Internal Rate of Return on Investment in Higher Education in Europe

The factor that enhances growth in semi-endogenous growth models is represented by the human capital, which is a factor endogenised via timing and resources spent on education by two sectors, in principle, education, and production sector (Rebelo, 1991). The human capital helps to diminish the effect of decreasing physical capital. In fact, models applying human capital rely on the productivity of the education sector rather than on the number of teachers. Lucas's (1988) model implies 7–10% endogenous growth rate, which is implausibly high compared to the actual rate of approximately 2%. Both models stick to a basic assumption of this class, i.e. direct correlation between the rate of economic growth and the productivity of the education sector in the long term. The current study follows closely the theoretical approach of Rebelo (1991). In order to meet this theoretical approach, the internal rates of return on investment in higher education are considered. This approach refers directly to the alternative costs of schooling, regarded as forgone wages during the high school years. The historical record for the estimates of this rate show even higher values. If the study confirms rates of >10%, then it would be evidence for a significant mismatch between macroscale models and estimates based on micro-foundations. While the data on the ‘Household Finance and Consumption Survey’ (HFCS; 2014–2016) for the 2016 wave were released in March 2020 for use in general research, this study elaborates the latest survey statistics. Surprisingly, despite these new data, the estimates are nearly the same as in 2014. A summary of the previous estimates is elaborated in the Results section.

The study is structured as follows: after this Introduction, the methodology for the calculation of the internal rate of return (IRR) on investment in higher education is described, followed by the calibration. After the Conclusions section, the detailed data applied in the calculations are provided in the Appendix.

The calibration of the above-described formulas relies in principle on the OECD data, particularly gross and net replacement rates for employed and unemployed persons, employment and unemployment rates, employment rate data by educational attainment, probability to achieve the degree based on graduation rate data, marginal tax rates based on gross-gross labour tax rate (including employers’ tax), factor productivity based on 10 years’ average for gross domestic product (GDP) per hour worked at constant prices, pension growth dynamics (per head, at current prices and current purchasing power parities [PPPs], in US dollars; old age data, excluding survivors in public and mandatory private schemes); retirement age; gross and net replacement rates; life expectancy at birth; age of entry into school; average years of schooling (Wittgenstein Projection: mean years of schooling, age 25+, total values); τ₀ = τ is taken as the average labour-time gross tax rate on earnings, taken from the progressivity tax model described by Jabłonowski (2018); thus, from the employee perspective, this excludes employers’ labour tax burden. The average tax rate on unemployment earnings was taken from the OECD statistics, with their absent or apparently erroneous specification, as the minimum tax rate observed in the OECD tax/benefit calculator. Both the rate of return on education r˜s \tilde rs and the labour market experience v were taken directly from the Mincer equation computed with the use of the standard ordinary least squares model based on the HFCS (2014–2016) country-specific data.

The already-mentioned disclaimer on such an approach applies, i.e. the net income was available only for Italy and Poland.

The discussion on the IRR methodology and the obtained results has a broad history in the literature. Although the issues of accumulation of human capital and its growth-spurring role are tempting, since the famous critique by Jones (2002), which pointed out that the so called AK models

AK model is the simplest type of the endogenous growth model, known from the growth theory. It assumes the e.g. a constant level of technology.

More generally, when inspecting Table 1, which summarizes past research, one may suspect that there is some issue in the comparability of the methodologies. A mutual correlation among countries within a selected year shows stability over time, i.e. developing countries exhibit higher rates than the developed European economies. However, there is no clear trend between years, i.e. it seems as if different vintages/authors/methodologies caused incomparability. Therefore, in order to formulate assumptions or theses on the IRR time trends, one should rather stick to a unified methodology/authorship usually applied by international institutions, such as the OECD or the World Bank.

The number of variables, assumptions and calculations applied in this study (see Appendix for details), as compared to the final outcome, i.e. list of numbers (IRR), is quite big and gives an indication of the complexity of this subject. Additionally, there is an ongoing discussion on the adequacy of the Mincerian equation with respect to correctness. Growiec and Groth (2018) claim that a correlation between years of schooling and productivity may be more than nuanced, which suggests that, generally, the IRR results should be taken with caution, especially when applied in the macroeconomic modelling of the semi-endogenous growth models. So, to measure the human capital stock in general equilibrium, one should maybe consider lower rates, as suggested by Lucas (1988). In other words, the fact that a subject is well settled in the economic literature does not harden such an approach for ages, since the regularity in the economic data will not always be taken as absolute laws as in physics. This study anyway does not go beyond its main aim and sticks to the classical approach by Mincer (1958).

Since the educational path affects the entire generation's lifespan, the process develops over decades. In the meantime, the older cohorts need to pass a difficult economic transformation into the market economy, so the older cohorts form Central Europe may be barely comparable with those in for instance Western Europe. In order to compare apples to apples, the detailed results and comments are divided into Central–Eastern Europe (CEE: HU, PL, EE, SK, LV, SI) and Western Europe (WE). Few additional, more technical remarks can be derived from Table 1, which are listed below: –

A clear gap in results for the CEE countries may stem for the convergence process, e.g. between 2001 and 2014, the number of persons with higher education grew from 15% to 43%; in Hungary, it grew from 15% to 38%.

Despite the applied cost formula, the weaknesses remain nearly the same, from e.g. a lack of inclusion of student loans, missing rental and travelling costs on the one hand to factors such as the following on the other hand: the co-financing of education by external, non-tax sources, the international support or earmarked assets, privatization receipts etc. These weaknesses may be responsible for the differences in theoretically similar countries. Finally, the representative household perspective also equalizes the net rates of return from the given branch of studies, which vary across the educational disciplines by costs (e.g. medicine) or their gross rates of return (e.g. law). However, the applied costs’ ‘generalization’ seems to overcome a problem of the internal financing structure of the education process among countries, especially in terms of rapidly increasing tuition fees in the best universities, which is needed to compute the unified rates of return for taxation purposes.

There might be several reasons for the obtained differences, one of which is the use of the HFCS data for the Mincer equation estimates and gross/net earnings. The application of the ‘social’ version of the education costs shows little difference, as compared with the corresponding values reported by DeLaFuente and Jimeno (2009) and Romele (2013). More recently, the study of Psacharopoulos and Patrinos (2018) indicates the rates on return, but from different vintages and authors, also for the WE countries.

The achieved results, ranging between 13% and 21%, are rather higher than those of Poteliene and Tamasauskiene (2014), although comparable and higher by ~7 pp. than historical records for the years 1996–2013. Especially, the most recent decennial summary of Psacharopoulos and Patrinos (2018) shows nearly halved values mostly for 2011, with a quite comparable methodology, which is also applied in a macroeconomic modelling context by Lucas (1988), with a range of 7%–10%. Are then current study outcomes believable? On the one hand, for the microeconometric estimate of the Mincer-type equation, they are sound and comparable. On the other hand, the methodology that includes pension factors may suggest that a comparison between methodologies may be the key to understand differences among years and authors. Anyway, all in all, an increasing trend in average IRR, in spite of differing authors and methodologies, suggests an increasing trend in valuation and importance of human capital based on high school degree, especially for CEE countries, resulting from rapid economic and cultural convergence. However, their direct use for the macroeconomic models of semi-endogenous growth, which apply the rate of return on investment in education to the life cycle path of wages, seems to yield much too high values.