While literature provides several examples of the individual and social benefits of adult learning, its role for economic growth and innovation is less clear and discussed, so far. This is, to some extent, due to limited differentiation between training and adult learning or further education. Dieter Dohmen provides some evidence that adult learning is a driver for economic growth and innovation.
The link between education in general and economic activity, such as on GDP, long-term economic growth and labour productivity has been investigated in several studies. Some studies found human capital to be a crucial factor in explaining the convergence of GDP per capita over time. Their results suggest that growth is mainly fostered by improving the skills of all society’s members rather than focusing strictly on skill development of highly talented individuals. However, the role of adult learning in this regard is less clear cut. Adult education may contribute to a reduction in the gap between the skills needed by employers and those held by employees and thus increase output.
Yet, there is some evidence that adult learning contributes to higher growth rates. Our own research could identify a positive relationship between AES participation rates and real GDP growth over time (FiBS/DIE 2013). The findings suggest a positive relation between AES participation (2007 and 2011) and real GDP growth rates. Although some cross-sectional relationship can be identified, this link becomes even stronger when we consider time lag effects. This suggests that countries with higher AES participation rates show higher growth rates one or two years later than countries with lower rates participation rates (after accounting for differences in economic performance and time effects). These results serve as a first indication of a positive relationship between participation in adult learning and economic growth, suggesting short term as well as long-term benefits for the countries of analysis. For those interested, some evidence is presented in greater detail at the end of this post.
The role of adult learning is even stronger when looking at the link to innovation. Here, various indicators – participation in adult learning (AL), the share of training enterprises, HR practices, employee participation in CVT courses, workplace learning and costs of CVT as share of total labour cost – show strong and significant linear relationships with innovation performance. The strongest correlation turns out for participation in AL according to LFS (2009) and innovation performance (2010); followed by the share of enterprises providing training for their employees.
Eventually, a ‘human capital formation factor’, representing a sample of indicators on participation in and provision of adult learning, explained two thirds of the variance in innovation performance between countries (Cedefop 2012). Here, the strongest impact factor is learning in the workplace or work organisation; this means that a demanding workplace is the strongest driver of innovation.
While this blog provided evidence concerning the macro-economic importance of adult learning, the major question is, how to advance it and how adult learning should look like in the future.
More in detail, Table 1 presents the results of a regression model (Fixed Effects (FE) vs. random effects (RE) estimation) and the inclusion of the lag of participation in adult learning as an independent variable (in FE 2 and RE 2, respectively). Regression results for all models suggest a positive relation between AES participation and real GDP per growth. This effect holds even when (additionally) controlling for the time lag of AES participation, which shows that it is not merely caused by serial correlation, i.e. differences in participation rates of countries being based on differences participation rates in the past. Furthermore, regarding FE 2 and RE2 (R²), the models with the highest goodness of fit value “within their estimation method”, both AES participation and the time lag of AES participation are significant, which suggests that participation in adult learning has a positive impact not only on short term but also on long term growth. Furthermore, the strength of the relationship between participation in AL and growth, as measured by the respective beta coefficient, is strong – regarding strictly RE results (RE1, RE2), even the strongest of all variables. Interestingly, GDP per capita and growth appear to have an only barely significant (FE1) or insignificant (FE2) relationship, when regarding FE models. Regarding RE models, suggests this relationship to be negative, if only barely significant in RE1. Overall, a negative relationship between GDP per capita and real GDP growth which can be explained by the fact that countries with high economic performance may have less scope for growth as they are approaching steady state growth rates.
|Real GDP growth||Real GDP growth||Real GDP growth||Real GDP growth|
|Time lag of AES participation (participation rate in previous year)||0.274**||0.355****|
|GDP per capita||3.248*||1.142||-0.389*||-0.477***|
Standardised Beta Coefficients. Significance levels: *p < 0.10, **p < 0.05, ***p < 0.01, ****p < 0.001
To sum up, results suggest that countries with higher AES participation rates show higher growth rates than countries with lower rates participation rates (after accounting for differences in economic performance and time effects).
Dieter Dohmen is Managing Director of FiBS – Research & Consulting, a Berlin-based research and consulting institute and a leading Think Tank on the economics of education and social affairs (www.fibs.eu). FiBS just started with another project on the macro-economic costs and benefits of further education and continuing VET