Study on the potential of education to cause macroeconomic imbalances and negative spillovers : Annex 4 : summary of literature review.
Date: 2016-06-06
Institutions: Directorate-General for Education, Youth, Sport and Culture
Subjects: cooperation in the field of education, economic consequence, education policy, labour market, macroeconomics, report, school-working life relations

Study on the potential of 
education to cause 
macroeconomic imbalances 
and negative spillovers 

Annex 4 

Summary of Literature Review 

March  2016 

 
 
 
 
 
 
 
 
 
EUROPEAN COMMISSION 

EUROPEAN COMMISSION 

Directorate-General for Education and Culture 

Directorate A — Modernisation of Education I: Europe 2020, country analysis, Erasmus+ coordination  

Unit A.1 — Europe 2020, Investment Plan, Education and Training 2020 

Contact: Mr P. Battaglia 

E-mail:  eac-unite-a1@ec.europa.eu   

European Commission 
B-1049 Brussels 

 
 
 
 
 
 
 
EUROPEAN COMMISSION 

Study on the potential of 
education to cause 
macroeconomic imbalances 
and negative spillovers 

Annex 4 

Summary of Literature Review 

A report submitted by ICF Consulting Services 
in association with 
Cambridge Econometrics and Technopolis 

Oskar Andruszkiewicz, George Barrett, Jan Franke, Richard Lewney, Hector 
Pollitt, Martin Sers, Máté Péter Vincze 

Directorate-General for Education and Culture 

April  2016 

EN 

 
 
 
 
 
 
 
 
 
Study on the potential of education to cause macroeconomic imbalances and negative 
spillovers – Annex IV 

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Study on the potential of education to cause macroeconomic imbalances and negative 
spillovers – Annex IV 

Table of Contents 

Annex 4 Summary of Literature Review ................................................................ 1 

A4.1 

Summary review of key literature ......................................................... 1 

A4.1.1  Educational inputs/ outcomes ........................................................... 1 
A4.1.2  Qualifications .................................................................................. 1 
A4.1.3  Productivity and earnings ................................................................. 2 
A4.1.4  Unemployment and inactivity ............................................................ 3 
A4.1.5  Effects of education on growth .......................................................... 4 
A4.1.6  Variables and indicators ................................................................... 5 
A4.1.7  Effects of productivity on trade deficit/ export share/ unit labour cost .... 6 

A4.2 

List of literature reviewed and summarised ............................................ 7 

A4.2.1  Policy papers .................................................................................. 7 
A4.2.2  Microeconomic studies ..................................................................... 7 

A4.2.2.1  Single country empirical studies (including studies using longitudinal 
 .............................................................................................. 7 
data) 

A4.2.2.2  Multi-country studies ................................................................. 8 

A4.2.3  Macroeconomic studies .................................................................... 8 

A4.2.3.1  Single country studies ............................................................... 8 

A4.2.3.2  Multi-country studies ................................................................. 8 

A4.2.3.3  Education and economic contribution of migrants .......................... 9 

A4.2.3.4  Academic literature – Productivity and trade balance ..................... 9 

A4.2.3.5  Academic literature – productivity and exports .............................10 

A4.2.4  Other literature (not summarised in section A2.3) ..............................10 
A4.2.5  Longitudinal data sources (country level) ..........................................11 

A4.3 

Item by item summary ......................................................................12 

A4.3.1  Policy papers .................................................................................12 

A4.3.1.1 
Macroeconomic Imbalances Procedure – In depth country reviews .................12 

In-depth review of macroeconomic imbalances – Italy 2014. 

A4.3.1.2 
Education at a Glance: OECD Indicators 2004. .............................................13 

The output of educational institutions and the impact of learning.  

A4.3.2  Microeconomic studies ....................................................................16 

A4.3.3  Single country studies (including longitudinal studies) .....................16 

A4.3.3.1  Beyond qualifications: labor market returns to cognitive skills and  
personality traits in urban Colombia ...........................................................16 

A4.3.3.2  An der zweiten Schwelle: Junge Menschen im Uebergang zwischen 
Ausbildung und Arbeitsmarkt. Ergebnisuebersicht des Jugendlaengsschnitts 
TREE – (At the second hurdle: young people in transition between education and 
the labour market. Overview of results from the longitudinal panel TREE 
[Transitions from Education to Employment) ...............................................17 

A4.3.3.3 
number 45  .............................................................................................19 

The Returns to Higher Education Qualifications - BIS research paper 

A4.3.3.4 
Outcomes - BIS research paper number 47 .................................................20 

The Long Term Effect of Vocational Qualifications on Labour Market 

 
 
 
 
 
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A4.3.3.5  Returns to Intermediate and Low Level Vocational Qualifications - 
BIS research paper number 53 ..................................................................22 

A4.3.3.6  Evaluation of the Impact of Learning Below Level 2 - BIS research 
paper number 150 ....................................................................................24 

A4.3.3.7  Estimating the Return to Investments in Education: How Useful is the 
Mincer Equation? ......................................................................................25 

A4.3.3.8  Education, Equality and Efficiency: An Analysis of Swedish School 
Reforms during the 1990s .........................................................................26 

A4.3.3.9  Do cognitive test scores explain higher US wage inequality? Review  
of Economics and Statistics .......................................................................28 

A4.3.3.10  Summary of the Danish PISA-PIAAC survey ................................30 

A4.3.3.11  Gesellschaftliche Kosten der Ausbildungslosigkeit in der Schweiz.  
(Social costs of low qualification in Switzerland) ...........................................32 

A4.3.3.12  Estimates of the Economic Return to Schooling for the United  
Kingdom 

 .............................................................................................33 

A4.3.3.13  The Occupations and earnings of young Australians: The rule of 
education and training. Longitudinal Surveys of Australian Youth. Research 
Report 55 

 .............................................................................................35 

A4.3.3.14  Pathways to success. How Knowledge and skills at age 15 shape 
future lives in Canada ...............................................................................37 

A4.3.3.15  A ‘causal’ estimate of the effect of schooling on full-time 
employment among young Australians ........................................................39 

A4.3.3.16  Low Achievers' Risky Pathways: PISA Literacy Scores as Predictors 
of Upper Secondary Enrolment and Graduation ............................................41 

A4.3.4  Multi-country studies ...................................................................42 

A4.3.4.1  Returns to Human Capital in Europe: A Literature Review .............42 

Post-school education and labour force participation positive 

A4.3.4.2 
relationship between a college/other post-school education qualification and full-
time employment chances at ages 24 to 26 in Canada and Australia ...............43 

A4.3.4.3 

The private and fiscal returns to schooling in the European Union ..45 

A4.3.4.4 
Evidence from French and British Compulsory Schooling Laws .......................47 

Is it enough to increase compulsory education to raise Earnings? 

A4.3.4.5 
Surveys 

The Returns to Education: Microeconomics. Journal of Economic 
 .............................................................................................49 

A4.3.4.6  Earnings Functions and Rates of Return: The Mincer Equation and 
beyond. In Eric A. Hanushek and Finis Welch (eds.). Handbook of the Economics 
of Education ............................................................................................52 

A4.3.4.7 
Outcomes and Social Behavior ...................................................................53 

The Effects of Cognitive and Noncognitive Abilities On Labor Market 

A4.3.4.8 
Further Analysis of the Returns to Academic and Vocational 
Qualifications ...........................................................................................56 

A4.3.4.9 

Investment in Human Capital and Personal Income Distribution .....58 

A4.3.5  Macroeconomic studies ...................................................................59 

A4.3.6  Single country studies .................................................................59 

A4.3.6.1  Unzureichende Bildung: Folgen fuer die oeffentlichen Haushalte .....59 

 
 
 
 
 
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A4.3.6.2  Kosten der Arbeitslosigkeit sind gesunken ...................................61 

A4.3.6.3  Measuring the Economic Impact of Further Education - BIS  research 
paper number 38 .....................................................................................62 

A4.3.7  Multi-country studies ...................................................................64 

A4.3.7.1 
PIAAC Data. OECD Economics Department Working Papers, No.  1209 ...........64 

Labour Market Mismatch and Labour Productivity: Evidence from  

A4.3.7.2  Economic Growth in a Cross Section of Countries. In: Quarterly  
Journal of Economics106 (2) , 407-443. ......................................................65 

The Role of Human Capital in Economic Development: Evidence from 

A4.3.7.3 
Aggregate Cross-Country Data. In: Journal of Monetary Economics 34(2), 143-
173. 

 .............................................................................................67 

A4.3.7.4  Human Capital, the Structure of Production, and Growth. In: The 
review of Economics and Statistics Vol. 91, pp.66-82. ...................................69 

A4.3.7.5 
countries. In: Statistics Canada – Catalogue no. 89-552, no 11. ....................72 

Literacy scores, human capital and growth across fourteen OECD 

A4.3.7.6  Schooling Data, Technological Diffusion and the Neoclassical Model. 
In: American Economic Review 91(2), 323-327. ...........................................73 

A4.3.7.7  Human capital in growth regressions: How much difference does data 
quality make? ..........................................................................................74 

A4.3.7.8  Optimal investment in human capital under uncertainty. Economics 
of Education Review .................................................................................77 

A4.3.7.9  Schooling, Labor-Force Quality and the Growth of Nations. In: 
American Economic Review 90(5), 1184-1208. ............................................79 

A4.3.7.10  The Role of Cognitive Skills in Economic Development..................80 

A4.3.7.11  The Economics of International Differences in Educational 
Achievement. In: Eric A. Hanushek, Stephen Machin and Ludger Woessmann 
(eds.). Handbook of the Economics of Education ..........................................83 

A4.3.7.12  How Much Do Educational Outcomes Matter in OECD Countries? ...86 

A4.3.7.13  Do better schools lead to more growth? Cognitive skills, economic 
outcomes, and causation ...........................................................................88 

A4.3.7.14  Growth Empirics: A panel data approach. ...................................90 

A4.3.7.15  Education for Growth: Why and for Whom? ................................92 

A4.3.7.16  Educational mismatch and firm productivity: Do skills, technology  
and uncertainty matter? IZA discussion paper 8885......................................94 

A4.3.7.17  A Contribution to the Empirics of Economic Growth ......................96 

A4.3.7.18  Long run growth and investment in education: Does  unemployment 
 .............................................................................................98 
matter? 

A4.3.7.19  Where Has All the Education Gone? World Bank Economic Review .99 

A4.3.7.20  Does learning to add up add up? The returns to schooling in 
aggregate data. In: E. Hanushek and F. Welch (Eds). Handbook of the 
Economics of Education ........................................................................... 102 

A4.3.7.21  Human Capital and Growth: Theory and Evidence. NBER Working 
Paper Series .......................................................................................... 104 

A4.3.8  Education and economic contribution of migrants ............................. 105 

 
 
 
 
 
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A4.3.8.1  Educational Achievements of Second Generation Immigrants: An 
International Comparison ........................................................................ 105 

A4.3.8.2 
Productivity Growth and the Trade Balance in the 1990s: the Role of 
Evolving Perceptions ............................................................................... 108 

A4.3.8.3 
Transmission mechanism among G7 countries ........................................... 110 

Productivity, external balance and Exchange rates: evidence on the 

A4.3.8.4 

Productivity shocks, Budget deficits and the current account ....... 112 

A4.3.8.5 

The terms of trade, productivity growth and the current account .. 114 

A4.3.8.6 

Productivity, terms of trade and the “home market effect” .......... 116 

A4.3.8.7  Notes on exports and productivity: a causal analysis .................. 117 

A4.3.8.8  Effects of Productivity and Import on Firm-Level Export .............. 119 

A4.3.8.9 
From Productivity to Exporting or Vice Versa? Evidence from Tunisian 
Manufacturing Sector .............................................................................. 121 

 
 
 
 
 
Study on the potential of education to cause macroeconomic imbalances and negative 
spillovers – Annex IV 

Annex 4 Summary of Literature Review 

Annex A4.1 provides a snapshot of the reviewed literature that focuses on (i) choice 
variables of educational inputs/ outcomes, (ii) effect of education on growth, and (iii) 
the link between productivity and trade deficit/ export share and unit labour cost. It is 
followed by A4.2 that provides the list of all positions that have been reviewed. Finally, 
Annex A4.3 presents an item by item summary of the key empirical research that has 
informed the present study. 

A4.1  Summary review of key literature  

A4.1.1  Educational inputs/ outcomes  

Research on determinants and variables of education inputs and outcomes are 
discussed in literature on ‘education production functions’. This field has taken a large 
variety of perspectives, with the principal goal to sort out the causal impacts of school 
factors (things that could be changed through policy) from other factors affecting 
achievement such as family background, peers, neighbourhood influences and similar 
(which are less accessible through policy change). 

Data availability restricted past research and policy discussions to measures on formal 
school attainment, i.e. years of schooling. Average years of schooling is, however, an 
incomplete and potentially misleading measure of education for comparing the impacts 
of human capital on national economies across countries. Simply because it implicitly 
assumes that a year of schooling delivers the same increase in knowledge and skills 
regardless of the education system. A simplification that may introduce strong bias 
depending on the context of the research. 

More recent research emphasizes the importance of qualifications (attainment level) or 
even more directly - actual skillset of people, ie. what they know - their cognitive skills 
- as opposed to how much time they have spent in schools. International benchmarks 
focus on direct measures of math and science skills through tests like TIMSS, PIAAC 
and PISA and have proved to be more robust than years of schooling in explaining 
growth. Yet, there are still number of caveats emphasised in the literature that must 
be borne in mind while using the data on cognitive skills i.e. importance of exogenous 
factors such as ‘grit’ and motivation, unequal distribution of males/ females between 
fields of study (i.e. STEM) or simply the still fairly limited time coverage of the OECD 
data. 

A4.1.2  Qualifications 

Low skills levels lead on average to early school leaving or only lower level 
qualifications, or qualifications in areas and from schools where competition is limited 
(because of lower value).  

Krueger and Lindahl (2001)1, reviewed the Mincer wage model that establishes a 
relationship between an individual’s earnings and the number of years they spent at 
school is the basis for microeconomic analysis of returns to education. 

ln 𝑊𝑖 = 𝛽0 +   𝛽1𝑆𝑖 + 𝛽2𝑋𝑖 +   𝛽1𝑋𝑖

2 +   𝜀𝑖 

The Mincerian wage equation is explained by Wi being the natural log of the wage for 
individual i, Si being years of schooling, Xi being experience, Xi
squared, and εi being a disturbance term. 

2 being experience 

It implies that the change in a country’s average level of schooling should be the key 
determinant of income growth. In contrast, macro growth literature typically sees 
economic growth as a function of the initial level of education of the population. In 

1 Krueger, A. B., and M. Lindahl (2001), Education for Growth: Why and For Whom? Journal of 
Economic Literature 

1 

 
 
 
                                           
Study on the potential of education to cause macroeconomic imbalances and negative 
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their paper, the authors use a semi-parametric varying-coefficient model that allows 
the coefficient on education to vary across countries (as in microeconometric 
estimates of the return to schooling). Their overall conclusion is that both initial level 
of education and changes thereof are positively correlated with economic growth. 

For example, McIntosh (2006)2 estimates the returns to each qualification on the basis 
of the earnings of all individuals who acquire them, rather than just individuals who 
acquire that qualification and progress no further. As a result of using the ‘all 
qualifications’ specification, the interpretation of the estimated coefficient on any 
particular qualification is the estimated difference in earnings between someone who 
holds that qualification and someone who does not, holding all other education 
achievements constant. The estimated returns should be viewed as cumulative across 
qualifications, and so can be summed to obtain the total returns to a combination of 
qualifications.  

Returns to academic qualifications  

  26% for a first degree,  
  16% for 2 or more A levels,  
  28% for 5 or more good (grade C or above) GCSEs. 

A4.1.3  Productivity and earnings 

A range of studies show that more schooling is associated with higher individual 
earnings (for example Psacharopoulos (1994), Card (1999), Harmon, Oosterbeek, and 
Walker (2003) Psacharopoulos and Patrinos (2004), and Heckman, Lochner, and Todd 
(2006)). 

Using a life-time income maximising model to determine the optimal length of 
schooling, taking into consideration the possibility of unemployment and the 
uncertainty regarding future income, Groot and Oosterbeek (1992) found, in line with 
other studies, that earnings increased with the level of education.  

The conclusion of a study from Harmon, Oosterbeek and Walker (2003) is that there is 
an unambiguously positive effect on the earnings of an individual from participation in 
education. The size of the effect seems large relative to the returns on other 
investments. The authors add considerations on credentials attained to a simple 
regression model, and find average returns to one year of education equal to: 16% for 
women and 9% for men.3 

Finally Temple (2001) examined a robust correlation between measures of human 
capital and growth across countries. Thus, earnings functions may be picking up 
genuine productivity effects rather than just reflecting signalling effects or omitted 
characteristics.  

In recent years, cognitive skills as measured by international achievement tests 
emerged as a factor of interest underlying long-run growth differences across OECD 
countries (Hanushek and Woessmann 2011a) and were found to be strongly correlated 
with individual earnings (the rate of return to schooling across countries is centered at 
about 10 percent with variations: returns appear higher for low income countries, for 
lower levels of schooling, and, frequently, for women) and to the distribution of 
income (Hanushek and Woessmann, 2008). 

2 Data from Labour Force Survey full time series from 1993 to 2001 in the UK. 
3 Method used here is to plot the average return for a number of popular credentials in the UK 
data against the average number of years of schooling for holders of these credentials. 

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Study on the potential of education to cause macroeconomic imbalances and negative 
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Regarding the distribution of income, Blau and Kahn (2005) already highlighted that 
greater dispersion of cognitive test scores4 can explain higher wage inequality in the 
US. When controlling for the test score, a one standard deviation increase in schooling 
is expected to raise the wages of American men by 16.6% and of American women by 
26.4%, compared to a non-US average of 6.4% for men and 10.3% for women. 

In addition, studies typically find that achievement in cognitive tests has a clear 
impact on earnings after controlling for differences in the quantity of schooling, the 
experiences of workers, and other factors that might also influence earnings 
(Hanushek and Woessmann, 2008). According to the same authors (2010), cognitive 
skills explained 3-13 percent of variance in individual earnings depending on gender 
and the specific distributional measure. 

Heckman, Stixrud, Urzua, (2006) summarised that non-cognitive skills strongly 
influence schooling decisions, and also affect wages given schooling decisions. In 
addition, schooling, employment, work experience and choice of occupation are 
affected by latent non-cognitive and cognitive skills. 

Bjorklund, A. and Kjellstrom, C. (2002) further finds in their study that the retirement 
age and employment rates could be of great consequence for the magnitude of the 
return to schooling. It also finds that postponing education leads to lower lifetime 
earnings. The study shows the Mincer equation can be extended in several way (Box-
Cox transformation or use a dummy variable for each year of schooling). The study 
focuses only on employed persons (more accurate data), only on men (no interruption 
in they work careers, less frequently in part-time), only on people who lived in 
Sweden when they grew up). Data used were the Swedish Level of Living Surveys 
from 1968, 1981 and 1991.  

Asplund’s (2001) results suggest that the effect of education on earnings is primarily 
due to its impact on individuals’ productivity. There is, indeed, no sign that the returns 
to education are decreasing along individuals’ life cycle. Furthermore, their distinction 
between private and public sector employees has not highlighted any differential in 
the returns to education in favour of the presumably screened group of public sector 
workers, hence rejecting the strong version of the screening hypothesis as well. 

A4.1.4  Unemployment and inactivity 

A proper qualification is the precondition for getting a (decent) job. Many of the young 
people without qualification (which is to a large extent corresponding with a lack of 
adequate skills) will be unemployed (‘unemployable’), or will just give up and go into – 
in fact involuntary – inactivity.  

The risk of unemployment in future prospects have no negligible impact on investment 
in education. Observations in 19 OECD countries between 1960-1990 showed that 
unemployment represents an inefficiency in human capital production, which can 
negatively impact on the human capital accumulation of the economy and finally 
reduce the returns from education (Mauro and Carmeci 2003).In addition, 
unemployment naturally prevents the person to acquire working experience, which 
negatively impacts the upgrading of their human capital (this finding is also in line 
with the Mincer wage equation). 

This could have an impact when individuals make decisions about the length of 
schooling. According to Groot and Oosterbeek (1992), the optimal length of schooling 
decreases with: (i) a decrease in the derivative of earnings with respect to schooling, 
(ii) an increase in the direct costs of schooling, (iii) an increase in the discount rate, 
(iv) and an increase in the educated wage range. Consequently if risk of 

4  The  International  Adult  Literacy  Survey  (IALS)  contains  the  results  of  comparable  cognitive 
tests administered in a number of countries (US, Canada, Netherlands, Sweden, Switzerland) in 
the areas of mathematics, prose literacy, and document-reading ability. Data from 1994-96. 

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Study on the potential of education to cause macroeconomic imbalances and negative 
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unemployment and inactivity are high, incentives to invest in education could be 
greatly hampered. 

A4.1.5  Effects of education on growth  

Modern theories of economic growth consider the accumulation of human capital and 
productive knowledge to be of similar importance to the accumulation of physical 
capital, the main cornerstone of neoclassical growth models. Human capital theory 
regards education as an investment into the upskilling of individuals, expecting to be 
rewarded through higher productivity in the future. An initial cost is incurred which 
includes direct costs as well as opportunity costs of the time spent on education. Wage 
levels are a standard proxy for productivity, assuming that in a functioning market 
economy employers will reward productive individuals (see section 2 of the main 
report). In contrast, the ‘screening hypothesis’ suggested that earning differentials, 
standardised for differences attributed to non-educational external factors, do not 
allow assumptions on productivity-enhancing effects of education attainment but are 
rather a reflection of ‘natural’ ability (Spence 1973). Issues of reverse causation and 
omitted externalities remain a recurring point of discussion, i.e. whether education 
actually ‘raises’ productivity or is only a proxy for ‘natural’ ability. 

A number of empirical approaches can be identified in literature to investigate the 
effects of human capital on growth. A first approach, prominent until the early 1990’s, 
included a specification and estimation of an ad-hoc equation relating growth in total 
or per capita output to a set of informally drawn up variables deemed important, most 
commonly built around years of schooling or type of qualification obtained using 
regression discontinuity designs (see Harmon and Walker 1995, Grenet 2009, 
Devereux and Hart 2010 for some examples). Chevalier, Harmon, Walker and Zhu 
(2004) have tested against the ‘screening hypothesis’ using changes in the minimum 
school leaving age in the UK as instruments. They find that the basic human capital 
assumption can be supported, albeit noting that their tests might suffer from limited 
data and robustness. 

The second approach estimates a structural relation outputs and their growth rate and 
relevant independent variables based on explicit theoretical assumptions presented in 
aggregate production function and often an additional technical progress function, 
often building on the Mincer earnings function (Mincer 1958). Some of the more recent 
empirical work under this strand, based on more complete and higher quality datasets 
across 21 OECD countries, (Barro 1991, De la Fuente, Domenech 2006 & 2008, de la 
Fuente 2011) suggest strong causal links between educational attainment, wage levels 
and employment security (ie. r2 above 0.6) and proposes a set of meta-estimates of a 
human capital coefficient in an aggregate Cobb-Douglas production function. 

Hanushek and Woessmann (2011) have shown in a sample of 24 OECD countries that 
the coefficient of determination is higher in regression analysis of skills and growth in 
GDP per capita compared to data on formal school attainment and growth. 

In contrast to this, a separate set of empirical studies using panel data techniques and 
controls for assumed heterogeneity of countries, produced negative results (Islam 
1995, Pritchett 2001), notably some of this research has found a negative correlation 
between human capital and growth for developed countries. A surprising result of 
Islam (1995) is that effects of human capital or education on economic growth are 
found to be not significant, sometimes null, or even negative, using the HUMAN 
variable from the Barro and Lee dataset (average schooling years in total population 
over the age of 25), and when using pooled estimation. Using his method of panel 
estimation, he finds coefficients of -0.0069 (for a sample of 67 developing countries) 
and -0.0450 (or a sample of 22 OECD countries) between years of schooling and real 
per capita GDP growth. 

It is possible to differentiate the level of achievement between basic skills (non-
tertiary school attainment or low test results in PISA, TIMSS or PIAAC) vs. high skills 

4 

 
 
 
Study on the potential of education to cause macroeconomic imbalances and negative 
spillovers – Annex IV 

(tertiary school attainment or top test results) to follow different models outlined 
above – see Hanushek and Woessmann (2011).  

Recent literature related to human capital and economic growth using more broadly 
available data on mean cognitive skills measured directly and not proxied, as in much 
literature before 2000, principally finds a more sizeable and more statistically 
significant link between human capital and growth. This more recent research 
benefitted strongly from replacing the traditionally used years of schooling and ordinal 
data (educational attainment achieved) with mean test scores from surveys such as 
PISA.  

Criticism of multi-variate regression analyses using skill tests often focusses on the 
shortcomings of the statistics per se, such as: 

  Reverse causalities 
  Omitted variable biases 

Hanushek and Woessmann (2009) have used instrumental variables and difference-in-
difference methods to exclude some of the most commonly assumed reverse 
causalities and omitted variable biases. Hanushek and Woessmann conclude in their 
work that economic institutions (e.g. clear property rights, openness to international 
trade, regulations and restrictions in labour, capital and product markets), contrary to 
other work, have no strong effect on long-run growth in national economies. Acemoglu 
et. al. (2005)5 offers an antagonistic view on the role of economic institutions in 
growth models. 

Another theoretical point of discussion is the role of basic skills vs. top skills in growth. 
Vandenbussche et al. (2006)6 assumes that innovations occur more often and are 
more intensive in high-skilled labour than the following imitation processes. This 
results in endogenous models where high-skill labour has a greater growth-enhancing 
effect in countries with largely technology-driven economies, whilst countries ‘further 
away’ from the cutting edge of technology benefit stronger from basic skills in human 
capital. However a large body of innovation research suggests that often innovation 
results from lucky coincidences, and imitation of these instances of luck requires 
highly-skilled specialists. 

There is furthermore a very strong conceptual argument for disaggregating by sector 
to explore the economic effects of different types of skill. Evidence of different 
patterns of wage returns to different kinds of education and training in European 
countries such as Germany and the UK serves indicate that employers in each country 
have tended to adopt very different patterns of sectoral specialisation. An 
appropriately refined statistical approach can be found in Ciccone and Papaioannou 
(2009), who use the cross-sectoral differences in growth according to the importance 
of human capital for each sector, instead of ‘simple’ within-country averages of test 
scores. 

A4.1.6  Variables and indicators 

Economic research has expanded the understanding of the benefits of education 
towards the end of the twentieth century. The literature typically separates private 
and social returns as well as monetary and non-monetary returns (see McMahon 1997 
and Psacharapoulos 2006). 

Economic research has expanded the understanding of the benefits of education 
towards the end of the twentieth century. A simple taxonomy of benefits and 

5 D. Acemoglu (2005): Institutions as a fundamental cause fo long-run growth, in P. Aghion and 
S.N. Durlauf (eds.): Handbook of Economic Growth, 385-472. 
6  J.  Vandenbussche  et  al.  (2006):  Growth,  distance  to  frontier  and  composition  of  human 
capital, Journal of Economic Growth, 11(2), 97–127. 

5 

 
 
 
                                           
Study on the potential of education to cause macroeconomic imbalances and negative 
spillovers – Annex IV 

education outcomes, is provided in Table A4.1. The literature typically separates 
private and social returns as well as monetary and non-monetary returns (see 
McMahon 1997 and Psacharapoulos 2006). 

Table A4.1  Returns on investments in education 

Market/monetary 

Private 

Social 

Higher Earning 

Higher productivity 

Higher skills in 
mathematics, literacy and 
problem solving 

higher GDP per capita 

Employability 

Higher net tax revenue 

Less unemployment 

Less reliance on government 
financial support 

Labour market flexibility  

Greater mobility 

Greater consumer efficiency  Lower crime rates 

Health benefits of individual 
and/or family 

Less spread of infectious 
diseases 

Higher voter participation 

Non-market/non-
monetary 

A4.1.7  Effects of productivity on trade deficit/ export share/ unit labour cost  

There is very clear-cut relationship between productivity and unit labour costs (ULC) 
stemming from the fact of how unit labour costs is computed. However, while the 
literature emphasises very important role of the ULC indicator at the micro level, it is 
far less reliable at the macro level due to caveats such as different labour capital/ 
intensity of specific sectors, or its simplicity which does not allow to capture other 
factors influencing competitiveness, even more in short than long run. In terms of the 
link between productivity and trade deficit, the literature largely fails to provide the 
clear-cut evidences on the character of effects of one on another. In fact, several 
publications stressed possible mechanism where increase in labour productivity may 
lead to increase in trade deficit arguing that higher productivity leading to higher 
wages and rise in aggregate consumption may eventually trigger off relatively higher 
import demand. In addition, much research points out to importance of other factors 
that may easily offset the role of productivity i.e. real interest rates, real exchange 
rate or number of exogenous factors such as changes in global trade and demand. 

Finally, while there is plethora of literature focusing on the link between productivity 
and export share at the firm level that shows strong positive correlation between both 
(no consensus on direction of causality), the research on the macro level is very 
limited and inconclusive. 

6 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Study on the potential of education to cause macroeconomic imbalances and negative 
spillovers – Annex IV 

A4.2  List of literature reviewed and summarised 

A4.2.1  Policy papers 

  European Commission (2014), The MIP 2014 in-depth review of Italy. 
  OECD (2004), The output of educational institutions and the impact of learning. 

In: Education at a Glance: OECD Indicators 2004. Paris: OECD, 146-187. 

A4.2.2  Microeconomic studies 

A4.2.2.1 

Single  country  empirical  studies  (including  studies  using 

longitudinal data) 

  Acosta, P., Cunningham, W., Muller, N. (2014), Beyond qualifications: labor 
market returns to cognitive skills and personality traits in urban Colombia.  

  Bertschy, K., Böni, E. & Meyer, T (2007), An der Zweiten Schwelle: Junge 

Menschen im Übergang zwischen Ausbildung und Arbeitsmarkt.  
Ergebnisuebersicht des Jugendlaengsschnitts TREE.  

  BIS research paper No 45 - The Returns to Higher Education Qualifications. 

London Economics 2011. 

  BIS research paper No 47 - The Long Term Effect of Vocational Qualifications on 

Labour Market Outcomes. London Economics 2011. 

  BIS research paper No 53 - Returns to Intermediate and Low Level 

Qualifications. London Economics 2011. 

  BIS research paper No 150 - Impact of Learning below Level 2. BMG Research 

and IER. 2013 

  Björklund, A. and Kjellstrom, C. (2002), Estimating the Return to Investments 
in Education: How Useful is the Mincer Equation?. In: Economics of Education 
Review 21(3) , 195-210. 

  Björklund, A., Edin, P.A., Freriksson, P. and Krueger, A. (2004), Education, 
Equality and Efficiency: An Analysis of Swedish School Reforms during the 
1990s. IFAU - Institute for Labour Market Policy Evaluation, Report 2004:1.  
  Blau, F. D., & Kahn, L. M. (2005), Do cognitive test scores explain higher US 

wage inequality? Review of Economics and Statistics 87(1), 184-193. 
  Danish Ministry of Education (2014), Summary of the Danish PISA-PIAAC 

survey.  

  Fritschi, T., Oesch. T. and Jann, B. (2009), Gesellschaftliche Kosten der 

Ausbildungslosigkeit in der Schweiz. Büro für Arbeits- und Sozialpolitische 
Studien BASS AG, Schlussbericht, Mai 2009, Bern.  

  Harmon, C. and Walker, I. (1995), Estimates of the Economic Return to 

Schooling for the United Kingdom. In: American Economic Review 85(5) , 1278-
1286. 

  Marks, G. (2008), The Occupations and earnings of young Australians: The role 
of education and training. Longitudinal Surveys of Australian Youth. Research 
Report 55. 

  OECD (2010), Pathways to success. How Knowledge and skills at age 15 shape 

future lives in Canada.  

  Rosdahl, A., Fridberg, T., Jakobsen, V. and Jørgensen, M. (2013), Færdigheder i 
læsning, regning og problemløsning med IT i Danmark. (Survey of Adult Skills 
in Denmark – Main report). Copenhagen: SFI – The Danish National Centre for 
Social Research. Report 13:28.  

  Rosdahl, A. (2014), Fra 15 til 27 år. PISA 2000-eleverne I 2011/12. (From 15 
to 27 years. The PISA 2000-students in 2011/12). Copenhagen: SFI – The 
Danish National Centre for Social Research. Report 14:13.  

  Ryan, C. (2003), A ‘causal’ estimate of the effect of schooling on full-time 

employment among young Australians. 

7 

 
 
 
Study on the potential of education to cause macroeconomic imbalances and negative 
spillovers – Annex IV 

  Stalder, B. E., Meyer, T. & Hupka-Brunner, S, (2009), Low Achievers' Risky 
Pathways: PISA Literacy Scores as Predictors of Upper Secondary Enrolment 
and Graduation. In: F. Rauner, E. Smith, U. Hauschildt & H. Zelloth (Eds.). 
Innovative Apprenticeships. Promoting Successful School-to-Work Transitions. 
17-18 September 2009, Turin, Italy. Conference Proceedings, 137-140. 

A4.2.2.2 

Multi-country studies 

  Asplund, R. and Telhado Pereira, P. (eds.) (1999), Returns to Human Capital in 
Europe: A Literature Review. Helsinki: ETLA, The Research Institute of the 
Finnish Economy. 

  Austen, S., MacPhail, F. (2010), Post-school education and labour force 

participation in Canada and Australia. 

  de la Fuente, A. and Jimeno, J. (2009), The private and fiscal returns to 
schooling in the European Union. In: Journal of the European Economic 
Association 7(6) , 1319-1360. 

  Grenet, J. (2009), Is it enough to increase compulsory education to raise 
Earnings? Evidence from French and British Compulsory Schooling Laws. 
  Harmon, Colm, Hessel Oosterbeek and Ian Walker (2003). The Returns to 

Education: Microeconomics. In:Journal of Economic Surveys 17(2) , 115-155. 
  Heckman, J., Lochner, L. and Todd, P. (2006), Earnings Functions and Rates of 
Return: The Mincer Equation and beyond. In: Eric A. Hanushek and Finis Welch 
(eds.). Handbook of the Economics of Education. Amsterdam: North-Holland , 
307-458. 

  Heckman, J., Stixrud, J. and Urzua, S. (2006), The Effects Of Cognitive and 

Noncognitive Abilities On Labor Market Outcomes and Social Behavior, Journal 
of Labor Economics, v24(3,Jul), 411-482. 

  Mcintosh, S. (2006), Further Analysis of the Returns to Academic and 

Vocational Qualifications. In: Oxford Bulletin of Economics and Statistics 68(2), 
225-251. 

  Mincer, J. (1958), Investment in Human Capital and Personal Income 

Distribution. In: Journal of Political Economy 66(4), 281-302. 

A4.2.3  Macroeconomic studies 

A4.2.3.1 

Single country studies 

  Allmendinger, J. et.al. (2012), Unzureichende Bildung: Folgekosten fuer die 

oeffentlichen Haushalte. 

  Bach, H., Spitznagel, E. (2008), Kosten der Arbeitslosigkeit sind gesunken. IAB 

Kurzberichte 14/2008. 

  BIS Research paper No 38 - Measuring the Economic Impact of Further 

Education. Cambridge Econometrics and IER. 2011. 

A4.2.3.2 

Multi-country studies 

  Adalet Mc Gowan, M., and Andrews, D. (2015), Labour Market Mismatch and 

Labour Productivity: Evidence from PIAAC Data. OECD Economics Department 
Working Papers, No. 1209. 

  Barro, R. (1991), Economic Growth in a Cross Section of Countries. In: 

Quarterly Journal of Economics 106(2), 407-443. 

  Benhabib, J. and Spiegel, M. (1994), The Role of Human Capital in Economic 
Development: Evidence from Aggregate Cross-Country Data. In: Journal of 
Monetary Economics 34(2), 143-173.  

  Ciccone, A., Papaioannou, E. (2009), Human Capital, the Structure of 

Production, and Growth" The review of Economics and Statistics Vol. 91, pp.66-
82.  

8 

 
 
 
 
Study on the potential of education to cause macroeconomic imbalances and negative 
spillovers – Annex IV 

  Coulombe, S., Tremblay, J.-F. and Marchand, S. (2004), Literacy Scores, 

Human Capital and Growth Across Fourteen OECD Countries. Ottawa: Statistics 
Canada.  

  de la Fuente, A. and Domenech, R. ( (2001), Schooling Data, Technological 
Diffusion and the Neoclassical Model. In: American Economic Review 91(2) , 
323-327.  

  de la Fuente, A. and Domenech, R. (2006), Human capital in growth 

regressions: How much difference does data quality make? Journal of the 
European economic Association, Volume 4, Issue 1, pp1-36. 

  Groot, W., & Oosterbeek, H. (1992), Optimal investment in human capital 

under uncertainty. Economics of Education Review 11(1), 41-49. 

  Hanushek, Eric A. and Kimko, D. (2000), Schooling, Labor-Force Quality and 
the Growth of Nations. In: American Economic Review 90(5) , 1184-1208.  
  Hanushek, E. A. and Woessmann, L.(2008), The Role of Cognitive Skills in 

Economic Development. In: Journal of Economic Literature 46(3) , 607-668. 
  Hanushek, E. A. and Woessmann, L.(2010), The Economics of International 

Differences in Educational Achievement. In: Eric A. Hanushek, Stephen Machin 
and Ludger Woessmann (eds.).Handbook of the Economics of Education, 
Volume 3. Amsterdam: North Holland, 89-200. 

  Hanushek, E. A. and Woessmann, L. (2011a), How Much Do Educational 
Outcomes Matter in OECD Countries? Economic Policy 26(67), 427-491. 
  Hanushek, E. A. and Woessmann, L. (2012), Do Better Schools Lead to More 
Growth? Cognitive Skills, Economic Outcomes, and Causation. In: Journal of 
Economic Growth 17(4) , 267-321. 

  Islam, N. (1995), Growth Empirics: A Panel Data Approach. In: Quarterly 

Journal of Economics 110(4) , 1127-1170. 

  Krueger, A., Lindahl, M. (2001), Education for Growth: Why and for Whom?. In: 

Journal of Economic Literature 39(4) , 1101-1136. 

  Mahy, B., et. al. (2015), Educational mismatch and firm productivity: DO skills, 
technology and uncertainty matter? IZA discussion paper 8885, February 2015. 

  Mankiw, G., Romer, D. and Weil, D. (1992), A Contribution to the Empirics of 
Economic Growth. In: Quarterly Journal of Economics 107(2) , 407-437. 

  Mauro, L., & Carmeci, G. (2003), Long run growth and investment in education: 

Does unemployment matter? Journal of Macroeconomics 25(1), 123-137. 
  Pritchett, Lant (2001). Where Has All the Education Gone?. In: World Bank 

Economic Review 15(3) , 367-391. 

  Pritchett, L. (2006), Does learning to add up add up? The returns to schooling 
in aggregate data. In: E. Hanushek and F. Welch. Handbook of the Economics 
of Education. Amsterdam: North-Holland, 635-695. 

  Romer, P. (1990), Human Capital and Growth: Theory and Evidence. In: 
Carnegie-Rochester Conference Serie on Public Policy 32 , 251-286 

A4.2.3.3 

Education and economic contribution of migrants 

  Dustmann, C., Frattini, T., Lanzara, G. (2011), Educational Achievement of 
second generation immigrants: an international comparison. Centre for 
Research and Analysis of Migration (CReAM). Discussion paper No 16/11. 

  OECD (2013), What do immigrant students tell us about the quality of 

education systems? PISA in Focus 33. 

A4.2.3.4 

Academic literature – Productivity and trade balance 

  Bussière, M. Fratzscher, M. and Müller, G.J. (2005), Productivity shocks, Budget 

deficits and The current account 

  Corsetti, G. Dedola, L. and Leduc, S. (2006), Productivity, external balance and 
Exchange rates: evidence on the Transmission mechanism among G7 countries  

9 

 
 
 
 
Study on the potential of education to cause macroeconomic imbalances and negative 
spillovers – Annex IV 

  Corsetti, G. Martin, P. and Pesenti, P.  (2006). Productivity, terms of trade and 

the “home market effect”. 

  Erceg, C.J., Guerrieri, L., and Gust, C. (2002), Productivity Growth and the 

Trade Balance in the 1990s: the Role of Evolving Perceptions 

  Iscan, T.B. (2000), The terms of trade, productivity growth and the current 

account. Journal of Monetary Economics. 

A4.2.3.5 

Academic literature – productivity and exports 

  Ayadi, M. and Matoussi, W. (2014), From Productivity to Exporting or Vice 

Versa? Evidence from Tunisian Manufacturing Sector. 

  Kunst, R. and Marin, D. (1989), Notes on exports and productivity: a causal 

analysis. 

  Jienwatcharamongkhol, V. (2013), Effects of Productivity and Import on Firm-

Level Export, Ratio Working Papers 225, The Ratio Institute. 

A4.2.4  Other literature (not summarised in section A4.3) 

  Acemoglu, D. et.al. (2013), Innovation, Reallocation and Growth, NBER 

Working Papers No 18993. 

  Bermperoglou, D., Pappa, E., & Vella, E. (2013), Spending cuts and their effects 

on output, unemployment and the deficit. Unpublished Manuscript. 

  Bonin, H. (2014), Der Beitrag von Auslaendern und kuenftiger Zuwanderung 

zum deutschen Staatshaushalt. 

  Bowles, S., Gintis, H. and Osborne, M.  (2001), Incentive-Enhancing 

Preferences: Personality, Behavior, and Earnings. 

  Braconier, H., Nicoletti, G., and Westmore, B. (2014), Policy challenges for the 

next 50 years. OECD Economics Department Policy Papers, No. 9. 

  Davis, J. (2003), Empirical Evidence on Human Capital Externalities. EPRI 

Working Paper Series 2003/5. 

  Doménech, R., & García, J. R. (2008), Unemployment, taxation and public 

expenditure in OECD economies. European Journal of Political Economy 24(1), 
202-217. 

  European Commission (2014), The economic case for education, Internal 

Working Paper. 

  Galor, O. (2011), Inequality, Human Capital Formation and the Process of 

Development. In: Eric A. Hanushek, Stephen Machin and Ludger Woessmann 
(eds.). Handbook of the Economics of Education. Amsterdam: North-Holland, 
441-493. 

  Geier, B., Hupka-Brunner, S., Gaupp, N. (2013), Chances of young adults from 
lower secondary schools with basic intellectual requirements in Switzerland and 
Germany: Mastering (or failing) the second threshold. 

  Harmon, C., Walker, I. and Westergaard-Nielsen, N. (eds.) (2001). Education 
and Earnings in Europe: A Cross Country Analysis of the Returns to Education. 
Cheltenham: Edward Elgar. 

  Lucas, R. (1988), On the Mechanics of Economic Development. In: Journal of 

Monetary Economics22(1) , 03-42. 

  McMahon, W. (1999), Education and Development, Measuring the Social 

Benefits.  

  Nelson, R., Phelps,E. (1966), Investment in Humans, Technological Diffusion 
and Economic Growth. In: American Economic Review 56(1-2) , 69-82. 
  Quintini, G. (2011), Right for the job: Over-qualified or under-skilled?, OECD 

Social Employment and Migration Working Papers, No. 120.  

  Reinberg, A., Hummel, M. (2007), Der Trend bleibt. Geringqualifizierte sind 

haeufiger arbeitslos. IAB Kurzbericht 18/2007. 

10 

 
 
 
 
Study on the potential of education to cause macroeconomic imbalances and negative 
spillovers – Annex IV 

  Temple, J. R. (2001), Growth effects of education and social capital in the OECD 

countries. OECD economic studies. 

A4.2.5  Longitudinal data sources (country level) 

  Australian Government, Department of Education and Training, Longitudinal 

Surveys of Australian Youths (LSAY), http://www.lsay.edu.au/  

  Statistics Canada, Youth in Transition Survey (YIS) 

http://www23.statcan.gc.ca/imdb/p2SV.pl?Function=getSurvey&SDDS=4435  

  University of Basel, Transitions from Education to Employment panel (TREE) 

https://tree.unibas.ch/daten/  

11 

 
 
 
 
 
Study on the potential of education to cause macroeconomic imbalances and negative 
spillovers – Annex IV 

A4.3  Item by item summary 

A4.3.1  Policy papers 

A4.3.1.1 

In-depth  review  of  macroeconomic  imbalances  –  Italy  2014. 
Macroeconomic Imbalances Procedure – In depth country reviews 

Table A4.2  European Commission (2014) 

Title 

Education 
Inputs, 
outputs 
and 
returns 
considered  

Countries 
covered 
Data used 
and 
timescales 
for data 
and 
scenarios 
Overview 

Year of 
publicatio
n: 2014 

Author:  European 
Commission 

In-depth review of 
macroeconomic imbalances – 
Italy 2014. Macroeconomic 
Imbalances Procedure – In 
depth country reviews 
Education inputs: Public spending on education as % of GDP 
Education outputs: Cognitive skills as measured by PISA and PIAAC, 
qualifications obtained (ISCED), Trends in International 
Mathematics and Science Study (TIMSS) and Progress in International 
Reading Literacy Study (PIRLS) 
Individual returns to education and skills: Employment status, age-
earnings profiles, individual salaries, labour productivity 
Italy 

PIAAC (2012) and PISA (2012), TIMSS (2011), PIRLS (2011), ISTAT 
Labour Force Survey's (LFS) (2009) 

The document describes potential sources for macroeconomic problems of 
Italy, in particular as regards possible sources that slur growth, 
productivity and the country’s trade balance. The document is mainly 
based around second party research and data analysis conducted by the 
European Commission (Eurostat and EIB data). The document 
subsequently suggests reform measures for each shortcoming/potential 
source of macroeconomic imbalances identified. 

Importantly, the document highlights the low education outcomes of Italy 
(in terms of PISA and PIAAC results), and particular challenges of labour 
market entrance, as well as low individual returns on education and skills 
overall, and in particular for young people. 

The study uses QUEST simulations, based on Hanushek (2013)7, to show 
that human capital weaknesses could explain a significant part of Italy’s 
productivity gap, i.e. .the extent to which the productivity of Italy’s 
economy lags behind the EU average and that of other EU Member 
States. Results from the Commission's QUEST III simulations to assess 
the impact of comprehensive packages of structural reforms across EU 
countries (including spillovers) indicate that human capital plays an 
important role in explaining Italy's productivity gap. Notably, closing half 
of the gap to the three best performing EU countries in the attainment 
rates at both ends of the skill scale could increase GDP by 8% over the 
baseline in the long run. This represents nearly half of the potential gain 

7 Hanushek E. A., Schwerdt G., Wiederhold S., Woessmann L. (2013), 'Returns to skills around 
the world: evidence from PIAAC', OECD Education Working Papers, No. 101. 

12 

 
 
 
 
                                           
Study on the potential of education to cause macroeconomic imbalances and negative 
spillovers – Annex IV 

from the whole set of reforms simulated. Because of the relevance of 
cohort effects to human capital reforms, Italy's gains from structural 
reforms over the first 10 simulation years are smaller than for other 
countries. 

The paper does use second party research without highlighting 
limitations of this research, as it is a document mainly geared towards 
policy makers. However, there are extensive limitations to the approach 
taken by Hanushek as discussed in the review found in Hanushek and 
Woessmann (2011a) (see A4.3.7.12). 

Description 
of main 
findings 

The paper states that Hanushek et al. (2013)8 find that in Italy, the 
return to skills for older workers (those aged 55-65) is 12.3 pps. higher 
than for young adults (aged 25-34), substantially outpacing the return 
according to cross-country evidence 

The paper highlights a high number of low achievers in Italy. According to 
2012 Eurostat data, Italy has the fourth highest share across the EU of 
population with only basic education and the lowest share of population 
with tertiary education. Particularly worrying are the low attainment rates 
for young cohorts ((around 25 per cent of  Italians aged 25-34 have less 
than upper secondary education (ISCED levels 0-2)) – The percentage of 
people between 15-29 not in employment nor in education or training 
(NEET) was 23.9 per cent in 2012, compared to an EU28 average of 15.8 
per cent. There are however large regional differences in this findings, 
with the North of the country generally faring better.  

There is also evidence of a difficult transition from education to labour 
market from education to work. The ISTAT Labour Force Survey's (LFS) 
ad-hoc module for 2009 showed that the average time between leaving 
education and starting the first job was 10.5 months for Italy, second 
only to Greece (13.5). Also, the share of NEET (young people aged 15- 
29 not in education, employment or training) was at 24% in 2012, the 
third highest in the EU. 
http://ec.europa.eu/economy_finance/publications/occasional_paper/201
4/pdf/ocp182_en.pdf  

Link 

A4.3.1.2 

The output of educational institutions and the impact of learning. 
Education at a Glance: OECD Indicators 2004. 

Table A4.3  OECD (2004) 

Title 

The output of educational 
institutions and the impact of 
learning. Education at a 
Glance: OECD Indicators 
2004. 

Author:  OECD 

Year of 
publication: 
2004 

Education 
Inputs, 
outputs 
and 

The paper considers various measures education inputs in terms of 
public and private educational expenditure, outputs in terms of level of 
educational attainment (ISCED levels), and various returns including 
employment/unemployment rates, earnings and social benefits. 

8 Ibid. 

13 

 
 
 
 
 
 
 
                                           
Study on the potential of education to cause macroeconomic imbalances and negative 
spillovers – Annex IV 

returns 
considered  

The following indicators were used:  
1)  Labour force participation (employment and unemployment ratios) by 

level of educational attainment (macroeconomic indicator) 

2)  Earnings of workers according to level of educational attainment 

(microeconomic indicator) 

3)  Private internal rates of return to investment in education: estimates 
the incentives for investment in education faced by working-age 
adults; benefits are calculated based on the difference between the 
earnings for the original and acquired qualifications, which are then 
adjusted for higher employment probability  (microeconomic 
indicator) 

4)  Social internal rates of return to investment in education: reflects the 
costs and benefits to society of investment in education; costs include 
the foregone production of output during study periods and the full 
cost of providing education, while benefits include the increased 
productivity as well as possible indirect benefits with economic 
repercussions, such as lower crime, better health, etc.  

5)  Effect of human capital growth on labour productivity: human capital 
was estimated from the levels of education completed and the years 
spent at each level 

All OECD member states 

The paper stratifies the inputs, outputs and returns described above 
according to country, age (25- to 64-year-olds included), year, gender. 
Data is derived from national labour force surveys, OECD, and De la 
Fuente, A. and Doménech, R. (2000). Earnings were reported weekly, 
monthly or annual depending on the country and part-time, part-year 
and seasonal employment was not included for all countries. 
The data covers mostly 1990-2000 as a timescale, with some exceptions 
depending on data availability in individual countries. 

The following points should however be remembered when interpreting 
the indicators: 
1)  Private internal rates of return do not account for unemployment 

benefits or non-wage income.  

2)  Information on social benefits other than productivity is not readily 

available and it is difficult to quantify such social benefits; externality 
effects are also not included in the social rates of return. 

3)  Internal rates of return are based on earnings assumptions that have 

Countries 
covered 

Data used 
and 
timescales 
for data 
and 
scenarios 

weak empirical basis. 

Overview  Main objective:  

This is an OECD report where the impact of education has been 
quantified in terms of employment and returns on investment. 

Method:  
Indicators of labour force participation, earnings, returns on investment 
and productivity were used to make comparisons according to country, 
year, age, gender and education level; non-monetary impacts of 
education, e.g. enhanced social status and better health, were not 
included. 

14 

 
 
 
 
 
 
 
Study on the potential of education to cause macroeconomic imbalances and negative 
spillovers – Annex IV 

Description 
of main 
findings 

Main empirical findings: 
1)  In most countries, employment ratios for tertiary education graduates 

are higher (by about 5 percentage points on average) than those for 
upper secondary graduates, perhaps because the former tend to 
remain in employment longer. 

2)  For men, there is a large difference (10 percentage points or more in 
22 out of 30 OECD countries) between the employment ratios of 
those with and those without an upper secondary qualification.  
3)  Employment ratios for women with lower secondary attainment are 

particularly low.  

4)  The gender gap in employment ratios decreases with increasing 
educational attainment. However, it is still evident in tertiary 
education graduates.  

5)  As expected from the employment statistics, unemployment ratios fall 

with higher educational attainment.  

6)  Since 1995, the advantage conferred by tertiary education in terms of 
greater employability has decreased on average. This is most evident 
in countries like Denmark, Portugal, Turkey and Switzerland, while 
the reverse is true in Austria and Germany. 

7)  In all countries, tertiary-level graduates earn substantially more than 
upper secondary and post-secondary graduates. In most countries, 
education beyond the upper secondary level attracts a high premium. 

8)  Women earn less than men even if they have the same educational 

attainment. Differences in career choices, time spent in the workforce 
and incidence of part-time work among men and women may explain 
the difference in earnings. 

9)  Higher private internal rates of return are obtained when individuals 

directly proceed from upper secondary to tertiary education compared 
to individuals who undertake tertiary education later in life because 
the former reap the benefits of higher education over a longer period. 

10) The rates of return are strikingly high after upper secondary 

education. The estimated values of the rates of return vary greatly 
among countries. 

11) Social internal rates of return are generally lower than the private 

internal rates of return because of the higher social costs of 
education. 

12) Working and studying part-time gives better returns at upper 

secondary level than when a 40-year-old studies full time. However, 
for tertiary education there is a variation based on gender and 
countries.  

13) Growth in cyclically adjusted GDP per hour worked is partially 

attributable to changes in the average human capital of the employed 
population. 

14) Between 1990 and 2000, Europe saw marked skilled upgrading, but 

little growth in employment. However, some countries saw an 
increase in GDP growth per employed person (e.g. US, Australia, the 
Netherlands, Sweden, Canada). 

  Overall conclusions: 
1)  There is a strong positive relationship between education and 

earnings. 

2)  Gender differences among the unemployment ratios vary according to 

educational attainment and country. 

3)  The social rates of return vary greatly according to country and 

education level. 

15 

 
 
 
 
 
Study on the potential of education to cause macroeconomic imbalances and negative 
spillovers – Annex IV 

4)  Human capital is a significant determinant of economic growth. In the 

OECD area, an additional year in education is estimated to affect the 
long-term effect on economic output by 3 to 6 per cent, measured in 
labour productivity growth 

5)  High rates of return can be interpreted in several ways. They could 

indicate a shortage of skilled workers, perhaps a temporary situation. 
They could also indicate an economic premium for a scarce resource 
like ability and motivation. 

6)  Micro- and macro-level indicators should be interpreted to identify the 

impact on individuals (micro-) and on the country as a whole (macro-
). Moreover, macroeconomic indicators are important for policy 
making. 

Link 

http://www.oecd-ilibrary.org/education/education-at-a-glance-2004/the-
output-of-educational-institutions-and-the-impact-of-learning_eag-2004-
4-en  

A4.3.2  Microeconomic studies 

A4.3.3  Single country studies (including longitudinal studies) 

A4.3.3.1 

Beyond  qualifications:  labor  market  returns  to  cognitive  skills 

and   personality traits in urban Colombia 

Table A4.4  Acosta, Cunningham, Muller (2014) 

Title 

Beyond qualifications: labor 
market returns to cognitive 
skills and personality traits in 
urban Colombia 

Authors: Pablo Acosta ; 
Wendy Cunningham ; Noel 
Muller 

Year of 
publicatio
n: 2014 

Education 
Inputs, 
outputs 
and 
returns 
considered  

The study covers cognitive skills in terms of reading proficiency. The 
survey also measures personality traits, in particular the so-called “short 
big five inventory”, and three additional items to capture grit, two to 
capture hostile attribution bias, and four to capture decision-making 
skills. 

Countries 
covered 

Data used 
and 
timescales 
for data 
and 
scenarios 

Colombia 

The study uses data from the World Bank’s Skills Toward Employment 
and Productivity (STEP) household survey, a multi-country study led by 
the World Bank assessing skills that matter and their links to the labor 
market. The survey measures cognitive skills in terms of reading 
proficiency. The survey also measures personality traits, in particular the 
so-called « short big five inventory », and three additional items to 
capture grit, two to capture hostile attribution bias, and four to capture 
decision-making skills. The analysis is based on an original dataset 
collected in 2012 by the World Bank, representative of Colombia 13 main 
metropolitan areas. 

Returns to education/skills are measured as hourly earnings, also 
collected through STEP. 

16 

 
 
 
 
 
 
Study on the potential of education to cause macroeconomic imbalances and negative 
spillovers – Annex IV 

Overview  Main objective:  

The objective of this paper is to estimate the returns to cognitive skills 
(a scaled reading proficiency score), personality traits (Big 5 model, 
Grit, Hostile bias, and decision-making scales) and educational levels on 
labor outcomes in urban Colombia. .  Colombia, as one of the countries 
worldwide with a very high income inequality, also exhibits a strong 
divergence in qualifications and skills. The authors assume that, in 
additional to other sources of income inequality (incentives structures, 
labor market barriers) skills might be a further source of income 
inequality. 

Method/Approach:  

Using the STEP household survey data, the authors then use an OLS 
regression approach based on Diaz (2012), where cognitive and non-
congnitive skills are derived from schooling in the following form. With 
S= the highest year of schooling and T= all skills. 

Si  =  δ0  +  δ1  +  Ti  +  δ2  Xi  +  µ 

which can be used to estimate Y=labour market outcome in the following 
form 

Yi= α+ β1 μi+ β2 Ti+β3 Xi+ εi 

Average results for the total working-age population (15-64) indicate a 
stronger role for cognitive skills as predictor of labor earnings than 
personality traits. Cognitive skills matter also for holding a formal job, 
being a white collar worker, a wage worker, but not for being employed. 
Generally, these effects no longer hold when controlling for education 
levels. Personality traits have mixed effects on these outcomes and 
across groups of interests (gender, wage worker and self-employed, but 
their effects generally hold when controlling for schooling 

The research is ongoing, there are now confirmed empirical results. 
Intermediary results can be found in the link provided below, which holds 
a draft of the final research paper. 

Description 
of main 
findings 

Link 

http://www.iza.org/conference_files/transatlantic_2014/acosta_p2727.pd
f  

A4.3.3.2  An der zweiten Schwelle: Junge Menschen im Uebergang zwischen 
Ausbildung 
des 
Jugendlaengsschnitts TREE – (At the second hurdle: young people 
in transition between education  and the labour market. Overview 
of  results  from  the  longitudinal  panel  TREE  [Transitions  from 
Education to Employment) 

Ergebnisuebersicht 

Arbeitsmarkt. 

und 

Table A4.5  Bertschy, K., Böni, E. & Meyer, T (2007) 

17 

 
 
 
 
 
 
 
 
 
 
 
 
Study on the potential of education to cause macroeconomic imbalances and negative 
spillovers – Annex IV 

Authors:  Bertschy, K., 
Boeni, E. and Meyer, T. 

Year of 
publication: 
2007 

Title 

An der zweiten Schwelle: 
Junge Menschen im 
Uebergang zwischen 
Ausbildung und Arbeitsmarkt. 
Ergebnisuebersicht des 
Jugendlaengsschnitts TREE – 
(At the second hurdle: young 
people in transition between 
education and the labour 
market. Overview of results 
from the longitudinal panel 
TREE [Transitions from 
Education to Employment) 

 Education outputs: highest qualification obtained (ISCED), PISA 
results in 2000 
Returns to education: Earnings, employment status 

Education 
Inputs, 
outputs 
and 
returns 
considered  

Countries 
covered 

Data used 
and 
timescales 
for data 
and 
scenarios 

Switzerland 

Transitions from Education to Employment (TREE) longitudinal panel 
study - The paper builds on data collected through the panel surveys 
TREE (Transitions from education to employment), which started 
following 6,000 individuals included in the Swiss PISA cohort in 2000 
and is ongoing. Data used was from 2000-2006. 

Overview  Main objective: 

Description 
of main 
findings 

The paper reports on the observed education pathways and labour 
market outcomes (income) as well as employment status of a cohort of 
Swiss students which were part of the Swiss PISA survey in 2000, 
during six years of continued education and labour market entry. The 
paper reports on data collected in 2006, at which time individuals 
included in the cohort were 22 years old. 

Those still in education or training in 2006 are mostly enrolled in the 
tertiary level (university, higher education). However, six years upon 
completion of compulsory schooling, about one in eight youths of the 
surveyed cohort is still enrolled in upper secondary level programmes. 
By 2006, approximately four in five young adults of the surveyed cohort 
have graduated from upper secondary education and training. About 
60% have obtained a certificate in vocational education and training 
(VET), while about 20% have acquired a diploma of general education. 
Approximately one fifth of the cohort has not obtained any post-
compulsory certificate or diploma up to 2006. About half of this group 
has left the education system by then, be it temporarily or for good. The 
risk of remaining without any postcompulsory certification is particularly 
high among youth with low socio-economic status, PISA low achievers 
and youth out of education and training during the first year following 
the end of compulsory school.  

Those having left the educational system by 2006 have found gainful 
employment in seven out of eight cases. Youth with a VET certificate 
have a significantly higher chance to be employed than those without 

18 

 
 
 
 
Study on the potential of education to cause macroeconomic imbalances and negative 
spillovers – Annex IV 

any post-compulsory certification. Around 6% of young people with 
school certificate/VET certificate are unemployed. Around 22% of young 
people without any school certificate/VET certificate are unemployed. 

Half of the gainfully employed part of the PISA/TREE cohort earns less 
than CHF4’200 per month (approx. €2’500). The median for (fulltime) 
gross monthly income among the labour force as a whole is at CHF5’700 
(approx. €3’400). Uncertified youths earn significantly less than those 
having obtained a VET/schooling certificate (3,800 CHF per month on 
average, compared to 4,300 CHF per month on average). There is also 
strong regional variation with young people in the German-speaking 
regions earning higher salaries than those jobholders from French and 
Italian speaking Switzerland.  The TREE results also show substantial 
income disparities by gender: Under comparable conditions and 
qualifications, young women earn about CHF500 or >10% less than 
men. 

TREE data also shows that a substantial minority experiences 
precariousness when performing this transition. About one fifth are 
underemployed, perform work on demand or have a working contract 
which is limited in time. Women’s risk of being precariously employed is 
almost twice as high (26%) as men’s (14%). 

Link 

https://tree.unibas.ch/fileadmin/tree/redaktion/docs/TREE_Ergebnis-
Update_2007.pdf  

A4.3.3.3 

The  Returns  to  Higher  Education  Qualifications  -  BIS  research 
paper number 45 

Table A4.6  BIS, research paper 45 (2011) 

Title: 

The Returns to Higher 
Education Qualifications 
- BIS research paper 
number 45 

Author:  
London economics  

Year of 
publication:  

Publisher: 
Department for 
Business, Innovation 
and Skills 

2011 

Education inputs, 
outputs and 
returns 
considered  

Measuring the economic value of the sector was to estimate the 
Net Present Value (NPV) associated with undertaking different 
qualifications and aggregating them up to the sector as a whole. 

Countries 
covered 

England 

Data used and 
timescales for 
data and 
scenarios 

Data: Labour Force Survey, Higher Education Funding Council 
for England and Higher Education Statistics Agency data. The 
data analysed covers the 1996 – 2009 timeframe. 

19 

 
 
 
 
 
 
 
Study on the potential of education to cause macroeconomic imbalances and negative 
spillovers – Annex IV 

Overview 

Analysis of the economic benefits associated with higher 
education qualifications. 

Description of 
main findings 

In aggregate, the earnings premium associated with 
undergraduate degrees increases as the grade of honours 
increases, although the incremental premium by grade of 
degree is much steeper for men than for women. For men, 
moving from a lower to an upper second class honours degree 
increases hourly earnings by 9.5 percentage points (compared 
to 4.4 percentage points for women), while moving from an 
upper second class honours degree to a first class honours 
degree increases hourly earnings by 4.2 percentage points 
(compared to 3.9 percentage points for women). 

Although positive, there is an uneven relationship between the 
grade of degree honours and the enhanced probability of being 
employed. Compared to an individual in possession of 2 or more 
GCE ‘A’ Levels, individuals in possession of lower or upper 
second class honours degrees are 3.2 or 4.4 percentage points 
more likely to be employed respectively, while a first class 
honours degree offers an employment boost of approximately 
2.8 percentage points. 

The analysis indicates that there are substantial earnings 
returns to Master’s degrees, with men and women posting an 
8.9% and 10.3% premium respectively compared to possession 
of an undergraduate degree. 

The analysis indicates that there is only a small difference in the 
marginal earnings returns associated with Foundation degrees 
(i.e., degrees combining academic and workplace skills), higher 
education Diplomas or ‘other’ forms of higher education 
qualification. The returns to these qualifications stand at 13.8%, 
17.1% and 7.1% (respectively) compared to 2 or more GCE ‘A’ 
Levels. There are some significant variations depending on 
gender, especially for Foundation degrees. Productivity is not 
covered by the analysis. 

The study assesses financial benefits from higher educations but 
does not cover the analysis of spillovers and productivity 
effects. 

Link 

https://www.gov.uk/government/uploads/system/uploads/attac
hment_data/file/32419/11-973-returns-to-higher-education-
qualifications.pdf  

A4.3.3.4 

The  Long  Term  Effect  of  Vocational  Qualifications  on  Labour 
Market Outcomes - BIS research paper number 47 

Table A4.7  BIS, research paper 47 (2011) 

Title: 

The Long Term Effect of 
Vocational Qualifications on 

Author:  
London Economics  

Year of 
publication:  

20 

 
 
 
 
 
 
 
Study on the potential of education to cause macroeconomic imbalances and negative 
spillovers – Annex IV 

Labour Market Outcomes - BIS 
research paper number 47 

Publishers: 
Department for 
Business, Innovation 
and Skills  

2011 

 Assessment of the long-term effect of vocational education and 
training on earnings, employment and benefit dependency. 

Education 
inputs, 
outputs 
and 
returns 
considered  

Countries 
covered 

UK 

Data used 
and 
timescales 
for data 
and 
scenarios 

Overview 

Description 
of main 
findings 

The analysis combines information on learner attainment from the 
Individual Learner Record (ILR) between 2002/03 and 2005/06; 
detailed earnings (between 2003/04 and 2009/10) and employment 
information (between 1999/2000 and 2009/10) from HM Revenue and 
Customs; and benefit receipt and duration information (between 
1999/00 and 2009/10) from the Department for Work and Pensions. 
Scenarios cover a period of seven years following qualification 
attainment.  The data matching processes were generally successful 
and resulted in a significant volume of data available for subsequent 
analysis.  

The findings indicate that there are strong positive effects of 
qualification attainment on both the long term earnings and 
employment outcomes of those completing learning aims relative to 
non completers. In addition, the analysis clearly illustrates that 
individuals attaining additional qualifications are significantly less likely 
to be benefit dependent. Results are unambiguous: education and skills 
acquisition result in improved labour market outcomes that persist for 
many years post attainment. The study also found that apprenticeships 
and Skills for Life literacy and numeracy learning have positive, long 
term impacts on wage and employment.   

Results in the impact of qualification on long term earnings - 
Qualification attainment at Level 1 of NVQ adds approximately 3% per 
annum to earnings in the seven years post completion. At level 2, the 
earnings premium is approximately zero in the first three years post 
completion; however, increases to approximately 6% after 7 years. At 
level 3, there are negative earning returns in the first four years post 
attainment however, a positive earnings return emerges by year 4 
post-acquisition and increases to more than 14% after seven years.  

Results of employment outcomes - Level 1 qualification completers also 
achieve a statistically significant employment boost from qualification 
attainment that is immediate and ongoing (between 1% and 4%). 
Level 2 completers achieve an average increase in employment 
outcomes of 4.2% over the seven years of analysis, which again 
increases as the time from qualification acquisition elapses (from 2.2% 
to 5.4%). Individuals acquiring level 2 and level 3 National Vocational 
Qualifications post-employment gains of approximately 8% by the end 
of the period (a large number of National Vocational Qualifications are 

21 

 
 
 
 
Study on the potential of education to cause macroeconomic imbalances and negative 
spillovers – Annex IV 

undertaken in the workplace while already in employment). 

Apprenticeship - In terms of other labour market outcomes, the 
analysis illustrates that there are also significant employment effects 
associated with acquiring Apprenticeships. The findings indicate that 
both Advanced and Foundation Apprenticeships are associated with an 
increase in the proportion of the year employed by between 4% and 
6%. 

Problems: 

The key methodological issue involves how to construct an appropriate 
counterfactual to assess the long term impact of qualification 
achievement. 

Link 

https://www.gov.uk/government/uploads/system/uploads/attachment_
data/file/32326/11-1035-long-term-effect-of-vocational-
qualifications.pdf 

A4.3.3.5  Returns to Intermediate and Low Level Vocational Qualifications - 

BIS research paper number 53 

Table A4.8  BIS, research paper 53 (2011) 

Title: 

Returns to Intermediate and 
Low Level Vocational 
Qualifications - BIS research 
paper number 53 

Author:  

London Economics 
Publishers: 
Department for 
Business, Innovation 
and Skills  

Year of 
publication:  

2011 

 Assess the impacts of vocational qualifications on earnings, as well as 
the probability of being employed.  

Education 
inputs, 
outputs 
and 
returns 
considered  

Countries 
covered 

UK, with the exception of the outcomes associated with 
apprenticeships, where the analysis was restricted to England.  

Data: Labour Force Survey information between 1996 and 2009. 

Data used 
and 
timescales 
for data 
and 
scenarios 

22 

 
 
 
 
 
 
 
Study on the potential of education to cause macroeconomic imbalances and negative 
spillovers – Annex IV 

Overview 

Description 
of main 
findings 

The research confirms the positive employment and earnings benefits 
to individuals (and to the exchequer) from gaining vocational 
qualifications at high levels (level 3) of the Business and Technology 
Education Council (BTEC) classification, Royal Society of Arts (RSA) and 
NVQ. Low or negative earnings returns are observed at lower levels 
(levels 1 and 2 of BTEC, RSA and NVQ), although positive earning 
returns to NVQ level 1 and 2 are observed in some areas, such as the 
construction sector. The study also highlights some important trends 
that will need to be monitored closely as programmes are expanded in 
future years. 

Marginal earnings returns: 

The findings illustrate that the returns to level 3 vocational 
qualifications are relatively significant. At the upper end of the 
spectrum, individuals in possession of BTEC level 3 qualifications 
achieve a 34.0% earnings premium over those in possession of no 
formally recognised qualifications; a 14.2% premium over those in 
possession of level 2 academic or vocational qualifications; an 18.9% 
premium over those in possession of level 2 vocational and/or level 1 
academic qualifications; and a 12.8% premium over those in 
possession of 5 or more GCSEs at grades A*-C. 

Those in possession of NVQ level 2 qualifications struggle to achieve 
any earnings return, with these individuals earning 2.0% less 
compared to those in possession of level 1 academic and/or vocational 
qualifications and a statistically insignificant return compared with 
possession of no formally recognised qualifications. 

The results generally follow those relating to level 3 and level 2 
qualifications detailed previously, with all qualifications generally 
offering a positive return over possession of no formally recognised 
qualifications (between 9.1% and 17.9% respectively). 

Average employment returns to qualification attainment: 

Compared to individuals in possession of 1 GCE ‘A’ level, where the 
enhanced probability of being employed on a full time basis is 
approximately 4 percentage points, possession of a City & Guilds level 
3 qualification increases the probability of being employed on a full 
time basis by as much as 12.8 percentage points, while a City & Guilds 
level 2 qualification increases the probability of being employed by 
approximately 7.5 percentage points. 

Returns to apprenticeship: 

The estimates indicate that there are significant employment returns 
associated with all forms of apprenticeship. Individuals in possession of 
recognised trade apprenticeships are 8.7% more likely to be employed 
compared to individuals in possession of level 2 academic or vocational 
qualifications, while individuals in possession of Advanced level 
apprenticeships have  a 13.8 percentage point greater probability of 
being employed. 

Link 

https://www.gov.uk/government/uploads/system/uploads/attachment_
data/file/32354/11-1282-returns-intermediate-and-low-level-
vocational-qualifications.pdf 

23 

 
 
 
 
Study on the potential of education to cause macroeconomic imbalances and negative 
spillovers – Annex IV 

A4.3.3.6 

Evaluation of the Impact of Learning below Level 2 - BIS research 
paper number 150 

Table A4.9  BIS, research paper 150 (2013) 

Title: 

Evaluation of the 
Impact of Learning 
Below Level 2 - BIS 
research paper number 
150 

Authors:  

BMG Research and the 
Institute for Employment 
Studies (IES) 

Year of 
publication:  

2013 

Publishers: Department for 
Business, Innovation and Skills  

 Evaluation of the impact of learning Below Level 2 in Further Education 
in England. 

Education 
inputs, 
outputs 
and 
returns 
considered  

Countries 
covered 

England 

Data used 
and 
timescales 
for data 
and 
scenarios 

Overview 

Analysis of learner records from the ILR system (years 2009 – 2012) 
and telephone survey with learners conducted between December 2012 
and February 2013 (survey completed with 4,000 learners undertaking 
English for Speakers of Other Languages (ESOL) courses). 

“Below Level 2 learning’ in this context comprises any learning which is 
funded by Government, through the Skills Funding Agency, which is 
aimed at developing skills and knowledge to a level Below Level 2 in 
the National Qualifications Framework. From an economic perspective, 
up-skilling the workforce should provide a boost to the UK’s 
competitiveness and opportunities for growth. In an increasingly 
globalised economy, Western nations have come to rely on a large pool 
of skilled labour as an important competitive advantage (HM Treasury, 
2006). At a point at which the UK economy is recovering from a period 
of recession, skills continue to provide a potential source of growth and 
innovation (BIS, 2012). 

Description 
of main 
findings 

Impact on employment status: 

The survey data suggests that the impact of Below Level 2 learning is 
to raise the overall employment rate in the Below Level 2 learner 
population (from before to after learning) from 47 to 54 per cent, a 
gain of 7 per cent (in addition, the self-employment rate rose from 2 to 
3 per cent). 

Impact on earnings: 

Four years after learning, this analysis estimates that 19 to 24 year 
olds who achieved Level 1 qualifications had a weekly average wage 
level which was around £32 higher than those who did not achieve. 

For learners aged 25 and over the comparable advantage was of 
around £12 per week. However, for both age groups, there were no 

24 

 
 
 
 
 
Study on the potential of education to cause macroeconomic imbalances and negative 
spillovers – Annex IV 

significant earnings benefits from learning at Entry Level. 

Impact on the economy 

Taking into account the lifetime gains to learners and the costs to the 
Exchequer, the analysis of benefits to the economy indicates that the 
return per pound of public investment, not accounting for deadweight, 
amounts to £16.70 for Entry Level and £21.60 for Level 1 provision, in 
the case of young learners (19-24 years old). Among the 25+ group of 
learners, the social return per pound of public investment is £2.70 for 
Entry Level provision and £5.90 for Level 1 programmes. 

Link 

https://www.gov.uk/government/uploads/system/uploads/attachment_
data/file/253585/bis-13-1261-evaluation-of-the-impact-of-learning-
below-level-2.pdf 

A4.3.3.7 

Estimating the Return to Investments in Education: How Useful is 
the Mincer Equation? 

Table A4.10 Bjorklund, A. and Kjellstrom, C. (2002) 

Title 

Estimating the Return 
to Investments in 
Education: How Useful 
is the Mincer 
Equation? 

Authors: Björklund, Anders and 
Christian Kjellstrom  

Year of 
publicatio
n: 2002 

 Education inputs: years of schooling 
Returns: earnings, employment 

Education 
Inputs, 
outputs 
and 
returns 
considered  

Countries 
covered 

Sweden 

Data used 
and 
timescales 
for data 
and 
scenarios 

Overview 

Swedish Level of Living Surveys from 1968, 1981 and 1991 
The focus only on employed persons (more accurate data), only on men 
(no interruption in they work careers, less frequently in part-time), only 
on people who lived in Sweden when they grew up) 

The goal of the study is to examine how well the schooling coefficient in 
standard Mincer equations approximates the marginal internal rate of 
return to education. 

Description 
of main 
findings 

The Mincer equation (see below) is very frequently used in labour 
economics: 

ln 𝑦 = ln 𝑦0 + 𝑟𝑆 + 𝛽1𝑋 + 𝛽2𝑋2 
Where the variables have the following meanings 𝑦 is earnings (𝑦0 is earnings 
of someone who has no education and no experience): S is years of schooling; 

25 

 
 
 
 
 
 
Study on the potential of education to cause macroeconomic imbalances and negative 
spillovers – Annex IV 

X is years of potential labour market experience. 

Often, the schooling coefficient is interpreted as the return to the 
investment in schooling. But this interpretation relies on six 
assumptions: 

 

 

 

 

 

 

(1) “The earnings measure captures the full benefits of the 
investment” 

(2) “The only costs of schooling are foregone earnings”   

(3) “The earnings function is separable in s and x so that ∂ ln y/∂s 
is independent of years of work experience” 

(4) “The length of working life is the same independent of the 
length of schooling” 

(5) “Schooling precedes work” 

(6) “The economy is in a steady state without any wage and 
productivity growth” 

The study examines Assumptions 3 to 6.  

The study shows the Mincer equation can be extended in several ways:  

  apply a Box-Cox transformation:  

  use a dummy variable for each year of schooling. This method 
enables to capture discrete shifts in the return to schooling; 

  derive alternative equations from other assumptions about the 

evolution of the investment ratio over time. 

The paper uses two regression strategies (based on a mix of the three 
extensions above) to examine how “deviations from the standard 
Mincer model affect the interpretation of the schooling coefficient”. 
Results highlight that It finds weaknesses in the specification of the 
Mincer equation. The study shows that: 

 

return to schooling for college education declined in the 
observation period (1968-1981), whilst return to high school 
education remained stable 

  The rates of return observed are strongly influenced by 

assumptions made on the length of working life and retirement 
age/individual retirement decisions, therefore aggregate 
coefficients might not be useful to measure individual returns. 

Link 

http://www.researchgate.net/profile/Anders_Bjorklund/publication/483
6663_Estimating_the_return_to_investments_in_education_how_useful
_is_the_standard_Mincer_equation/links/0deec521e3c71e1891000000.
pdf  

A4.3.3.8 

Education, Equality and Efficiency:  An  Analysis of  Swedish School 
Reforms during the 1990s 

Table A4.11 Bjorklund, A. et.al. (2004) 

Title 

 Education, Equality and 
Efficiency: An Analysis of 
Swedish School Reforms 

Authors: Björklund, Anders, 
Per-Anders Edin, Peter 
Freriksson and Alan Krueger 

Year of 
publication: 
2004 

26 

 
 
 
 
 
 
 
Study on the potential of education to cause macroeconomic imbalances and negative 
spillovers – Annex IV 

during the 1990s 

Publisher: IFAU - Institute 
for Labour Market Policy 
Evaluation 

Education inputs: years of schooling 
Returns: earnings 

Education 
Inputs, 
outputs 
and 
returns 
considered  

Countries 
covered 

Focus on Sweden.  
Comparison with other OECD countries: Canada, Denmark, France, 
Germany, Italy, Japan, Netherlands, UK, US 

OECD. 1007-2000 

Data used 
and 
timescales 
for data 
and 
scenarios 

Overview 

This study focuses on the monetary returns to schooling. 

Description 
of main 
findings 

The study uses a simple Mincer-specification with a linear years-of-
schooling variable to estimate the long run perspective on the 
education wage premium. It finds an internal rate of return for 
secondary schooling of 6% and for tertiary schooling of 10% (Sweden). 
This is in line with OECD figures (see table below, providing comparison 
for other OECD countries – estimates are in %) 

Table 1. 

Internal private rates of returns to upper secondary and 
tertiary education, 1997-2000 

Country 

Canada 

Denmark 

France 

Germany 

Italy 

Japan 

Netherlands 

Sweden 

Upper 
secondary, 
Males 

Upper 
secondary, 
Females 

Tertiary, 

Males 

Tertiary, 
Females 

13.6 

11.3 

14.8 

10.8 

11.2 

6.4 

7.9 

6.4 

12.7 

10.5 

19.2 

6.9 

NA 

8.5 

8.4 

8.1 

9.4 

13.9 

10.1 

12.2 

11.7 

9.0 

6.5 

7.5 

8.3 

NA 

6.7 

12.0 

12.3 

0.0* 

11.4 

10.8 

United Kingdom 

15.1 

NA 

17.3 

15.2 

United States 

16.4 

11.8 

14.9 

14.7 

27 

 
 
 
 
 
Study on the potential of education to cause macroeconomic imbalances and negative 
spillovers – Annex IV 

Source: OECD (2002), Education at a Glance, Organization for Economic 
Cooperation and Development, Paris.  

Notes: The earnings difference between females with lower and upper 
secondary schooling in Sweden is not large enough to produce a positive 
rate of return. 

The study investigates whether differences in wage dispersion are 
driven by differences in the supply of skills, and finds that 

  There is a direct effect from skill dispersion on wage dispersion. 
The size of effect depends on the value of certain skills, which is 
effected by wage levels and labour market demands in countries 

  There is also an indirect effect of skills on wage , as the net 
supply of skills is assumed to affect the value/price of skills 
  However, the study fails at making strong statements about the 

exact size / level of skill differences contributing to wage 
inequality across countries –given the limited number of cases 
investigated.  

Link 

http://cep.lse.ac.uk/seminarpapers/06-02-04-BJO.pdf  

A4.3.3.9  Do  cognitive  test  scores  explain  higher  US  wage  inequality? 

Review  

of Economics and Statistics  

Table A4.12 Blau, F. and Kahn, L. (2005) 

Title 

Do cognitive test scores 
explain higher US wage 
inequality? Review of 
Economics and Statistics  

Authors: Francine Blau 
and Lawrence Kahn 

Year of 
publication: 
2005 

Education outputs: literacy, numeracy 
Returns: earnings 

Education 
Inputs, 
outputs 
and 
returns 
considered  

Countries 
covered 

Data used 
and 
timescales 
for data 
and 
scenarios 

United States 

Microdata from the 1994-6 International Adult Literacy Survey (IALS); 
data available on gender, employment status, schooling, age, weeks 
working the previous year, full-/part-time status, weeks worked in the 
past year, industry, occupation and earnings (available for Canada, 
Switzerland, Sweden, the Netherlands and the US). 
Cognitive skills were estimated from the average scores for tests on 
prose literacy, document literacy and quantitative literacy 
Measures of educational attainment, age, average IALS score 

Overview  Main objective:  

The US has higher levels of wage inequality and lower levels of 
unemployment than other OECD countries. This divergence may be 
explained by the differences in labour market institutions, market 
factors and characteristics of the labour force. Previous attempts have 
focused on population heterogeneity and labour market prices, but with 

28 

 
 
 
 
 
 
Study on the potential of education to cause macroeconomic imbalances and negative 
spillovers – Annex IV 

Description 
of main 
findings 

controls for levels of schooling and age and labour market experience. 
Importantly, labour skills will not be uniform across countries and even 
within countries for the same years of schooling. Instead, test scores 
that show performance on the same international tests, could be a 
better way of measuring cognitive skills.  
Therefore, the authors choose to use test score differentials to explain 
wage inequality among countries. 

Method/Approach:  
The authors use a full-distributional accounting method devised by 
Juhn, Murphy and Pierce (1993). 
They study the wage determination process in several countries and 
examine the impact of cognitive test scores on wages in each country. 
They approximate each country’s wage rate from the weekly earnings 
of full-time workers and estimate separate wage structures for men 
and women. 

The paper does not provide a clear definition of wage inequality, it 
seems to insinuate that wage inequality is derived from the variance of 
wage levels across a country. 

Main empirical findings:  

1)  The wage inequality was higher for the US overall and when 

compared to other countries the difference in wage inequality 
between the US and other countries was relatively small at the top 
of the wage distribution, while it was much higher at the bottom of 
the wage distribution. 

2)  American do substantially worse on tests than the non-US average, 
especially at the bottom of the wage distribution, but perform about 
the same at the median and better at the top. 

3)  In models that exclude education, a one standard deviation increase 
in test scores raises wages by 10% to 24.2% for men and 7.7%. to 
25.3% for women compared to the 1994-6 IALS results, on average 
4)  When education is included, the change in the coefficient on the test 

score is significant for both men and women in the US. 

5)  Furthermore, the US has the highest returns to cognitive skills. A 
one standard deviation increase in test scores is predicted to 
increase wages by 16.4 (controlling for education) and 24.2% (not 
controlling for education) compared to non-US averages of 10.3 and 
13.7%, respectively.  

6)  In addition, test scores are estimated to increase women’s wages by 

22.1% in the US compared to 15.1% elsewhere, when education is 
not included, but Dutch women have the highest returns to 
education when education is included. Canadian women also 
perform better than the US in both models when returns to 
education are estimated. 

7)  The coefficients on education are positive and significant in all 

cases. 

8)  When controlling for the test score, a one standard deviation 

increase in schooling is expected to raise the wages of American 
men by 16.6% and of American women by 26.4%, compared to a 
non-US average of 6.4% for men and 10.3% for women. 

9)  The greater dispersion of test scores in the US does not account for 
its higher returns to education compared to the other countries 

29 

 
 
 
 
 
 
Study on the potential of education to cause macroeconomic imbalances and negative 
spillovers – Annex IV 

studied. Thess observations are very unclear 

10) In men, the distribution of schooling, age and test scores explain on 
average 47% of the higher US gap in US wage inequality in the 
middle and bottom of the wage distribution. In women, the effect is 
smaller. 

11) Wage coefficient and residual effects account for 77% of the 
differences between the US and other countries on average.  
12) The impact of adding the test score to the human capital equation 
which controls for age and educations is that the effect on the 
average measured characteristics increase to 0.048 log points from 
0.016 log points. 

13) The difference between the US and other countries decreases when 

the analysis is restricted to native workers and immigrant workers 
are ignored. 
Overall conclusions: 
1)  Greater dispersion of cognitive test scores can explain higher wage 

inequality in the US. 

2)  Higher labour prices and greater residual inequality (or within-group 
inequality) play important roles in determining wage inequality in 
both men and women. 

3)  Labour prices in US remain high even after controlling for the 

selectivity of the wage samples. 

Link 

http://www.nber.org/papers/w8210.pdf  

A4.3.3.10  Summary of the Danish PISA-PIAAC survey 

Table A4.13 Danish Ministry of Education (2014) 

Title 

Summary of the Danish 
PISA-PIAAC survey 

Authors:  Anders Rosdahl, 
Torben Fridberg, Vibeke 
Jakobsen and Michael 
Jørgensen  

Year of 
publication: 
2014 

Education 
Inputs, 
outputs and 
returns 
considered  

Countries 
covered 

Data used 
and 
timescales 
for data and 
scenarios 

Education outputs considered: cognitive skills as measured by 
PISA and PIAAC 

Denmark 

The report studies the approx. 46,500 persons born in 1984 who were 
enrolled in an educational institution in the spring of 2000 and lived in 
Denmark in 2011/12. The group makes up about 90 per cent of the 
1984 birth cohort. The report is primarily based on responses from 
1,881 representatively selected persons from the group mentioned. 
Their reading proficiency was tested in PISA 2000 when they were 
15-16 years old, and again in PIAAC in 2011/12, when they were 
about 27 years old. Identification throughout time was possible 
through ‘civil registration numbers’ of the cohort which are assigned 
to each Danish citizen on birth. Just as there is sampling error 
because only part of the 1984 birth cohort were interviewed and 
tested in PISA and PIAAC and not the entire cohort, there is a certain 

30 

 
 
 
 
 
 
Study on the potential of education to cause macroeconomic imbalances and negative 
spillovers – Annex IV 

degree of uncertainty associated with the measurement of skills. The 
uncertainty arises because each respondent is not asked to solve a 
very large number of tasks (which would be far too time-consuming), 
but only a limited number of tasks. The analyses in this report seek to 
take both types of uncertainty into consideration, e.g. in the 
determination of whether the difference between the skills of two 
groups is statistically significant.  

The PISA-PIAAC respondents are representative of young people aged 
about 15-16 in 2000 who were enrolled in an educational institution in 
the spring of 2000, and who still (or again) lived in Denmark in 
2011/12. Through weighting that takes into account both sampling 
design in PISA 2000 and attrition in PIAAC, the results of the report 
aim to be representative of the approx. 46,500 young people, born in 
1984, in this group.  
It should be noted that the PISA participants who also participated in 
the follow-up in PIAAC in 2011/12 cannot be viewed as being fully 
representative of all young people in the same age group in Denmark 
in 2011/2012. The main reason is that persons born in 1984 who 
have immigrated to Denmark after PISA 2000 are obviously not 
included in the group of the 1,881 PISA-PIAAC respondents.  

The aim of the PISA-PIAAC survey was to examine how the 15-16-
year-old students who participated in PISA 2000 perform 12 years 
later in the PIAAC survey. Further details can be found in the primary 
research (only available in Danish) 

  Rosdahl, Anders, Torben Fridberg, Vibeke Jakobsen and Michael 

Jørgensen (2013) Færdigheder i læsning, regning og 
problemløsning med IT i Danmark. (Survey of Adult Skills in 
Denmark – Main report). Copenhagen: SFI – The Danish National 
Centre for Social Research. Report 13:28. 
http://www.oecd.org/site/piaac/Denmark_1328-Danskernes-
kompetencer.pdf  

  Rosdahl, Anders (2014) Fra 15 til 27 år. PISA 2000-eleverne I 
2011/12. (From 15 to 27 years. The PISA 2000-students in 
2011/12). Copenhagen: SFI – The Danish National Centre for 
Social Research. Report 14:13. 
http://eng.uvm.dk/~/media/UVM/Filer/Udd/Folke/PDF14/Jun/140
619%20pisa%20piaac2.pdf  

Overall, the PISA-PIAAC survey shows that there is a clear link 
between the reading proficiency at the age of 27 and the course of 
the young people’s lives after basic school. Even when taking the 
importance of the course of development of the young people into 
consideration, there is a clear link between their reading proficiency 
as measured in PISA 2000 and their proficiency about 12 years later 
in PIAAC. The better the reading proficiency at the age of 15-16 
years, the better the reading proficiency at the age of 27. 

About 61 per cent of the respondents with the poorest reading 
proficiency in PISA still belong in the poorest third in PIAAC. Just over 
27 per cent belong to the “middle group” in PIAAC, while about 11 per 
cent of the respondents with the poorest reading proficiency in PISA 
belong to the best third in PIAAC. 

31 

Overview 

Description 
of main 
findings 

 
 
 
 
 
 
 
Study on the potential of education to cause macroeconomic imbalances and negative 
spillovers – Annex IV 

■  About 38 per cent of all respondents in the middle group on the 

PISA proficiency reading scale also belong to the middle group in 
PIAAC. Just over 27 per cent have moved “downwards” in the 
distribution, while approx. 34 per cent have moved “upwards” and 
belong to the best third in PIAAC.  

■  Of those who had the best reading proficiency in 2000, just over 58 
per cent were still among the best third in 2011/12 in PIAAC. 31 
per cent belonged to the “middle” third in PIAAC, while 11 per cent 
belonged to the poorest third. 

Then who moved “upwards” in the reading proficiency distribution? 
According to the report, it is particularly those whose parents have 
attained further education; those who have completed further 
education; those who have not received transfer income (due to 
unemployment or sickness); and those who live in another region in 
2012 than in 2000. Who have remained in the group of the poorest 
readers in 2012? The report shows that it is particularly those who 
have completed basic school or vocational training as the highest level 
of education and those who have received transfer income for a long 
period. 

Link 

http://eng.uvm.dk/~/media/UVM/Filer/English/PDF/140626%20PISA-
PIAAC%20engelsk%20sammenfatning.pdf  

A4.3.3.11  Gesellschaftliche  Kosten  der  Ausbildungslosigkeit  in  der  Schweiz. 

(Social costs of low qualification in Switzerland) 

Table A4.14 Fritschi, T., Oesch. T. und Jann, B. (2009) 

Title 

Gesellschaftliche Kosten der 
Ausbildungslosigkeit in der 
Schweiz. (Social costs of low 
qualification in Switzerland) 

Authors:  Fritschi, T., 
Oesch, T. and Jann, B. 

Year of 
publication: 
2009 

 Education outputs: low qualification, defined as the lack of VET 
certificate or a certificate attesting schooling attainment of at least 3 
years 
Returns: Earnings in CHF (2008 values) 

Education 
Inputs, 
outputs 
and 
returns 
considered  

Switzerland 

Swiss labour force survey SAKE for 2008 (Schweizer 
Arbeitskraefteerhebung SAKE) 

Countries 
covered 

Data used 
and 
timescales 
for data 
and 
scenarios 

32 

 
 
 
 
 
 
 
 
 
 
 
Study on the potential of education to cause macroeconomic imbalances and negative 
spillovers – Annex IV 

Overview  Objective 

The paper summarises the results of a research project which 
attempted to estimate the social costs of low qualification in 
Switzerland. Low qualification is defined as the lack of a VET or 
schooling certificate at high school level.  

Method 
The research uses data from the Swiss labour force survey SAKE for 
2008 (Schweizer Arbeitskraefteerhebung SAKE). Low qualification is 
defined as the lack of a VET certificate or a certificate attesting 
schooling attainment of at least 3 years (Maturitaetszeugnis, 
Fachmittelschul-Diplom and others). 
Statistical matching is conducting in the SAKE data for 2008, to identify 
individual of the same age, sex, location and migration background/lack 
of migration background, to isolate the level of education as far as 
possible. The research however is limited to the variables collected in 
SAKE. 

Two comparisons are then drawn. First, between individuals without 
VET/schooling certificate and those with a highschool/VET certificate. 
Secondly, a comparison between individual without VET/schooling 
certificate and those with at least a highschool certificate (including 
individuals with university degrees and other forms of tertiary 
education). These two comparisons are assumed to provide minimum 
and maximum divergence of educational outcomes and the associated 
returns/costs of education. 

As regards the first comparison, the difference in income is 15,298CHF ( 
EUR 9,4849) per year on average. The probability of being employed is 
7.6 % lower for low-qualified individuals in the first comparison. The 
probability of receiving a pension/benefit because of health impairments 
is 2.3x times higher for low-qualified individuals. The probability to 
receive social benefits is 1.9x higher. 

Regarding the second comparison, the divergence in all variables is 
equal or greater than above. In terms of income differences, low-
qualified individuals earn 23,259CHF (EUR14,41210 ) less on average 
than individuals with at least a highschool certificate. 

Description 
of main 
findings 

Link 

http://www.buerobass.ch/pdf/2009/ausbildungslosigkeit_schlussbericht
_def.pdf  

A4.3.3.12  Estimates  of  the  Economic  Return  to  Schooling  for  the  United 

Kingdom 

Table A4.15 Harmon, C. and Walker, I. (1995) 

Title 

Estimates of the Economic Return 
to Schooling for the United 

Authors: Harmon, 
Colm and Ian Walker  

Year of 
publication: 

9 9  Using average exchange rate in 2008: 0.62, according to www.xe.com  

33 

 
 
 
 
 
 
 
 
                                           
 
 
Study on the potential of education to cause macroeconomic imbalances and negative 
spillovers – Annex IV 

Kingdom 

1995 

Education inputs: year of schooling, school leaving age 
Returns: earnings 

Education 
Inputs, 
outputs 
and 
returns 
considered  

Countries 
covered 

Data used 
and 
timescales 
for data 
and 
scenarios 

Overview 

Description 
of main 
findings 

United Kingdom 

Family Expenditure Survey. Employed males aged 18-64  
1978-1986 

This study makes a summary of biases associated with Ordinary Least 
Squares (OLS). It makes a recap of previous methods used to deal with 
endogeneity. Then, the study proposes its own method to deal with OLS 
bias, by using an Instrumental Variable (IV) approach.  
Results find an important bias in OLS. The IV return to schooling is 
around 16%. 

According to the study, sources of bias associated with Ordinary Least 
Squares (OLS) estimates of the return to schooling include:  

  positive correlation between schooling and its return in an 

optimising investment model => upward bias; 

  ability bias: when an unobservable factor is correlated with 

schooling and with wages => upward bias; 

  measurement error => downward bias; 

  discount-rate bias: when individuals with higher discount rates 

choose less education in an optimising model => downward bias.  

The paper summarises previous methods used to deal with 
endogeneity. These include:  

 

include a proxy for ability (i.e. IQ tests) => this method leads to an 
upward bias in OLS estimates; 

  use twins to eliminate differences in innate ability or motivation => 

this method suffers from several problems, including that of 
measurement error. 

 

treat ability as a fixed effect and use panel data => this method 
suffers from endogeneity problem  as the of return can only be 
identified from individuals who return to school. 

  Use an IV approach and exploit natural variation in data caused by 

exogenous influences on the schooling decision (i.e. season of birth, 
draft lottery proximity of college to home, sex of child etc.) 

This study also uses an Instrumental Variables (IV) approach. It 
exploits the impacts of two policy changes that raised the minimum 

34 

 
 
 
 
 
 
Study on the potential of education to cause macroeconomic imbalances and negative 
spillovers – Annex IV 

school-leaving age in the UK (1944 and 1973).  

Results (returns to schooling – wages):  

  OLS yield a return to schooling of 6% (i.e. a 6% increase in lifetime 

earnings per additional year of schooling); 

 

IV yield a return to schooling 16%(i.e. a 16% increase in lifetime 
earnings per additional year of schooling). 

The study then checks the quality of the IV estimates. It also tests for 
the time stability (by interacting the schooling variable with year 
dummies and estimating over each cross section separately).  An 
ordered probit model yields comparable results to the IV results. 

Link 

http://w3.ced.unicz.it/upload/migali/Walker_etal_1995.pdf 

A4.3.3.13  The  Occupations  and  earnings  of  young  Australians:  The  rule  of 
education  and  training.  Longitudinal  Surveys  of  Australian  Youth. 
Research Report 55 

Table A4.16 Marks, Gary (2008) 

Title 

The Occupations and earnings of 
young Australians: The rule of 
education and training. Longitudinal 
Surveys of Australian Youth. 
Research Report 55 

Author: Marks, 
Gary 

Year of 
publication: 
2008 

Education inputs: participation in and completion of different types of 
post-secondary education and training 
Returns: occupation status, earnings 

Education 
Inputs, 
outputs 
and 
returns 
considered  

Countries 
covered 

Australia 

Data used 
and 
timescales 
for data 
and 
scenarios 

Sample of young people who were first interviewed when they were in 
Year 9 in 1995 and subsequently interviewed annually until 2005 (24 
years old) 
The estimates are based on person-year data from all waves on the 
longitudinal study. This is positive as it means biases that could result 
from sample attrition are minimised. 

Overview  The goal of this study is to understand the effect of post-secondary 

education and training on the occupation and earnings of young people. 

Description 
of main 
findings 

The study uses multi-level regression analyses to examine the impact of 
the participation in and qualifications from different types of post-
secondary education and training on:  

 
 

(1) occupational status; 
(2) earnings. 

35 

 
 
 
 
 
 
 
 
 
Study on the potential of education to cause macroeconomic imbalances and negative 
spillovers – Annex IV 

The estimates are net of the impacts of other characteristics (i.e. prior 
labour market experiences of work and unemployment, literacy and 
numeracy, school type, socioeconomic background and ethnicity) 

(1) Results show that overall, there is a positive relationship between 

education level and employment. The probability of employment is 
higher for those who complete courses. Gender does not have an 
influence on occupation status, but other characteristics do (such as 
age and prior experience) 

Apprenticeship 

Traineeship 
TAFE Certificate 

Participation 
in … increases 
employment 
probability by: 

Qualification 
from … increases 
employment 
probability  by: 
-2% (decrease in 
employment 
proba) 

-1% (decrease 
in employment 
proba) 

TAFE Diploma  
University Diploma  
Bachelor Degree 
Post-Graduate 
University  

5% 
8% 

Note: only significant results are shown. 

2% 
8% 
14% 
5% 

(2) Results show that overall, there is a positive relationship between 

education level and earnings. Overall, earnings are higher for those 
who complete courses. Other characteristics have an impact, such as 
gender, previous unemployment record, socioeconomic background, 
catholic school etc.  

Apprenticeship 
Traineeship 
TAFE Certificate 

TAFE Diploma  
University Diploma  
Bachelor Degree 
Post-Graduate 
University  

Participation 
in … increases 
earnings by: 
19% 
21% 
-8% (decrease 
in earnings) 

Qualification from 
… increases 
earnings by: 
23% 
8% 
5% 

14% 
17% 
31% 

5% 
-30% (decrease 
in earnings) 

Note: only significant results are shown. 

Overall, post-school education and training leads to higher status 
occupations and higher earnings compared to not doing any further study 
or training. However, there are differences between different types of 
post-secondary education and training. For instance, in terms on 
earnings, apprenticeships and bachelor degrees tend to be more 
beneficial than TAFE certificates or traineeships.  

36 

 
 
 
  
 
 
 
 
 
 
 
 
 
 
 
 
 
Study on the potential of education to cause macroeconomic imbalances and negative 
spillovers – Annex IV 

Link 

http://research.acer.edu.au/cgi/viewcontent.cgi?article=1108&context=l
say_research   

A4.3.3.14  Pathways  to  success.  How  Knowledge  and  skills  at  age  15  shape 

future lives in Canada 

Table A4.17 OECD (2010) 

Title 

Pathways to success. How 
Knowledge and skills at age 
15 shape future lives in 
Canada 

Author: OECD 

Year of 
publication: 
2010 

Education 
Inputs, 
outputs 
and 
returns 
considered  

Background factors: Speaker of minority language, number of 
siblings, second-generation immigrant, family income, nuclear family, 
mother’s education or father’s education. 
Education inputs: access to university and college, persistence in 
post-secondary education, choice of field of study.  
Education outputs: PISA scores, highest grade level completed 
(ISCED), patterns of transition in a pathway (students moving to 
education, inactivity and work). 
Returns: hourly earnings, employment status. 

Countries 
covered 

Data used 
and 
timescales 
for data 
and 
scenarios 

Overview 

Description 
of main 
findings 

Canada 

OECD PISA and HRSDC (Department of Human Resources and Skills 
Development Canada) 
2000-2010 

This paper explains how the PISA system works and describes the 
Canadian educational system. It also explains how Canada performed in 
PISA 2000. The paper then examines the different pathways taken by 
students to college, university and work since 2000.  

Predicting success: Key characteristics of youth affecting 

transitions to education and the labour market.  

This paper examines the impacts of key characteristics (such as PISA 
scores, minority language, number of siblings, second-generation 
immigrant, family income, nuclear family, mother’s education or father’s 
education) on two outcomes:  

■  Educational attainment (assessed as the highest grade level 

completed). The model used establishes a sequence of probabilities 
of continuing education. 

■  Transition patterns (between education, inactivity and work). 

Multivariate (multinomial) is used, as this method is best suited to 
outcomes that are not continuous. 

Results show that after adjusting for background factors, the 
relationship between reading proficiency and educational attainment is 
strong.  

37 

 
 
 
 
 
 
Study on the potential of education to cause macroeconomic imbalances and negative 
spillovers – Annex IV 

If the reading 
score increases by 
1 standard 
deviation, then… 

Reduction in 
secondary 
school non-
completion  

For men 

For women 

17.4% 

31.5% 

Increase in the 
proportion 
attending post-
secondary 
education  
12.6% 

11.4% 

The association between high achievement and the transition pattern 
education-education is strong. This means high achievers are more 
likely to continue their studies than low achievers who are more likely to 
begin working or looking for a job. What is more, high achievement is 
negatively correlated with unsecure transition education-inactivity.   

Acquiring Human Capital: The relationship of PISA reading 
proficiencies and the pathway to higher education  

This paper uses a multivariate model to examine the impacts of key 
characteristics (such as demographic and socio-economic 
characteristics, geographic characteristics or educational antecedents) 
on three outcomes:  

 

 

 

(1) Access to university and college;  

(2) Persistence in post-secondary education; 

(3) Choice of field of study. 

Results: 
(1) The paper finds that even after adjusting for background factors, 

high achievers are more likely to attend post-secondary education. 
Indeed, students scoring at “level 5” on PISA literacy tests are 
around 20 times more likely to attend university than other 
students. Females have an advantage over males in terms of both 
college and university attendance (independently of other 
characteristics). Students whose parents have university education 
are 4.6 times more likely than others to enter university. 

(2) The paper does not find strong relationships between high 

achievement in PISA and the persistence in post-secondary 
education. Higher achievement does not result in higher level of 
persistence in post-secondary education. Females are 1.5 times 
more likely to have obtained qualification and 1.5 times more likely 
to still be studying (independently of other characteristics). 

(3) The variance explained by the model is small (0.12) as regards the 
choice of study fields. The paper finds that higher achievers (“level 
5”) are 1.9 times more likely to study pure sciences. Gender 
differences are strong with respect to field of study. However, 
socioeconomic variables such as parental education, parental 
occupation or income do not have a clear impact. 

Competent pathways to work: PISA scores and labour market 

returns.  

This paper examines the impacts of key characteristics (such as PISA 
reading scores, family factors, school factors, employment factors, early 
work exposure, educational attainment and province of residence) on 

38 

 
 
 
 
 
 
 
Study on the potential of education to cause macroeconomic imbalances and negative 
spillovers – Annex IV 

earnings patterns (hourly earnings). Because gaps exist between hourly 
wages for men and women, the paper considers these two groups 
separately. First, the paper looks at the relationship between earnings 
and PISA reading scores on their own. Then the paper adjusts for 
background and educational variables. This is useful to understand 
whether the association between achievement and earnings is 
independent from other characteristics. 

Results for males: The explanatory power of the model is weak as it 
accounts for only 12% of the total variance in hourly earnings. The 
relationship between achievement and earnings is small (and even null 
when adjusting for background characteristics).  

Results for females: The explanatory power of the model is weak as it 
accounts for only 10% of the total variance in hourly earnings. Higher 
achievement in reading tests is positively (0.048) related to higher 
earnings. This result is significant even after adjusting for background 
characteristics.  

The paper uses the same analysis for the impact of achievement on 
unemployment. Again, the explanatory power of the models is weak 
(4% for males, 5% for females). Results show that a one deviation 
increase in reading scores reduces the probability of unemployment by 
2.5% for males (1.9% for females). However, the effect is null when 
adjusting for background characteristics. 

Link 

http://www.oecd.org/fr/canada/pathwaystosuccess-
howknowledgeandskillsatage15shapefuturelivesincanada.htm  

A4.3.3.15  A  ‘causal’  estimate  of  the  effect  of  schooling  on  full-time 

employment among young Australians 

Table A4.18 Ryan, Chris (2003) 

Title 

A ‘causal’ estimate of the effect of 
schooling on full-time 
employment among young 
Australians 

Author(s) 
publisher: Ryan, 
Chris 

Year of 
publication: 
2003 

Education inputs: years of schooling  
Returns: full time employment 

Education 
Inputs, 
outputs 
and 
returns 
considered  

Countries 
covered 

Data used 
and 
timescales 
for data 
and 
scenarios 

Overview 

Australia 

Two Longitudinal Surveys of Australian Youth (LSAY) cohorts 
The Youth in Transition 1975 birth cohort (YIT 75) and the Longitudinal 
Surveys of Australian Youth Year 9 cohort (LSAY 95) fall either side of 
the policy change and should capture its impact.  

This paper exploits a policy change that occurred in South Australia in 

39 

 
 
 
 
 
 
Study on the potential of education to cause macroeconomic imbalances and negative 
spillovers – Annex IV 

the mid-1980s to generate a ‘causal’ estimate of the effect of schooling 
on full-time employment outcomes.  

In the mid-1980s in South Australia, the Early Years of School policy 
changed the way students progressed through junior primary school, 
This caused these students to obtain an additional year of schooling 
compared with their predecessors.  

This paper exploits this policy change to understand how an additional 
year of schooling can have impacts on full-time employment. 

The policy affected individuals born between October-February and 
those born between July-September. Those born between March-June 
were unaffected: they are a natural comparison group for assessing the 
impact of the policy.  

Two methods are used in this paper: 

  A difference in difference: isolate the effect of the policy change by 

comparing the pre- and post-policy implementation full-time 
employment outcomes of the group affected by the policy change 
with the change in the full-time employment outcomes of the 
control group who were not affected. 

  A difference in difference in difference: also take into account 

differences with individuals with same education levels from other 
jurisdictions. 

The model relies on several assumptions:  

 

 

“the jurisdiction- specific effects on the youth labour market do not 
change over time”; 

“there are no interaction effects between the following variables: 
historical patterns, characteristics of the group, trends in other 
factors common across jurisdictions, factors that influence the 
youth labour market in that jurisdiction in that year, differences in 
the estimated employment outcomes of groups that arise from the 
way the data were collected, the additional year of schooling they 
obtained because of the policy change and factors unique to that 
group in that jurisdiction in that year”; 

 

“either other policies that affect the youth market are constant over 
time or all jurisdictions adopt policies with similar effects other than 
the one under consideration”. 

A further limitation of the model is that estimates apply only for 
individuals who leave school and do not undertake full-time post-school 
studies immediately. 

Description 
of main 
findings 

The paper finds that one additional year of school results in an increase 
in the probability of being employed full-time just in the first year out of 
school of 11 percentage points. Note there is an offsetting effect of 8 
percentage points on that probability: indeed, individuals are older when 
they leave school, and wages are institutionally age-based in the 
Australian youth labour market. 

Link 

http://research.acer.edu.au/cgi/viewcontent.cgi?article=1037&context=
lsay_research  

40 

 
 
 
 
 
Study on the potential of education to cause macroeconomic imbalances and negative 
spillovers – Annex IV 

A4.3.3.16  Low  Achievers'  Risky  Pathways:  PISA  Literacy  Scores  as 

Predictors of Upper Secondary Enrolment and Graduation 

Table A4.19  Stalder, Meyer,  Hupka-Brunner (2009) 

Title 

Low Achievers' 
Risky Pathways: 
PISA Literacy 
Scores as 
Predictors of 
Upper Secondary 
Enrolment and 
Graduation 

Authors: Stalder, B. E., Meyer, T. & 
Hupka-Brunner, S 
Publishers: F. Rauner, E. Smith, U. 
Hauschildt & H. Zelloth (Eds.). 
Innovative Apprenticeships. Promoting 
Successful School-to-Work 
Transitions. 17-18 September 2009, 
Turin, Italy. Conference Proceedings, 
137-140 

Year of 
publication: 
2009 

Education outputs: PISA reading literacy level, transition to and 
completion of upper secondary education  

Education 
Inputs, 
outputs 
and 
returns 
considered  

Switzerland  

Swiss Youth panel TREE (Transitions from Education to Employment). 
5,500 young people from all parts of Switzerland who have participated 
in PISA 2000 

Countries 
covered 

Data used 
and 
timescales 
for data 
and 
scenarios 

Overview 

This paper looks at the impact of PISA reading literacy level on: 

 

 

the transition to upper secondary education;  

the completion of upper secondary education.  

The focus is on low achieving students and their chance to enter 

vocational education and training (VET). 

Description 
of main 
findings 

To determine the impact of literacy score on graduation chances, the 
paper uses a multinomial regression analysis. Controls in the analysis 
include: school track at lower secondary level, educational situation in 
the first year after compulsory school, migrant background, social 
background and gender.  

Results show that higher achievers have better changes to obtain 
vocational or academic matura (certificates which grant access to 
tertiary education).  

An important factor on graduation changes is the educational situation 
in the first year after compulsory school. Individuals who are not in 
upper secondary education in the year after compulsory school have 
more chances never to go to upper secondary school.  

Link 

http://www.adapttech.it/old/files/document/8670INAP.pdf#page=141  

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A4.3.4  Multi-country studies 

A4.3.4.1  Returns to Human Capital in Europe: A Literature Review 

Table A4.20 Asplund, R. and Telhado Pereira, P. (1999) 

Title 

Returns to 
Human Capital 
in Europe: A 
Literature 
Review 

Authors: Asplund, Rita and Pedro 
Telhado Pereira  
Publisher: ETLA, The Research 
Institute of the Finnish Economy 

Year of 
publication: 
1999 

 Education inputs: years of schooling 
Returns: earnings  

Education 
Inputs, 
outputs 
and 
returns 
considered  

Countries 
covered 

15 European countries are covered: Austria, Denmark, Finland, France, 
Germany, Greece, Ireland, Italy, Netherlands, Norway, Portugal, Spain, 
Sweden, Switzerland, United Kingdom 

Data used 
and 
timescales 
for data 
and 
scenarios 

Overview 

The book provides an overview of empirical research, no empirical 
research is performed specifically for the book. 

The goal of the book is to provide a comprehensive overview of the 
current state of knowledge in Europe concerning returns to human 
capital in general and to education in particular.  

Description 
of main 
findings 

The common point of departure to estimate returns to education in the 
empirical research is the Mincer equation. This is based on 3 
assumptions:  

 

 

 

individuals live and work for an infinite number of years => this is 
obviously wrong. 

the natural logarithm of (1+r) is equal to r;  

the only cost that people face during their studies is forgone 
earnings (or, alternatively, that the student pays the extra cost of 
studying by working part-time) 

The book provides a summary of Ordinary Least Estimates of returns to 
years of schooling (based on earnings) reported by countries. 
Estimates in the table below are in % change to lifetime earnings.   

42 

 
 
 
 
 
 
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The main limitation with this analysis is that it is not possible to 
compare the different estimates between countries (different datasets / 
different methodologies).  

Link 

https://www.etla.fi/wp-content/uploads/b156.pdf  

A4.3.4.2  Post-school  education  and  labour  force  participation  positive 
relationship  between  a  college/other  post-school  education 
qualification  and  full-time  employment  chances  at  ages  24  to  26 
in Canada and Australia 

Table A4.21 Austen, Siobhan, MacPhail, Fiona (2010) 

Title 

Post-school education and 
labour force participation 
positive relationship 
between a college/other 
post-school education 
qualification and full-time 
employment chances at 
ages 24 to 26 in Canada and 
Australia 

Authors: Austen, Siobhan, 
MacPhail, Fiona 

Year of 
publication: 
2010 

Education inputs: Year 12 or 13 completion, parental education 
Education outputs: post school education choices, academic 
achievement at school 
Returns: employment status 

Education 
Inputs, 
outputs 
and 
returns 
considered  

Countries 
covered 

Canada and Australia 

43 

 
 
 
 
 
 
 
Study on the potential of education to cause macroeconomic imbalances and negative 
spillovers – Annex IV 

Longitudinal Surveys of Australian Youth and the Canadian Youth in 
Transition Surveys 
1995 and 1998 cohorts examined in 1999 

Data used 
and 
timescales 
for data 
and 
scenarios 

Overview  The goal of this study is to understand why in 2006, 52% of the 20-24 
years old in Canada held a post-school qualification while only 38% did 
in Australia. The assumption is that systems in the two countries have 
different characteristics, which impact on post-school choices young 
people make. 

The study focuses on three pathways for young school leavers:  

  university; 

  vocational education and training (VET) in Australia / college in 

Canada; 

  no post-school education. 

The paper also examines the impact of education on labour market 
participation. 

Description 
of main 
findings 

To measure the relationship between a range of school leaver 
characteristics (such as gender, indigenous status, location, language, 
disability, type of school, Year 12 or 13 completion, parental education 
and academic achievement at school) and the choice of post-school 
pathway, the study uses multinomial logit regression techniques. 

In 2006, 52% of the 20-24 years old in Canada held a post-school 
qualification while only 38% did in Australia. The paper observes that 
rates of engagement in university education are similar between 
Australia and Canada. However, a larger proportion of Canadians 
participate in college education than Australians do in VET. This could 
be due to the fact that in Australia, the relationship between 
participation in non-university education and academic 
achievement/parental education is weak while it is strong in Canada.  

The study also analyses the employment likelihood after different 
choices of education. Specifically, the “likelihood that a transition from 
employment to non-employment would occur between the ages of 22 
and 25 was modelled for young Australian women employed in 2003”. 
This method faces a potential selection bias: it might happen that these 
women are different from other young Australian women. To solve this, 
the paper uses a 2-stage procedure: first, it estimates the probability 
that a woman with particular characteristics would be represented in 
the 2003 sample; then it uses an employment transition regression. 
Results show that in Australia, completion of a non-university education 
does not improve the probability of full-time employment at age 25. 
However, in Canada, the relationship between non-university education 
and full-time employment chances is positive. 

Link 

Downloadable from http://www.lsay.edu.au/   

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A4.3.4.3 

The private and fiscal returns to schooling in the European Union 

Table A4.22 De la Fuente, A. and Jimeno, J. (2009) 

Title 

The private and fiscal returns 
to schooling in the European 
Union 

Authors: de la Fuente, 
Angel and Juan 
Francisco Jimeno 

Year of 
publication: 
2009 

Education inputs: years of schooling 
Returns: earnings, employment status 

Education 
Inputs, 
outputs 
and 
returns 
considered  

Countries 
covered 

14 member states of the EU: Austria, Belgium, Denmark, Finland, 
France, Germany, Ireland, Italy, Italy, Netherlands, Portgual, Spain, 
Sweden, UK 

Many different sources, mainly from OECD 

Data used 
and 
timescales 
for data 
and 
scenarios 

Overview 

The goal of this paper is to develop a framework for the quantitative 
analysis of: 

 

 

 

the private returns to schooling; 

the fiscal returns to schooling; 

the effect of public policies on private incentives to invest in 
education. 

Description 
of main 
findings 

For each of the 14 countries considered, De La Fuente constructs 
estimates of the private return to an additional year of schooling. For 
this, he takes into account: 

 

 

 

the effects of education on wages and employment probabilities 
after allowing for academic failure rates,  

the direct and opportunity costs of schooling,  

the impact of personal taxes, social security contributions and 
unemployment and pension benefits on net lifetime incomes.  

De La Fuente also attempts to measure the effects of different policies 
on the returns to education, by introducing a number of tax and 
subsidy interventions and measures fiscal returns of individual 
interventions. 

A main limitation of this work is that the return of education is a rather 
simple expression introduced with some strong simplifying 
assumptions, i.e.: 

 

 

“the experience premium on wages is assumed to be a function of 
potential experience rather than of actual years of employment, and 
to grow at a constant rate”;  

“the probability of employment is assumed to be an increasing (and 
time-invariant) function of schooling”; 

45 

 
 
 
 
 
 
 
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 

“tax rates are assumed to depend only on the agent's status (that 
is, on whether he is employed, unemployed or retired) and do not 
change over time as his income rises with technical progress and 
experience”. 

To construct quantitative measures of the impact of various public 
policies on individual incentives to invest in education, De La Fuente 
then applies the private returns formula under different scenarios. He 
starts from a hypothetical scenario in which there is no government 
intervention and sequentially introduces: 

  1) educational subsidies and the public provision of free education;  

  2) personal income taxes, including employee social security 

contributions; 

  3) unemployment insurance and housing benefits for the 

unemployed; 

  4) retirement benefits. 

The main results are as follows:  

  Research estimates an increase in private returns assuming a one-

year increase in schooling, starting from currently observed average 
attainment levels,  would be between 7.5% and 10% in most EU 
Member States, compared to the currently observed average 
attainment levels 

 

“On average, different direct subsidies to education considered raise 
returns by 45% [across EU member states] while personal taxes 
and unemployment benefits reduce them by 8% and 22% 
respectively and pensions raise them by 2.4%”.  

Further results show that “in most countries the tax system generates 
only modest disincentives to invest in further education at observed 
average attainment levels”. 

Link 

http://digital.csic.es/bitstream/10261/4340/1/73708.pdf  

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A4.3.4.4 

Is  it  enough  to  increase  compulsory  education  to  raise  Earnings? 
Evidence from French and British Compulsory Schooling Laws 

Table A4.23 Grenet, J. (2009) 

Title 

Is it enough to increase compulsory 
education to raise Earnings? 
Evidence from French and British 
Compulsory Schooling Laws 

Author: Julien 
Grenet 

Year of 
publication: 
2009 

Education inputs: years of schooling 
Returns: earnings, employment 

Education 
Inputs, 
outputs 
and 
returns 
considered  

Countries 
covered 

France 
England and Wales 

Data used 
and 
timescales 
for data 
and 
scenarios 

Overview 

Description 
of main 
findings 

French Enquete Emploi (EE) survey (1990-2002) 
Quarterly Labour Force Survey (QLFS) (1993-2006) 

The study compares two reforms that raised the minimum school 
leaving age to 16 in France (1967) and in England and Wales (1972). 
It uses a Regression Discontinuity Design estimation strategy. 

Results show that while the French law did not have impact on 
earnings, the British reform led to a 6 to 7% increase in hourly wages 
per additional year of compulsory schooling. This difference may be 
due to the fact that “the fraction of individuals holding no 
qualifications dropped sharply after the introduction of the new 
minimum school leaving age in England & Wales whereas it remained 
unchanged in France”. 

The study begins with a literature review.  
Harmon and Walker (1995):  

  Estimate Mincerian earnings equation in which schooling is 
instrumented by the minimum school leaving age faced by 
individuals.  

 

Identify impact of increased educational attainment by comparing 
average schooling and earnings across pre- and post-reform 
cohorts 

  2SLS estimates of the returns to schooling (based on earnings) 

are higher than the OLS naive estimates (15% vs. 6%) 

  Limitation: the study does not involve a proper control group, so it 

does not neutralise cohorts fixed effects 

Acemoglu and Angrist (2000); Oreopoulos, (2006, 2007): 

  Exploit variations in the timing of compulsory schooling laws 

across states or regions  

  Use either a Difference in Difference framework or Regression 

Discontinuity Design framework.  

47 

 
 
 
 
 
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  Returns to schooling (based on earnings) = 7% - 14% 

But some other studies used the same method and found smaller 
returns to schooling (based on earnings): 

  Meghir and Palme (2005): 1.4%        

  Pischke and von Wachter (2008): 0% 

  Oosterbeek and Webbink (2007): 0% 

  Devereux and Hart (2010): 0% for women,  4% - 7% for men 

As said above, the study uses a RDD strategy to estimate the impact 
of an additional year of schooling on earnings. The steps for the 
estimation are the following:  

  Baseline first stage specification: Si =𝛼0 + 𝛼1Ti + g(Xi – c) + 𝜖i 

  Reduced form specification: ln Wi = 𝛽0 + 𝛽1Ti + g(Xi – c) + 𝜖i 

  Returns to compulsory schooling are estimated by 2SLS: 

 

ln Wi =𝛾0 +𝛾1Si + g(Xi – c) + 𝜖i 

S: age left full-time education / T: dummy = 0 if individual I born 
before the cut-off date c and =1 if born after /  W: wage 

We therefore have  = returns to compulsory education of those 
individuals who were compelled to increase their educational 
attainment as a result of the new compulsory schooling requirement 

This method relies on one assumption i.e.: “raising compulsory 
schooling induces some individuals to increase their own educational 
attainment but no one to drop out earlier from school”. 

The two reforms had a similar impact on average educational 
attainment (expressed as years of schooling): the length of schooling 
was increased in both case by about 0.3 year. However, they had 
different effects in terms of returns to schooling (based on earnings): 
while the French law did not have impact on earnings, the British 
reform led to a 6 to 7% increase in hourly wages per additional year 
of compulsory schooling.  
The study performs a couple of robustness checks. The results are 
consistent with different specifications.  

Interpretation of the differences between England/Wales and France:  
“The fraction of individuals holding no qualifications dropped sharply 
after the introduction of the new minimum school leaving age in 
England & Wales whereas it remained unchanged in France”. This 
means the French reform failed to improve the distribution of 
qualifications compared to the British reform. This could be due to the 
characteristics of the French education system (i.e. organisation of 
vocational tracking, prevalent of grade repetition, decentralisation of 
the junior secondary schooling system).  
Therefore, it is possible that the differences between England/Wales 
and France are due to the role of academic credentials. “The actual 
quantity of education may be less important than credentials in 

48 

 
 
 
 
 
 
 
 
 
Study on the potential of education to cause macroeconomic imbalances and negative 
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determining the returns to compulsory schooling”.  

Link 

http://www.parisschoolofeconomics.com/grenet-
julien/WP/Grenet_2011.pdf  

A4.3.4.5 

The  Returns  to  Education:  Microeconomics.  Journal  of  Economic 
Surveys  

Table A4.24 Harmon, C., Oosterbeek, H., Walker, I. (2003) 

Title 

The Returns to Education: 
Microeconomics. Journal of 
Economic Surveys  

Authors: Harmon, Colm, 
Hessel Oosterbeek and 
Ian Walker  

Year of 
publication: 
2003 

Education inputs: years of schooling 
Education outputs: credentials  
Returns: earnings, employment 

Education 
Inputs, 
outputs 
and 
returns 
considered  

Countries 
covered 

Data used 
and 
timescales 
for data 
and 
scenarios 

Overview 

Various countries 

Various datasets  
No clear timescale (1980-1995?) 

The focus of this paper is on “education as a private decision to invest 
in human capital and the estimation of the rate of return to that 
private investment”. The study find that “there is an unambiguously 
positive effect on the earnings of an individual from participation in 
education. The size of the effect seems large relative to the returns on 
other investments”. 

Description 
of main 
findings 

Implications from considering schooling as an investment decision:  

  The Internal rate of return (IRR) is the discount rate that equates 

the present value of benefits to the present value of costs. 

  Direct education costs lower the net benefits of schooling. 

 

 

If the probability of being employed is higher with more schooling, 
then an increase in unemployment benefit would erode the reward 
from undertaking education. But if gap between educated and 
non-educated widens or if opportunity costs of schooling 
decreases, the net effect on the incentive to invest in schooling is 
positive. 

If education and job training are complements, more schooling 
may imply a greater likelihood of receiving work related training 
while being employed. 

49 

 
 
 
 
 
 
Study on the potential of education to cause macroeconomic imbalances and negative 
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  There may be non-pecuniary benefits associated with education. 

 

Increases in individual incentives to invest in schooling would be 
offset by changes in wage distribution if supply of educated labour 
increases (and that of less educated falls). 

Regression analysis  
Problem of bias in the Mincer specification (i.e. unobserved ability). 
Three ways to solve this:  

 

Include direct measures of ability in the regression 

  Exploit within-twins differences in wages and education  

  Use IV that affect schooling but not the wage directly.  

This study uses multivariate regression methods to derive estimates 
of the effect of education on wages. Results are shown in the table 
below. Returns vary by country and by gender. Results are robust to 
changes in specification. 

Men (and % of 
earnings 
explained by 
variable used) 
5.8% 

Women (and % of 
earnings 
explained by 
variable used) 
6.8% 

7.3%  

8.1% 

7.2% 

7.9% 

Returns to 
education 

When age is used as 
the proxy for 
experience 
Using potential 
experience (age – 
education leaving 
age) 
Using recorded 
experience 
(weighted number 
of years of part time 
and full time work 
since leaving full 
time education) 

A limitation to these results is that the method considers only working 
employees. Return to schooling might be biased because of that. 

The study then looks at the returns to education by focusing on 
credentials: it plots the average return for a number of popular 
credentials in the UK against the average number of years of 
schooling for holders of these credentials. It finds that returns to 
education equal to 16% for women and 9% for men. 

The study then examines returns to schooling using a quantile 
regression rather than OLS. It finds that the returns to schooling are 
higher for those at the very top of the wage distribution compared to 
those at the very bottom.  

50 

 
 
 
 
 
 
 
 
 
 
 
 
 
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Signalling 
Considering education as a signal 
The value of education as a signal is equal to: 

 

 

the difference between the returns to educations for employees 
versus the self-employed; or 

the difference between the returns to educations for public versus 
private employees. 

The paper finds that the signalling component of education is quite 
small. 
A problem with the analysis that self-employment is not random but 
cannot be explained with observable variables 

The paper then include ability measures to distinguish between ability 
and productivity. The results are the same: the signalling component 
of education is small. A problem with the analysis is that ability 
measures should be uncontaminated by the effects of education and 
should indicate ability to be productive.  

Endogenous schooling 
Instrumental Variables:  

 

IV estimates are higher than OLS 

  A common problem with IV estimations is instrument relevance 

and validity. 

  Two examples of IV estimations of the return to schooling:  

a)  Compare wages of people who left school at 16 when the 

minimum school leaving age was raised to 16, with wages of 
those that left school at 15 just before the minimum was raised 
to 16. 

  Use smoking behaviour (smoking when young is a sign of having a 
high discount rate). Estimated return to schooling: 16% for men 
and 18% for women 

Panel estimators: 

  Panel data is used to address heterogeneity (it treats unobserved 

heterogeneity as a fixed effect) 

  However, the use of panel data is limited because it is only 

possible to observe earnings following the completion of schooling: 
information is needed on earnings before AND after schooling, and 
as such it is available only for those who return to education later 
in their lives.  

Twins or siblings data: 

  Exploit belief that siblings are more alike that a randomly selected 

pair of individuals => the goal is to eliminate omitted ability bias 
or motivation bias. 

  However, this method suffers from measurement errors. Recent 

studies have attempted to solve this issue.  

  The major weakness is that no attention has been given to why 
twins have different levels of education. It is not obvious that 
within-twins differences in education are random.  

51 

 
 
 
 
 
 
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Matching methods:  

  One study by Blundell et al (2001) referenced in the research 
considered the estimation of the return to schooling using IV, 
control function approaches and propensity score matching 
methods. Returns based on matching methods were between 
those using OLS and IV estimates when considering the effect of 
the treatment on the treated. Returns based on matching methods 
were however lower than OLS an IV estimates when considering 
the effect of the treatment on the non-treated.  

Link 

http://www.economists.nl/files/20070906-
HarmonOosterbeekWalkerJES2003.pdf 

A4.3.4.6 

Earnings Functions and Rates of Return: The Mincer Equation and 
beyond. In Eric A. Hanushek and Finis Welch (eds.).  Handbook  of 
the Economics of Education 

Table A4.25 Heckman, J., Lochner, L., Todd, P. (2006) 

Title 

 Earnings Functions and Rates 
of Return: The Mincer 
Equation and beyond. In Eric 
A. Hanushek and Finis Welch 
(eds.). Handbook of the 
Economics of Education. 

Authors: Heckman, 
James J., Lance J. 
Lochner and Petra E. 
Todd  

Year of 
publication: 
2006 

 Education inputs: years of schooling 
Returns: earnings  

Education 
Inputs, 
outputs 
and 
returns 
considered  

Countries 
covered 

Data used 
and 
timescales 
for data 
and 
scenarios 

Overview 

United States 

U.S. 1940-2000 decennial Censuses 
Current Population Survey (CPS): 1964-2006 

The goal of this paper is to examine different approaches to estimating 
marginal internal rates of return for different schooling levels.  
The paper explores the relationship between: internal rates of return to 
schooling, interest rates, and the earnings function. It does so by using 
nonparametric approach and by taking into account income taxes, 
direct schooling costs, and the length of working life that depends on 
schooling levels. 
Then, the paper looks at how taxes, tuition, variable length of working 
life, and changes in the relationship between earnings, schooling and 

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experience affects “internal rates” of return to education. 

Description 
of main 
findings 

The internal rate of return is defined as the discount rate that equates 
the present value of two potential income streams. It is useful when 
assessing whether to increase or decrease expenditure on education: 
it is assumed to be profitable “to take additional schooling if the 
marginal internal rate of return exceeds the opportunity cost of 
funds”. 

The coefficient on schooling in a Mincer regression of log earnings on 
years of schooling and years of post-school work experience is 
interpreted as an estimate of the internal rate of return. This is based 
on assumptions:  

 

 

 

 

 

(1) no direct or psychic costs of schooling (i.e. costs of added 
stress or losses to quality of life),  

(2) no income taxes,  

(3) no loss of working life with additional years of schooling,  

(4) earnings functions are multiplicatively separable in experience 
and schooling,  

(5) marginal returns equal average returns, which, together with 
multiplicative separability, implies linearity of log earnings in 
schooling over the observed range of schooling choices 

The returns (based on earnings) obtained by the paper’s method differ 
from Mincer returns. 
The paper finds higher returns to schooling (based on earnings) than 
Mincer for high school completion: 24% in 1940 (52% in 2000) 
(results for white men). Results indicate that returns to schooling have 
been increasing over time.  
The paper also finds higher returns to schooling (based on earnings) 
than Mincer for college completion: 26% in 1940 (29% in 2000) 
(results for white men). 

Link 

http://economics.uwo.ca/people/lochner_docs/earningsfunctionsnber1
3780.pdf   

A4.3.4.7 

The  Effects  of  Cognitive  and  Noncognitive  Abilities  On  Labor 
Market Outcomes and Social Behavior 

Table A4.26 Heckman, J., Stixrud, J., Urzua, S. (2006) 

Title 

The Effects Of Cognitive and 
Noncognitive Abilities On Labor 
Market Outcomes and Social 
Behavior 

Author(s): Heckman, 
James J., Jora Stixrud 
and Sergio Urzua  
Publisher: Journal of 
Labor Economics 

Year of 
publication: 
2006 

Education 
Inputs, 

Education inputs: years of schooling  
Education outputs:  

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outputs 
and 
returns 
considered  

Cognitive skills: arithmetic reasoning, word knowledge, paragraph 
comprehension, mathematical knowledge, and coding speed 
Non-cognitive skills: degree of control individuals feel they possess 
over their life and perceptions of self worth 
Returns: earnings, employment, productivity 

Countries 
covered 

Data used 
and 
timescales 
for data 
and 
scenarios 

Overview 

Description 
of main 
findings 

United States  

National Longitudinal Survey of Youth 1979 

This paper presents an analysis of the effect of cognitive and non-
cognitive skills on wages, schooling, work experience, occupational 
choices and the propensity to engage in risky behaviour.  

The study first uses conventional approaches, such a standard least 
squares analysis of the effects of cognitive and non-cognitive skills on 
wages.  
Results show that for men, cognitive ability explains 9% of the 
variance of log wages while non-cognitive explains only 0.9% of the 
variance. For women, the figures are similar: 12.4% for cognitive 
ability and 0.4% for non-cognitive ability.  
However, this model is subject to several limitations:  

 

IQ tests and achievement tests are different; 

  There is a reverse causality problem: “each additional year of 

schooling increases cognitive (and non-cognitive) abilities. Thus, 
OLS estimates likely overstate the contribution of ability to wages 
and understate the contribution of schooling to wages”. 

  Skill scores are imperfect measures of individuals’ true abilities  as 

they are affected by non-cognitive and unobservable variables such 
as ‘grit’. 

The study then develops a more complex model. It postulates the 
existence of two underlying factors representing latent cognitive and 
non-cognitive ability. Conditioning on the observables, these factors 
account for all of the dependence across choices and outcomes 

Main results  are stated below and illustrated by graphs (graphs are for 
women results only; the observations are similar for men)  

  Whilst “[…]cognitive skills explain much more of the variance of 

(log) wages, their effects on (log) wages (as measured by skill 
gradients) are similar to the effects of the non-cognitive traits.” For 
the first decile of skills, log wage is around 2. For the 10th decile, 
log wage is more than 2.5. 

54 

 
 
 
 
 
 
 
 
 
Study on the potential of education to cause macroeconomic imbalances and negative 
spillovers – Annex IV 

             Y-axis: log wage  

 

“The gradient on non-cognitive skills is greater than it is for 
cognitive skills. The effects of both cognitive and non-cognitive 
ability on employment cumulate over the lifecycle into effects on 
work experience” (true for both genders. the graph illustrates this 
for women). The probability of employment is fairly stable for all 
deciles of cognitive skills (at around 70%). For non-cognitive skills, 
the probability of employment associated with the first decile is a 
little more than 20%; for the 10th decile, the probability is almost 
equal to 100%. 

55 

 
 
 
 
 
 
 
 
 
 
Study on the potential of education to cause macroeconomic imbalances and negative 
spillovers – Annex IV 

Link 

http://www.uaedreform.org/site-der/wp-
content/uploads/2013/08/Heckman-2006-NBER-Cognitive-and-
noncognitive-skills-on-labor-market-outcomes.pdf  

A4.3.4.8 

Further  Analysis  of  the  Returns  to  Academic  and  Vocational 
Qualifications 

Table A4.27 McIntosh, S. (2006) 

Title 

 Further Analysis of the 
Returns to Academic and 
Vocational Qualifications 

Authors: Mcintosh, 
Steven 

Year of 
publication: 
2006 

Education outputs: qualifications from different types of schooling 
Returns: wages 

Education 
Inputs, 
outputs 
and 
returns 
considered  

Countries 
covered 

Data used 
and 
timescales 
for data 
and 
scenarios 

United Kingdom 

UK Labour Force Survey  
Timescale: 1993-2001 
Limitation of the data: before 1996, the education section of the survey 
only asked about the three highest qualifications that respondents 
possessed. The survey did not ask about all qualifications. This means 
that in some cases there will inconsistencies when considering the full 
time series from 1993 to 2001. 

Overview  The goal of the study is to disaggregate the estimated returns (based 

on earnings) to different qualifications along the following dimensions: 

 

 

time; 

type of sector (public / private); 

  age cohort; 

  highest school qualification.  

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Description 
of main 
findings 

The strength of this paper is that it does not only use the highest 
qualifications achieved by individuals; instead, it uses information on 
all qualifications held by individuals. This means that the returns 
(based on earnings) to each qualification are estimated by the earnings 
of all individuals who acquire them, rather than by individuals who 
acquire that qualification and no further qualification. 
Estimated coefficients on any particular qualification correspond to “the 
estimated difference in earnings between someone who holds that 
qualification and someone who does not, holding all other education 
achievements constant”. These returns are cumulative across 
qualifications: they can be summed to obtain the total returns to a 
combination of qualifications. 

Results concerning returns to qualifications (based on earnings) can be 
found in the tables below. Returns depend greatly on the types of 
qualification. Overall, men face higher returns to academic qualifications 
but lower returns to vocational qualifications. 

Returns to 
academic 
qualifications 
(based on 
earnings)  

first degree 

2 or more A levels 

5 or more good 
(grade C or 
above) GCSEs 

Returns to 
vocational 
qualifications 
(based on 
earnings)  

Graduate level 
professional 
qualifications 

Nursing 
qualifications 

Teaching 
qualifications 

HNC/HNDs 

ONC/ONDs 

For men 

For women 

24-28% 

15-17%  

26-31%  

25-27% 

14-16% 

25-30% 

For men 

For women 

36-45% 

35-40% 

6-14%  

15-18% 

5-8%  

27-32% 

13-15% 

8-13% 

7-9% 

5-11% 

An important observation is that the returns were constant over the 
time period considered. What is more, returns usually do not vary over 
the working lifetimes of individuals.  
Note that for academic qualifications, returns are higher in the private 
sector.  

Link 

http://dera.ioe.ac.uk/4679/1/RR370.pdf  

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A4.3.4.9 

Investment in Human Capital and Personal Income Distribution 

Table A4.28 Mincer, J. (1958) 

Title 

Investment in Human Capital and 
Personal Income Distribution 

Authors: Mincer, 
Jacob  

Year of 
publication: 
1958 

Education inputs: length of training  
Returns: earnings 

Education 
Inputs, 
outputs 
and 
returns 
considered  

Countries 
covered 

Data used 
and 
timescales 
for data 
and 
scenarios 

Overview 

United States 

US Census (1940-1960) 

The paper analyses earnings and distribution of earnings through a focus 
on labour market experience or on-the job training and educational 
services. The model provides an analysis of how training and experience 
influence the differences in earnings across individuals and how they 
determine the inequality and skewness of earnings. 

Description 
of main 
findings 

The paper begins with a “simple model” relying on the following 
assumptions: 

  All individuals have identical abilities and equal opportunities to enter 

any occupation;  

  Occupations differ in the amount of training they require; 

  1 year of training reduces earning life by 1 year; 

  The flow of income receipts is steady during the working life. 

According to Mincer, the costs of training depend on (1) deferral of 
earnings for the period of training and (2) costs of educational services 
and equipment. 
Assuming that the costs of educational services and equipment are null, 
then Vn, the present value of life-earnings of individuals with (n) years 
of training is: 

𝑙
Vn = 𝑎n ∫ (
𝑛

e-rt) 𝑑t =

𝑎𝑛

𝑟

 (e-rn – e-rl) 

l: length of working life + length of training / a: annual earnings / r: rate 
at which future earnings are discounted 

 of annual earnings of persons differing by d years of 

The ratio 
training is found by equating V(d) = V (n-d) 
This ratio is a positive function of n. This means that relative income 
differences between persons with 10 and 8 years of training are larger 

58 

 
 
 
 
 
 
 
 
 
 
 
Study on the potential of education to cause macroeconomic imbalances and negative 
spillovers – Annex IV 

than those between individuals with 4 and 2 years of training. 
However, Mincer assumes that changes with n are negligible and sets  
= k. In other words: “Annual earnings corresponding to various 

levels of training differing by the same amount d differ by a 
multiplicative factor k” 
Mincer gets to the same conclusion even when considering expenses of 
training. 

Mincer extends his model by introducing experience on the job (i.e. 
amount of time spent on the job). Hi model shows that experience 
influences productivity more strongly in jobs that normally require more 
training. “The steeper growth of performance throughout the span of 
working life in occupations with higher levels of training implies that the 
growth of earning is also greater”.  

The main implications from Minder’s model are that:  

 

 

“Differences in training result in differences in levels of earnings 
among occupations as well as in differences in slopes of life paths of 
earnings among occupations” 

“The higher the occupational rank, the higher the level of earnings 
and the steeper the life-path of earnings” 

The main limitation of the study is that the model was “not adapted to 
predict absolute or even relative magnitudes of parameters in the 
component distributions”. It does not “tell how much of the observed 
sizes of parameters or of the difference between parameters of relevant 
components is accounted for by the factors built into the model”. 

The paper finishes with an empirical analysis that shows that inequality 
in earnings increases with age, schooling level and occupational rank.   

Link 

http://down.cenet.org.cn/upfile/35/2006814124319197.pdf  

A4.3.5  Macroeconomic studies 

A4.3.6  Single country studies 

A4.3.6.1 

Unzureichende Bildung: Folgen fuer die oeffentlichen Haushalte 

Table A4.29 Allmendinger et.al. (2012) 

Title 

Education 
Inputs, 
outputs and 
returns 
considered  

Unzureichende 
Bildung: Folgen fuer 
die oeffentlichen 
Haushalte 

Author(s): Jutta Allmendinger, 
Johannes Giesecke, Dirk 
Oberschachtsied 

Year of 
publication: 
2012 

Education Outputs: level of qualification obtained (ISCED) 
Returns to education and costs of insufficient education: wage 
levels, productivity, income tax, benefit costs, costs of social 
insurance. 

59 

 
 
 
 
 
 
 
 
 
 
Study on the potential of education to cause macroeconomic imbalances and negative 
spillovers – Annex IV 

Countries 
covered 

Data used 
and 
timescales 
for data and 
scenarios 

Germany 

The study uses three distinct datasets, which are combined to create 
a sample of individual sbetween 22 and 55 years of age. 

It uses German census data (Mikrozensus -MZ) collected between 
2005-2007, an annual household panel survey which includes 
370,000 households and 820,000 individuals. All four years, the 
surveyed households are replaced. The Mikrozensus is an obligatory 
survey, therefore the response rate is usually above 90%. 

The so-call socio-economic panel (Sozio-oekonomisches Panel -
SOEP) collected between 2002-2008, a longitudinal panel survey 
which has been running since 1984 in West Germany and since 1990 
in the regions of the former GDR. Variables include household 
composition, employment, occupations, earnings, health and 
satisfaction indicators 

German earnings and income tax statistics ( Stichprobe zur Lohn- 
und Einkommensteuerstatistik - LES). Collected every three years, 
this dataset contains anonymised earnings and tax details for a 
representative sample of employees in Germany. No details are 
provided on the timescales used from this dataset. 

Overview 

This study is a detailed analysis of the social costs of low 
qualifications in Germany, using data on unemployment statistics, 
census and income statistics. Some of the used data is longitudinal.  

Low qualifications are defined as the lack of school certificates or 
professional training certificates. 

The social costs of low educational attainment is assumed to be 
represented by the function below 

KJ = BSZj - BSzj - BSZrz ; J∈{Az1. …Azn}  with BSz = ∑ (𝑇

𝑡=𝑙 Rt)∗(1+i)-t 

Four types of costs are then calculated over the assumed working life 
of individuals (21 – 55 years of age): Income tax (and lack thereof), 
transfers (and lack thereof), and income and expenditure of 
unemployment benefit insurances. Therefore, the study covers the 
main types of costs which typically affect public budgets in Germany. 
Further impacts on the public budget, such as consumable taxes, 
income and spend of pension and health systems are excluded.  

Costs are then calculated for a cohort of 920,000 individuals which, 
in 2008, are 21 year old, of which 15.4 % (142.000 individuals) are 
considered to be low qualified.  

Description of 
main findings 

The study describes the calculated social costs in detail as aggregate 
figures and per-head figures.  

The study estimates the average per-head costs of low qualifications, 
along the types of costs above, to be 22,000 EUR /y within the 35 
years observed. However, there is strong regional disparity between 
different German regions which partly hold competencies in the area 

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Study on the potential of education to cause macroeconomic imbalances and negative 
spillovers – Annex IV 

of education and transfer services. 

Link 

http://www.awo-obb-
jugend.de/uploads/media/Bertelsmann_Unzureichende_Bildung.pdf  

A4.3.6.2 

Kosten der Arbeitslosigkeit sind gesunken 

Table A4.30 Bach, Hans-Uwe, Spitznagel, Eugen (2008) 

Title 

Kosten der Arbeitslosigkeit 
sind gesunken 

Authors:  Hans-Uwe Bach, 
Eugen Spitznagel  
Publisher: IAB 
Kurzberichte 14/2008 

Year of 
publication: 
2008 

Education 
Inputs, 
outputs and 
returns 
considered  

Countries 
covered 

Data used 
and 
timescales 
for data and 
scenarios 

Overview 

Education outputs: qualifications obtained  
Returns: Employment status 

Germany 

German official statistics on benefits, social insurance for 2007 
German official statistics on unemployment for 2007 

Main objective: 
The paper provides estimates of the social costs of unemployment in 
Germany, updating previous calculations provided by the authors. 

Method/Approach: 

The paper considers different categories of people included in official 
statistics, who are considered not in employment. The paper then 
goes on the focus on the group of social benefit beneficiaries. The 
empirical analysis then provides average costs for different transfer 
services this group benefits of, based on averages for the year 2007 

Description 
of main 
findings 

Main findings:  
People receiving benefits created social costs of EUR 17,900 on 
average in 2007. This amounted to a total spent of public budget of 
EUR 35 billion and EUR 33 billion of lower tax income, a total cost of 
EUR 68 billion.  

Link 

http://doku.iab.de/kurzber/2008/kb1408.pdf  

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A4.3.6.3 

Measuring  the  Economic  Impact  of  Further  Education  -  BIS 
research paper number 38 

Table A4.31 BIS (2011) 

Title: 

Author:  Cambridge economics 

Measuring the Economic 
Impact of Further Education - 
BIS research paper number 
38 

Publisher: Department for Business, 
Innovation and Skills 

Year of 
publication:  

2011 

The objective of this paper was to estimate the Net Present Value (NPV) 
associated with undertaking different qualifications and aggregating 
them up to the sector as a whole. 

Education 
inputs, 
outputs 
and 
returns 
considered  

Countries 
covered 

UK 

Data used 
and 
timescales 
for data 
and 
scenarios 

Overview 

Data: BIS in-house analysis of the Labour Force Survey; focus on 
2008/09 further education (FE) funding allocations. 

This analysis suggests that the magnitude of the NPV estimates is more 
sensitive to the assumptions about the benefits than about the costs. 
Even relatively small differences in the wage and employment premia11 
could have a significant impact on the NPV estimates. For example, a 
10% change in the wage premia could change the total NPV by 9%. 
There is also considerable uncertainty around the wage returns to non-
first qualifications. Under an extreme case where these generated zero 
wage returns, this would reduce the NPV of the FE system by around 
12%. However, this is an extreme assumption – it seems unlikely that 
there are no returns for the average learner from undertaking a 
particular qualification. 

Description 
of main 
findings 

Employment returns: Not only could qualifications increase the wages 
which individuals earn in employment, but they could also increase the 
probability of being in employment over the course of their lifetime. 

Spillovers: The benefits of enhanced productivity could be captured by 
their employers in the form of higher profits or increased 
competitiveness or increased wages for other workers, and by co-
workers or other employers due to the transfer of knowledge. Dearden, 
Reed and Van Reenen (2005) suggest that the increase in productivity 

11 The research paper defines wage premia as ‘estimates of the additional wages, in percentage 
terms, that an individual is likely to receive over the rest of their working life, having achieved a 
particular  qualification,  compared  to  a  similar  individual  who  does  not  hold  that  particular 
qualification (average returns) or only has qualifications at the level below (marginal returns)’. 
Employment  premia  are  calculated  based  on  the  difference  in  employment  rates  for  groups  of 
individuals with different levels of previous highest qualification. 

62 

 
 
 
 
 
                                           
Study on the potential of education to cause macroeconomic imbalances and negative 
spillovers – Annex IV 

from training is double the increase in wages. Dearden et al (2005) 
consider only productivity spillovers at an industry level, but is the only 
source that has attempted to quantify spillovers. 

By improving skill levels, further education provision is assumed to raise 
labour productivity and the employment rate (the economic activity of 
the working-age population, and success in matching workers to jobs). 

Wage premia: Based on the ‘sophistication of use’ argument, the wage 
returns from Skills for Life are assumed to increase as prior qualification 
levels increase. The Borghans & Bas ter Weel (2004) evidence implies a 
wage premium of 2.5% compared to individuals with no prior 
qualifications, and 5% compared to those previously qualified to level 1 
of National Vocational Qualifications (NVQ). A return of 5% is then 
assumed in the model for all levels of prior qualification at level 2 of 
National Vocational Qualifications and above. 

Employment premia:  

Percentage point increase in the probability of employment Below L2 to 
L2: 4.8; L2 to L3: 2; L1 to L3: 6.8 

Baseline rate of employment Below L2 to L2: 88.9; L2 to L3: 93.7; L1 to 
L3: 88.9 

Percentage increase in probability of employment Below L2 to L2: 5.4; 
L2 to L3 2.1; L1 to L3: 7.7. 

Results: 

The NPV per achievement is higher for Apprenticeships - £136,000 
compared to £67,000; 

The NPV per start is also higher for Apprenticeships - £99,000 compared 
to £49,000. The ratio of NPV per achievement and NPV per start is 
broadly the same for Apprenticeships and NVQs in the workplace, 
reflecting the fact that success rates are broadly similar across the two 
routes; 

The NPV per pound of state funding is less markedly higher for 
Apprenticeships than is NPV per achievement and NPV per start. This is 
because of the higher. 

Problems: 

Literature on employment effects is less well-developed than that on 
wage effects. There is therefore greater uncertainty about the extent to 
which FE increases an individual’s lifetime employment status. This is 
particularly an issue for learning undertaken in the workplace. Due to 
the lack of evidence on the employment-enhancing effects of vocational 
qualifications, the approach taken in this study is necessarily a simplified 
one, based on analysis of the Labour Force Survey data. The authors 
observe that there is a lack of evidence on productivity spillovers, and 
make reference to pre-existing literature which points to productivity 
increases from wage increases and training. 

Link 

https://www.gov.uk/government/uploads/system/uploads/attachment_d
ata/file/32329/11-816-measuring-economic-impact-further-
education.pdf  

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A4.3.7  Multi-country studies 

A4.3.7.1 

Labour Market Mismatch and Labour Productivity: Evidence from 
PIAAC  Data.  OECD  Economics  Department  Working  Papers,  No. 
1209 

Table A4.32 Adalet Mc Gowan, Muge, and Andrews, Dan(2015) 

Title 

Labour Market Mismatch and 
Labour Productivity: Evidence 
from PIAAC Data. OECD 
Economics Department 
Working Papers, No. 1209 

Authors: Adalet Mc Gowan, 
Muge, and Andrews, Dan 
Publisher: OECD 

Year of 
publication: 
2015 

The study considers education outputs in terms of the highest ISCED 
qualification obtained, and the literacy proficiency, as reported by 
individuals through the PIAAC survey. 

Education 
Inputs, 
outputs 
and 
returns 
considered  

Countries 
covered 

Austria, Belgium, Czech Republic, Denmark, Estonia, Finland, France, 
Germany, Italy, Japan, Korea, the Netherlands, Norway, Poland, the Slovak 
Republic, Spain, Sweden, the United Kingdom, and the United States 

Data used 
and 
timescales 
for data 
and 
scenarios 

The study uses a small sample of industry data across 19 OECD countries 
(205 firms) and OECD data on qualification of workers, as well as PIAAC 
results on skill levels of workers in the 19 countries investigated – the data 
is based on PIAAC results released in 2013 and collected in 2012/2013, as 
well as a harmonised cross-country industry dataset for corresponding 
years – the industry data is based on ORBIS, a commercial database by 
Bureau van Dijk and aligned with the distribution of firms from the 
Structural Demographic Business Statistics, collected by OECD& Eurostat.  

Overview  Main objective:  

The paper explores the link between skill and qualification mismatch and 
labour productivity using cross-country industry data for 19 OECD 
countries, and mismatch indicators aggregated from OECD PIAAC survey 
released in 2013. 

Method:  
Qualification mismatch is determined by a benchmark of ISCED level 
needed to find employment, based on a poll of firms, and the actual ISCED 
qualification levels held by workers (PIAAC data). Skills mismatch is 
determined by self-assessment of workers, validated by quantitative 
information on skill levels (literacy proficiency as tested through PIAAC). 
Individual workers were then allocated to a sample of firms at the 1-digit 
industry level drawn from ORBIS and SDBS business databases. 
Using OLS regression analysis, the correlation between different types of 
mismatch and labour productivity are then estimated at the industry level.  
A number of sensitivity tests, such as controlling for managerial quality, are 
implemented. 

Description 
of main 
findings 

Main empirical findings: 
The research finds that higher skill mismatch is associated with lower labour 
productivity through a less efficient allocation of resources. A higher share 
of under-qualified workers is associated with both lower allocative efficiency 
and within-firm productivity, i.e. a lower ratio of high productivity to lower 
productivity firms. However, the authors warn that allocative efficiency is 

64 

 
 
 
 
 
 
 
Study on the potential of education to cause macroeconomic imbalances and negative 
spillovers – Annex IV 

varying largely across countries, and therefore conclusions across countries 
should not be drawn. 

The study finds furthermore that differences in aggregate labour 
productivity at any point in time will reflect two factors. First, average 
differences in within-firm productivity – measured by the unweighted 
average of firm productivity, irrespective of each firm’s relative size – which 
is increasing in the ratio of high productivity to low productivity firms within 
an industry. Second, the extent to which, all else equal, it is the more 
productive firms that command a larger share of industry employment (i.e. 
allocative efficiency), which will be the outcome of the shift in resources 
across firms in previous periods. 

The study finds a strong negative correlation between over-skilling and 
productivity (For the 10th percentile of workers who are over-skilled [i.e. the 
10th percentile with the highest self-assessed mismatch], effect on weighted 
individual productivity is a change of -0.0094, statistically significant at the 
1% level, and negative impact on allocative efficiency in the industry of -
0.0124, statistically significant at 10% level). Furthermore, the negative 
correlation between over-skilling and labour productivity results from its 
effect on allocative efficiency; that is, in industries with a higher share of 
over-skilled workers, the more productive firms find it more difficult to 
attract suitable labour, in order to expand their operations. The authors find 
that this effect is also economically significant.  

The authors suggest that the allocation of skills can account for a 
considerable proportion of cross-country labour productivity differences. If 
interpreted causally, the estimates suggest that for instance Italy , which 
has very high skill mismatch and low allocative efficiency, could close one-
fifth of its gap in allocative efficiency with the United States if it could 
reduce its mismatch within each industry to that corresponding to the OECD 
best practice. 

The authors also confirm the economic significance of mismatches. They 
suggest that one standard deviation increase in over-skilling is associated 
with a 6% reduction in allocative efficiency and a 4% decrease in overall 
labour productivity. 

For the 10th percentile of workers considered underqualified (i.e. the 10th 
percentile with the lowest qualification in a given job), the study also find a 
statistically significant effect on weighted in-firm productivity (-0.0129) and 
allocative efficiency within the industry (-0.0087).  

Link 

http://www.oecd.org/eco/growth/Labour-Market-Mismatch-and-Labour-
Productivity-Evidence-from-PIAAC-Data.pdf  

A4.3.7.2 

Economic  Growth  in  a  Cross  Section  of  Countries.  In:  Quarterly 
Journal of Economics106 (2) , 407-443. 

Table A4.33 Barro, Robert (1991) 

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Study on the potential of education to cause macroeconomic imbalances and negative 
spillovers – Annex IV 

Title 

Economic Growth in a Cross 
Section of Countries. In: 
Quarterly Journal of 
Economics106 (2) , 407-443. 

Author: Robert Barro 

Year of 
publication: 
1991 

Education 
Inputs, 
outputs 
and 
returns 
considere
d  

Countries 
covered 

Education inputs: school-enrolment rates at the secondary and primary 
levels 

Returns considered: per-capita GDP, fertility rates, physical investments 

Control variables: political stability, market distortion index 

The data covers 118 countries. A full list of countries covered in the data is 
provided in an Annex. EU-28 Member States covered are Denmark, Finland, 
France, Germany (West), Greece, Ireland, Italy, Luxembourg, Malta, 
Netherlands, Portugal, Spain, Sweden, United Kingdom,  

Data used 
and 
timescale
s for data 
and 
scenarios 

Barro uses two proxies for human capital, the 1960 values of school-
enrolment rates at the secondary (SECGO) and primary levels (PRIM60). 
These variables, based on information from the United Nations, measure 
number of students enrolled in the designated grade levels relative to the 
total population of the corresponding age group. Barro then uses growth 
rates of real per-capita GDP between 1960-1985 as the main dependent 
variable. 

Barro uses control variables for political instability drawn from Bank’s 
data12, namely REV = the number of revolutions and coups per year, and 
ASSASS = number per million population of political assassinations per 
year. He uses controls for different types of economic systems based on 
Gastil13, categorising countries into primarily socialist, mixed between 
socialist and free enterprise, and primarily free enterprise. Purchasing-
power-parity (PPP) numbers for investment goods are used to control for 
market distortions, based on Summers and Heston.14 

Data sources are to some extent unclear and not (or no longer) publicly 
available, however the paper suggests that data from Summers and 
Heston15, the United Nations, the World Bank, Banks16 and other sources. 
Barro and Wolf17 provide details on the data set, the publication is however 
not retrievable online. 

12 Banks, Arthur S., "Cross-National Time-Series Data Archive," Center for Social Analysis, State 
University of New York at Binghamton, September 1979. 
13 Gastil, Raymond D., Freedom in the World (Westport, CT: Greenwood Press, 1987). 
14 Summers, Robert, and Alan Heston, "A New Set of International Comparisons of Real Product 
and Price Levels: Estimates for 130 Countries," Review of lncome and Wealth, XXXIV (19881, 1-
25.  
15 Summers, Robert, and Alan Heston, "A New Set of International Comparisons of Real Product 
and Price Levels: Estimates for 130 Countries," Review of lncome and Wealth, XXXIV (19881, 1-
25.  
16 Banks, Arthur S., "Cross-National Time-Series Data Archive," Center for Social Analysis, State 
University of New York at Binghamton, September 1979. 
17 Robert Barro and Holger C. Wolf, "Data Appendix for Economic Growth in a Cross Section of 
Countries," National Bureau of Economic Research, November 1989. 

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Study on the potential of education to cause macroeconomic imbalances and negative 
spillovers – Annex IV 

Overview  The empirical analysis in this paper uses school-enrolment rates as proxies 
for human capital. For a given starting value of per capita GDP, a country's 
subsequent growth rate is positively related to these measures of initial 
human capital. Barro finds that 1960 primary- and secondary-school 
enrolment rates are positively correlated with 1960–1985 growth in real 
per capita GDP. 

Descriptio
n of main 
findings 

For 98 out of a 118 countries investigated for the period 1960-1985, the 
growth rate of real per capita GDP is positively related to initial human 
capital (coefficient of 0.0250 in relation to school enrolment rates in primary 
school (s.e. 0.0056), and 0.305 for the rate of enrolment in secondary 
school(s.e. 0.0079)). Countries with higher human capital also have lower 
fertility rates ( coefficient of -1.27 between average fertility rate between 
1965-85 and primary school enrolment rates in 1960 (s.e. 0.51), and –2.62 
if compared to secondary school enrolment rates(s.e. of 0.67)) and higher 
ratios of physical investment to GDP. Growth is inversely related to the 
share of government consumption in GDP for a smaller sample of 76 
countries (coefficient -0.12, with a s.e. of 0.03), but insignificantly related 
to the share of public investment. 

Link 

http://www.econ.nyu.edu/user/debraj/Courses/Readings/BarroGrowth.pdf   

A4.3.7.3 

The  Role  of  Human  Capital  in  Economic  Development:  Evidence 
from  Aggregate  Cross-Country  Data.  In:  Journal  of  Monetary 
Economics 34(2), 143-173. 

Table A4.34 Benhabib, Jess and Mark Spiegel (1994) 

Title 

The Role of Human Capital in 
Economic Development: 
Evidence from Aggregate 
Cross-Country Data. In: 
Journal of Monetary 
Economics 34(2), 143-173. 

Authors:  Jess Benhabib and 
Mark Spiegel 

Year of 
publicatio
n: 1994 

Education outputs: educational attainment in ordinal data (Barro and 
Lee specification) 
Returns: per-capita growth rates, physical investments 

Education 
Inputs, 
outputs 
and 
returns 
considered  

Countries 
covered 

Datasets used cover a variety of countries, the research constructs 
different samples of 42 countries and 129 countries. 

Data used 
and 
timescales 
for data 
and 

Income, population, and labor force data data are acquired from the 
Summers and Heston18 data set, covering the years 1965-85 and 130 
countries. 
Barro and Lee19 data is used as well – this is a data set on educational 
attainment that we have constructed for 129 countries over five-year 

18 Summers, Robert, and Alan Heston, "A New Set of International Comparisons of Real Product 
and Price Levels: Estimates for 130 Countries," Review of lncome and Wealth, XXXIV (19881, 1-
25.  

67 

 
 
 
 
 
 
                                           
Study on the potential of education to cause macroeconomic imbalances and negative 
spillovers – Annex IV 

scenarios 

Overview 

periods from 1960-1985. It contains male and female attainment of the 
adult population at four levels: no-schooling. primary. secondary, and 
higher education. 
A third dataset used is Kyriacou20, which includes country level estimates 
of human capital and literacy for 42 countries. 

The authors calculate cross-country growth regressions, using estimates 
of physical and human capital stock, to understand how human capital 
(specifically the educational attainment of the labour force) affect the 
output and growth of an economy.  

The authors pioneer a complex production function allowing for 
interactions between productivity (A), human capital (H) as well as 
growth and allowing for diffusion of technology between countries 
representing the productivity “frontier” and follower countries. 

The research uses an endogenous growth model based on Romer21, which 
allows human capital (expressed as the average level of education 
attainment) to influence productivity through an effect on the capacity of 
nations to innovate. Furthermore, previous work by Nelson and Phelps22 is 
included in the model to allow levels of educational attainment to affect 
the speed of technological catch-up and diffusion. 

The neoclassical model used is built around the aggregate production 
function presented below, whereas H= Human capital 

(log Yr – log Y0) = (log Ar – log A0)+ 𝑥 (log Kr – log K0) 

+ 𝛽(log Lr-logL0) + 𝛾(log Hr-log H0) 

+(log 𝜀r – log 𝜀0). 

An alternative endogenous growth model is then developed for 
comparison, in which the term with the log of human capital has been 
replaced with the average level of the log of human capital over the 
investigated time period 

(log Yr - log Y0) = (log Ar – log A0)+𝑥(log Kr – log K0) 

+ 𝛽(log Lr – logL0) + 𝑦(1/T ∑ (𝑟

0 log Ht)  

+(log𝜀r – log𝜀0). 

19Robert  J.  Barro,  Jong-Wha  Lee,  1993,  International  Comparisons  of  Educational  Attainment, 
NBER Working Paper No. 4349. 
20  Kyriacou,  George,  Level  and  growth  effects  of  human  capital,  1991,  Working  paper  91-26 
(C.V. Starr Center, New York, NY).  
21  Romer,  Paul,  1990,  Endogenous  technological  change,  Journal  of  Political  Economy  98, 
S71lSlO2.  
22  Nelson,  Richard  and  Edmund  Phelps,  1966,  Investment  in  humans,  technological  diffusion, 
and economic growth, American Economic Review: Papers and Proceedings 61, 69975.  

68 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
                                                                                                                                
Study on the potential of education to cause macroeconomic imbalances and negative 
spillovers – Annex IV 

Description 
of main 
findings 

The authors find that the results of their growth regressions depend, not 
surprisingly, largely on the production functions used. Using a standard 
Cobb-Douglas aggregate production function which uses different measures 
of human capital as an ordinary input into the function, the results indicate 
that human capital does not have a significant influence on per-capita 
growth rates (-.0.041 when using average literacy (s.e. 0.057), -.0.029 
when using Barro and Lee’s data on average educational attainment (s.e. 
0.071)). Using an alternative production function, modelling total factor 
productivity as a function of the average level of education, and accounting 
for the initial levels of income, the authors find a positive role of human 
capital (0.128, s.e. 0.055). In particular, the applied combination of an 
assumed effect of education on domestic innovation capacity and catch-up 
speed produces regression results quite different from regressions using a 
neoclassical model (“standard growth regressions”). The results also 
suggest that the transition towards a common growth rate (“convergence 
effect”) are rather long and innovations set by leading countries could 
change the rates of following countries. The authors conclude that “that the 
role of human capital is indeed one of facilitating adoption of technology 
from abroad and creation of appropriate domestic technologies rather than 
entering on its own as a factor of production.” 

Link 

http://federation.ens.fr/wheberg/parischoeco/formation/fcses/_boitdocu/0
607s1_lect02_b.pdf  

A4.3.7.4  Human  Capital,  the  Structure  of  Production,  and  Growth.  In:  The 

review of Economics and Statistics Vol. 91, pp.66-82. 

Table A4.35 Antonio Ciccone & Elias Papaioannou (2009) 

Title 

Human Capital, the Structure 
of Production, and Growth. 
In: The review of Economics 
and Statistics Vol. 91, pp.66-
82. 

Authors: Antonio Ciccone & 
Elias Papaioannou  

Year of 
publication: 
2009 

Education outputs: Ordinal data on educational attainment, based on 
Barro and Lee 
Returns considered: Value-added, employment numbers 

Education 
Inputs, 
outputs 
and 
returns 
considered  

Countries 
covered 

A sample of 44 countries is considered in this paper. A list of individual 
countries considered is not provided. 

Data used 
and 
timescales 
for data 
and 
scenarios 

Data on value added and employment at the country-industry level come 
from the Industrial Statistics of the United Nations Industrial Development 
Organization (UNIDO INDSTAT3(revision 2, which reports value added and 
employment data for 28 manufacturing industries at the 3-digit 
International Standard Industrial Classification (ISIC) level for a large 
number of countries. The specific variables used are the annual logarithmic 
growth rate of value added (V AGRs,c; the deflator used is the U.S. 
producer price index) and the annual logarithmic change of employment 
(EMP GRs,c) over the 1980-1999 period. Countries with data for less than 
10 industries and less than five years of data in the 1980’s and in the 
1990’s for each country-industry are excluded. The U.S. is excluded from 

69 

 
 
 
 
 
 
Study on the potential of education to cause macroeconomic imbalances and negative 
spillovers – Annex IV 

the sample because it is used for benchmarking the schooling intensity of 
industry sectors. These specifications create a sample of 44 countries and 
1049 observations for industry value added growth and a sample of 47 
countries and 1134 observations for industry employment growth. 

The data source for the industry-level measure of schooling intensity is the 
1980 Integrated Public Use Microdata Series covering US industries. 
Data for country-level human capital is average years of schooling of the 
entire population from the latest update of the Barro and Lee database23. 
For sensitivity analysis proxies of human capital that are based on the 
share of the population with a completed secondary education are used, as 
well as the Hanushek and Kimko (Table A2.40Table A4.40) schooling 
quality indicator. Real per capita GDP and the capital output ratio come 
from the Penn World Tables and Klenow and Rodriguez-Clare24 
respectively. 

The study uses several data sources to collect data for possible control 
variables. Data on trade openness are taken from Wacziarg and Welch.25 
The rule of law indicator comes from the International Country Risk Guide 
database (see Knack and Keefer, 1995) and the measure of property 
rights institutions from the Polity IV26 database. 

Timescales considered are 1980-1999 for the principal analysis, however 
due to patchy coverage of the UNIDO database for later years additional 
samples are analysed for 1980-89 and 1980-1995. 

This paper tests whether countries with higher education levels 
experienced faster value-added and employment growth (raise in 
employment in specific industries) in more compared to less schooling-
intensive industries in the 1980s and 1990s. It also examines the link 
between improvements in schooling and growth in schooling-intensive 
industries. Schooling intensive industries are derived from US data on the 
average level of educational attainment (by years of education and hours 
worked) across 28 industries in the United Stated in 1980. The most 
industries with the highest educational attainment in 1980 are Petroleum 
Refineries, Printing and Publishing and (Industrial and other) Chemicals, 
whilst the least schooling intensive sectors are Leather, Apparel, Footwear 
and Textile. 

The research then builds an endogenous growth model, which considers 
the supply of high and low human capital separately (Mc,t and Lc,t in country 
c at time t), evolving efficiency levels of the two types of labor Ac,t for L 
and M, and deriving from that efficiency levels in a given country and a 
world-frontier efficiency, which is constantly growing. The model then 
captures country-level efficiency (D) and industry-specific technology levels 
(E) to produce output Xs,c,t in industry s, country c and time t according to: 
Xs,c,t = Dc,tEs,t(Ac,tL)1-s(Ac,tM)s 

Overview 

23 Robert J. Barro, Jong-Wha Lee, 2010, A new data set of educational attainment in the world, 
1950-2010. 
24  Peter  J.  Klenow,  Andres  Rodriguez-Clare,  2005,  Externalities  and  Growth.  NBER  Working 
Paper No. 11009. 
25 Romain Warcziarg, Karen Horn Welch, 2003, Trade Liberalization and Growth: New Evidence, 
NBER Working Paper No. 10152. 
26 http://www.systemicpeace.org/polity/polity4.htm  

70 

 
 
 
 
 
 
 
                                           
Study on the potential of education to cause macroeconomic imbalances and negative 
spillovers – Annex IV 

The steady-state output for a given sector s in a given country c is then 
determined as 

X*s,c,t = D c,t Es,t Lc,t (

∅ 

𝐿
𝑐

𝐿
𝑔𝐿+ ∅
𝑐

 AtL,W) l-s (

∅ 

𝑀
𝑐

𝑔𝑀+ ∅

𝑀
𝑐

 AtMW Hc)s 

Steady-state production in schooling intensive industries relative to less 
schooling intensive industries in countries c and q is then compared 
through 

∗ 
𝑍𝑐
∗ 
𝑍𝑞

 = [

𝐻𝑐  
𝐻𝑞  

 ] [ ∅

 (∅𝑐
 (∅𝑞

𝑀/∅𝑐
𝐿 )
𝐿 )
𝑀/∅𝑞

  (

 (

𝐿 
𝑔𝐿+ ∅𝑐
𝑀 
𝑔𝑀+∅𝑐
𝐿 
𝑔𝐿+ ∅𝑞
𝑀 
𝑔𝑀+∅𝑞

  )

] 

)

Description 
of main 
findings 

This comparative function of production implies a factor supply effect and 
a technology adoption effect of human capital H on the production of 
country c, compared to country q. This means an increase in human 
capital results in an increase in the relative supply of the factor used by 
schooling intensive industries and therefore greater production in the 
schooling intensive industries.  

There are some limitations of the research linked to the data used. The 
quality and coverage of UNIDO data decreased sharply for the 1990’s – 
Therefore the authors consider alternative timescales which are however 
less extensive than the initial time from 1980-1999.  Also, it has to be 
considered that the educational attainment level used (Barro and Lee 
2001) is ordinal in nature, and does not allow a high degree of 
disaggregation. 

The paper finds that value added and employment growth in schooling-
intensive industries (industries with a high average level of educational 
attainment) was significantly faster in economies with higher initial levels 
of schooling (measured by average levels of educational attainment). This 
link becomes stronger when sampling only countries that were open to 
international trade.  

The authors also find that the observed link between initial schooling (in 
1980) and subsequent shifts of the production structure towards schooling-
intensive industries (observing the timescales outlined above) is robust to 
controls for physical capital endowments, for financial development, for 
indicators of rule of law, property rights institutions, and initial levels of 
income. These effect prevails when different proxies for schooling (the 
share of the population with a completed secondary education) are used, 
and when quality of schooling based on the work of Hanushek and Kimko is 
used(Table A4.40) . The authors find furthermore that countries which saw 
greater improvements in schooling experienced faster shifts in production 
towards schooling-intensive industries (industries with a high average level 
of educational attainment). 

71 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
Study on the potential of education to cause macroeconomic imbalances and negative 
spillovers – Annex IV 

Link 

http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.370.1694&rep
=rep1&type=pdf  

A4.3.7.5 

Literacy  scores,  human  capital  and  growth  across  fourteen  OECD 
countries. In: Statistics Canada – Catalogue no. 89-552, no 11. 

Table A4.36 Serge Coulombe, Jean-François Tremblay, and Sylvie Marchand (2004) 

Title 

Literacy scores, human 
capital and growth across 
fourteen OECD countries. 
In: Statistics Canada – 
Catalogue no. 89-552, no 
11. 

Authors: Serge Coulombe, 
Jean-François Tremblay, and 
Sylvie Marchand 

Year of 
publication: 
2004 

Education 
Inputs, 
outputs and 
returns 
considered  

Countries 
covered 

Data used 
and 
timescales 
for data and 
scenarios 

Overview 

Description 
of main 
findings 

Education outputs: literacy skills 
Returns: GDP per capita in purchasing power parities (PPP), GDP per 
worker in PPP 

Belgium, Canada, Switzerland, Germany, Denmark, Finland, United 
Kingdom, ireland, Italy, Netherlands, Norway, New Zealand, Sweden 
United States 

Human capital indicators are based on the results of the 1994 
International Adult Literacy Survey (IALS), which tested the skills of 
individuals aged between 16 and 65 over three domains of literacy – 
prose, quantitative and document.  

In contrast to human capital indicators based on schooling enrolment and 
attainment, these indicators provide a direct measure of the quality of 
human capital and are not subject to the usual problems related to the 
comparability of education systems across countries. 

Data on GDP per capita and GDP per worker is taken from the Penn World 
Tables (version 6.1) 

This is an analysis of the relationship between human capital and growth 
across OECD countries by making the best use of direct measures of 
human capital based on literacy scores. However, this paper is not 
strongly empirical, but rather summarises latest empirical research on the 
topic. 

Findings: 

On the Mankiw, Romer and Weil’s (1992) Cross-Country Estimation they 
explained that MRW’s work aimed at verifying the ability of the Solow 
growth model to “explain international variation in standards of living.” 
The authors assume assumed a Cobb-Douglas production function with 
constant returns to scale and decreasing returns to capital, augmented 
with the exogenous level of technological progress and stock of human 
capital. Following Islam (1995), the authors use a panel approach to 
estimate growth rates for country I at time t by: 

72 

 
 
 
 
 
 
Study on the potential of education to cause macroeconomic imbalances and negative 
spillovers – Annex IV 

∆𝑌i,T = F (Yi,0, Zi, 𝜀i). 

Z collects variables such as the investment ratio and the growth rate of 
population for country i. 

Second, they suggest that raising the average literacy and numeracy skill 
level of the workforce, and reducing the proportion of workers at the 
lowest level of skill, could yield significantly higher levels of growth in 
GDP per capita. An individual country which raises its average literacy 
score by one percent increases labour productivity by 2.5 per cent, and 
GDP per capita by 1.5 per cent (higher than other countries which remain 
at the average literacy level), respectively.  

The results of various regressions suggests that productivity is mostly 
influenced by the effect of skill and human capital accumulation on the 
general labour force, rather than their effect on highly specialized 
employment only. 

One of the most puzzling results in the empirical literature on human 
capital and growth is, however, that, when the sample under study is 
restricted to the OECD countries, the effect of human capital or education 
on economic growth is not significant, sometimes null, or even negative 
(see A4.3.7.14). 

The main limitation repeated by the authors throughout the text is that 
human capital indicators might not be very comparable at the cross-
country level given the wide variety of educational systems around the 
world. 

Link 

http://publications.gc.ca/collections/Collection/CS89-552-11E.pdf  

A4.3.7.6  Schooling  Data,  Technological  Diffusion  and  the  Neoclassical 
Model. In: American Economic Review 91(2), 323-327. 

Table A4.37 de la Fuente, Angel and Rafael Doménech (2001) 

Title 

Schooling Data, Technological 
Diffusion and the Neoclassical 
Model. In: American 
Economic Review 91(2), 323-
327. 

Authors: de la Fuente, Angel 
and Rafael Doménech  

Year of 
publicatio
n: 2001 

Education inputs: years of schooling 
Education outputs: educational attainment obtained (ordinal data similar 
to Barro and Lee) 

Education 
Inputs, 
outputs 
and 
returns 
considered  

Countries 
covered 

Japan, Austria, Belgium, Denmark, Finland, France, Germany (west), 
Greece, Ireland, Italy, Netherlands, Norway, Portugal, Spain, Sweden, 
Switzerland, United Kingdom, Canada, United States, Australia, New 
Zealand. 

73 

 
 
 
 
 
 
Study on the potential of education to cause macroeconomic imbalances and negative 
spillovers – Annex IV 

Data used 
and 
timescales 
for data 
and 
scenarios 

Overview 

Construction of new educational-attainment series for a sample of 21 OECD 
countries (1960-1990). They have collected all the information they could 
find on educational attainment in OECD countries, both from international 
publications and from national sources.  

The data is then compared to previous data by Barro and Lee (1996) - the 
authors suggest that they have increased the signal-to-noise ratio in the 
data by exploiting a number of additional sources not used by Barro and 
Lee. The authors have also attempted to eliminate sharp breaks in the 
series, reinterpreting some of the data by referring to somewhat broader or 
narrower schooling categories than the ones reported by sources. 

The paper argues that poor data quality has previously been the main 
reason for counterintuitive results on human capital and growth, and the 
relevant coefficients will in many cases have been biased towards zero, 
even if the overall specifications of previous research were sensible. The 
authors specifically criticise data previously collected by Barro and Lee, 
and attempt to offer an improved dataset for 21 OECD countries form 
1960-1990. 

The authors then use the new data for a simple growth regression 
(nonlinear least squares approach), which extends an standard aggregate 
production function with human capital by allowing for technological 
diffusion and for differences in permanent total factor productivity (TFP) 
across countries.  

Δqit = Γ0 + 𝛾I + 𝜂t + 𝛼Δkit 

+ 𝛽Δ ℎit + 𝜆bit +𝜀it 

Description 
of main 
findings 

The constructed data differs significantly from Barro and Lee. Whilst barro 
and Lee display a large number of sharp breaks between individual levels 
of attainment. The authors illustrate the improved quality of their data by 
showing the fitted distribution of annualized growth rate of average years 
of schooling for all countries and all years in each country. The range of 
distribution for the new dataset is between 0.15 % and 2%, whilst the 
distribution of Barro and Lee ranges from -1.35 % to 7.8%. The authors 
also eliminate implausible observations, which in the Barro and Lee data 
constituted 15.9 % (negative observations) and 19 % (observations 
exceeding 2% growth in years of schooling on average). 

The regressions performed link guman capital variable ß from the authors’ 
data as well as the Barro and Lee data with total factor productivity. This 
show significant differences between Barro&Lee data (coefficient of -0.057, 
s.e. of 0.88) and the new dataset (coefficient of 0.271, s.e. of 2.53).  

Link 

http://www.iae.csic.es/investigatorsMaterial/a9167122008archivoPdf5015
6.pdf  

A4.3.7.7  Human  capital  in  growth  regressions:  How  much  difference  does 

data quality make?  

Table A4.38 De la Fuente, A. and Domenech, R. (2006) 

74 

 
 
 
 
 
 
 
 
 
 
 
Study on the potential of education to cause macroeconomic imbalances and negative 
spillovers – Annex IV 

Title 

Human capital in growth 
regressions: How much 
difference does data quality 
make?  

Authors: de la Fuente, Angel 
and Domenech, Rafael 

Year of 
publicatio
n: 2006 

Education inputs: years of schooling 
Returns: earnings, employment status 

Education 
Inputs, 
outputs 
and 
returns 
considered  

Countries 
covered 

21 OECD countries: Germany, Australia, Canada, USA, Switzerland, New 
Zealand, Denmark, Austria, Japan, Norway , Finland, Netherlands, 
Sweden, UK, France, Belgium, Ireland, Italy, Greece, Spain, Portugal  

Data used 
and 
timescales 
for data 
and 
scenarios 

Dataset constructed by De La Fuente and Domenech for the purpose of 
this paper only 
1960-1995 

Data sources used: 
1) Census figures and enrolment data to construct time series of 
educational attainment: Barro and Lee (1993, 1996, 2000)  
2) Enrolment data to construct time series of educational attainment: 
Nehru, Swanson, and Dubey (1995) 
3) Educational attainment series for the adult population of a sample of 21 
OECD countries between 1960 – 1990 (1995): Fuente and Doménech 
(2002) or http://iei.uv.es/∼rdomenec/human/human.html. 

De La Fuente and Domenech compare his results with previous findings 
using different databases. 

Overview  Main objective: 

The authors argue that the ‘discouraging’ results obtained in the recent 
empirical literature on the relationship between human capital and growth 
could be partly explained by the poor quality of existing data on 
educational attainment. 
Empirical investigations of the contribution of human capital accumulation 
to economic growth have often produced discouraging results. These 
counterintuitive results on human capital and growth can be attributed to 
deficiencies in the data. The paper shows that improvements in data 
quality lead to larger and more precise estimates of schooling coefficients 
in growth regressions.  
The authors find that methodological differences in the construction of 
different schooling series lead to substantially different results in terms of 
educational attainment levels across countries and of their evolution over 
time. Although the various studies generally coincide when comparisons 
are made across broad regions (e.g., the OECD vs. LDCs in various 
geographical areas), the discrepancies are very important when the focus 
of the analysis centers on industrialized economies.  

The authors develop new estimates of educational attainment for a sample 
of OECD countries for the adult population of a sample of 21 OECD 
countries. They attempted to increase the signal-to-noise ratio27 in these 

27 A signal-to-noise ratio refers to the ratio of useful information to false or irrelevant data in a 
data set. 

75 

 
 
 
 
 
 
 
                                           
Study on the potential of education to cause macroeconomic imbalances and negative 
spillovers – Annex IV 

Description 
of main 
findings 

data by using multiple data sets and previously unexploited information. 
The time-series are prepared using a two-step approach. First, time-series 
are prepared based on information contained in international publications 
and national sources. Second, the authors engaged with education 
correspondents in the statistical offices of member countries, who 
validated the data and provided additional information to help improving 
the time-series.  
The authors use their indicators and a number of previously available 
estimates of educational attainment and examine their performance in 
several growth specifications. 

Main empirical findings: 
1) The authors find a clear positive correlation between data quality and 
the size and significance of human capital coefficients in growth 
regressions, i.e. indicators that account for the signal-to-noise ratio 
and data breaks show a stronger correlation between educational 
attainment and growth in comparison with other standard measures of 
educational attainment. Using different data sets on schooling leads to 
different growth results, as evidenced by different coefficients in the 
Cobb–Douglas aggregate production function. 

2) The true value of the elasticity of output with respect to years of 

schooling is almost certainly above 0.60, that is, at least twice as large 
as the largest estimate of reference in the previous literature. 

3) They also found that there is a systematic relationship between data 
quality and the size of ordinary least squares (OLS) estimates of the 
coefficient of human capital.  

4) The overall correlation between data sources on estimates of average 
years of schooling is high with industrialized countries and socialist 
economies display much higher attainment rates than the least 
developed countries (LDCs). However, there are significant 
discrepancies between the data sets, both over time and across 
countries.  

Measuring Data Quality 
The authors demonstrate that the information content is higher in recent 
datasets than in older ones by constructing “reliability ratios”. The 
research shows that more reliable datasets provide higher OLS estimates 
of the coefficient of human capital. This is due to smaller measurement 
errors. De La Fuente and Domenech conclude that if measurement error 
was null, the “true value of β would be of at least 0.50”. 

Overall conclusions: 
The results clearly support the view that weak data are likely to be one of 
the main reasons for the discouraging results obtained in the recent 
empirical literature on human capital and growth, as does the work by 
Krueger and Lindhal (2001) and Cohen and Soto (2001), and suggest that 
the contribution of investment in education to productivity growth is 
sizable.  

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spillovers – Annex IV 

Link 

http://www.iae.csic.es/investigatorsMaterial/a9167121855archivoPdf2774
.pdf 

A4.3.7.8  Optimal 

investment 
Economics of Education Review  

in  human  capital  under  uncertainty. 

Table A4.39 Groot, W. and Oosterbeek, H. (1992) 

Title 

Optimal investment in human 
capital under uncertainty. 
Economics of Education 
Review  

Authors: Wim Groot and 
Hessel Oosterbeek  

Year of 
publication: 
1992 

Education outputs: years of schooling 
returns: earnings, employment status 

Education 
Inputs, 
outputs 
and 
returns 
considered  

Countries 
covered 

Data used 
and 
timescales 
for data 
and 
scenarios 

Netherlands 

Data from the Dutch Central Bureau of Statistics and the US Bureau of the 
Census 
Unemployment rate (For the Netherlands: Labour Force Survey 1979 and 
1985; For the US: Statistical Abstracts of the USA); earnings (For the 
Netherlands: Wage structure survey 1979 and Annual Wage Survey 1985; 
For the US: Population reports “Money Income of Households, Families 
and Persons in the United States”) 

Overview  Main objective:  

Most human capital models assume that future income streams are known 
with certainty. Actually, many decisions regarding human capital are 
taken long before workers enter the labour market. School goers, for 
example, generally have very limited knowledge about employment and 
the labour market as well as returns on investment. The authors 
attempted to build a human capital model that accounts for uncertainty 
and unemployment. 

Method/Approach:  
The authors extend the human capital model taking into account three 
empirical facts: 

 

Individuals consider future employment/unemployment prospects 
when making decisions about the length of schooling; 

  Post-education earnings are not known with certainty; 

 

Job opportunities rise with length of schooling, as it is possible to get a 
job below one’s educational level but not above. 

The authors build a life-time income maximising model to determine the 
optimal length of schooling, taking into consideration the possibility of 
unemployment and the uncertainty regarding future income. The notion of 
uncertainty is important in the authors’ assumptions, as it introduces 
variance with respect to the opportunities offered by schooling and the 

77 

 
 
 
 
 
 
 
 
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spillovers – Annex IV 

Description 
of main 
findings 

returns on the investment in schooling. This is to say that schooling 
decisions are typically taken at an age at which an individual has a very 
limited knowledge about the labor market. 
According to the model, the optimal length of schooling decreases with: 
(i) a decrease in the derivative of earnings with respect to schooling, (ii) 
an increase in the direct costs of schooling, an increase in the discount 
rate, (iii) and an increase in the educated wage range. 
Since people with higher educational attainment have a lower chance of 
becoming unemployed and vice versa, the authors took the probability of 
unemployment as a function of the length of schooling and assumed that 
it will remain the same over time. They also assumed that unemployment 
benefits were independent of previous earnings, that unemployment did 
not depreciate wages, and that workers would not accept wages lower 
than unemployment benefits. 

In the empirical studies, earnings of males aged between 35 and 44 and 
working full-time were used to control for the effects of gender, 
participation behaviour and work experience. 

Main empirical findings:  

1)  In the Netherlands, the unemployment rate decreased with the level of 
education, while in the US, the unemployment rate was highest for 
high-school dropouts and lower for individuals who attended 
elementary school, but did not start high school (Table 1- 4, p476): 

  Netherlands: individuals with ‘Primary school’ as maximum level of 
education experienced 23% unemployment, while individuals with 
‘University’ as maximum level of education experienced 5% 
unemployment (in 1985). 

  US: individuals with ‘Elementary school’ as maximum level of 

education experienced 5.58% unemployment, while individuals with 
‘College’ as maximum level of education experienced 4.32% 
unemployment (in 1985). 

2)  Earnings increased with the level of education in both countries. In the 

Netherlands, the highest rate of return was observed for higher 
vocational education, while in the US, it was completed college (1979) 
and completed high school (1985). 

  Netherlands: individuals with ‘Primary school’ had a marginal return to 
investment on education of 2.73%, while individuals with ‘University’ 
had a marginal return to investment on education of 6.18% (in 1985). 
The highest marginal return to investment on education can be found 
among those that reached ‘Higher vocational’ training (10.17%) 

  US: individuals with ‘Elementary school’ had a marginal return to 

investment on education of 5.02%, while individuals with ‘College’ had 
a marginal return to investment on education of 9.79% (in 1985). The 
highest marginal return to investment on education can be found 
among those that reached ‘High School training (12.73%) 

3)  The model also suggested a drop of 12-20% in the rate of return to 

education between 1979 and 1985 in the Netherlands, while for most 
educational levels the rate of return increased in the US. 

4)  Uncertainty has a depressing effect on the rates of return, but the 

magnitude of the effect is small.  

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Overall conclusions: 
1)  Differences between different levels of schooling in unemployment rates 

and uncertainty in earnings tend to decrease the rates of return 
slightly. 

2)  The decreased rate of return may result from changes in employment 
opportunities (i.e. changes at the level of industries or the overall 
economy) and uncertainty (i.e. limited knowledge of individuals about 
the labor market). 

Link 

http://www.economists.nl/files/20070906-GrootOosterbeekEER1992.pdf  

A4.3.7.9  Schooling,  Labor-Force  Quality  and  the  Growth  of  Nations.  In: 

American Economic Review 90(5), 1184-1208. 

Table A4.40 Hanushek, Eric A. and Dennis D. Kimko (2000) 

Title 

Schooling, Labor-Force 
Quality and the Growth of 
Nations. In: American 
Economic Review 90(5), 
1184-1208. 

Authors: Hanushek, Eric A. 
and Dennis D. Kimko.   

Year of 
publication: 
2000 

Education input: measure of schooling quality QL1 developed by authors, 
explained below 
Education outputs: years of schooling 
Returns: real per capita GDP growth 

Education 
Inputs, 
outputs 
and 
returns 
considered  

Countries 
covered 

Data used 
and 
timescales 
for data 
and 
scenarios 

Australia, Belgium, Brazil, Canada, Chile, China, Finland, France, Germany 
(West), Hong Kong, Hungary, India, Iran, Ireland, Israel, Italy, Japan, 
Jordan, Korea (republic of), Luxembourg, Mozambique, Netherlands, New 
Zealand, Norway, Philippines, Poland, Portugal, Singapore, Spain, 
Swaziland, Sweden, Switzerland, Taiwan, Thailand, United Kingdom, 
United States, USSR. 

Data for country-level human capital is average years of schooling of the 
entire population fis the 1991 version of the Barro and Lee database 
Growth data used are baseline estimates of 1960-1990 growth measured 
in real per capita GDP sourced from Summers and Heston. 
To construct a measure of schooling quality, the authors use direct 
observations of cognitive skills (mathematics, science skills) are included 
for 31 countries, building on six voluntary international tests of student 
achievements in mathematics and science. Four of these tests were 
undertaken by the International Association for the evaluation of 
Educational Achievement (IEA), and two by the International Assessment 
of Education Progress organisation (IAEP). 

Overview 

This paper constructs new measures of human capital quality, based on 
on student cognitive performance on various international tests in 
mathematics and science. Labour force quality differences this way prove 
to have extremely strong effects on growth rates. 

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The paper argues that previous attempt to measure human capital, 
through proxies of enrolment in primary or secondary schools (Romer, 
Barro) and similar data have not accurately represented either the 
relevant stock of human capital of the labor force of changes in this stock 
during periods of educational and demographic transition. Whilst Barro 
and Lee (1993) have improved the data and introduced schooling stock 
variables based on individual country survey and census data, a main 
problem remained the lack of adjustment for the quality of schooling in 
individual countries.  

The innovative aspect of this research is that it uses deviation from the 
mean results in a large variety of cognitive skills tests of students as a 
variable for the quality of schooling (QL1) and, in an alternative scenario, 
deviations from the U.S. international performance in the cognitive skills 
tests considered (QL2).  

Description 
of main 
findings 

The paper finds, for a sample of 31 countries outlined above, that the 
Barro-Lee measure of schooling quantity, together with initial per capita 
income in 1960, produces a R2 value of 0.33. When the new measure of 
labour force quality QL1 is added, the R2 value changes to 0.73, a highly 
significant result. 

Link 

http://www2.econ.iastate.edu/classes/econ520/Huffman/documents/Scho
olingLaborForceQualityandtheGrowthofNations.pdf  

A4.3.7.10  The Role of Cognitive Skills in Economic Development 

Table A4.41 Hanushek, Eric A. and Ludger Woessmann (2008). 

Title 

The Role of Cognitive Skills in 
Economic Development 

Authors:  Eric A. Hanushek 
and Ludger Woessmann  
Publisher: Journal of 
Economic Literature  

Year of 
publication: 
2008 

Education outputs: highest level of school completed, literacy 
Returns: earnings, GDP per capita 

Education 
Inputs, 
outputs 
and 
returns 
considered  

Countries 
covered 

Data used 
and 
timescales 
for data 
and 
scenarios 

Numerous countries are covered in the different literature reviewed 

1) Psacharopoulos and Patrinos (2004) for measuring impacts of school 
attainment on individual income; multiple authors estimated these effects 
for African countries (Table 1, p. 622). 
2) OECD Adult Literacy Survey (IALS) – measuring cognitive skills28  
3) Hanushek and Zhang (2008) estimate returns to school attainment and 
to literacy scores for the thirteen countries where continuous measures of 
individual earnings are available.  

28 Note: the study dates back to 2008 – In 2013, the OECD published results from the Survey of 
Adult skills (PIAAC) that are not reflected in the study. 

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4) Alan Heston, Robert Summers, and Bettina Aten (2002) for GDP per 
capita 1960 – 2000 
5) Hanushek and Kimko (2000) and Woessmann (2002, 2003) added 
international achievement tests to measure the impact on the GDP. 
6) OECD PISA test survey 
7) IEA TIMMS and IEA PIRLS 

Overview  Main objective: 

Description 
of main 
findings 

The main objective of the paper is to summarise the existent empirical 
evidence on the relationship between cognitive skills and individual 
earnings, distribution of income, and economic growth. A particular 
attention is paid to the robustness of the relationship between education 
and economic outcomes across tests of alternative specifications and 
hypotheses about the underlying determinants of outcomes.  

Method / approach: 
The authors develop a conceptual framework (expressed in eight 
equations shown in pp. 610 – 612) to discuss the relationship between 
cognitive skills and individual earnings, distribution of income, and 
economic growth. However, the evidence presented in the paper comes 
from reviewing other exiting empirical studies. 

Main findings: 

  Evidence of the impact of school attainment on individual incomes 
There are several studies that show that more schooling is associated with 
higher individual earnings (Psacharopoulos (1994), Card (1999),  
Harmon, Oosterbeek, and Walker (2003) Psacharopoulos and Patrinos 
(2004), and Heckman, Lochner, and Todd (2006)).  “The rate of return to 
schooling across countries is centered at about 10 percent for a person’s 
working life with variations in expected ways based largely on scarcity: 
returns appear higher for low income countries, for lower levels of 
schooling, and, frequently, for women (Psacharopoulos and Patrinos 
2004)”.  
There is also evidence of positive spillovers / externalities of education, 
and, consequently of a relatively higher social return on investment to 
education in comparison with the private return. “Recent studies indeed 
find evidence of externalities of education in such areas as reduced crime 
(Lochner and Enrico Moretti 2004), improved health of children (Janet 
Currie and Moretti 2003), and improved civic participation (Thomas S. Dee 
2004, Kevin Milligan, Moretti, and Oreopoulos 2004)”. 

 

Impacts of Cognitive Skills on Individual Incomes—Developed 
Countries 

New studies have emerged looking at the relationship between cognitive 
skills on individual incomes. These studies have benefitted from new 
existing longitudinal datasets that link cognitive achievement measure 
(students’ performance on standardized test) with subsequent labour 
market outcomes. 
These analyses typically find that measured achievement has a clear 
impact on earnings after controlling for differences in the quantity of 
schooling, the experiences of workers, and other factors that might also 
influence earnings. Most of the studies used US data. 
A limited number of additional studies are available for developed 
countries outside of the United States. For instance, Steven McIntosh and 
Anna Vignoles (2001) find strong returns between numeracy and literacy 
skills and wages in the United Kingdom. 

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  Evidence from the International Adult Literacy Survey (IALS) 
Hanushek and Zhang (2008) estimate returns to school attainment and to 
literacy scores for the thirteen countries where continuous measures of 
individual earnings available. With the exception of Poland, literacy scores 
have a consistent positive impact on earnings. The (unweighted) average 
of the impact of literacy scores is 0.093 (i.e. one standard deviation 
increase wages in 9.3%).  

  Quantity of Schooling and Economic Growth 
From a theoretical viewpoint: 
- education increases the human capital inherent in the labour force, which 
increases labour productivity and thus transitional growth towards a higher 
equilibrium level of output  
- education may increase the innovative capacity of the economy, and the 
new knowledge on new technologies, products and processes pro motes 
growth  
- education may facilitate the diffusion and transmission of knowledge 
needed to understand and process new information and to implement 
successfully new technologies devised by others, which again promotes 
economic growth  
There is vast literature that relies on cross-country growth regressions and 
has identified a strong positive correlation between quantitative measures 
of schooling (usually school attainment) and economic growth. The 
authors own estimations show that each year of schooling is statistically 
significantly associated with a long-run growth rate that is 0.58 percentage 
points higher (measured in real per capita GDP). The association is 
somewhat lower (at 0.32 percentage points higher) but still significant 
when the model control by regional differences (i.e. regional dummies are 
added to the regression). 
A considerable controversy has emerged in the literature about whether it 
is the level of years of schooling (as would be predicted by several models 
of endogenous growth) or the change in years of schooling (as would be 
predicted in basic neoclassical frameworks), which is the more important 
driver of economic growth. The authors add the change in years of 
schooling over 1960–2000 to their estimations and find that change in 
years of schooling is not statistically significant with the sole exception of 
the sample of twenty-three OECD countries, and there it is sensitive to the 
inclusion of Korea. 

  Cognitive Skills and Economic Growth 
Measuring cognitive skills and economic growth, instead of school 
attainment, in growth regression improves the significance of the 
contribution of education to economic growth. Using data on national 
student achievement tests through Hanushek and Dennis D. Kimko (2000) 
find a statistically and economically significant positive effect of the 
cognitive skills on economic growth in 1960–90. 

Summary of main empirical findings found in the literature: 
1) Models that include direct measures of cognitive skills can account for 
about three times the variation in economic growth than models that 
include only years of schooling; including the cognitive skills measures 
makes the coefficient on years of schooling go to zero; and the 
estimates of such more inclusive models are far more robust to 
variations in the overall model specification.  

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2) The rate of return to schooling across countries is centered at about 10 
percent with variations in expected ways based largely on scarcity: 
returns appear higher for low-income countries, for lower levels of 
schooling, and, frequently, for women. 

3) Each year of schooling is statistically significantly associated with a 
long-run growth rate that is 0.58 percentage points higher than 
without the additional year of schooling.  

4) Adding the international achievement test measures to a model that 
includes only a base specification (initial income and educational 
quantity) considerably helps to explain the variance in the GDP per 
capita growth (in the study model R2 increased from 0.25 to 0.73).  
5) The effect of cognitive skills on economic growth is indeed significantly 

higher in the sample of non-OECD countries (at 0.56 percentage 
points higher real per capita GDP growth per additional year of 
schooling) than in the sample of OECD countries (at 0.26 percentage 
points higher real per capita GDP growth per additional year of 
schooling).  

7) Additionally, the authors simulate changes in GDP due to reforms that 
aim at increasing PISA scores. They model different scenarios in term 
of the time it takes for the reform to be implemented. Irrespectively of 
the timeframe all reforms achieve a 0.50 standard deviation 
improvement in PISA scores (p649). The model also assumes 
expenditure in education of 3.5% of GDP. The simulation shows that, 
for instance, a twenty-year reform implemented in 2005 (and 
completed in 2025) would have led to a 5% greater real GDP by 2037. 
A thirty-year reform (completed in 2035), in turn, would have yielded 
more than 5% higher real GDP by 2041. 

Overall conclusions: 
There is strong evidence that the cognitive skills of the population—rather 
than mere school attainment—are powerfully related to individual earnings 
and to economic growth. New empirical results show the importance of 
both minimal and high level skills, the complementarity of skills and the 
quality of economic institutions, and the robustness of the relationship 
between skills and growth. International comparisons incorporating 
expanded data on cognitive skills reveal much larger gaps in education 
outputs between developing countries and OECD countries than deficits 
highlighted from data containing school enrollment and attainment.  

Link 

http://hanushek.stanford.edu/sites/default/files/publications/Hanushek%2
BWoessmann%202008%20JEL%2046(3).pdf  

A4.3.7.11  The  Economics  of  International  Differences 

in  Educational 
Achievement.  In:  Eric  A.  Hanushek,  Stephen  Machin  and  Ludger 
Woessmann (eds.). Handbook of the Economics of Education  

Table A4.42 Hanushek, E. and Woessmann, L. (2010) 

Title 

The Economics of 
International Differences in 
Educational Achievement. In: 
Eric A. Hanushek, Stephen 
Machin and Ludger 

Authors: Eric A. Hanushek, 
Ludger Woessmann  

Year of 
publication: 
2010 

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Woessmann (eds.). 
Handbook of the Economics 
of Education  

Education outputs: cognitive skills 
Returns: earnings, per-capita GDP, income distribution 

Education 
Inputs, 
outputs 
and 
returns 
considered  

Countries 
covered 

Data used 
and 
timescales 
for data 
and 
scenarios 

Various groups of countries covered in the literature reviewed – the 
empirical analysis performed by authors themselves focusses on 
1) OECD: International Adult Literacy Survey (IALS)29  
2) OECD: PISA data 
3) Hanushek and Kimko (2000) and Woessmann (2002, 2003) added 
international achievement tests to measure the impact on the GDP 
4) Hanushek and Zhang (2009) for the returns to cognitive skills (based 
on IALS results) 
5) Lynn and Vanhanen (2002, 2006) for the international IQ scores  

Overview  Main objective: 

The objective of this paper is to review the economic literature on 
international differences in educational achievement, restricting itself to 
comparative analyses that are not possible within single countries and 
placing particular emphasis on studies trying to identify suitable 
variables/measures that could be used in empirical analysis. The authors 
look into the relationship between the level of cognitive skills, individual 
earnings, income distribution and economic growth. 

Method / approach: 
The paper contains an extensive literature review and complements this 
with empirical analysis. The economic literature review focuses on two 
type of studies both different directions: research designed to understand 
the (i) underlying determinants of cognitive skills and (ii) research focused 
on the consequences of skill differences. The authors also provide an 
overview and critical assessment of the different available international 
datasets on educational achievement.  
 The literature review includes the following topics: 

  Table 5: Within-country studies on student background and 

educational achievement pp 97-98 

  Table 6: Cross-country studies on student background, school inputs, 

and educational achievement pp 99-100  

  Table 8: Within-country studies on institutions and educational 

achievement, p102 

  Table 9: Cross-country studies on institutions and levels of educational 

achievement, pp103-104 

  Table 10: Cross-country studies on institutions and equity of 

educational achievement, p105 

  Table 11: Studies on cognitive skills and individual labour-market 

outcomes, p106 

29 Note: Since 2013, data on the sequel to IALS, called PIAAC, have been available. 

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Description 
of main 
findings 

Each table includes information on the name of the study, countries in the 
empirical analysis, topic of investigation (e.g. Skill compression and wage 
distribution, measurements (e.g. achievement, labour-market outcomes, 
institutions), estimation method (e.g. Cross-section log-linear OLS), and 
results  

Summary of main empirical findings found in the literature: 
1) When cognitive skills are added to a model that just includes initial 
income and years of schooling, the share of variation in economic growth 
explained by the model (the adjusted R2) jumps from 0.25 to 0.73. 
2) The conclusion from the various models of the impact of national IQ 
scores on economic outcomes is that IQ provides another potential 
measure of cognitive skills.  
3) Variations in cognitive skills explain 3-13 percent of variations in 
earnings depending on gender and the specific distributional measure, but 
they are not the dominant determinant. Differences in prices, i.e. the 
rewards to different levels of skills, explain a considerably larger part of 
the observed variation (between 28-55 per cent) 
4) After controlling for the initial level of GDP per capita and for years of 
schooling, the test-score measure of math and science skills features a 
statistically significant effect on the growth in real GDP per capita for 
1960-2000. (Hanushek and Woessmann 2009 find a correlation coefficient 
of 2.015, with a s.e. of 10.68) 

Overall conclusions: 
1) Variations in skills measured by the international tests are strongly 
related to individual labor-market outcomes and, perhaps more 
importantly, to cross-country variations in economic growth.  

2)  Determinants of international educational achievement:  
-  Students’ family background is an important factor for educational 

achievement.  

-  There is limited evidence on the relationship between school inputs 

(e.g. class size, expenditure levels) and educational achievement. 
3) Economic Consequences of International Educational Achievement: 
-  Cognitive Skills and Individual labour-Market Outcomes: Evidence 

suggest that both skills and formal education play a role explaining 
labour market outcomes 

-  Cognitive Skills and the Distribution of Economic Outcomes: Cognitive 

skills do play a role in explaining earnings differentials. 

-  Cognitive Skills and Macroeconomic Growth: The vast literature of 

cross-country growth regressions has tended to find a significant 
positive association between quantitative measures of schooling and 
economic growth. But, average years of schooling are a particularly in 
complete and potentially misleading measure of education for 
comparing the impacts o human capital on the economies of different 
countries. Empirical analyses building on aggregate measures of 
cognitive skills have reached dramatically different conclusions than 
most of the prior growth analysis built on school attainment measures 
of human capital. 

Link 

http://ftp.iza.org/dp4925.pdf  

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A4.3.7.12  How Much Do Educational Outcomes Matter in OECD Countries? 

Table A4.43 Hanushek, Eric and Woessmann, Ludger (2011a) 

Title 

How Much Do Educational 
Outcomes Matter in OECD 
Countries? 

Authors: Eric A. Hanushek and 
Ludger Woessmann;  
Publisher: Economic Policy 

Year of 
publicati
on: 2011 

Education inputs: cumulative expenditure per student 
Education outputs: Cognitive skills (test score), years of schooling, 
highest level of attainment (educational attainment) 
Returns: GDP per capita 

Education 
Inputs, 
outputs 
and 
returns 
considered  

Countries 
covered 

All 24 OECD member states are covered: Australia, Austria, Belgium, 
Canada, Czech Rerpublic, Denmark, Finland, France, Gemrany, Greece, 
Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, Mexico, 
Netherlands, new Zealand, Norway, Poland, Portugal, Slovak Republic, 
Spain, Sweden, Switzerland, Turkey, United Kingdom, United States.  

Data used 
and 
timescales 
for data 
and 
scenarios 

Cognitive skills (test score), years of schooling, years, country, cumulative 
expenditure per student according to country (OECD, 2007). 

Quantitative educational attainment data are taken from the latest version 
of the Barro and Lee (2010) database (data version 1.0, 3/10);  

Average annual growth rate in GDP per capita, percent addition to annual 
GDP, percent increase in GDP, GDP per capita, PISA math score from 
OECD  

GDP per capita and GDP growth per capita data are taken from the Penn 
World Tables [Heston et al., 2002]. 
NB. Data on cognitive skills and economic growth was obtained for the 24 
OECD countries. However, some data from an expanded group of 50 
countries including non-OECD countries were also used. In this case, there 
was a trade-off between adding observations and restricting economic 
relationships for uniformity across countries. 

Overview  Main objective: 

Despite more research into the economics of growth, there is a dearth of 
robust evidence for policies that might promote long-run growth. Most of 
the robust evidence is regarding basic economic institutions with policy 
implications for developing countries or short- to medium-term growth in 
developed countries. Moreover, there is great variation among the long-
run growth experiences of different developed countries. Therefore, the 
authors wished to investigate whether improved human capital, as 
measured by cognitive skills, could explain long-run growth differences 
among OECD countries.  

Method/approach:   
The authors use an ‘endogenous growth’ model where improved skills 
yield a permanently higher growth rate and a ‘neoclassical growth’ model 
where skills affect the level of income but effects on growth are transitory 
and the economy returns to its previous long-run growth rate. 
Cognitive skills were measured according to Hanushek and Woessmann 
(2009). Test scores obtained from international tests of maths, science 

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and reading were normalised for each age group and subject to the PISA 
standard of mean 500 and individual standard deviation of 100. Then all 
the available test scores were aggregated to the country level and 
interpreted as an index of the human capital of the population of each 
country. If scores [and skills] change over time, some measurement error 
will be introduced. 
The authors rejected the ‘average years of schooling’ measure for 
evaluating human capital as it assumes that a year of schooling delivers 
the same increase in knowledge and skills regardless of cross-country 
variations in factors like quality, education system, family background, 
etc. 
The authors use cross-country growth regressions for years 1960-2000 
for different measures of educational achievement. The usual limitations 
of reverse causality and omitted variable bias, thus apply.  
Three education reform scenarios were simulated: 
(i) 

increase average performance by 25 PISA points (one-quarter 
standard deviation),  
bring each country to the Finland average i.e. 546 PISA points, and  
bring all countries to a minimum of 400 PISA points. 

(ii) 
(iii) 

Description 
of main 
findings 

For the projections, the authors calculated the time path of the annual 
growth rate engendered by education reform, modelled the future 
expansion of GDP with and without the education reform, and calculated 
the total value of the reform by aggregating the discounted values of the 
annual differences between the GDP with reform and the GDP without 
reform. Sensitivity analyses were performed for alternative parameter 
choices such as growth, time horizon, speed of reform, working life, etc. 

The analysis also has its limitations: the model cannot take account of the 
different drivers of growth for the countries analysed, nor their change 
over time; the measurement of human capital is reduced to one single 
index; migration is not included. 

Main empirical findings:  

1)  OECD-sample growth variance explained by the model increases from 
56% to 83% when measuring human capital by cognitive skills rather 
than years of schooling  

2)  Years of schooling in 1960 are significantly associated with average 

annual growth rates in GDP per capita from 1960-2000. However, this 
association becomes insignificant when the test-score measure of 
cognitive skills in included. The latter measure shows a highly 
significant association with growth. 

3)  The current value of reforms in education varies by country, depending 

on current economic and educational performance.  

4)  Basic skills have substantial growth pay-offs in OECD countries and the 

return to top skills is lower, not higher, in the OECD. 

5)  Evidence from both within and across countries shows a positive impact 
of competition among schools, of accountability and student testing, 
and of local school autonomy in decision making.  

6)  Simulation exercises suggest that the aggregate gains across all OECD 
countries range from $90 trillion to $275 trillion for the different policy 
alternatives tested. Sensitivity analyses of these simulations by 
changing specifications and parameters consistently show that the large 
returns to schooling remain. In terms of each scenario (Table 7, p55): 

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(i) 

(ii) 

(iii) 

If all OECD countries boost their average PISA scores by 25 points 
an aggregate gain of $90–123 trillion of OECD GDP (0.47% higher 
annually) is expected. 
if all the OECD countries achieve the Finnish PISA score (546 
points), the gain is expected to be around $275 trillion (average 
annual growth rate 0.93% higher),  
If all countries achieve a minimum score of 400, the aggregate 
gain is expected to be $226 trillion (average annual growth rate 
0.8% higher).  

These figures have been calculated for growth rates in 2070 following the 
introduction of reforms in 2010. 

Overall conclusions:  
7)  Within the OECD, long-run growth is closely related to cognitive skills 

as measured on international achievement tests.  

8)  Quality of a country’s economic institutions such as having secure 
property rights or an open economy do not help us understand 
differences in long-run growth rates among OECD countries.  

9)  Tertiary attainment plays no specific role in OECD growth.  
10) Direct measures of educational outcomes such as cognitive skills 

measures from international achievement tests emerge as a strong 
policy factor underlying long-run growth differences across countries. 

The question of how to improve human capital still remains for policy 
makers. Simply increasing school resources does not guarantee results. 
Emerging research suggests that teaching quality and institutional 
elements involving choice, competition, accountability, school autonomy, 
etc. are important for higher achievement.  

Link 

http://www.econstor.eu/bitstream/10419/46396/1/640577113.pdf  

A4.3.7.13  Do better schools lead to more growth? Cognitive skills, economic 

outcomes, and causation 

Table A4.44 Hanushek, Eric A. and Ludger Woessmann (2012) 

Title 

Do better schools lead to more 
growth? Cognitive skills, 
economic outcomes, and 
causation 

Authors: Eric A. Hanushek 
and Ludger Woessmann  
Publisher: Journal of 
Economic Growth 

Year of 
publication: 
2012 

Education outputs: cognitive skills, years of schooling 
Returns: real per capita GDP growth rate 

Education 
Inputs, 
outputs 
and 
returns 
considered  

Countries 
covered 

64 countries are covered by this paper – a list of countries covered is 
provided in Table 11(pp. 308-309).  

Data used 
and 

Data sources: 
Average annual growth rate in GDP per capita, 1960–2000.  

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Cognitive skills (data on cognitive skills from 64 countries); years of 
schooling (from an extended version of the Cohen and Soto (2007) data).  

timescales 
for data 
and 
scenarios 

Overview  Main objective:  

To develop a new metric for the distribution of educational achievement 
across countries that can further track the cognitive skill distribution within 
countries and over time. 

There is a vast body of literature that has established a strong positive link 
between quantitative measures of schooling and economic growth. 
However, analyses using average years of schooling as the human capital 
measure implicitly assume that a year of schooling delivers the same 
increase in knowledge and skills regardless of the education system. This 
type of analysis also fails to account for the quality of non-school factors 
and assumes that formal schooling is the primary source of education. 
The authors challenge that assumption by setting a growth model that uses 
‘cognitive skills’, rather than years of schooling, as a proxy for human 
capital.  

Method/approach:  
The authors use a transformation of the PISA maths and science scores to 
arrive to a score that can be compared across countries and over time. 
Each PISA assessment has varying country participation and has different 
test construction so that the variance of scores for each assessment cannot 
be assumed to be constant. Their approach is built on the observed 
variations of country means for a group of countries that have well 
developed and relatively stable educational systems over the time period. 
The authors then use cross-country growth regressions to measure the 
relationship between the assessment scores and country GDP growth (from 
50 countries in the period 1960-2000). 
Possible problems with reverse causality and omitted country variables are 
dealt with using alternative estimation approaches, including instrumental 
variables, difference-in-differences among immigrants in the U.S. labour 
market, and longitudinal analysis of changes in cognitive skills and growth 
rates. 

Description 
of main 
findings 

Main empirical findings:  

1)  One standard deviation increase in test scores is associated with a two-
percentage point higher average annual growth rate in GDP per capita 
across 40 years. – This is based on the same regressions presented by 
the authors previously. 

2)  Sensitivity analyses show that the estimated effect of cognitive skills on 
growth is fairly robust when faced with different samples, specifications 
and measurements.   

3)  Basic literates and high performers have independent relationships with 

growth, the latter being larger in poorer countries.  

Overall conclusions 

1)  Cognitive skills are important for explaining international differences in 

long-run growth rates.  

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2)  School policy can be an important instrument to spur growth, but simply 

investing in further schooling without ensuring commensurate 
improvements in cognitive skills does not lead to economic returns.  

3)  Cross-sectional growth regressions provide insights into long-term 

development but their interpretation may be affected by endogeneity 
biases. 

Link 

http://hanushek.stanford.edu/sites/default/files/publications/Hanushek%2B
Woessmann%202012%20JEconGrowth%2017(4).pdf 

A4.3.7.14  Growth Empirics: A panel data approach.  

Table A4.45 Islam, Nazrul (1995) 

Title 

Growth Empirics: A panel data 
approach.  

Author: Nazrul Islam  
Publisher: The Quarterly 
Journal of Economics. 

Year of 
publicatio
n: 1995 

Education outputs: average years of schooling in the total population over 
the age of 25 
Returns: real per capita GDP growth 

96 countries in total are included in the data, covering 17 European 
countries: Austria. Belgium, Denmark, Finland, France, Germany, Greece, 
Ireland, Italy, Holland, Norway, Portugal, Spain, Sweden, Switzerland, 
Turkey, United Kingdom 

Data sources: 
1) Summers-Heston (1988) for the data sets on growth empirics 
2) Barro and Lee (1993) for the data on human capital in a wide selection of 
countries  

Education 
Inputs, 
outputs 
and 
returns 
considere
d  

Countries 
covered 

Data used 
and 
timescale
s for data 
and 
scenarios 

Overview  Main objective: 

The main objective of the paper is to advocate and implement a panel data 
approach by allowing for differences in the production function across 
countries. This paper advances the seminal work of Mankiw, Romer and 
Weil (1992) 30(Table A2.48, M-R-W) and is one of the first papers to 
implement a panel data approach. 

Method / approach: 
The virtues of the panel data approach are exemplified using the M-R-W 
approach to the Solow mode that uses a Cobb-Douglas production function 
with labour augmenting technological progress. The M-R-W approach was 
applied to single-countries and the author shows the difference in results 
when applying the model to a panel data set. 

30 Mankiw, Gregory, David Romer and David Weil (1992). A Contribution to the Empirics of Economic Growth. 
In: Quarterly Journal of Economics 107(2) , 407-437. 

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spillovers – Annex IV 

Descriptio
n of main 
findings 

In terms of empirical strategy, the author uses a Least Square with Dummy 
Variables dummy (LSDV) estimator and Minimum Distance (MD) estimator. 
To keep consistent with the M-R-W the author conducts the analysis for 
three samples: (1) NONOIL (79 countries), (2) INTER (67 countries), (3) 
OECD (22 countries).  

Thus, instead of using average values across a time span, the author uses 
values from every five years between 1960-1995 to generate panel data on 
human capital and growth, which is used in a growth regression. 

Main empirical findings: 
Results are shown pp1140-1146 

  Single country estimation: Shows a very slow rate of conversion and a 
high level of output with respect to capital, which is one of the reasons 
why M-R-W suggest including human capital a as production factor. 
(NONOIL 0.8346; INTER, 0.7628; OECD 0.6036 for the LSDV 
estimations. Table IV, p1146). 

  LSDV and MD estimations: return to capital becomes smaller and in line 
with more ‘accepted values’ (NONOIL 0.4398; INTER, 0.4575; OECD 
0.2047 for the LSDV estimations. Table IV, p1146). 

Introducing human capital in the regression model, the author finds a 
number of surprising results. One of the most puzzling results of this 
research is that effects of human capital or education on economic growth 
is not significant, sometimes null, or even negative, using the HUMAN 
variable from the Barro and Lee data (average schooling years in total 
population over the age of 25). This depends heavily on the regression 
model used. In single cross-sections Islam finds a coefficient of 0.1335 and 
0.1062 (both not statistically significant) for the INTER and OECD sample, 
outlined above. When using pooled estimation, the results change rather 
drastically. He finds coefficient values of 0.0544 (NONOIL sample), -0.0077 
(INTER sample) and 0.0174 (OECD sample), none of which are statistically 
significant. Using his method of panel estimation, he finds coefficients of -
0.1990 (NONOIL, marginally significant), -0.0069 (INTER, not significant) 
and -0.0450 (OECD, not significant). The authors offers several 
explanations for these surprising results. First, he attributed a large margin 
of measurement error to the introduction of the temporal dimension, i.e. 
the panel character of the used human capital data. He points to De 
Gregorio (1991), who has produced similar results. He also suggests that, 
as the the Barro and Lee data on years of schooling are a very partial 
measure of the rate of investment in human capital and did not account for 
differences in the quality of schooling, particularly less developed countries 
appear to have progressed, however the true levels of human capitals have 
not increased that much. Islam therefore assumes that for his approach, a 
“negative temporal relationship between the human capital variable used 
and economic growth within countries […]. Since panel estimators rely 
more on ‘within’ variation, we find that in panel results the negative 
temporal relationship surfaces more forcefully”. The second response 
offered by the author is simply that the production function including 
human capital should be more complex, allowing for interactions between 
productivity (A), human capital (H) as well as growth and allowing for 
diffusion of technology between countries representing the productivity 
“frontier” and follower countries. 

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Overall conclusions 
The author suggests that international differences in income per capita are 
best explained using an augmented Solow growth model with the main 
explanatory variables being differences in savings, educational attainment 
and population growth.  Results on the elasticity of physical capital and 
output go more in line with the expected values when using the developed 
panel data approach.  Furthermore, the panel data approach used predicts 
that countries with similar technologies and rates of accumulation and 
population growth should converge in income per capita, however the 
convergence is occurring at a slower rate than previous research suggests. 

Link 

http://dept.ku.edu/~empirics/Courses/Econ915/papers/Islam95.pdf  

A4.3.7.15 

Education for Growth: Why and for Whom? 

Table A4.46 Krueger, Alan and Mikael Lindahl (2001)  

Title 

Education for Growth: Why 
and for Whom? 

Authors: Alan Krueger and 
Mikael Lindahl  
Publisher:Journal of Economic 
Literature 

Year of 
publicatio
n: 2001 

Education outputs: years of schooling 
Returns: income per capita, GDP per capita 

Education 
Inputs, 
outputs 
and 
returns 
considered  

Countries 
covered 

A sample of 34 countries is used for the authors’ own regressions. The 
European countries included in this are: Austria, Belgium, Bulgaria, 
Czechoslovakia, Denmark, east Germany, Finalnd, France, Hungary, 
Ireland, Italy, Netherlands, Norway, Poland, Romania, Spain, Sweden, 
Switzerland, United Kingdom, West Germany. 

Data used 
and 
timescales 
for data 
and 
scenarios 

Log income per capita (derived from family income data for 34 countries 
from the World Values Survey); annualised change in log GDP per capita 
(derived from Summers and Heston’s [1991] Penn World Tables Mark 
5:6) 
Average years of schooling data sets from Kyriacou (1991) and Barro and 
Lee (1993), weighted mean years of schooling (derived from education 
data for 34 countries from the World Values Survey), change in years of 
schooling, change in log capital per worker (Benhabib and Speigel, 1994), 
change in log work force (derived from Summers and Heston’s [1991] 
Penn World Tables Mark 5:6)  

Overview  Main objective:  

Micro labour literature has produced several estimates of monetary 
returns to schooling, while macro growth literature has investigated the 
possible impact of the level of schooling on a country’s GDP growth rate. 
The Mincer wage model that establishes a relationship between an 
individual’s earnings and the number of years they spent at school is the 
basis for microeconomic analysis of returns to education. It implies that 
the change in a country’s average level of schooling should be the key 
determinant of income growth. In contrast, macro growth literature 
typically sees growth as a function of the initial level of education (i.e. the 

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spillovers – Annex IV 

Description 
of main 
findings 

initial average of whatever variable is used to measure educational 
outcomes in the first year of investigation).  
The authors discuss these two distinct, but related strands of literature 
and try to reconcile them. 

Methods/approach:  
The authors use a variable-coefficient model that allows the coefficient on 
education to vary across countries (as in microeconometric estimates of 
the return to schooling). They explore if the significant effect of initial 
level of schooling on growth remains in the aforementioned model and 
also if the linearity assumption of the initial level of education is relaxed.  
Aggregate data were used to estimate the effect of education on GDP 
growth because this relationship can generate insights into endogenous 
growth theories and may allow one to discriminate between alternative 
theories. Moreover, the external returns to human capital could also be 
captured.   One limitation is the low reliability of both country-level and 
individual level education data. 

Main empirical findings:  

1)  The change in schooling has little effect on GDP growth when the 
growth equation is estimated with high frequency changes (i.e. 5 
years), the effect is positive and statistically significant over 10 or 20 
years.  

2)  The variable-coefficient model and relaxation of the linearity 

assumption for the initial level of education shows that the positive 
influence of the initial level of education on growth is sensitive to 
econometric restrictions that are rejected by the data. 

3)  They also show that Benhabib and Speigel’s finding (1994) that a log 
change in the years of schooling has an insignificant effect on the 
annualised growth rate of GDP does not hold true if physical capital is 
excluded from the equation or is held constant at 0.20-0.35 rather 
than 0.5. The 0.20-0.35 range is consistent with a 0.65 to 0.80 share 
for labour in the national income. As a result, the schooling change 
rises and becomes statistically significant. 

4)  For individuals, investments in education, especially for those who 

traditionally acquire low levels of schooling, generates a substantial 
payoff. 

5)  For countries, the results consistently indicated that a statistically 
significant and positive association of education with growth only 
exists for countries with the lowest level of education. This contradicts 
other work presented in this literature review. 

Overall conclusions: 
1)  The change and initial level of education are positively correlated with 

economic growth.  

2)  Measurement errors in education severely attenuate the estimates of 
the effect of the change in schooling on GDP growth, but these errors 
are unlikely to cause a spurious positive association between the initial 
level of schooling and GDP growth across countries. 

3)  Countries with higher initial levels of schooling tended to have larger 

returns. 

4)  It is unclear whether social returns to schooling exceed the private 
returns. However, the available evidence suggests that positive 

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spillovers – Annex IV 

externalities like lower crime rates and reduced welfare bills are more 
likely to emerge from investments in disadvantaged groups. 
Within countries, the causal effect of education on earnings can be 
estimated well by taking education as exogenous; however, this result 
cannot be extrapolated to a cross-country scenario. Any policy changes to 
improve education are likely to be accompanied by other policies for 
enhancing growth resulting in a possible omitted-variable bias in cross-
country analyses.  

Link 

http://www00.unibg.it/dati/corsi/91015/49249-
jel%202000_kruegerlindahl.pdf  

A4.3.7.16 

Educational  mismatch  and 

firm  productivity:  Do  skills, 

technology and uncertainty matter? IZA discussion paper 8885  

Table A4.47 Mahy, Benoit, et. al. (2015) 

Title 

Educational mismatch and 
firm productivity: Do skills, 
technology and uncertainty 
matter? IZA discussion paper 
8885  

Authors:  Mahy, Benoit ; 
Rycx, Francois ; Vermeylen, 
Guillaume 

Year of 
publication: 
2015 

Education outputs: educational attainment/qualifications 
Returns: productivity 

Education 
Inputs, 
outputs 
and 
returns 
considered  

Countries 
covered 

Data used 
and 
timescales 
for data 
and 
scenarios 

Belgium 

Data sources: 
The study uses a combination of two large datasets covering the years 
1999-2010. The merger of the two datasets has been realized by 
Statistics Belgium, using the firms’ social security numbers. The first 
dataset is the “Structure of Earnings Survey” (SES), carried out by 
Statistics Belgium. It covers all firms that are operating in Belgium, 
employ more than 10 workers and have economic activities within 
sections B to N of the NACE Rev. 2 nomenclature. The second main data 
source is a firm-level survey called the “Structure of Business Survey” 
(SBS), also carried out by Statistics Belgium. This survey provides 
financial information (e.g., firm-level value added and gross operating 
surplus per worker).  

It is unclear from the paper which source was used to determine the 
qualifications obtained by workers. 

Overview  Main objective:  

The paper examines whether the impact of educational mismatch (the 
extent to which employees have a much lower or much higher 
qualification than is required for their job, in this paper measured by the 
workers’ self-assessment), measured in educational 
attainment/qualifications, on productivity, varies depending on (i) the 

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spillovers – Annex IV 

Description 
of main 
findings 

level of skills required by jobs, (ii) the degree of technological/knowledge 
intensity, and (iii) the uncertainty of the economic context. 

Method/Approach:  
The authors use a realised matching approach to compute the required 
level of education for a given job by taking the mode of workers’ years of 
education within each ISCO 3-digit occupation (113 categories) in 
Belgium. 

Firm-level productivity is then estimated using  four different methods: 
pooled ordinary least squares (OLS), a fixed-effects (FE) model, the 
generalized method of moments (GMM) estimator developed by Arellano 
and Bover (1995) and Blundell and Bond (1998), and a more structural 
approach suggested by Levinsohn and Petrin (2003, hereafter LP). 

The impact of over-/undereducation is then tested on different 
stratifications of the database. the first divides firms into two groups: one 
with essentially “low-skilled jobs” and gathers 7,183 firm-year 
observations; and a second represents firms with essentially “high-skilled 
jobs” and gathers 5,107 firm-year observations. A second stratification 
divides the dataset into two groups according to the Eurostat HT/KIS 
nomenclature, effectively indicating the technology and knowledge 
intensity of firms, with 3,888 high tech/knowledge firms and 8,402 low-
tech/knowledge firms. 

Main empirical findings:  

The authors provide first evidence on whether the direct relationship 
between educational mismatch and firm productivity varies across 
working environments. Using detailed Belgian linked employer-employee 
panel data for 1999-2010, they find the existence of a significant, positive 
(negative) impact of over- (under-)education on firm productivity. 
Moreover, their results show that the effect of over-education on 
productivity is stronger among firms: (i) with a higher share of high-
skilled jobs, (ii) belonging to high-tech/knowledge-intensive industries, 
and (iii) evolving in a more uncertain economic environment. Findings 
suggest furthermore that the impact of under education decrease with 
increasing worker age. Interaction effects between under-education and 
working environments are less clear-cut. However, economic uncertainty 
is systematically found to accentuate the detrimental effect of under-
education on productivity. 

Firm productivity is estimated to rise by 1.3% on average when the (one 
year lagged) required level of education in a firm increases by one year 
(e.g. the required year of schooling assumed to be required for a firm 
increases by one year). As for variables of educational mismatch, results 
show that mean years of over-education have a significant and positive 
impact on firm productivity, whereas under-education has a negative and 
significant impact on firm productivity. Firm productivity increases 
(decreases) on average by 1.8% (0.5%) the year after a one-unit 
increase in mean years of over- (under-)education. 

As regards sensitivity to qualifications required in a job/sector, following a 
one unit increase in mean years of required education (over-education) 

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the productivity of firms with a larger share of high-skilled jobs is 
estimated to increase on average by 3.3% (7.0%), whereas the 
productivity of firms with more low skilled jobs is found to increase by 
2.3% (3.3%).  

As regards sensitivity to education level required in high/low 
technology/knowledge firms, increasing the level of over-education by one 
year leads, the next year, to a 5.0% increase in high-tech/knowledge 
firms’ productivity, i.e., 2.0% more than in low-tech/knowledge ones. 
Then, under-education is estimated to decrease productivity by 1.3% in 
low-tech/knowledge firms, while the point estimate is nonsignificant in 
high-tech/knowledge ones. 

The research does not draw any conclusions on effects on the economy 
overall, however provides useful evidence on the role that the mix of 
qualifications plays in the productivity across different types of industry. 

Link 

http://ftp.iza.org/dp8885.pdf  

A4.3.7.17  A Contribution to the Empirics of Economic Growth 

Table A4.48 Mankiw, Gregory, David Romer and David Weil (1992). 

Title 

A Contribution to the Empirics 
of Economic Growth 

Authors: N. Gregory Mankiw, 
David Romer and David Weil  
Publisher: Quarterly Journal 
of Economics  

Year of 
publicatio
n: 1992 

Education outputs: school enrolment rates, qualifications obtained 
Returns: GDP per working-age person 

Education 
Inputs, 
outputs 
and 
returns 
considered  

Countries 
covered 

The research covers 121 countries listed in an Annex. The European 
countries covered are: Austria. Belgium, Cyprus, Denmark, Finland, 
France, Germany (West), Greece, Iceland, Ireland, Italy, Luxembourg, 
Malta, Netherlands, Norway, Portugal, Spain, Sweden, Switzerland, 
Turkey, United Kingdom 

Data used 
and 
timescales 
for data 
and 
scenarios 

Data from the Real National Accounts (Summers and Heston 1988); the 
data set included annual real income, government and private 
consumption, investment and population data for most countries except 
those that were centrally planned between 1960 and 1985.  
Log GDP per working-age person in 1985; log difference GDP per 
working-age person 1960-1985 
Investment rate (average 1960-1985); population growth rate (average 
1960-1985); SCHOOL (population aged 12 to 17 enrolled in secondary 
education [UNESCO yearbook] x fraction of working-age population aged 
15 to 19) 

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spillovers – Annex IV 

Overview  Main objective:  

Description 
of main 
findings 

The authors argue that the predictions of the Solow model are consistent 
with the evidence that saving and population growth affect income levels. 
More than half the cross-country variation in per capita income can be 
explained by these two variables. However, the model does not predict 
the magnitude of the effects accurately. Therefore, the authors expand 
the Solow model.  

Method/Approach:  
The authors extend Solow growth model by including the accumulation of 
physical and human capital as variables. They then test the new model 
using real-world data. They estimate the model by ordinary least squares 
and ask how much of the cross-country variation in income can be 
accounted for. 
The authors use the percentage of the working age population that is in 
secondary school as a proxy for the rate of human-capital accumulation. 
The working age was defined as 15 to 64 years. 
Three different groups of countries are sampled from the overall 121 
countries: A sample of 96 countries excluding all countries for which oil 
production is the dominant industry (NON-OIL), A sample including all 22 
OECD countries (OECD) at the time, and a sample of 75 countries 
excluding countries for which the Summers-Heston data indicated a large 
likelihood of measurement errors (Intermediate) 

Main empirical findings:  

1)  The augmented model accounts for about 80 percent of the cross-

country variation in income. The correlation coefficients for saving and 
population growth have the sign predicted by the assumed 
macroeconomic Solow model and are significant for two out of three 
samples (NON-OIL and Intermediate) 

2)  The estimated impacts of saving and labour force growth are much 

larger than predicted by the model. 

3)  Leaving out human capital affects the coefficients for physical capital 

investment and population growth. 

4)  In the US, the minimum wage averaged about 30 to 50% of the 

average wage in manufacturing. Since the former is roughly the return 
to labour without human capital, the return to human capital is 
expected to be 50 to 70% of the total labour income in the US. 

5)  As higher savings lead to higher income, there is a higher steady-state 
level of human capital even if the proportion of income attributed to 
human-capital accumulation is unchanged. 

6)   
Overall conclusions: 
1)  The authors conclude that adding human capital to the Solow model 
improves its performance. In this model, output is produced from 
physical capital, human capital and labour, and it is used to invest in 
physical capital and human capital, and consumption. 

2)  The model has several implications: (a) there are no substantial 
externalities to the accumulation of physical capital; (b) the 
accumulation of physical capital and population growth have a larger 
impact on per capita income than implied in the original Solow model; 
and (c) countries with similar technologies and rates of accumulation 
and population growth should converge in per capita income. 

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3)  The augmented Solow model attributes cross-country differences in 
income to differences in savings, education and population growth. 
The Solow growth model and endogenous growth models are both able to 
explain certain aspects of growth. Hence, one should not be discarded in 
favour of the other.  

Link 

http://eml.berkeley.edu/~dromer/papers/MRW_QJE1992.pdf  

A4.3.7.18 

Long 
unemployment matter?  

run  growth  and 

investment 

in  education:  Does 

Table A4.49 Mauro, L. and Carmeci, G. (2003) 

Title 

Long run growth and 
investment in education: 
Does unemployment matter?  

Authors: Luciano Mauro and 
Gaetano Carmeci  
Publisher: Journal of 
Macroeconomics 

Year of 
publication: 
2003 

Education outputs: years of schooling, enrolment in secondary school 
Returns: per capita GDP, unemployment rate 

Education 
Inputs, 
outputs 
and 
returns 
considered  

Countries 
covered 

Data used 
and 
timescales 
for data 
and 
scenarios 

19 OECD countries. The paper does not include a list of countries covered. 

Data sources: 
Data from 19 OECD countries 1960-1990, 95 observations 
Log of per capita output (GDP and investment to GDP ratio data from 

Penn World Table 5.6) 

Unemployment rate (OECD Labour Force Statistics); secondary enrolment 
(Barro and Lee, 1996); average schooling years in the total population 
(aged 15 and over; from Barro and Lee, 2000) 

Overview  Main objective:  

In a growth theory context, human capital investment can be regarded as 
a combination of two inputs: schooling and working time. Hence, 
unemployment represents an inefficiency in human capital production, 
which can negatively impact on the human capital accumulation of the 
economy. In this case, unemployment can reduce the returns from 
education. The authors wished to explore this idea theoretically and 
develop an appropriate model, which they have tested using empirical 
data. 

Method/Approach:  
The authors build an endogenous growth model with a stylized imperfect 
labour market that determines an equilibrium employment rate. Since 
unemployment prevents the acquisition of working experience, it 
negatively impacts on the efficiency of human capital production. The 
steady state implication of this model is a negative relationship between 
long run growth and the unemployment rate. Transitional dynamics are 
also considered for the model. 
The model distinguishes between human capital accumulation from 

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spillovers – Annex IV 

schooling and from working. The flow of resources invested in education 
increases human capital according to a function in which the 
unemployment rate enters as an argument. The model ignores 
uncertainty, taxes, benefits, and the output price is normalised to 1. Firms 
are assumed to equalise the wage to the marginal productivity of labour.  

Main empirical findings:  

1)  The two measures of human capital (percentage growth rate of the 

average year of schooling and secondary school enrolment rate) are not 
statistically correlated with long run per capita growth. 

2)  Unemployment is negatively (significantly) influences long-run per 

capita growth. Moreover, once unemployment is introduced into the 
model, the two proxies of human capital become statistically significant. 
(NOTE: the authors do not make an estimation of the ratio, but from 
the coefficients we can estimate that 1% decrease in unemployment 
leads to a 14% increase in GDP per capita). 

3)  Take together this empirical evidence support a growth model (with an 
imperfect labour market) where the unemployment rate influences the 
efficiency of human capital accumulation. 

Overall conclusions: 
1)  Economies starting from a relative human capital shortage display 

lower growth rates than those starting from a relative human capital 
abundance. 

2)  Unemployment negatively influences (significantly) long run per capita 

growth. 

3)  Schooling investment positively and significantly influences the growth 

of economies when labour market inefficiency is controlled for. 
4)  Labour market imperfections may play a role in long run growth. 

http://ac.els-cdn.com/S0164070403000107/1-s2.0-S0164070403000107-
main.pdf?_tid=4a2f1c58-65d4-11e5-9c1f-
00000aacb361&acdnat=1443439995_a4245f60f61c7d1aa79920d1a90a04
0a  

Description 
of main 
findings 

Link 

A4.3.7.19  Where  Has  All  the  Education  Gone?  World  Bank  Economic 

Review 

Table A4.50 Pritchett, Lant (2001) 

Title 

Where Has All the Education 
Gone? World Bank Economic 
Review 

Author: Lant Pritchett  

Year of 
publication: 
2001 

Education outputs: years of schooling 
Returns: growth in CUDIE capital per worker, growth of GDP per worker 

Education 
Inputs, 
outputs 
and 
returns 
considered  

Countries 
covered 

All 129 countries within the Barro-Lee dataset are investigated, excluding 
Kuwait, Gabon, Norway and Ireland. European countries included are 

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spillovers – Annex IV 

Austria, Belgium (French), Bulgaria, Cyprus, Czech republic, Denmark, 
France, Germany (west), Greece, Hungary, Netherlands, Norway, 
Portugal, Spain, Sweden, Switzerland, United Kingdom. 

Data used 
and 
timescales 
for data 
and 
scenarios 

Years of schooling (Barro-Lee 1993 dataset); years of schooling (Nehru et 
al., 1995 dataset), wage increment to a year’s schooling (10% across all 
years of schooling), CUDIE (Cumulated, Depreciated, Investment Effort) 
from King and Levine (1994) and Nehru and Dhareshwar (1993), growth 
in CUDIE (Cumulated, Depreciated, Investment Effort)  capital per worker  
An important criticism is that years of schooling do not reflect learning. 
Growth rate of per worker educational capital (estimated in this study), 
growth of GDP per worker (Penn World Tables, Mark 5) 

Overview  Main objective:  

People with more education have higher wages. In addition, if, as 
believed, there were positive externalities to education, average income 
would rise if more people were educated. Moreover, positive externalities 
would mean that the impact of education on aggregate output would be 
greater than the sum of the individual impacts. To test for these 
externalities, consistent macro- and micro-economic models are 
required. One example is the augmented Solow model, which predicts 
that the ‘impact of education (without any external variables considered) 
is represented by the share of educational capital in total factor income. 
This impact can be estimated using microeconomic evidence on wage 
increments to capital. However, the Solow model, consistently estimates 
less impact of education on growth than is assumed. Besides, cross-
national data suggest ‘negative’ externalities. Thus, there is a paradox. 
The author attempts to resolve this paradox using the available macro 
and micro evidence about the impact of education on economic growth.  
Furthermore, he tries to explain why there are differences in the impact 
of education on growth across countries. 

The paper summarises some of the evidence that possibly explains the 
difference in the impact of education on growth, especially in the context 
of developing countries. Some of the reasons are the quality of 
education, the employment destination of the educated workforce and 
labour market dynamics. For example, the employment of surplus 
educated labour by the government (as an employer of last resort) can 
reduce growth of output per worker by as much as 2% per year (from a 
base case growth of 2.5%; Gelb et al., 1991). Smaller growth returns 
from expanding education may also result from changes in demand for 
and supply of educated labour leading to changes in the wage premium 
for educated workers. Finally, in countries with a poor quality of 
education, an additional year of schooling may have no impact on skills, 
and hence, growth and productivity is expected to vary depending on the 
quality of teachers, educational facilities, etc.  

Methods/approach:  
The author starts with the standard production function specifications of 
growth at the macroeconomic level. Next, he builds aggregate measures 
of education capital from microeconomic data on education and its 
returns. Finally, he examines the relationship between the 
macroeconomic model and the newly computed aggregate measure. 
The extended Solow approach allows simple non-regression-based 
estimates of how significant changes in the educational capital are. In 
these calculations, the physical capital share was estimated to be 0.4, 

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spillovers – Annex IV 

which is slightly high but consistent with evidence, while the labour share 
was fixed at 0.6. One of two methods was used to calculate how much of 
the labour share (in terms of wages) could be attributed to human 
capital: (1) a ratio of the unskilled wage to the average wage or (2) 
attributing a wage premium to each education level by determining the 
proportion of the labour force in each attainment level and using this to 
calculate the educational capital share. 
Estimates of the growth rate per worker educational capital were created 
using two cross-national time-series data sets (Barro and Lee; Nehru et 
al.), which provided estimates of the years of schooling of the labour 
force. Then, using Mincer’s equation (1974), which assumes a linear 
relationship between the natural log of the wage and years of schooling, 
a measure of educational capital was derived. 
Growth-accounting regressions were then performed. There may be 
‘omitted variables,’ which could create an upward bias towards the 
independent variable regressed. Moreover, the available data have 
insufficient power to identify the impact of schooling on schooling capital 
and of schooling capital on output.  

Description 
of main 
findings 

Main quantitative/empirical results:  
1)  The estimate of the impact of growth in educational capital on growth 
of GDP per worker is negative and insignificant. Adding the initial level 
of GDP per worker to the regression model does not change this result. 

2)  The growth of educational capital shows a large, statistically very 

significant and negative effect on total factor productivity growth. This 
observation remains consistent for different estimations of the physical 
and human capital shares. The estimated coefficient shows the same 
trend for different subgroups of the data as well as variations in the 
data used for education, CUDIE or GDP, comparing OECD countries to 
developing countries. 

3)  Lack of a control for quality of education causes an upward bias in the 

estimates. 

 Overall conclusions:  
By large, educational capital growth has a positive association with total 
factor productivity (TFP) on average, however the strength of this effect 
varies largely across countries and falls short of what the authors initially 
assumed. Disappointing strength of effects for some developing countries 
could be explained, according to the authors, through two avenues. First, 
the institutional/governance setting could have been “sufficiently perverse” 
so that the education capital growth would have decreased economic 
growth rates. Secondly, the authors argue, the marginal returns to 
education could have fallen very fast whilst the supply of education 
expanded, and the demand for higher educated labor was stagnant. 

There are 3 possible explanations for the differences among countries in 
the impact of schooling on economic growth: 
1)  In some countries, schooling may have created cognitive skills that are 
privately remunerative, but devoted to socially wasteful activities.  
2)  The demand for educated labour may have grown at different rates in 

different countries, resulting in different returns to schooling in different 
countries. 

Depending on the country and according to the results of the regression 
analysis performed, the effects of schooling vary largely.  

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Link 

http://wber.oxfordjournals.org/content/15/3/367.full.pdf+html?hwshib2=
authn%3A1443010252%3A20150922%253Aa1791f8d-f608-4681-8fac-
b7331c49e8a1%3A0%3A0%3A0%3A0GGLdwYcfGQ%2FCDUs9eI8jQ%3D
%3D  

A4.3.7.20  Does  learning  to  add  up  add  up?  The  returns  to  schooling  in 
aggregate data. In: E. Hanushek and F. Welch (Eds). Handbook of 
the Economics of Education 

Table A4.51 Pritchett, Lant (2006) 

Title 

Does learning to add up add 
up? The returns to schooling 
in aggregate data. In: E. 
Hanushek and F. Welch (Eds). 
Handbook of the Economics of 
Education 

Author: Lant Pritchett 

Year of 
publication: 
2006 

 Education outputs: Enrolment in secondary education, adult illiteracy rate, 
years of schooling 
Returns: CUDIE (Cumulated, Depreciated, Investment Effort) per worker, 
change in capital per worker  

Education 
Inputs, 
outputs 
and 
returns 
considered  

Countries 
covered 

The research mainly works with a sample of OECD countries, including: 
Australia, Netherlands, Italy, Norway, Belgium, Japan, UK, Austria, 
Germany, Denmark, France, USA, Switzerland, Finland, New Zealand, 
Sweden, Canada. 

A large sample of 88 countries, including both OECD countries and 
developing countries, is used where educational attainment data is 
sourced from the Barro-Lee data. 

Data used 
and 
timescales 
for data 
and 
scenarios 

Growth per capita output (based on Maddison, exact source is unclear), 
output per worker, growth of output per worker, rate of return to 
schooling, growth of schooling capital per worker, wages. 
Enrolment in secondary education (Mitchell for 1900, WDI for 1990), adult 
illiteracy rate (UNESCO), years of schooling (Barro-Lee dataset), schooling 
per worker, CUDIE (Cumulated, Depreciated, Investment Effort) per 
worker, change in capital per worker  

Overview  Main objective:  

To explore the impact of differences in economic environment on returns 
to schooling. 

Methods/approach:  
The authors provide stylised facts on the growth and schooling trends and 
in the relationship between schooling and growth. They complement the 
analysis with some additional estimates. They based their estimations on 
cross-country growth regressions (which as the authors point out may 
have ‘omitted variables,’ which can create an upward bias towards the 
independent variable regressed; the available data have insufficient power 
to identify the impact of schooling on schooling capital and of schooling 
capital on output). 

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Description 
of main 
findings 

Main findings 
The paper points to some stylised facts on growth and schooling: 
1)  Rate of growth of output per worker in the leading industrial countries 

has been quite steady for a very long time while the first fact of 
schooling is that it has expanded massively over the course of a 
century (changing from 5.7 % on average to 95.9 % on average in the 
OECD countries investigated) 

2)  There has been a massive expansion in the schooling of the labor force 
in nearly every country in the world (Table 2, p10), but also a nearly 
universal and massive slowdown in economic growth. 

3)  Adding schooling into growth accounting models lead to low or negative 

‘growth residuals’ (which capture technological change), which is 
puzzling. The implication is that the level and growth of aggregate 
schooling per worker will do, at best, a modest amount in explaining 
the growth of output per worker.  

Summary of empirical findings:  

1)  Growth in capital per worker (CUDIE) explains 46.1% of the variance 
in a cross-country growth regression model of 88 countries (Table 5, 
p19). 

2)  Estimated return to schooling is lower if difference in CUDIE is 

accounted for. 

3)  On average, across all countries the schooling capital share of the 
wage bill is between one-third and half. The wage increment is 
assumed to be uniform across all levels of schooling. 

4)  Under certain assumptions the authors calculate the differential 
between (i) macro-Mincer—the impact of a year of schooling on 
aggregate output— (ii) the micro-Mincer—the impact of a year of 
schooling on an individual’s wages: 

 

If range of micro-Mincer estimates is 9.9% then the difference is 3.5 
to 6.5 percentage points at the primary level (because opportunity 
costs are low instruction costs are a higher fraction of total cots) and 
1.7-3.3 percent at the secondary level. (Assumptions: The calculations 
assume the only private cost is wage foregone, a working life of 45 
years, 15 percent tax rate, and 11.5 percent discount rate. At these 
assumptions a 9.9 percent wage increment is sufficient to induce a 15 
year old to complete a ninth year of schooling at zero instructional 
cost). 

 

If range of micro-Mincer estimates are between 6 and 12 percent this 
means that the aggregate impact should be between 50 and 25 
percent higher than the aggregate of the individual impacts   

5)  The wage premia is attaining ‘Tertiary education’ between 7.69-7.63 

versus no schooling (baseline = 1), across all analysed countries (which 
include OECD and no-OECD countries) (p53) 

6)  Under certain assumptions, (human) schooling capital explains 73% of 

the wage bill for OECD countries (p53) (Assumptions: (wage 
increments are: primary16 percent, secondary 12 percent, tertiary 8 
percent)) 

Overall conclusions 

1)  The impact of more schooling on aggregate output varies according to 

country. 

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2)  Schooling per worker does not have sufficiently large fluctuations at 

the required frequencies to have an effect on growth rates in the short 
or medium term.  

3)  While long-term cross-sectional studies do not have a problem with 
differences in output versus schooling dynamics like in the short to 
medium term ones, they do not allow for certain statistical models, 
such as fixed effects models.31  

4)  If a large proportion of growth is attributed to schooling, one has to 

assume that contributions from other variables such as CUDIE are very 
low.  

5)  The implication of an empirically poor aggregate payoff from past 

increases in schooling is not “don’t educate,” but rather “reform now so 
that investments (past and present) in schooling will pay off.”  

6)  Structural change or technical change that is skill enhancing leads to 
greater demand for educated labour, and thus can affect economic 
returns to schooling. 

Link 

http://www.sciencedirect.com.ezproxy.sussex.ac.uk/science/handbooks/1
5740692/1  

A4.3.7.21  Human  Capital  and  Growth:  Theory  and  Evidence.  NBER  Working 

Paper Series 

Table A4.52 Romer, P. (1989) 

Title 

Human Capital and Growth: 
Theory and Evidence. NBER 
Working Paper Series 

Author: Paul M. Romer 

Year of 
publication: 
1989 

Education outputs: literacy 
Returns: income, per capita growth of GDP 

Education 
Inputs, 
outputs 
and 
returns 
considered  

Countries 
covered 

The sample covers all market economies included in the Summers and 
Heston database, excluding the oil-producing countries. This leaves a 
sample of 112 countries, which are not listed in the publication. 

Data used 
and 
timescales 
for data 
and 
scenarios 

1) World Data table compiled by Robert Sumers and Alan Heston (1988) for 
the information on national income accounts 
2) Statistical Yearbooks by UNESCO for the measures of human capital 
3) Cross-country variation in per capita growth rates and investment are 
collected from Romer (1987, 1989a.)  

Overview  Main objective: 

The objective of the paper was to demonstrate how a more general model 
could be used to guide the analysis of data on human capital and 
endogenous growth.   
The paper outlines a theoretical framework for thinking about the role of 
human capital in a model of endogenous growth. The framework pays 

31 https://en.wikipedia.org/wiki/Fixed_effects_model  

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particular attention to two questions: What are the theoretical differences 
between intangibles like education and experience on the one hand, and 
knowledge or science on the other? And How do knowledge and science 
actually affect production?  

Method / approach: 
The author developed a theoretical framework – in addition to the usual 
relationships between the rate of growth of inputs and the rate of growth of 
outputs suggested by growth accounting, he included the level of human 
capital variables to explain the rate of growth of output.  
In the empirical part, the author developed a model. The empirical part of 
the paper illustrates how this observation can be exploited by focusing on 
literacy as a measure of human capital.  

Description 
of main 
findings 

Main empirical findings: 
1) The results demonstrate a weak consistency between the data and the 

theoretical framework 

2) The initial level of literacy helps explain investment, but it is correlated 

with other variables like life expectancy at birth 

3) The results find only weak evidence that the initial level of income is 
negatively related to the rate of growth. The only measure of the 
growth of a human capital variable considered here, the change in the 
level of literacy, does not have an independent effect in the growth 
equation, but it does help explain the rate of investment, as one would 
expect. 

4) The interesting, and from the point of view of conventional theory 
unexpected finding is that the initial level of literacy also helps explain the 
subsequent rate of investment, and thereby the subsequent rate of income 
growth.  

Link 

http://www.nber.org/papers/w3173.pdf  

A4.3.8  Education and economic contribution of migrants 

A4.3.8.1 

Educational  Achievements  of  Second  Generation  Immigrants:  An 
International Comparison 

Table A4.53 Dustmann et.al. (2011) 

Title 

Educational Achievements of 
Second Generation 
Immigrants: An International 
Comparison 

Year of 
publication: 
2011 

Author(s): Dustmann, 
Christian, Frattini, 
Tommasso, Lanzara, 
Gianandrea 
Publisher: Centre for 
Research and Analysis of 
Migration (CReAM) 

Education 
Inputs, 
outputs 
and 
returns 

Education inputs: Family background, education attainment of 
parents 
Education outputs: achievement (PISA test scores in reading and 
mathematics) 

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spillovers – Annex IV 

considered  

Countries 
covered 

Data used 
and 
timescales 
for data 
and 
scenarios 

Various OECD countries  

OECD Programme for International Student Assessment (PISA) 
database, 
European Union Labour Force Survey,  
European Social Survey 

2003-2008 

Overview  This paper looks at the educational achievements of second generation 

immigrants in several OECD countries. 

Description 
of main 
findings 

The educational achievement of immigrant children varies across 
countries. It is strongly related to the achievements of parents. The 
advantages linked to having well-educated parents decrease when 
parental background characteristics are taken into account.  

The paper then looks at the difference between scores achieved by 
Turkish children in Turkey and scores achieved by Turkish children in 
host countries. The study finds that children of Turkish immigrants score 
higher than children in Turkey (conditionally and unconditionally on 
parental background characteristics). Immigrant children’s educational 
advantage could be explained by higher school quality relative to that in 
the home country. 

The paper is based on two assumptions:  

 

 

Importance of educational attainment of parents (there is evidence 
that this differs greatly between countries); 

Importance of intergenerational correlation between parent and 
child generation (there is evidence that parental attainment and the 
attainment of children are correlated; this differs between 
countries). 

First, the paper looks at the differences in reading and mathematics test 
scores between second generation immigrant and native children at age 
15 in various countries. The model used includes dummies such as: 
family background, peer quality, school characteristics, percentage of 
immigrants at school and languages. The results show that in some 
countries, there are no gaps between second generation immigrant and 
native pupils. However, there are other countries in which gaps are 
significant (for instance, up to 88 PISA points in reading tests and up to 
75 PISA points in mathematics tests in Finland). One limitation of the 
paper is that it ignores cross-country differences in the composition of 
the immigrant population. However, this might be an important factor to 
consider as it could be one of the reasons behind cross-country 
differences in immigrants-natives test score gaps. 

In a second step, the paper looks at how children of Turkish immigrants 
perform in host countries (Austria, Belgium, Switzerland, Germany and 
Denmark), compared to native children and Turkish children in Turkey.  
The model used includes dummies such as family background, peer 
quality, school inputs and language.  The conclusions of the paper are 
that children of Turkish immigrants score higher than children in Turkey 

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spillovers – Annex IV 

but lower than native children (conditionally and unconditionally on 
parental background characteristics). Another finding is that parental 
education is an important factor in explaining the test gaps between 
children. 

Link 

http://www.ucl.ac.uk/~uctpb21/Cpapers/CDP_16_11.pdf  

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A4.3.8.2  Productivity Growth and the Trade Balance in the 1990s: the Role 

of Evolving Perceptions 

Table A4.54 Erceg et al (2002) 

Title:  

Productivity Growth and the 
Trade Balance in the 1990s: 
the Role of Evolving 
Perceptions 

Authors: Christopher J. Erceg, 
Luca Guerrieri, and 
Christopher Gust 

Year of 
publication
: 2002 

Publishers: Board of 
Governors 

of the Federal Reserve 
System 

Education 
inputs, 
outputs and 
returns 
considered  

Inputs: Labour productivity, total factor productivity  

Outputs: trade balance, imports/exports, real exchange rate  

Countries 
covered 

US 

Data used 
and 
timescales 
for data and 
scenarios 

Overview 

Second half of the 1990s 

Focus on the importance of productivity shocks in accounting for 
salient features of the U.S. economy during the second half of the 
1990s, including the substantial deterioration of the trade balance. 
The economy's response to a rise in productivity growth is highly 
sensitive to agents' perceptions about the degree of persistence of the 
shock process. The model can account for over half of the 
deterioration in the trade balance over this period. 

The estimates of Glick and Rogoff (1995)32 predict that a 1 percent 
increase in U.S. productivity unmatched abroad would cause at most 
a 0.2 percentage point decline in the trade balance as a fraction of 
GDP.  

Description 
of main 
findings 

Background: Output per hour in the non-farm business sector rose 
almost 2.5 percent from 1996 to 2001. At the same time, there was a 
sharp deterioration in the U.S. trade deficit, which declined from 1 
percent of GDP in 1997 to over 4 percent of GDP in 2000. 

The model analyses the response to a shock that raises the growth 
rate of labour-augmenting technical change by one percentage point 
(at an annual rate) over the entire simulation horizon of 20 quarters.  

1.  Effects of the shock under the assumption that agents correctly 

ascertain that the shock will have highly persistent effects on the 

32 Glick, R. and Rogoff K. (1995), Global versus country-specific productivity shocks and the current account, 
Journal of Monetary Economics 35, 159–192. 

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spillovers – Annex IV 

future growth rate of productivity:  

  The shock raises output growth by about 1 percentage point per 

year over the simulation horizon. 

  Because the productivity shock is perceived to be heavily “back-
loaded" in this case (in the sense that the shock is expected to 
raise the future level of productivity much more than current 
productivity), the shock has very strong effects on domestic 
demand. 

  With a sharply rising projected future income profile, households 

desire to markedly reduce their savings.  

  The rise in the expected marginal product of capital induces a 

strong increase in investment spending.  

  With domestic short-term real interest rates increasing relative to 

foreign short-term real rates, the terms of trade initially 
appreciate considerably.  

  The appreciation of the terms of trade induces a substantial fall in 
the export share of GDP, which falls nearly 1 percentage point by 
two years after the shock.  

  The import share of GDP also ratchets up markedly due to both 

the boom in domestic demand, and to the terms of trade 
appreciation.  

  The shock induces a deterioration in the real trade balance equal 

to nearly 2 percent of GDP after eight quarters. 

2.  Effects of the shock under the assumption that agents mistakenly 

expect productivity growth shocks to be transitory:  

  Domestic demand rises much less sharply than in the case in 

which productivity growth is expected to rise persistently, in part 
because the less steeply rising output profile induces a smaller rise 
in consumption GDP. 

  The effects of the productivity shock on the trade balance are also 

much smaller in this case. In particular, the trade balance 
deteriorates by only about 0.5 percent of GDP after eight quarters, 
or one quarter as much as under perfect information. 

  The smaller deficit in part reflects that the import share of GDP 

rises much less due to the smaller stimulus to domestic demand. 

  The terms of trade are basically unchanged initially in this case 

(since short-term real rates rise by less), also serving to cushion 
the fall in exports, and to reduce the rise in the import share. 

3.  Effects of the shock under the assumption that agents attach a 

non-negligible weight to the innovations of each type: 

  The responses of key variables look like a weighted average of the 
two extreme cases: initially, the responses look very similar to the 
case in which agents perceive shocks to be temporary. 

  Eventually, agents revise upward their assessment of the 

persistence of the shock to productivity growth, and the responses 

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Study on the potential of education to cause macroeconomic imbalances and negative 
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look similar to the case in which agents perceive the shock as 
completely permanent. 

 

In this case, the trade balance deteriorates by about 1-1/4 to 1-
1/2 percent of GDP between two and three years after the 
occurrence of the shock, or roughly half the magnitude of the 
nearly 3 percent deterioration in the trade balance that occurred 
between 1997 and 2000. About 2/3 of the deterioration in the 
trade balance implied by the model is accounted for by a shift in 
the ratio of investment to output, again reasonably in line with the 
data. 

Limitation: a number of features make it more difficult to explain the 
large deterioration in the trade balance by way of productivity shocks 
only. For example, without habit persistence, a positive innovation to 
productivity would lead to a greater initial response of consumption 
and hence imports; thereby, magnifying the deterioration in the trade 
balance.  

Link 

http://www.federalreserve.gov/events/cbw/ercegetal_aug30.pdf  

A4.3.8.3  Productivity,  external  balance  and  Exchange  rates:  evidence  on 

the Transmission mechanism among G7 countries 

Table A4.55 Corsetti et al (2006) 

Title:   

Productivity, external 
balance and 

Exchange rates: evidence 
on the Transmission 
mechanism among G7 
countries 

Authors: Giancarlo 
Corsetti, Luca Dedola and 
Sylvain Leduc 

Year of 
publication: 
2006 

Publishers: NBER working 
paper series 

Education 
inputs, 
outputs and 
returns 
considered  

Countries 
covered 

Data used 
and 
timescales 
for data and 
scenarios 

Overview 

Inputs: domestic labour productivity in manufacturing 

Outputs: consumption, net exports, price of non-tradables, external 
account, exchange rate, terms of trade 

Five G7 countries: US, Japan, Germany, UK, Italy 

1973-2004 

Large variety of sources, but most of the data from the OECD Economic 
Outlook Database   

Investigate the international transmission of productivity shocks. 
Shocks that increase permanently domestic labour productivity in 
manufacturing raise relative consumption, deteriorate net exports, and 
raise the relative price of non-tradables. Moreover, the deterioration of 

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Description 
of main 
findings 

the external account is fairly persistent, especially for the US. The 
response of the real exchange rate and the terms of trade differs 
across countries. Productivity growth in the US tradable sector does not 
necessarily improve the US trade deficit, nor deteriorate the US terms 
of trade, at least in the short and medium run. 

Harrod-Balassa-Samuelson hypothesis: A widespread view of the 
transmission mechanism holds that a productivity increase in the 
traded goods sector of a country should simultaneously lower the 
international relative price of domestic tradables (i.e. it should worsen 
the country’s terms of trade) and raise the relative price of domestic 
non-tradables. 

Assumptions: 

The study focuses on productivity shocks to the tradable sector, rather 
than to the economy as a whole, because the theory’s predictions are 
starker for the former than for the latter.  

Whereas previous studies mostly focused on the link between 
productivity and real exchange rates, the study places significant 
emphasis on the joint dynamics of net trade and international relative 
prices (including the price of tradables). 

The only type of shock which affects the long-run level of average 
labour productivity in manufacturing is a permanent shock to 
technology 

Results:  

Positive productivity shocks in each of the countries in our sample raise 
domestic manufacturing output and aggregate consumption relative to 
an aggregate of other industrial countries. In response to such shocks, 
the trade balance worsens, its deterioration being persistent over time; 
the price of domestic tradables in terms of nontradables falls.  

The study find that the real exchange rate’s response to productivity 
shocks is heterogeneous across countries. In the case of the US and 
Japan - the two largest and least open economies in our sample - 
productivity gains lead to a short-run appreciation in all our three 
measures of the international relative prices. The price response is 
instead not significant for Germany, at least in our baseline 
specification. In the case of the UK and Italy - the smaller and more 
open economies in our sample - permanent depreciations are detected 

US: 

1.  The median impact effect of the shock on relative manufacturing 

output and aggregate consumption is slightly negative but 
statistically insignificant in the short run; both variables however 
converge to a permanently higher level after three years 

2.  The long-run increase in both these variables is of the order of 0.5 

percent, against a permanent increase of 1.5 percent in the 
productivity differential.  

3.  The technology shock leads to a prolonged, statistically significant 

fall in both net exports and the relative price of domestic tradables. 

4.  The CPI-based RER temporarily appreciates in the aftermath of the 

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shock, and then goes back to its previous long-run level 

Other countries: 

For all countries, a positive shock increases the consumption 
differential after a few quarters; it decreases both the nominal net 
trade relative to GDP as well as the relative price of manufacturing in 
terms of services/overall CPI 

The deterioration of net exports over GDP is stronger in the UK and 
Germany, where it is also permanent; in Italy this variable displays a 
similar qualitative behaviour but is significantly negative only for a 
couple of quarters one year after the shock. As in the US case, these 
permanent effects reflect the assumption that the net-trade-to-GDP 
ratio is nonstationary, in line with results from unit root tests. In Japan 
net exports ó modelled as stationary ó reach a minimum 8-10 quarters 
after the shock, and then slowly revert to their baseline value. Finally 
the relative price of manufactured goods in terms of services falls 
permanently in all countries, although significantly so only in Germany 
and Italy. 

Final section to examine whether the identification strategy is able to 
detect the true effect of a positive technology shock on the terms of 
trade and the real exchange rate, when this effect can be either an 
appreciation or a depreciation. If the identifying assumption that the 
only source of unit root in labour productivity in manufacturing is 
correct, the empirical findings are unlikely to be driven by some bias 
inherent in the approach. 

Link 

http://www.nber.org/papers/w12483.pdf 

A4.3.8.4  Productivity shocks, Budget deficits and the current account 

Table A4.56 Bussière et al (2005) 

Title:  

Productivity shocks, 
Budget deficits and 
The current account 

Authors: Matthieu Bussière, 

Marcel Fratzscher and Gernot J. 
Müller 

Publishers: European Central Bank 

Year of 
publication: 
2005 

Inputs: budget deficits, productivity shocks  

Outputs: current account 

21 OECD countries 

Annual data from 1960-2003 

OECD Economic Outlook database 

Education 
inputs, 
outputs and 
returns 
considered  

Countries 
covered 

Data used 
and 
timescales 
for data and 
scenarios 

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Overview 

Description 
of main 
findings 

Evaluate empirically the relative importance of budget deficits and 
productivity shocks for the determination of the current account. The 
study finds little evidence for a contemporaneous effect of budget 
deficits on the current account, while country-specific productivity 
shocks appear to play a key role. 

Literature focusing on productivity as an important factor in the 
determination of the current account 

  Glick and Rogoff (1995)33: While global productivity innovations 

stimulate investment, they should not affect the current account, 
since they have a simultaneous impact on all countries. The 
distinction between country-specific and global productivity shocks 
is supposed to play a key role in accounting for the low (in absolute 
value) correlation of investment and the current account.  

  Mann (2002)34: In the late 1990s, the budget deficit and the current 
account moved into different directions: "productivity gains in the 
U.S. economy (…) attracted foreign investors", triggered investment 
and induced a current account deficit. 

  Kollman (1998)35: Productivity shocks appear to be "the main 

source of fluctuations in net exports" for the US during the period 
1975-1991, and seem to have contributed to the sharp drop in US 
net exports in the first half of the 1980s. 

Background in the US:  

  mid 1980s: Twin Deficits (current account and budget deficit 

reached record highs): a reduction in public saving, i.e., an increase 
in the budget deficit, should ceteris paribus lead to a reduction of 
net savings of the economy, i.e., the current account 

  1990s: current account and budget balance were moving in 

opposite directions. Importance of the (anticipated) productivity 
gains in the U.S. since the mid-1990s, which triggered both huge 
capital inflows and high consumption expenditure and investment, 
both reflected in the sizeable trade deficit. 

  2002-2005: Twin Deficits again 

Structural model of an open economy in which investment and 
consumption and eventually the current account respond to exogenous 
shocks to productivity and the budget deficit.  

Assumption: a fraction of the households behave as spenders and 
spend their disposable income in each period, while the rest of the 
population behaves as savers and consumes its permanent income, 
thus smoothing resources inter-temporally.  

Results:  

  The country specific productivity has a significant impact on the 

33 Glick, R. and Rogoff K. (1995), Global versus country-specific productivity shocks and the current account, 
Journal of Monetary Economics 35, 159–192. 
34 Mann, C. (2002), Perspectives on the U.S. Current Account Deficit and Sustainability, Journal of Economic 
Perspectives, 16(3), 131-152. 
35 Kollman, R. (1998), US Trade Balance Dynamics: the Role of Fiscal Policy and Productivity Shocks and of 
Financial Market Linkages, Journal of International Money and Finance, 17, 637-669. 

113 

 
 
 
 
                                           
Study on the potential of education to cause macroeconomic imbalances and negative 
spillovers – Annex IV 

current account in most countries (a 1% increase in country specific 
productivity would trigger a decrease in the current account balance 
by 14% basis points of average GDP).  

  The global productivity measure does not have a significant impact 
on the current account. The only exception is the US, for which the 
coefficient of the global productivity measure is negative and 
significant at the 10% level. One likely explanation is that the 
weight of the US in the global mean is very high, implying that for 
this country, the country specific and the global series are not very 
different. 

  There is little evidence for a Twin Deficit relationship (i.e. effect of 
the budget balance on the current account), the estimates being 
generally smaller than 10 percent and almost always insignificant 

Limitation: From a policy perspective, such estimates imply that a 
reduction in the U. S. budget deficit may not suffice to significantly 
decrease its sizeable current account deficit. However, the estimates 
correspond to an average over a long time horizon: the response of the 
current account to a reduction in the budget deficit could be higher due 
to specific factors (in particular, due to the composition of the fiscal 
adjustment).  

Link 

https://www.ecb.europa.eu/pub/pdf/scpwps/ecbwp509.pdf  

A4.3.8.5 

The terms of trade, productivity growth and the current account 

Table A4.57 Iscan, T. B (2000) 

Title:  

The terms of trade, 
productivity growth and 
the current account 

Authors: Talan B. Iscan 

Year of 
publication: 
2000 

Publishers: Journal of 
Monetary Economics  

Inputs: global and country-specific productivity in tradables and 
nontradables 

Outputs: Current account, investment 

Education 
inputs, 
outputs 
and 
returns 
considered  

Countries 
covered 

Canada, France, West Germany, Italy, Japan, United Kingdom, and the 
United States 

Data used 
and 
timescales 
for data 
and 
scenarios 

Overview 

Annual data from the International Sectoral Data Base compiled by the OECD. 

1971-1987 

Extend the analytical framework provided by Glick and Rogoff. Analyse 
the impact of country-specific and global productivity shocks on 

the current account and investment 

114 

 
 
 
 
 
 
 
Study on the potential of education to cause macroeconomic imbalances and negative 
spillovers – Annex IV 

The current account responds more than investment to country-specific 
traded productivity growth. 

Global traded productivity and country-specific nontraded productivity 
growth have no effect on the current account, but they have a 
significant impact on investment.  

The global component of nontraded productivity is negligible and has 
no significant impact on either the current account or investment.  

The response of the current account and investment to relative prices 
(the terms of trade and exchange rate) are insignificant. 

Glick and Rogoff (1995)36 derive analytically tractable current account 
and investment equations when there are global and country-specific 
productivity shocks. Their findings:  

Country-specific productivity shocks affect the current account more 
than investment, because both consumption and investment respond 
to changes in productivity inducing a larger response by the current 
account.  

Global productivity shocks have no impact on the current account 
because, in the face of a global shock, countries with similar 
endowments and technologies adjust their consumption and 
investment symmetrically 

Model:  

The model considers a two-sector small open economy model. The 
model allows for consumption smoothing and substitutability between 
tradable and nontradable goods. 

While in this framework country-specific tradable productivity growth 
causes the current account's response to be greater than that of 
investment, this is not the case for nontradable productivity. Because 
domestic consumption of nontradables cannot change more than 
output, in the case of nontradable productivity growth, the current 
account moves in tandem with investment. In contrast, a  permanent) 
change in country-specific tradable productivity not only leads to a 
response in the current account via investment, but also causes 
tradable consumption to change by more than output. Global 
productivity growth, however, has quite different implications: while 
global components of productivity changes have no impact on the 
current account, whether they originate from tradables or 
nontradables, they affect investment 

Limitations: choice of estimator when the cross-sectional coefficients 
are possibly heterogeneous and potential endogeneity of relative 

price variables  

Description 
of main 
findings 

36 Glick, R. and Rogoff K. (1995), Global versus country-specific productivity shocks and the current account, 
Journal of Monetary Economics 35, 159–192. 

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A4.3.8.6  Productivity, terms of trade and the “home market effect” 

Table A4.58 Corsetti et al (2006) 

Title:  

Productivity, terms of 
trade and the “home 
market effect” 

Authors: Giancarlo Corsetti , 
Philippe Martin and Paolo Pesenti  

Year of 
publication: 
2006 

Education 
inputs, 
outputs 
and 
returns 
considered  

Countries 
covered 

Data used 
and 
timescales 
for data 
and 
scenarios 

Overview 

Publishers: Journal of 
International Economics  

Inputs: GDP growth, growth of domestic R&D 

Outputs: terms of trade growth 

20 OECD countries 

1980-2004 

This paper analyses the international transmission and welfare 
implications of productivity gains and changes in market size when 
macroeconomic adjustment occurs both along the intensive margin of 
trade (changes in the relative price of existing varieties of tradable 
goods) and the extensive margin (creation and destruction of 
varieties). The study draws a distinction between productivity gains 
that enhance manufacturing efficiency and gains that lower the cost of 
firms’ entry and of product differentiation 

A common view in trade and growth theory is that an increased supply 
of domestic goods is associated with the deterioration of a country’s 
terms of trade, as the additional domestic supply is absorbed by 
international markets at falling prices. By the same token, stronger 
internal demand for domestic output reduces a country’s supply of 
exports and improves its international prices. A key welfare implication 
is that domestic productivity gains are transmitted positively to the 
country’s trading partners worldwide, thanks to changes in relative 
prices. 

The quantitative analysis by Ghironi and Melitz (2004)37 also predicts 
terms of trade appreciation in response to productivity shocks which 
symmetrically reduce production costs and the costs of firms’ entry. 

Description 
of main 
findings 

Background:  

GDP growth has a negative impact on relative export prices (and 
significant almost at the 5% level), while R&D growth has a positive 
and significant effect (at the 1% level). 

37  Ghironi,  F.  and  Melitz  M.  (2004),  International  Trade  and  Macroeconomics  Dynamics  with  Heterogenous 
Firms, NBER Working Paper No. 10540. 

116 

 
 
 
 
 
                                           
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spillovers – Annex IV 

However, when using country fixed effects to control for unobserved 
country characteristics that may affect the behaviour of their terms of 
trade. In this case, GDP growth is weakly positively correlated with 
relative export prices. But the positive and significant correlation with 
the growth rate of R&D remains robust. At a very minimum, these 
results suggest that the conventional view of the relation between 
growth and terms of trade does not tell the whole story 

Model: a two-country world economy with an endogenous set of goods 
supplied by imperfectly competitive firms 

endogenous labour supply. 

love for variety and its positive and welfare implications.  

do not model nontradable goods and deliberately focus on a static 
framework, abstracting from heterogeneity in productivity or trade 
costs and other microeconomic complexities 

Results: 

The international transmission and welfare implications of productivity 
growth crucially depend on whether such growth reduces the marginal 
costs of producing goods or the costs of creating new firms and 
varieties: 

A country that gains efficiency in manufacturing expands its output and 
exports, but experiences a deterioration of its terms of trade. 

On the contrary, the terms of trade improve with product diversification 
driven by efficiency gains in setting up new firms and changing goods’ 
attributes. 

The macroeconomic impact of efficiency gains that reduce entry costs 
is similar to that of changes in market size ó for a given level of GDP 
per capita, countries with a larger population tend to have stronger 
terms of trade. Firms’ entry and terms of trade are enhanced by 
domestic demand due to government expenditure 

When a country improves its international prices via product 
diversification, its trade partners are hurt by higher import prices, but 
they benefit from the availability of a large variety of goods: the 
welfare spillovers need not be negative. With love of variety, 
sufficiently low trade costs imply that equilibrium changes in domestic 
varieties available to foreign consumers more than compensate the 
adverse movements in their terms of trade 

Link 

https://www.researchgate.net/profile/Paolo_Pesenti/publication/50095
21_Productivity_Spillovers_Terms_of_Trade_and_the_'Home_Market_E
ffect'/links/00b495272b11931be2000000.pdf  

A4.3.8.7  Notes on exports and productivity: a causal analysis 

Table A4.59 Kunst, R and Marin, D (1989) 

Title: 

Notes on exports and 
productivity: a causal analysis 

Authors: Robert 
Kunst and Dalia Marin 

Year of 
publication: 
1989 

117 

 
 
 
 
 
 
 
Study on the potential of education to cause macroeconomic imbalances and negative 
spillovers – Annex IV 

Education 
inputs, 
outputs 
and 
returns 
considered  

Countries 
covered 

Data used 
and 
timescales 
for data 
and 
scenarios 

Overview 

Publishers: Review 
of Economics and 
Statistics 

Inputs and Outputs: exports; price competitiveness; productivity 
(output per employee in the manufacturing sector); world output 

Austria 

1966-1985 

Time series analysis  

The observed co-movement between productivity and export growth 
suggest a direct link between the two variables. . Explore causal 
relationship between productivity and exports.   

Causal hypotheses: 

  assume productivity to be largely induced by demand prospects via 

export growth/ 

  exports concentrate investment in the most efficient sectors of the 

economy 

  higher export growth allows to gain from economies of scale 

  stronger exposure to international competition increases the pressure 
on the export industries to keep costs low and provides incentives for 
the introduction of technological change  

  growth of exports has positive externalities on other sectors 

  consider productivity to determine the rate of growth of exports and 

to be independent of export demand. 

  competitive performance in export markets is attributed to market 

power achieved through innovation 

  causal link in both directions 

The paper performs a statistical test of the causal hypotheses 

The causality analysis indicates no causal link from exports to 
productivity.  

The null of no causality from productivity to exports has to be rejected 
at conventional levels.  

Description 
of main 
findings 

Results:  

Using an Unrestricted Vector Autoregression (UVAR) Framework: the 
causality test for the terms of trade suggests a causal link from 
productivity and OECD outputs, respectively, on the terms of trade and 
no causation from exports on the terms of trade 

118 

 
 
 
 
 
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Using a Subset Model Autoregression (SMAR): absence of any causality 
from exports to productivity  

Link 

https://www.researchgate.net/profile/Robert_Kunst/publication/240947
95_On_Exports_and_Productivity_A_Causal_Analysis/links/0912f50f48d
0b0dc1b000000.pdf  

A4.3.8.8 

Effects of Productivity and Import on Firm-Level Export 

Table A4.60 Jienwatcharamongkhol, V (n.d) 

Title:  

Effects of Productivity 
and Import on Firm-
Level Export 

Authors: Viroj 
Jienwatcharamongkhol 

Publishers: Lund University 

Year of 
publication: 
N.d 

Education 
inputs, 
outputs 
and 
returns 
considered  

Countries 
covered 

Data used 
and 
timescales 
for data 
and 
scenarios 

Overview 

Inputs: - 

Outputs:  - 

Sweden 

1997-2006 

It is well known that productive firms are more likely to become 
exporters. 

Investigate the interaction effects of productivity and import on firm’s 
export participation and intensity 

Results: the interaction effects are heterogeneous across industries. For 
basic and high-tech industries, firms with higher productivity are more 
likely to export and with greater extent, following previous import 
activities. The direct productivity effect is greater for basic low-skilled 
industries than capital-intensive high-tech industries. 

Literature also finds a connection between productivity and import, i.e. 
self-selection and learning-by-importing.  

According to the theoretical model by Melitz (2003)38 and Bernard et al. 
(2003)39, the sunk costs of establishing an international network set the 

38  Melitz,  M.  (2003),  The  impact  of  trade  on  intra-industry  reallocations  and  aggregate  industry  productivity, 
Econometrica, 71 (6), pp. 1695-1725. 

119 

 
 
 
 
 
 
 
 
                                           
Study on the potential of education to cause macroeconomic imbalances and negative 
spillovers – Annex IV 

Description 
of main 
findings 

threshold, or trade barrier, so that only big and highly productive firms 
can afford and self-select themselves into foreign markets.  

Eaton, Kortum, and Kramarz (2004)40 first observe this pattern in French 
manufacturing firms and their paper inspires a number of studies in 
other countries, which also find similar results. Furthermore, the 
productivity premia or a gain in productivity found in exporters are also 
documented and confirm the prediction from the self-selection literature. 
In a survey of 54 firm-level studies covering in total 34 countries by 
Wagner (2007)41, the conclusion is that ”exporters are more productive 
than non-exporters, and the more productive firms self-select into 
export markets.” 

Hypotheses of the paper:  

previous productivity, measured by TFP, and import, in terms of total 
value, positively affect current export, in terms of participation (whether 
to export or not) and intensity (how much to export). Besides the direct 
effects above, there is a potential interaction effect between productivity 
and import themselves. Two directions to this relationship can be 
observed. On one hand, productivity is positively related to import 
because of the high entry costs and only more productive firms can 
become importers. On the other hand; an import of intermediate inputs 
can increase firms productivity through global specialisation, technology 
transfer, and variety effect 

the interaction between productivity and import in the past positively 
affects current export, for both participation and intensity. More 
productive firms are more likely to export and with greater extent in the 
current period, following import in the previous period. 

Model: 

model the export as a function of past productivity measured as TFP and 
import, controlling for annual shocks and country fixed effects, as well as 
various firm- and country-specific characteristics variables, which are 
usually employed in a gravity equation 

Results 

The interaction term total factor productivity * imports show positive 
and significant coefficients across six industries. This suggest that more 
productive firms are more likely to engage in export, provided their past 
import experience 

The hypothesis of positive interaction effects on exports (H2) seems to 
apply for almost all industries, whereas the hypothesis of positive direct 
effects of productivity and import (H1) applies well for low capital-
intensive industries, Food and Textile, fairly well for the high capital-
intensive industries, Electrics and Vehicles, but not so well for medium 
capital-intensive ones, Chemicals and Plastics 

Link 

http://www.freit.org/WorkingPapers/Papers/FirmLevelTrade/FREIT558.p
df  

39 Bernard, A. et al. (2003), Plants and productivity in international trade, NBER Working Paper Series, 7688. 
40 Eaton, J., Kortum, S.,  and Kramarz, F. (2004), Dissecting trade firms, industries, and export destinations, 
Federal Reserve Bank of Minneapolis. 
41 Wagner, J. (2007), Exports and productivity: A survey of the evidence from firm-level data. World Economy, 
30 (1), 60–82.  

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A4.3.8.9 

From  Productivity  to  Exporting  or  Vice  Versa?  Evidence  from 
Tunisian Manufacturing Sector 

Table A4.61 Ayadi, M and Matoussi, W (2014) 

Title:  

From Productivity to 
Exporting or Vice Versa? 
Evidence from Tunisian 
Manufacturing Sector 

Authors: Mohamed Ayadi 
and Wided Matoussi 

Year of 
publication:
2014 

Publishers: African 
Development Bank Group 

Education 
inputs, 
outputs 
and 
returns 
considered  

Countries 
covered 

Data used 
and 
timescales 
for data 
and 
scenarios 

Overview 

Description 
of main 
findings 

Tunisia 

2004-06 

1323 manufacturing firms 

Institut National de la Statistique of Tunisia 

Explore the link between firm's productivity and exporting: examine 
whether more productive firms self-select into export markets, and 
whether exporters achieve productivity improvements through 
learning-by-exporting effects. 

Also explore the link between innovation (as a channel linking 
productivity to exporting) and exporting 

Method: Compare exporters from non-exporters; then compare fully 
exporting firms from others  

Results: fully exporting firms self-select more often into export markets 
and; therefore, have much less to gain from exporting because of their 
likely longer prior exporting experience. 

Regressions:  

model the self-selection effect as the probability of firm i exporting in 
period t, regressed on lagged exporting, lagged sales and lagged firm 
characteristics (including output). 

model learning-by-exporting as a simple linear regression of firm i 
sales in period t on lagged sales, lagged exporting and other lagged 
firm characteristics 

Results: 

Self selection: when looking at exporters vs non-exporters: the 
coefficient on output is positive (0.254) but not statistically significant. 
When looking at totally exporting firms vs others: the coefficient on 
output is positive and highly significant (8.84***) 

121 

 
 
 
 
 
 
 
 
  
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spillovers – Annex IV 

Learning-by-exporting: when looking at exporters vs non-exporters: 
the coefficient on exports is positive and significant (1.426***). When 
looking at totally exporting firms vs others: the coefficient on exports is 
positive and almost significant (0.611) 

Link between innovation and exporting 

Innovation is a key factor that affects productivity.  

Two equations:  

model the exporting activity: probability of firm i exporting in period t 
is regressed on lagged exporting, lagged innovation and lagged control 
variables capturing some of the firm characteristics 

model the innovation activity: linear regression of firm i innovation in 
period t on lagged innovation and exporting and other firm 
characteristics. 

Results:  

Exporting equation: when looking at exporters vs non-exporters: 
prior innovation activity increases the likelihood of current export 
status (2.948***). When looking at totally exporting firms vs others: 
There is a strong statistical support for the positive impact of lagged 
innovation on exporting ability (3.131***) 

Innovation equation: when looking at exporters vs non-exporters: 
positive coefficient on lagged exporting status is positive (0.052***) . 
When looking at totally exporting firms vs others: the positive and 
significant coefficient on lagged exporting status is along expected lines 
(0.068***) 

Link 

http://www.afdb.org/fileadmin/uploads/afdb/Documents/Publications/
WPS_No_214_From_Productivity_to_Exporting_or_Vice_Versa_Evidenc
e_from_Tunisian_Manufacturing_Sector_BB.pdf 

122 

 
 
 
 
 
 
 
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