Because of rising life expectancy and declining birth rates, reducing early retirement and prolonging employees’ working lives have become major goals on social policy agendas. In this context, ensuring the adaptability and employability of the existing workforce has become an important issue. The aim of this article is to conduct a review of the most recent literature on employment outcomes of adult learning. The review covers literature published in English since 2010.
Because of rising life expectancy and declining birth rates, reducing early retirement and prolonging employees’ working lives have become major goals on social policy agendas (Hasselhorn & Apt, 2015; Höfacker, 2010; Walker & Foster, 2013; Walker & Maltby, 2012). Active ageing has therefore gained widespread currency across Europe since the beginning of the new millennium (Foster, 2012) and governments are taking more and more policy action to encourage ageing workers to extend their working lives and their employers to retain them (Raemdonck, Beausaert, Fröhlich, Kochoian, & Meurant, 2015).
Whilst pension reforms have been the primary strategy of governments to date, this approach often fails to address the real barriers to older adults’ continuing participation in the labour force. Countries worldwide are therefore experimenting with structural reforms that introduce greater flexibility into work and retirement, removing softer barriers such as ageism among employers and providing older adults with targeted support to remain in or re‐enter the workforce. In this context, ensuring the adaptability and employability of the existing workforce has become an important issue. According to Foster (2012), there are two approaches to active ageing: a narrow economic and productivist approach focusing on participation in the labour market – advocated by the OECD (2006) and the European Commission (2012) – and a more comprehensive and multidimensional approach advocated by WHO (2002). This article focuses on the narrow approach with an emphasis on employment policy aimed at extending working life beyond the age of 50.
Different perceptions of the factors impacting older workers’ labour market participation and early retirement behaviour stem from differences in the notion of whether their exit is voluntary or involuntary and whether it is mainly affected by labour supply or labour demand (Hasselhorn & Apt, 2015; Jensen & Øverbye, 2013; Midtsundstad, 2015; Nilsson, 2016; Phillipson & Smith, 2005; Poulsen, Fridriksson, Tomasson, Midtsundstad, Mehlum, Hilsen, Nilsson, & Albin, 2017). The most influential theoretical frameworks in the retirement literature have focused on the retirement decision and been dominated by economic analyses (Midtsundstad, 2015; White, 2012). Economic theory links financial opportunities and constraints to labour supply, explaining why pull factors such as payment levels, taxes, age restrictions, pension scheme eligibility criteria and other welfare arrangements are of critical importance in the decision to retire or to continue working (Engelhardt, 2012; Midtsundstad, 2015). Another significant part of the research focuses on involuntary retirement and emphasises the importance of push factors in the labour market and at the company level, such as structural adjustments, rationalisations and other factors pushing older, less productive or less skilled workers out of the labour market (Midtsundstad, 2015). Today, there is little doubt that both pull and push factors influence the timing of retirement (Midtsundstad, 2015; Nilsson, 2016; Radl, 2013).
Early retirement is closely linked to the level of education and skills. Educated people typically continue to work longer (OECD, 2013). There is also a strong correlation between the level of education and occupation. The level of education therefore indirectly influences wage levels, pension and retirement options, working conditions, working environments and opportunities for continued training and development – all factors that influence the decision to retire (Hasselhorn & Apt, 2015; Midtsundstad, 2015; Nilsson, 2016). It also affects individuals’ health and work ability (Poulsen et al., 2017) and hence their ability to find a job and keep it. Last, but not least, the level of education impacts life expectancy and how healthy and thus active one can expect to be when finally retiring (Poulsen et al., 2017).
According to the OECD (2017), lifelong learning opportunities and inclusive labour markets will be essential to ensure that workers of all educational backgrounds are able to extend their working lives. Some studies also suggest that competence development and upgrading throughout the professional career (lifelong learning) may be important for older people to meet job requirements, especially in workplaces where labour and competence requirements are constantly changing (e.g. Ilmarinen, 2003; Yeatts, Folts, & Knapp, 1999).
The aim of this article is to review the most recent literature on adult learning and older workers’ labour market participation. Because of the scarce literature on the employment outcomes of adult learning found in earlier literature reviews, this review is not limited to scientific articles, but also includes book chapters and some grey literature, such as reports and working papers. It covers literature published in English since 2010. Before reviewing the publications found, a brief overview is given of the literature discussed in earlier reviews.
2 SUMMARY OF EARLIER LITERATURE REVIEWS
Myers, Frenette, Sweetman, Dostle, and Langlois (2014) provided an analysis of the empirical evidence on the outcomes associated with participation in adult learning. They examined the relationship between foundational learning (basic skills), higher education and workplace learning and a wide range of intermediate and final outcomes (financial and non‐financial) for individuals, for companies and for society. According to the review, evidence on the relationship between adult learning and financial outcomes is mixed. Whilst there is strong evidence to suggest that, in general, adult learners who engage in higher education experience financial gains, the returns are roughly the same as when the education is acquired earlier in life. They also found numerous studies investigating the extent to which individuals benefited financially from participating in workplace learning. This literature is, according to the authors, consistent in finding positive correlations between earnings and workplace‐related training, although estimates vary dramatically. However, they found few studies and therefore little evidence of whether and how adult learning affects older workers’ employability and labour market participation.
Another literature review by Field (2012) which examined the financial impact of lifelong learning and its impact on well‐being (such as health, self‐esteem and confidence) found that much of the research literature (up to 2011) focused on the gains experienced by the individual but less frequently on the benefits for companies and society. Furthermore, most of the literature concerns work‐related training and not general adult learning and has mainly focused on training for unemployed people and other vulnerable groups (single parents and the disabled) and to a limited degree on the impact of adult learning on the extension of working life. According to Field (2012), the research on returns to adult learning is inconclusive. Some studies find that it leads to a rise in average earnings, others that it has little or no impact on earnings, or that it only pays off for men, or that it gives negative wage benefits for relatively low‐level upgrading. Work‐based training is shown in some studies to be associated with higher wages, Field claims that this finding is not consistent across the literature, although, improvement in numeracy and literacy seems to increase earnings. The main conclusion, however, is that most studies of the economic effects of adult learning find that those who invest in new skills tend to be rewarded with higher wages.
In sum, the research in the field can be categorised as follows: 1) studies that describe the variation in participation rates among different groups, 2) studies that explore and/or investigate factors that explain the differences in participation rates in adult learning, adult education, workplace learning, etc., and 3) studies that investigate the effects or outcomes of different types of adult learning for individuals, companies and society. In addition, there are studies that discuss and partly evaluate different EU strategies, government strategies and different policy measures that are implemented in order to increase older peoples’ employability and participation in lifelong learning. In the following, I focus on studies published after 2009 and articles that investigate the impact of adult learning on older workers’ employment and labour market participation. However, in order to be able to grasp some of the mechanisms behind differences in outcomes, I also include some studies which explore other factors that could explain or shed light on the differences currently found in participation in lifelong learning and its outcomes.
The aim of this article is to investigate whether studies on the effects of adult learning or lifelong learning find that further learning and education have an impact on the employability of older workers, defined here as the ability to find and maintain employment.
According to Petticrew and Roberts (2006), systematic literature reviews are ways of making sense of large bodies of information and contributing to answer questions about what works and what does not. Systematic reviews, however, require a transparent and systematic process where you clearly define a research question, search for studies, assess their quality and synthesise the findings. As underscored by Armstrong, Hall, Doyle and Waters (2011, p. 147): ‘this requires an understanding of the literature, including gaps and uncertainties, clarification of definitions related to the research question and an understanding of the way in which these are conceptualised in the existing literature’.
According to earlier reviews (Myers et al., 2014; Field, 2012), few studies in the field investigate the connection between adult learning and employability. In order to unearth relevant literature on this issue, I therefore chose a broad approach and conducted a scoping review. Scoping reviews map the existing literature or evidence base and identify research gaps and summarise research findings. They differ from systematic reviews in several ways. The key phases according to Armstrong et al (2011) are: 1) identifying the research question, 2) identifying relevant studies, 3) study selection, 4) charting the data, 5) collating, summarising and reporting the results, and 6) optional consultation.
In this review, adult learning and education are defined as formal and informal education and learning obtained at an age beyond common graduation age. Employability is broadly defined as the ability to find and maintain employment, including upward and downward mobility. I also include studies that investigate whether further education and learning increase earnings, because this has been the usual way to measure outcomes from adult learning in earlier studies. In order to clarify whether adult learning and training seem to have a causal effect on labour market participation or not, most studies reviewed are based on longitudinal data or panel datasets. In order to shed light on possible mechanism and processes, I have also included studies that investigate and discuss some possible intermediate variables which influence the outcomes of adult learning.
The main strategy used to identify relevant literature was a search in Web of Science for scientific articles, using the following combination of keywords: lifelong learning OR adult learning OR adult education OR workplace learning AND older workers AND employ* OR employability. It produced 42 articles. After carefully examining the articles, I found that only two investigated the effects of adult learning on employment and/or earnings. This search was combined with a broader search in Google Scholar in order to find not only scientific articles, but also relevant chapters, reports and papers published in or since 2010 (up to April 2018). When searching Google Scholar, I used the following combination of keywords: ‘adult learning’ OR ‘lifelong learning’ AND ‘older workers’ AND ‘employability ‘AND ‘effect’. The search produced 711 publications. After carefully examining each publication (abstract and summary), only 20 were found to deal with the effects of lifelong learning or adult learning on older workers’ employability. I also searched for relevant articles in the European Journal of Education. Finally, I searched for relevant publications in the reference list of the articles found through the Web of Science and Google Scholar searches. In total, I found 27 articles that investigated the effects of adult learning or adult education on employability or labour market participation that were published after 2009.
4 RESULTS OF THE REVIEW (2010–2018)
Staying employable and participating longer in the labour market are important political goals for most European Governments. This review focuses on studies that investigate adult learning and training’s impact on employment, including work‐related earnings and occupational mobility. The studies are presented in Table 1.
|Author, publication, year and country||Population (dataset, n, sex, age etc.)||DV = dependent variable = outcomes||Design (type and follow‐up period)||Statistical analysis||Findings|
|TV = treatment variable = type of adult learning|
|IV = intermediate variables|
|Barbieri, P., Cutuli, G., Lugo, M., & Scherer, S. (2014)||The Italian Household Longitudinal Survey (ILFI) 1997‐2005||DV = occupational prestige score, occupational mobility (= 5‐points increase or decrease in the CAMSIS score); employment stability and unemployment exit||Five waves during the period 1997‐2005, including retrospective information on the life course||Panel fixed‐effects models, event history piecewise constant exponential models||Adult learning and work‐related training reduces unemployment risks, promote employability, foster job quality and upward mobility|
|N vary between analyses|
|TV = training = attending a course or learning program in concomitance to employment|
|Individuals born after 1940 (aged between 15‐64 years)|
|Buchler, S., Chesters, J., Higginson, A., & Haynes, M. (2014)||The Household Income and Labour Dynamics in Australia (HILDA) survey (ten waves; 2001‐2010)||DV = AUSEI06 occupational status scale (0‐100; mean 40.2)||Follow‐up 2001‐2010||Linear fixed‐effects regression model||Com. adult education leads to a 5.4‐point increase in occ. status (on average); fixed‐term employees benefit more than permanent and causal employed, those not employed, and those not in the labour force. Greatest eff. after 3 years and decrease thereafter|
|TV = completion of an educational qualification. as adult or not (lagged. time since comp.)||Interaction terms (5 models tested)|
|n = 60.660 obs.; 9.908 individuals Aged between 20 and 54 in 2001 (30 to 64 by wave 10)|
|Buchholz, S., Unfried, J., & Blossfeld, H. P. (2014)||Adult cohort of the National Educational Panel Study (NEPS) – panel study, retrospective monthly information for 11 000 individuals (1944‐1988)||DV = risk of becoming unemployed, chances of exiting unemployment, and chances of direct mobility (persons CAMSIS scores >5 point = upward mobility; >0 = downward mobility)||Retrospective data; monthly||Logistic regression models. Include dummy variables for different’ time pieces’||Participating in COT reduces the risk of becoming unemployed for men. VPC increases both men and women risk of becoming unemployed. Participation in COT increases men’s risk of remaining unemployed. FSE increases women’s chances of being reemployed|
|TV = formal secondary schooling = FSS; vocational preparation courses (offered by employment agencies) = VPC; certificated occupational training = COT; uncertificated occupational training = UOT|
|N = 8.392 individuals|
|Csanádi, G., Csizmady, A., & Róbert, P. (2014)||The Hungarian Household Panel (HHP) conducted by TARKI (2600 household surveyed yearly between 1992‐1997, follow up a sub sample in 2007)||DV = length of unemployment; upward and downward mobility (using the ISCO occupational codes and ISEI values)||Follow up 1992 – 2007||Standard logistic regression methods||FAE seem to shorten the length of unemployment, especially for women.|
|Retrospective data merge with original HHP sources||Participation in FAE increase the chances of upward mobility; especially HE for men and USE for women. FAE also seem to decrease the chances of downward mobility, but only for men|
|TV = formal adult education (FAE) (CE = compulsory education; USE = upper secondary education; HE = higher education)|
|Dordoni, P., Van der Heijden, B., Peters, P., Kraus‐Hoogeveen, S., & Argentero, P. (2017)||Survey data from a large Italian financial institution||DV = intentions to retire IV = one organizational factor = perceived support for learning; and one job‐related factor = perceived negative age stereotypes on older workers’ productivity||The data were analysed using Structural Equation Modelling (SEM) separately for the two age groups, 55–60 and 61–65||The results show that perceived support for learning appears to be negatively related to intention to retire (via participation in employability‐enhancing activities), while perceived negative age stereotypes are related positively to intention to retire|
|n = 2,082 workers aged 55 years and over (response rate = 74.7%)|
|The majority of respondents were male, between 55 and 60 years old, held a high school leaving certificate, had a managerial role, and had worked for the bank for more than 30 years|
|Elman, C., & Weiss, F. (2014)||National Longitudinal Survey of Youth 1979‐2010 (NLSY79)||DV = wages (= annual labor income logged at age 45), occupational rank (= socioeconomic index score at age 45) and employability (= employment for at least 21 out of 24 months after age 43 and 44)||Follow up from age 25‐45, 30‐45, 35‐45, 40‐45||OLS regression, separate for men and female||GED and FAS increase wages for women. OJT and FAS, but not GED, increase occ. rank. for both men and women|
|Individuals born between 1961‐1964||Compared with those not attaining education|
|OJT and GED increase women’s employability|
|n = 2.164|
|United States||TV = General education (GED), formal adult schooling (FAS) = enrollment in reg. school after leaving initial schooling, and on‐the‐job training>1 month (OJT)|
|Froehlich, D.E. (2017)||Cross‐sectional survey data from 139 Austrian bank managers Response rate 69.5% of which 43% were females and 43% were aged between 36 and 45 (41% older)||DV = self‐reported learning outcomes (rate themselves against their peers based on their most recent performance appraisal; rate their learning progress over the last year regarding technical knowledge, risk‐management, customer orientation, and willingness to change etc.)||Hypothesized path model, tested using maximum Likelihood Estimation||The results indicate that older bank managers are more likely to adapt a surface‐disorganized learning approach, which in turn, has a negative effect on learning outcomes|
|TV = learning approach (index’s based on several questions)|
|Hamplová, D., & Simonová, N. (2014)||The Social Cohesion Survey from 2005/2006 (SCS 2006)||DV = Upward mobility = >five‐points increase on the CAMSIS scale; downward mobility = ||Retrospective, covering the period 1989 to 2005/06
||Random effect logit models
||Increases the likelihood of upward prestige mobility but does not protect against downward mobility. The effect on upward mobility seem to be stronger among women than among men
|N = 3460; 1654 men and 1806 women|
|TV = last formal educational upgrading|
|The Czech Republic|
|Hannekam, S. (2015)||Cross‐sectional survey among older workers (50+) – all registered with a job agency specialized in older workers and currently in work||DV = career success (= self‐reported salary level), career satisfaction (previously validated scales) and wish to continue employment||Mixed method approach||Multile regression analysis||The quantitative analysis showed that older workers who possess social skills and are able and motivated to continuously develop themselves throughout their careers experience more career success and are more satisfied with their career, which then leads them to wish to continue employment.|
|Content analysis was used to analyse the qualitative data|
|The Netherlands||TV = social and interpersonal skills (previously validated scales) and continuous learning ability|
|N = 920|
|The average age = 61 years; 62% were male and 38% female; 49% had higher education, 32% had intermediate education and 18% lower general education|
|The qualitative analysis showed that the older workers were aware of the need to stay up‐to‐date. However, the need to constantly stay informed about the newest, mainly technological, developments was sometimes perceived as a threat, and therefore pushed older workers into retirement, since it negatively affected their career satisfaction|
|In‐depth interviews with 11 respondents|
|Hällsten, M. (2012)||STAR‐collection of administrative register data||DV = employment (= income in fixed prices incl. social transf. related to employment above ½ of median earnings of those 40‐46 year olds) earning (= log of earning above this cut‐point).||Follow up||Matching technics combined with fixed effect panel data methods||Late degree increased employment rate with 18% and earning while employed with 12%|
|Compared with those not attaining education|
|TV = attain tertiary education obtained between 1988‐2003|
|Individuals age above 30 (born 1940‐1965) with upper‐secondary schooling|
|n = 36.233|
|Kilpi‐Jakonen, E., Kosyakova, Y., Stenberg, A., Vono de Vilhena, D. & Blossfeldt, H‐B. (2012)||Different longitudinal datasets (country specific)||DV = labour market outcomes = probability of being employed||Find that educational upgrading tends to increase employment opportunities, although effects depend on the labour market status of those studying. The effects also vary between men and women and between countries. According to the authors, this is probably due to differences in institutional and labour market contexts|
|TV = educational upgrading|
|Sweden, Great Britain, Spain and Russia|
|Kilpi‐Jakonen, E. & Stenberg, A. (2014)||Population register data from Statistics Sweden||DV = incidence of employment = annual earnings above 100.000 SRK; and annual earnings = natural logarithm of annual earnings>100.000 SEK. All measured in 2010||Follow up 1994‐2010||Regression analysis||Increased employment probabilities and increased earnings. Least likely to benefit are older men, and benefits are greater for tertiary adult education. In general, benefits are most consistent for women|
|cohorts: 1952, 1957, 1962 and 1967||Compared with those not enrolled in education in 1994‐1995|
|n = 270.000|
|Male = 55.6%|
|TV = upgrading = increase in schooling by at least 1 year between 1994 and 2010|
|Kilpi‐Jakonen, E., Sirnio, O., & Martikainen, M. (2014)||Longitudinal register dataset from Statistics Finland. Individuals born between 1931 and 1981. Graduating at age 31 or later in the period 1993‐1995 (under the recession) or 19999‐2001 (after the recession)||DV = average number of months of unemployment per year, and annual income relative to others (divided in deciles by calendar year)||Follow‐up 1987‐2007||Repeated measures linear regression and the method of generalized estimation equation with an exchangeable correlation structure||Adult learning slightly reduces the experience of unemployment. Mainly graduation at the tertiary level that improves income, but less for adult than for younger graduates, and less for women than men|
|Separate analysis for men and women, for those who graduated during and after recession and for different educational levels. Comparing DV before and after upgrading, and adult graduates with young graduates|
|TV = year of graduation (upgrading) = reference point|
|N = 51.916 individuals|
|Kosyakova, Y. (2014)||The Russia Longitudinal Monitoring Survey – household panel (RLMS‐HSE)||DV = self‐reporter changes in occupational class (mobility) based on ESeC class scheme; and move from precarious jobs/unemployment to a good job||Follow‐up 2000‐2010||Event history analysis technics. Separate models for men and women||FAL pay offs for the employed and for those in precarious labour market situations. Employer‐sponsored NFAL do also have a positive eff|
|Up to age 55 for women and age 60 for men|
|TV = adult learning in the year prior to transition; formal (FAL), tertiary and non‐tertiary and non‐formal adult learning (NFAL) – employer sponsored or not|
|Kristensen, N. (2012)||Danish employer‐employee register data||DV = timing on retirement||Option Value model||The results indicate no significant effects of post‐secondary vourses on the timing of retirement, and significant but small effects following basic education or vocational courses|
|TV = government co‐sponsored training (hybrid between formal education and on‐the‐job‐training) (1980‐2004); basic courses, vocational and technical courses and post‐secondary courses|
|Individuals born 1936‐1944, all being active in the labour market in 2001 (when 57‐65 years old)|
|Jepsen, C., Troske, K. & Coomes, P. (2014)||Administrative data from Kentucky||DV = earning returns||Following 20‐60‐year‐old students who entered the state’s community college system during the 2002‐3 and 2003‐4 school years||Finds substantial heterogeneity in returns across fields study. Degrees, diplomas, and for women, certificates – correspond with higher levels of employment|
|TV = completing community college certificates, diplomas or associate degrees|
|Nordlund, M., Stehlik, T. & Strandh, M. (2013)||LISA Longitudinal register data||DV = income||Follow up 1992‐2003||OLS regression separately for each 12 years||Increased earnings on both short and long term|
|TV = increase in formal education 1992‐1997 (change in recorded level of education)|
|n = 263.841||Compared with those not attaining education|
|Age 21‐34 in 1992|
|McMullin, P., & Kilpi‐Jakonen, E. (2014)||British Household Panel Study (BHPS) 1991‐2008||DV = upward and downward prestige mobility and the absolute level of prestige (CAMSIS scores)||Follow up 1991‐2008||Discrete event history analysis for repeated events and fixed‐effects linear regression models||FAL has strong positive eff on prestige mobility, but not so much with regard to mobility. Highest return for tertiary degree, and relative stable and high returns for A‐level and equivalent qual. Fewer return for NFAL|
|Separate models for men and women|
|TV = formal adult learning (FAL); obtaining a new degree, a new tertiary diploma, a qual. equiv. A‐level, and lower sec qual.; non‐formal adult learning (NFAL)|
|Picchio, M. and van Ours, J. C. (2013)||Data from Netherland from the European Community Household Panel (ECHP)||DV = reemployment prospects later in life (the probability of remaining employed)||Follow up 1996‐2001||Multivariate discrete response model for panel data||Found that firm‐provided training significantly increases future employment prospects (training leads to retaining). This also holds for older workers, suggesting that firm‐provided training may be an important instrument to retain older workers at work|
|TV = firm‐provided training|
|Using semiparametric technics to control for the endogeneity of training participation|
|Individuals older than 26 and younger than 64 years, either employed or unemployed|
|n = 7.257 individuals|
|Saar, E., Unt, M., & Roosmaa, E. L. (2014)||The Estonia Family and Fertility Survey 2004‐2005 (FFS). Individuals 20‐79||DV = occupational mobility = mobility between first and second job after compl. highest educational level. Upward mobility = International Socio‐Economic Index (+6.5‐point); downward mobility = any change downward||Life‐coarse approach which contains retrospective event histories on major life careers||Logistic regression analysis (with repeated events in the case of latter) – competing risk model||Women are more likely to experience upward occupational mobility after participation in formal adult education than men|
|Estonia||TV = attainment of highest education in adulthood|
|Stenberg, A., de Luna, X., & Westerlund, O. (2014)||Swedish population register data 1982‐2007||DV = average annual earnings||The method used is difference‐in‐difference propensity score matching||The findings imply positive effects for women, but no significant average earning effects for men. Authors underlines the importance of long follow up periods|
|TV = formal education|
|42‐55‐years‐olds who enrolled 1994‐1995|
|Stenberg, A., de Luna, X., & Westerlund, O. (2012)||Individuals with eleven or fewer years of schooling||DV = the likelihood of postponing retirement||Follow‐up over a 20‐25‐years period||The result was that adult education does not seem to have any effect on the inclination to postpone early retirement|
|TV = adult education|
|Compared people who completed adult education at 42‐ and above with people of the same age who did not complete such an education|
|Stenberg, A. & Westerlund, O. (2013)||Swedish longitudinal population register data 1982‐2010||DV = timing of retirement||Follow up 1994‐2010||Control for endogeneity using propensity score matching; robustness checks||Finds that higher education increases labour market survival rates when aged 61‐66 by about 5% points – and if enrolment occurs at age 42 the effect is about 1% earnings return per year|
|TV = first time enrolment in higher education at age 42 or later|
|Aged 42‐55 (time of enrolment) (60‐73 in 2010)N = 159 760|
|Stenberg, A. & Westerlund, O. (2016)||Swedish population register data 1982‐2011||DV = annual earnings||Follow up 1994‐2011||Difference‐i‐difference set‐up that accounts for individual time invariant (fixed) unobserved characteristics correlated with earnings (checking for potential ability bases)||Finds substantial long run earnings returns on higher education, but these only fully emerge after approximately ten years|
|TV = first time enrolment in higher education|
|29 to 55‐years‐olds who enrolled 1992‐1993|
|Calculations indicate that benefits for society exceeds cost|
|Triventi, M. & Barone, C. (2014)||The International Adult Literacy Survey (IALS) 1994‐1998, covering 25.903 individuals in 22 different countries||DV (micro‐analysis) = gross personal income from wages, salary or self‐employment||Cross‐sectional data||Two‐stages strategy: First stage, estimating a regression models for each individual country (using interval regression), then use the estimated coefficient to assess whether they are related to a set of macro‐variables or not (using estimated dependent variables regression)||Adult learning experiences are positively rewarded in the labour market across a large set of industrialized countries, and the magnitude of economic returns to non‐formal training is systematically larger in the short run than returns to formal adult education|
|DV (macro‐analysis) = estimates for the wage returns to formal education and non‐formal training|
|Employees with upper secondary school between 22‐64 years old and employees with tertiary education between 28‐64 years|
|22 different industrialised countries||TV (micro analysis) = whether respondent had any adult learning experience; if the experience was job‐related or attended only for personal interest; formal adult education upgrades and non‐formal training|
|Returns to adult learning vary considerably across industrial countries probably due to the institutional differenced, especially the formal adult educational system. However, the effects of macro‐economic variables appear small, with the exception of unemployment rates|
|TV (macro‐analysis) = institutional|
|variation: unemployment rate, index of economic development (GDP per capita, expenditure in Research & development as percentage of GDP, mean years of education attained in the population), and proportion of small firms (<20 employees) in each country|
|de Vilhena, D. V., & Gamundi, P. M. (2014)||Panel Survey on Inequalities in Catalonia (PaD) 2002‐2009||DV = leaving unemployment (= move from unemployment in wave 1 to employment in wave 2); occupational class upgrade between waves; and leaving precarious jobs (= jobs without contract, part time jobs, fixed term contracts, self‐employed without employee, full‐time working poor etc.)||Follow‐up 2002‐2009||Discrete‐time event‐history models for repeated events. Separate models for the different types of learning||Significant and positive effect of formal adult upgrading on mobility, but only for men. Negative effect for non‐formal education (both employer‐sponsored and non‐employer‐sponsored) on downward mobility, but only for men. No effect on precarious jobs and leaving unemployment of neither formal education or non‐formal learning|
|N = 9544 individuals, excluding those <25 years and those above the retirement age|
|TV = increase in formal educational attainment (FAE) and finishing employer or not employer funded non‐formal adult learning during the last 12 months (NFAL)|
|Wahler, S., Buchholz, S., Jensen, V. M., & Unfried, J. (2014)||Administrative register data from Statistics Denmark, including the Integrated Database for Labour Market Research (IDA), covering employees and employers||DV = risk of becoming unemployed, chances of exiting unemployment, and chances of direct mobility (10% increase of salary = upward and 5% decrease = downward mobility)||Follow‐up 1981‐2009||Regression models with robust standard errors and clustered by the individual. Separate models for men and women||All adult learning except NFAL substantially decreases the risk of becoming unemployed, and increases the chances of (re)entering the labour market for unemployed. Participating in VUSE increases upward and decreases downward mobility, whereas TE increases both upward mobility and the risk of downward mobility; GUSE affect men’s income chances negatively|
|TV = six different measures of adult learning, both non‐formal (NFAL) and formal FAL (VUSE = vocational upper secondary education; GUSE = general upper secondary education = tertiary education)|
|Denmark||Cohorts from 1955‐1980|
|N = 1.949.781 individuals|
4.1 Returns to formal adult education
Several studies over the last eight years have tried to investigate the effects of formal adult learnings on employment and labour market participation using longitudinal data (see Table 1). Although the effects of upgrading seem to vary according to whether the participants acquired basic skills or a degree from upper secondary school or tertiary school, the overall effect seems to be positive (Elman & Weiss, 2014; Hällsten, 2012; Kilpi‐Jakonen, Kosyakova, Stenberg, Vono de Vilhena, & Blossfeldt, 2012; Kristensen, 2012; Nordlund, Stehlik, & Strandh, 2013; Stenberg & Westerlund, 2013, 2016). Wahler, Buchholz, Jensen, and Unfried (2014) also found that adult education (especially vocational upper‐secondary education) reduced the unemployment risk and increased the reemployment chances for the unemployed in Denmark, whilst de Vilhena & Gamund (2014) found no such effect in Spain. Barbieri, Cutuli, Lugo, and Scherer (2014), on the other hand, found that adult education reduced unemployment in Italy and Csanádi and Csizmady (2014) found that formal adult education shortened the length of unemployment in Hungary. In addition, a few studies investigated whether educational upgrading had any impact of the likelihood of exiting precarious work (de Vilhena & Gamund, 2014; Kosyakova, 2014). However, de Vilhena & Gamund (2014) did not find any such effect in Spain, whilst Kosyakova (2014) found that new tertiary‐level qualifications increased the likelihood of exiting a precarious labour market position in Russia. Many studies also studied the impact of formal adult educations on wages, salaries or earnings related to work (often used as a proxy for employment) and found that, in most cases, it led to increased earnings (Jepsen, Troske & Coomes, 2014; Hällsten, 2012; Nordlund et al., 2013; Kilpi‐Jakonen & Stenberg, 2014; Kilpi‐Jakonen, Sirnio & Martikainen, 2014; Stenberg, deLuna & Westerlund, 2012, 2014), although, Elman and Weiss (2014) did not find that additional adult education affected wages in the US.
Whether formal adult education had an impact on occupational or social mobility, also varied a great deal between studies. Some found that further education had a positive impact on upward mobility, but did not prevent downward mobility (Hamplová & Simonová, 2014; McMullin, & Kilpi‐Jakonen, 2014), whilst others found that educational upgrading had a positive impact on upward mobility, and likewise, reduced the chances of downward mobility (Buchler, Chesters, Higginson, & Haynes, 2014; Wahler et al., 2014), or that formal upgrading as adults only impacted women’s upward mobility(Csanádi, Csizmady, & Róbert,2014; Saar, Unt, & Roosmaa, 2014).
All in all, the effects of formal and non‐formal education seem to be greatly influenced by the labour market status of those who upgrade their skills (whether they are employed, unemployed, have a fixed‐term contract or are permanently employed) and their level of education (whether primary, general or vocational upper‐secondary, or tertiary education) (Kilpi‐Jakonen et al., 2012). Most studies also found gender differences in outcomes. In general, women seemed to gain more from educational upgrading than men (Hällsten, 2012; Elman & Weiss, 2014; Kilpi‐Jakonen et al., 2014; Bucholz et al., 2014; Csanádi et al., 2014; Saar et al., 2014; Stenberg, de Luna, & Westerlund., 2014). The effects also depended on whether it was a short–term or a long‐term effect. Stenberg and Westerlund (2016), for example, found that the positive effect of obtaining higher education late in life first emerged about 10 years after enrolment. Last but not least, the effect of adult learnings on employment varied considerably between countries (Blossfeld, Kilpi‐Jakonen, de Vilhena, & Buchholz., 2014). The divergent effects observed were influenced by differences in data and methodologies used, but it is also reasonable to attribute some of the differences to institutional differences and differences between the educational systems, the welfare systems and the labour markets, as well as the population’s level of education (Kilpi‐Jakonen et al., 2012; Triventi & Barone, 2014; de Vilhena, Kilpi‐Jakonen, Schührer, & Blossfeld, 2014).
4.2 Returns to non‐formal adult learnings
Whilst formal adult learning on average seems to increase employment outcomes, the impact of non‐formal learning and training is mixed (Blossfeld et al., 2014; de Vilhena & Gamundi, 2014; Wahler et al., 2014). Picchio and van Ours (2013) found that firm‐provided training for older workers significantly increased future employment prospects in the Netherlands and Elman and Weiss (2014) found that on‐the‐job training increased women’s employability in the US. Unlike what one might expect, however, non‐formal adult learning in Denmark seemed to increase the risk of becoming unemployed and decrease the chances of (re)entering the labour market for the unemployed (Wahler et al., 2014). A similar result was found in Spain: both non‐formal and formal education had a negative effect on the chances of being reemployed among those who were unemployed in the first place (de Vilhena & Gamundi, 2014). However, in Italy, non‐formal adult learning seemed to reduce the risk of unemployment (Barbieri et al., 2014). Furthermore, one study showed that the economic returns to non‐formal training were on average systematically greater in the short run than returns to formal adult education (Triventi & Barone, 2014).
4.3 Intermediate factors that influence the outcomes
In addition to the above mentioned studies, three cross‐sectional studies emphasise the importance of the organisational environment and the individuals’ learning strategies and how they may impact on the outcomes of adult learning. Although they do not measure the direct effects of adult learning, they focus on individual and workplace factors that could help to better understand the processes and mechanisms that influence differences in participation in adult learning and why the outcome is not always what we expect it to be.
Dordoni, Van der Heijden, Peters, Kraus‐Hoogeveen, and Argentero (2017) studied the impact of older workers’ participation in employability‐enhancing‐activities on their intentions to retire in relation to perceived support for learning and negative age stereotypes on older workers’ productivity. The results indicate that having a supporting and non‐age‐discriminatory work environment could be important for employment‐enhancing activities to promote longer working careers.
Froehlich (2017) investigated whether the approach to informal learning at work was an important mediating variable in the relationship between age and learning outcomes (broadly defined as any gains in skills or job and career performance) and found that the older managers became, the more they seemed to adopt a surface‐disorganised learning approach which, in turn, decreased their learning outcomes. Hannekam (2015), who examined how social skills and continuous learning ability influenced the career success and career satisfaction of older workers, found that older workers who possessed social skills and were able and motivated to continuously develop experienced greater career success and were more satisfied with their career and, hence, wished to continue to be employed. As underscored by the author the need to constantly stay informed about technological developments was, however, sometimes perceived as a threat which pushed older workers into retirement, since it negatively affected their career satisfaction.
The data in these three studies are cross‐sectional and cover small and select groups of older workers. No causal interference can therefore be drawn.
The studies reviewed here found some evidence that adult learning increased older workers’ employability and work‐related earnings. The quality of the data and methodologies in the studies reviewed do, however, vary considerably and few document a causal effect between adult education and learning and subsequent employability. We therefore still need to study in more detail how and why formal and non‐formal adult learning contribute to employability, i.e., the ability to find and maintain employment. There is also a need for more longitudinal studies based on population registered data in order to determine whether there is a causal interference between adult learning and training and older workers’ labour market participation, using advanced statistical methods. In other words, we still need to answer the question whether adult learning and training increase the number of years in employment amongst older workers or not and if it contributes to postponed retirement.
Moreover, the positive outcomes of lifelong learning observed cannot always be generalised across time periods. Although some studies could find a positive effect of lifelong learning on employability in one period, the same effects might not be seen in later periods. As Hällsten (2012) mentions, the risk of over‐education and educational inflation has risen over the years and this could have a negative impact on the returns of lifelong learning in the future. Studies also show different effects of different types of adult learning and training.
The studies reviewed also highlight the importance of conducting separate analyses for different types of adult learners and considering how outcomes could vary between groups of older employees according to gender, age, initial education level, labour market status, etc. As the comparative study by Kilpi‐Jakonen et al. (2012) shows, the outcomes will in most cases also vary between countries because of different institutional and labour market contexts. The results based on data from one country cannot therefore always be applied to others. There is therefore a need to examine the effects on employment of different types of adult learning and training, and for whom and in which situation and context they have an effect.
6 CONCLUDING REMARKS
Although adult learning is often described as one of the main strategies to increase the labour market participation of older workers, surprisingly few studies have examined whether adult learning actually increases the employment possibilities of older workers, although the number of relevant studies have increased lately. The number of current studies is also too small and the quality of the data and methodology used in most studies too weak to conclude that adult learning (formal or informal) has a causal effect on older workers’ employability.
The most promising studies investigate labour market outputs for adults who enrol in education and/or finish a formal education late in life (e.g., after 40) using longitudinal register data and advanced statistical methods and tests, like Stenberg and Westerlund (2013, 2016). They find that adult learning (tertiary upgrading after 42) both increases older workers’ earnings (and employability) and the likelihood of postponed retirement. It is, however, unclear whether these results can be generalised to other countries with different educational systems, levels of education, labour markets and welfare states.
However, although educational upgrading late in life may increase the likelihood of longer working lives, there are still some unsolved problems. Today, there is a social gradient in adult learning leading to increased rather than decreased social inequalities in skill, labour market participation and earnings over the life course (Blossfeld et al., 2015). Those in most need of educational upgrading in order to be employable, such as low skilled individuals with health problems who need to change jobs and those at risk of losing their jobs, participate least in educational upgrading. There are different explanations to this paradox linked to differences in cognitive/intellectual abilities, learning problems, different experiences with the educational system earlier in life, lack of suitable educational options, problems with financing further education, etc.
Thus, if adult learning and upskilling late in life should lead to prolonged working lives for all, the most vulnerable groups of older workers must be motivated and find it profitable to invest in further education (i.e., their expected profit or advantages must exceed the expected cost) and there must be appropriate adult education/learning options available. In other words, there has to be easy access to education and skill upgrading and it must not be too expensive to enrol in courses or classes. Furthermore, there must be ways to finance one’s lives while studying. And last, but not least, we need to know more about how to motivate the less skilled older workers; those who are most vulnerable to labour market exclusion in old age.
This work was funded by The Research Council of Norway as part of the project “Silver lining – a study of employability and learning trajectories of Late Career Learners” grant number 255210.
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