education. Technical education does not have a significant effect in the case of informal training. Apart from ‘Other’ religions, the relative log odds decrease for Islam and Christianity as compared to the Hindu religion individuals for receiving informal training. On the other hand, the relative log odds of Christianity and Other religions are higher than Hinduism in the case of formal training. The relative log odds increase for all social groups as compared to the STs for both formal and informal training. The relative log odds increase for both secondary and tertiary sector as compared to the primary sector. This result holds true for both formal and informal training.

Also from marginal effects, we observed that individuals from urban area have higher probability of getting formal as well as informal vocational training as compared to the individuals from rural area. Similarly, male and single individuals have higher probability of getting formal training as compared to female and ever-married individuals respectively while probability for informal training for males is lower than their female counterparts. Individuals with any formal education have higher probability of getting formal training as compared to the illiterate individuals and similar results exist in the case of informal training except for the individuals with higher education, where the probability of informal training are lower. Individuals with any technical education have higher probability of getting formal training as compared to those having no such education but the probability is lower for the informal training. In the case of religion, individuals from Christianity and other religions have higher probability than individuals belonging to Hindu religion but in the case of informal training, probability of receiving informal training is lower for individuals from Islam and Christianity religion while it is higher for other religions as compared to the individuals from Hindu religion. As far as social groups are concerned, individuals from SCs, OBCs and general households have higher probability of getting formal as well as informal training as compared to the individuals from STs households. Individuals working in secondary and tertiary sector have higher probability of receiving formal as well as informal training as compared to those working in primary sector.

In the second part of this section, we will discuss the effect of formal vocational training on wages using the multiple regression model. We have run multiple regression model separately for three sectors, i.e. agriculture, manufacturing and services as well. In Table  8 given, we observe that overall, having formal training increases the wage by 4.7% compared to a person without any training. The effect is the highest in the primary sector where a person with formal vocational training has 36.9% higher wages. If we analyse across sectors, we observe the significant effect of formal training on wages of the workers in primary and secondary sectors (wages are higher by 36.9% and 17.6% for a person with formal training as compared to a person without any training). In the case of the tertiary sector, the effect of formal training on wages is insignificant. One of the possible reasons for this could be the fact that service sector employers do not recognize the formal training certificates of the individuals.

This paper attempted to identify the factors, which affect an individual’s participation in formal vocational training. We found that being an urban dweller increases the odds of participating in formal vocational training. It may be indicative of lack of proper vocational training facilities in the rural areas. Further, being male increases the odds of receiving vocational training, implying that certain special interventions are required to encourage the girls to enrol in formal vocational training programmes.

We saw the impact of vocational training on the wages of an individual at an overall level and at the sectoral level. We found that having formal training increases the wage by 4.7% in the overall economy as compared to a person without any training. The effect is highest in the primary sector, where the individuals with vocational training had a wage increase of 36.9%. Workers with formal vocational training in the secondary sector had an increase in wages by 17.6%. This clearly indicates that there are good economic returns, which come with formal vocational training, and it makes sense to invest resources in vocational training.

Abbreviations

gross domestic product

Industrial Training Institutes

National Sample Survey Office

Other Backward Castes

Scheduled Castes

Scheduled Tribes

Technical and Vocational Education and Training

vocational education and training

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Kumar, R., Mandava, S. & Gopanapalli, V.S. Vocational training in India: determinants of participation and effect on wages. Empirical Res Voc Ed Train 11 , 3 (2019). https://doi.org/10.1186/s40461-019-0078-y

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Received : 20 July 2018

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DOI : https://doi.org/10.1186/s40461-019-0078-y

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