SERIESPAPERDISCUSSIONIZA DP No. 7850

Returns to Skills around the World:
Evidence from PIAAC
Eric A. Hanushek
Guido Schwerdt
Simon Wiederhold
Ludger Woessmann
December 2013

Forschungsinstitut
zur Zukunft der Arbeit
Institute for the Studyof Labor

Returns to Skills around the World:
Evidence from PIAAC
Eric A. Hanushek
Hoover Institution, Stanford University,
CESifo, NBER and IZA

Guido Schwerdt
University of Siegen,
CESifo, and IZA

Simon Wiederhold
ifo Institute, University of Munich

Ludger Woessmann
University of Munich, ifo Institute,
CESifo and IZA

Discussion Paper No. 7850
December 2013

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IZA Discussion Paper No. 7850
December 2013

ABSTRACT
Returns to Skills around the World: Evidence from PIAAC *Existing estimates of the labor-market returns to human capital give a distorted picture of therole of skills across different economies. International comparisons of earnings analyses rely
almost exclusively on school attainment measures of human capital, and evidence
incorporating direct measures of cognitive skills is mostly restricted to early-career workers in
the United States. Analysis of the new PIAAC survey of adult skills over the full lifecycle in 22
countries shows that the focus on early-career earnings leads to underestimating the lifetime
returns to skills by about one quarter. On average, a one-standard-deviation increase innumeracy skills is associated with an 18 percent wage increase among prime-age workers.
But this masks considerable heterogeneity across countries. Eight countries, including all
Nordic countries, have returns between 12 and 15 percent, while six are above 21 percent
with the largest return being 28 percent in the United States. Estimates are remarkably robust
to different earnings and skill measures, additional controls, and various subgroups.
Intriguingly, returns to skills are systematically lower in countries with higher union density,
stricter employment protection, and larger public-sector shares.

JEL Classification:
Keywords:

J31, I20

cognitive skills, education, labor market, earnings, international comparisons

Corresponding author:
Ludger Woessmann
Ifo Institute for Economic Research at the University of Munich
Poschingerstr. 581679 Munich
Germany
E-mail: woessmann@ifo.de*

We would like to thank Andreas Schleicher and William Thorn from the OECD for access to and help
with the international PIAAC data, as well as Dan McGrath, Eugene Owen, and Saida Mamedova fromthe National Center for Education Statistics and the PIAAC team at GESIS for providing access to the
U.S. and German continuous-earnings data, respectively. Simon Wiederhold and Ludger Woessmanngratefully acknowledge financial support from the European Union’s FP7 through the LLLight’in’Europe
project. Simon Wiederhold also gratefully acknowledges the hospitality of the OECD in Paris during
the initial phase of work on this paper.

1. Introduction
The skills of the population are generally viewed as a key ingredient in modern knowledgebased economies (e.g., Hanushek and Woessmann (2008)). However, existing evidence on the
returns to skills in the labor market is surprisingly limited, coming almost exclusively from
earnings of early-career workers in the United States. As a result, any sense of how rewards toskills evolve over the work life or of how they might differ across economies is absent.1 New
international data from the Programme for the International Assessment of Adult Competencies(PIAAC) dramatically changes the ability to understand how economies value skills. Using
these data, this paper provides new insights into the value of skills in different economic settings
by developing estimates of the earnings returns to cognitive skills across the entire labor force
for 22 countries.
Unlike the case of the returns to school attainment, obtaining estimates of the value ofindividual cognitive skills has had to rely on a small number of specialized data sets. Whileassessments of the achievement of students are common, tested students are seldom followedfrom school into the labor market where the impact of differential skills can be observed.
Developing estimates of the returns to skill has most generally relied on U.S. panel data that
permit observing the subsequent earnings of youth who can be followed into their initial jobs.2
However, estimates based on such early-career earnings are likely to suffer from downward
lifecycle bias as people with higher lifetime earnings show systematically steeper earnings
growth (Haider and Solon (2006)). Furthermore, individual skills may take time to be revealed(Altonji and Pierret (2001)). Importantly, returns to skills may also depend on a country’s
specific labor-market and social institutions.
The main prior source of international comparisons of returns to cognitive skills hascome from analyses of the International Adult Literacy Survey (IALS), collected in the mid-

1

There has been extensive estimation of returns to school attainment as found in Mincer earnings functions
with Psacharopoulos and Patrinos (2004) providing estimates for some 98 countries. Nonetheless, these estimatesprovide limited comparative information that can indicate how different economies value skills (see the discussionin Hanushek and Woessmann (2012)). Note also that, for expositional simplicity, we simply use common but
imprecise language interpreting the earnings impact of additional years of schooling as the return to schooling; see
Heckman, Lochner, and Todd (2006).
2
See, for example, Bishop (1989), Murnane, Willett, and Levy (1995), Neal and Johnson (1996), Mulligan(1999), Murnane et al. (2000), Lazear (2003), and Chetty et al. (2011). Bowles, Gintis, and Osborne (2001) provide
an early survey of studies of achievement effects, and Hanushek and Woessmann (2008) and Hanushek and Rivkin(2012) survey more recent evidence.

1

1990s.3 However, the returns from two decades ago may no longer be good indicators of thesituation in economies that have undergone substantial technological change (Autor, Levy, andMurnane (2003); Goldin and Katz (2008); Acemoglu and Autor (2011)). Moreover, while
informative, the IALS analyses suffer from a number of limitations: a restricted number of 15
countries with relevant data, relatively small sample sizes, and skill tests that assess very basic
competencies.
The new PIAAC survey, conducted under the auspices of the Organisation for Economic
Co-operation and Development (OECD) in 2011-2012 and released in the fall of 2013, provides
a number of advantages over existing datasets. First, while again observing people across theentire work life, it covers more countries (currently 22, but ultimately up to 33). Second, it
provides substantially larger sample sizes per country (more than twice the effective IALS
sample sizes), allowing richer analyses in subgroups.4 Third, PIAAC substantially extends thedepth and range of measured skills including an attempt to assess problem-solving skills intechnology-rich environments.
This paper makes three contributions to the literature. First, we place individual earnings ina more general human capital model and then document the pattern of returns to cognitive skills,
focusing on variations in overall earnings impacts across 22 countries. Second, we provide
evidence on the sensitivity of these returns to varying samples, measures, and specifications.
Third, we describe how these returns differ according to features of country economies including
union density and various labor and product market regulations.
Our results confirm that estimates based on early-career earnings underestimate the lifetime
returns to skills, in our analyses by an average of about one quarter. Across the 22 countries, aone-standard-deviation increase in numeracy skills is associated with an average 18 percent wage
increase among prime-age workers. Moreover, because of measurement errors in skills, theseestimates should be thought of as lower bounds on the return to skill.
But this overall measure of returns to skill also masks considerable cross-country
heterogeneity: Returns are below 15 percent in eight countries, including all four participating3

See Leuven, Oosterbeek, and Ophem (2004), Denny, Harmon, and O’Sullivan (2004), Hanushek and Zhang(2009), and Barone and van de Werfhorst (2011) along with the review in Hanushek and Woessmann (2011). Other
country-specific uses of the IALS data are found in McIntosh and Vignoles (2001), Green and Riddell (2003), andGreen and Riddell (2013) who also use data from the 2003 follow-on Adult Literacy and Life Skills Survey (ALL).
4
In total, the sample of prime-aged, full-time workers is more than three times as large in PIAAC as in IALS.

2

Nordic countries, and above 21 percent in six countries, with the largest return being 28 percent
in the United States. Estimated returns tend to be largest for numeracy and literacy skills andsmaller for problem-solving skills, although the relative importance of different skill dimensions
varies across countries. Estimates prove highly robust to different earnings measures, additional
controls, and various subgroups. Finally, exploiting the cross-country dimension of our analysis,
we find that returns to skills are systematically lower in countries with higher union density,
stricter employment protection, and larger public-sector shares.
In the following, Section 2 provides our conceptual framework and derives our empiricalmodel. Section 3 introduces the PIAAC data. Section 4 presents our empirical results on returns
to skills in the different countries, going into the details of model specifications, different skill
domains, heterogeneous returns, and robustness checks. Section 5 presents stylized facts about
what accounts for differences in skill returns across countries. Section 6 concludes.

2. Conceptual Framework and Empirical Model
Even though the labor economics literature is replete with analyses of how workers’ wages
are determined, we believe that it has settled on a stylized model that in the main derives from
common measurement and data deficiencies. We begin by broadening the perspective on humancapital and wages to include recognition of elements of skill determination. We follow this withdetails of our empirical approach.
2.1 Conceptual Framework
Most analyses of individual earnings rest on a very simple underlying model of humancapital, which we can write simply as (Hanushek and Woessmann (2008)):y   H 

(1)where individual earnings (y) are a function of the labor-market skills, or human capital, of theindividual (H). The stochastic term (ε) represents idiosyncratic earnings differences, generally
presumed in empirical analyses to be orthogonal to H.
Human capital is nonetheless a latent variable. A central part of the critique of early humancapital ideas was that human capital was inherently an elusive concept that lacked any
satisfactory measurement. Early arguments, for example, suggested that differences in earnings

3were caused by skill or human capital differences and thus that measurement of human capital
could come from observed wage differences – an entirely tautological statement. To be usefuland verifiable, it is necessary to specify the measurement of H.
The contributions of Jacob Mincer were especially important in establishing the course of
empirical work. Mincer (1970, 1974) argued that a primary motivation for schooling wasdeveloping the general skills of individuals and, therefore, that it made sense to measure humancapital by the amount of schooling completed by individuals. From this, Mincer showed how
wage differentials could be significantly explained by school attainment and, in a more nuanced
form, by on-the-job training investments. This insight was widely accepted and has dictated theempirical approach of a vast majority of empirical analyses in labor economics through today.
It has obviously been productive to rely on this formulation, because information on school
attainment is frequently measured and reported in surveys and censuses that also containearnings information. Propelled by readily available data on school attainment within and acrosscountries, economists have devoted considerable energy to estimating the returns to additional
years of schooling in formulations that are based on substituting years of schooling (S) for H inequation (1). This work has been summarized and interpreted in a number of different places.5
But Mincer’s empirical innovation has perhaps been too successful as it has also led
researchers to ignore many important and continuing measurement issues. Implicitly, thestandard Mincer formulation assumes that schooling is the sole systematic source of skill
differences.
In a different branch of research, a vast amount of research has delved into “educational
production functions.” This work has considered how, besides schools, also other factors enter
into the determination of skills.6 This line of research indicates that skills are affected by a range
of factors including family inputs (F), the quantity and quality of inputs provided by schools(which we incorporate as the function Q(S), where S is school attainment), individual ability (A),and other relevant factors (X) that include labor-market experience, health, and so forth. Alongwith a stochastic term (ν), assumed uncorrelated with the other determinants of H, we can write alinear version of this general model simply as:
5

See early discussions in Mincer (1970) and Griliches (1977) and more recent reviews in Card (1999) andHeckman, Lochner, and Todd (2006). In an international context, see Harmon, Oosterbeek, and Walker (2003) and
Psacharopoulos and Patrinos (2004).
6
See, for example, the general discussion in Hanushek (2002).

4

H   F  Q(S )   A   X 

(2)

Some empirical applications of this production function model have simply substituted
equation (2) into equation (1) and directly estimated the impact of various inputs into theeducational process on income, presumably arising from their impact on relevant labor-market
skills. More commonly, however, estimation is restricted just to equation (2) where skills (H)are measured by achievement tests. This analysis also matches more closely current educational
policy debates that are centered on improving levels of student achievement and reducing thevariance in such outcomes across schools and population groups.
This work on the determinants of skills implies that it is inappropriate just to substitute asingle input into skills (importantly, such as S) into equation (1) to estimate the impact of humancapital on earnings. It is highly unlikely that the stochastic term, ε, is orthogonal to school
attainment once it is recognized that it includes the other determinants of skills.
There is a long line of research focused on whether omitting individual ability, A, leads tobias in the estimated returns to schooling if more able students tend to attain more schooling.7
But the more general issue is that schooling is just one of a series of well-researched factors that
affect individual skills. The natural interactions among these various factors and school
attainment suggest that the simple estimation of the schooling-earnings relationship is prone tosevere biases that are difficult to correct in any satisfactory way.
Our estimation follows an alternative approach built on direct measures of cognitive skills.8
Consider test score measures of the relevant human capital skills – that is, standardized
assessments of numeracy and literacy achievement. If these measures, denoted C, completely
captured variations in H, the key parameter (γ) of equation (1) could be directly estimated byusing C as the measure of H. Further, if school quantity, S, were added to the estimation
equation, it would have no independent influence (in expectation), because the relevant skills
were fully contained in C.

7

See Card (1999). Indeed, one approach to looking at the returns to schooling is simply to estimate a standard
Mincer earnings function with an additive test score that is alternatively interpreted as ability or as school quality.
But neither is consistent with the educational production functions of equation (2). Skill tests are to varying degrees
subject to the influences depicted in equation (2), and thus are not generally thought of as fixed ability.
Alternatively, as a measure of school quality, such measures should modify the impact, or marginal product, of timespent in formal schooling. See also Hause (1972) on the functional form.
8
This view is closely related to the empirical analysis in Neal and Johnson (1996) and Murnane et al. (2000).

5But few people believe that existing test data are complete measures of H. Instead, theachievement test measures, C, are best thought of as error-prone measures of H:
C  H (3)

With classical measurement error, one would expect the estimate of γ in equation (1) to be biased
toward zero, and, if S and C are positively correlated as would be expected, the coefficient on S
in the same estimated equation would be biased upward, even if S had no independent effect over
and above its relationship with C. This simple model would imply that the coefficient on C
would be a lower bound on the impact of human capital on incomes.9
The discussion is further complicated by the fact that part of the return to cognitive skills
undoubtedly comes through continuation in school. There is substantial U.S. evidence thatstudents who do better in school, either through grades or scores on standardized achievement
tests, tend to go farther in school.10 Murnane et al. (2000) separate the direct returns to measured
skill from the indirect returns of more schooling and suggest that perhaps one-third to one-half ofthe full return to higher achievement comes from further schooling. Similarly, Rivkin (1995)
finds that test score variation captures a considerable proportion of the systematic variation in
high-school completion and in college continuation, to the extent that test score differences canfully explain black-white differences in schooling. Indeed, this relationship between cognitive
skill and further investments in formal schooling is implicit in the idea of skill complementarities
over the life cycle, as development by James Heckman and his co-authors (e.g., Cunha et al.
(2006); Cunha and Heckman (2007)) and summarized by the stylized conclusion that “skill
begets skill.” Early measures of human capital contained in cognitive skills during the schoolingprocess index future additions to human capital, again thought of in terms of the quality of skills.
2.2 Empirical Model
Following this conceptualization, our baseline empirical model is simply an analog to a
Mincer equation except that it is built on measured cognitive skills (C) instead of years ofschooling:
9

Unfortunately, while information on test reliability would help to bound the potential effects of measurement
error, we do not have such information for these tests. Note also that the standard test-retest measures of reliability
will themselves understate the amount of error in our measure, because we are interested in using the tests to measure
the concept of human capital – which is undoubtedly broader than the test constructs that have been developed.
10
See, for example, the early work by Dugan (1976) and Manski and Wise (1983).

6

ln yi   0  1Ci   2 Ei   3 Ei2   4 Gi   i(4)where yi is the hourly wage of individual i, E is years of actual labor-market experience,11 G is agender indicator, and ε is a stochastic error. The focus of our attention is β1, the earnings gradientassociated with measured human capital. (We frequently refer to this simply as the “return toskill.” It does not, however, correspond to a rate of return calculation, not only because of thegeneral arguments in Heckman, Lochner, and Todd (2006) but also because we have no indicationof the cost of achieving any given level of skill). To show the empirical relevance of school
attainment, we will also report estimations that add attainment to this model, but it is difficult toassess whether any independent impact of school attainment implies either an impact of schoolingthrough, say, a non-cognitive channel or through the measurement errors in cognitive skills.
More than in other empirical analyses, there is a question about the appropriate functional
form of the earnings relationship. In the investment paradigm developed by Mincer (1974), alog-linear relationship between earnings and human capital is quite natural. However, as alluded
to earlier, there is reason to suspect that the earnings gradient for cognitive skills is lower in early
career spans and higher later. First, because skills are difficult to observe, it may take time forfirms to learn about relevant differences among workers (e.g., Altonji and Pierret (2001)).
Second, since early careers often involve finding the right match of skills with employers (e.g.,
Jovanovic (1979)), early career observations would tend to understate the full value of skills
when observed in imperfect job matches. Third, Haider and Solon (2006) and Böhlmark and
Lindquist (2006) show that current earnings are a good proxy for lifetime earnings only whenobserved between the mid-30s and late-40s. Earnings observed at earlier ages can give rise to
substantial downward lifecycle bias because the measurement error at earlier ages is not ofclassical type, but mean-reverting: Workers with high lifetime earnings have particularly steep
earnings trajectories.12 All this suggests that any empirical analysis must be sensitive to positionin the career. We will therefore restrict our baseline analysis to prime-age workers and test forpossible age patterns in the returns to skill.

11

Although actual work experience might in part be endogenous to skill levels, robustness analyses described
below indicate that results hardly change when using potential experience (age minus year of schooling minus six)
instead of actual experience.
12
Estimates of the intergenerational earnings elasticity have been found to double when sons’ earnings aremeasured in their 30s rather than in their 20s; see Solon (1999) and Black and Devereux (2011) for reviews. Similarbias might be expected for estimates of the return to skills based on earnings observed early in the career.

7

A caution to our direct estimation approach, however, is that none of the studies of the
impact of cognitive skills on either earnings or school attainment have provided any convincing
analysis of the causal impact of scores. Unlike the efforts to identify the rate of return to school
attainment through a variety of means, the estimation based on measured achievement has
stopped without providing any substantial evidence that the observed variation in cognitive skills
is truly exogenous. The concern is that other influences on earnings – say, non-cognitive skills
or direct influences of families on earnings – are omitted from the estimation, leading to standard
omitted variables bias in the analysis of cognitive skills. To gauge the possible empiricalrelevance of such concerns, we will test for the robustness of our baseline model to additional
controls such as parental education. Furthermore, to the extent that any bias of the absolute
returns to skills is similar across countries, the cross-country pattern of our analysis should still
give a correct picture of the relative returns to skills across countries. Nonetheless, a causal
interpretation of the estimated coefficient of cognitive skills remains to be established.
Even in the absence of convincing identification strategies for cognitive skills, we believethat these estimates provide an indication of the potential earnings impacts of policies that lead to
improved skills. The consistency of the estimated impacts across different model specifications
and different subsamples that we will see below provides support for the underlying importanceof cognitive skills, and we know of no alternative model that would consistently produce our
stylized results.

3. The PIAAC Data
Our primary data source is the Programme for the International Assessment of Adult
Competencies (PIAAC). Developed by the OECD and collected between August 2011 and March
2012,13 PIAAC provides internationally comparable data about skills of the adult populations in
24 countries,14 22 of which can be used in our analysis:15 Austria, Belgium (albeit just Flanders),
13

Exceptions are Canada (November 2011 to June 2012) and France (September to November 2012).
Nine additional countries plan to implement PIAAC in 2014: Chile, Greece, Indonesia, Israel, Lithuania,
New Zealand, Singapore, Slovenia, and Turkey.
15
We do not use data for Australia and the Russian Federation in our analysis. The Australian data are notavailable to us at the time of writing. According to the OECD (2013), data for the Russian Federation are
preliminary, may still be subject to change, and are not representative of the entire Russian population because they
do not include the population of the Moscow municipal area. Based on the relatively small sample of 574 available
observations, we estimate a return to skills of 6.8 percent in our baseline model on the available data from theRussian Federation, but this may be biased downwards by the neglect of the capital region.
14

8

Canada, Cyprus, the Czech Republic, Denmark, Estonia, Finland, France, Germany, Ireland,
Italy, Japan, Korea, the Netherlands, Norway, Poland, the Slovak Republic, Spain, Sweden, theUnited Kingdom (specifically England and Northern Ireland), and the United States.
PIAAC was designed to measure key cognitive and workplace skills needed for individuals
to advance in their jobs and participate in society. In each participating country, a representative
sample of adults between 16 and 65 years of age was interviewed at home in the language of
their country of residence. The standard survey mode was to answer questions on a computer,but for respondents without computer experience there was also the option of a pencil-and-paperinterview.16 The countries used different sampling schemes in drawing their samples, but thesewere all aligned to known population counts with post-sampling weightings. Our analyses
employ sample weights in the estimations throughout.
The survey included an assessment of cognitive skills in three domains: literacy, numeracy,
and problem solving in technology-rich environments.17 The tasks respondents had to solve
were often framed as real-world problems, such as maintaining a driver’s logbook (numeracy
domain) or reserving a meeting room on a particular date using a reservation system (problemsolving domain). The domains, described more completely in OECD (2013), refer to key
information-processing competencies and are defined as Literacy: ability to understand, evaluate, use and engage with written texts to participate
in society, to achieve one’s goals, and to develop one’s knowledge and potential;
 Numeracy: ability to access, use, interpret, and communicate mathematical informationand ideas in order to engage in and manage the mathematical demands of a range ofsituations in adult life; Problem solving in technology-rich environments: ability to use digital technology,
communication tools and networks to acquire and evaluate information, communicate
with others and perform practical tasks.
PIAAC measures each of the three skill domains on a 500-point scale. For analytical
purposes, we standardize scores in the subsequent regression analyses to have a within-country16

On average across countries, 77.5 percent of assessment participants took the computer-based assessment
and 22.5 percent took the paper-based assessment. A field test suggested no impact of assessment mode (OECD(2013)).
17
Participation in the problem-solving domain was optional; Cyprus, France, Italy, and Spain did notparticipate in this domain.

9

mean of zero and a within-country standard deviation of one.18 All three scales are intended tomeasure different dimensions of a respondent’s skill set. IALS, the predecessor of PIAAC,
suffered from pairwise correlations of individual skill domains that exceeded 0.9, making it
virtually impossible to distinguish between different skills. The score domains in PIAAC areless strongly correlated with an individual-level correlation between numeracy and literacy(problem-solving) of 0.85 (0.76). In our preferred empirical specification, we focus on
numeracy skills, which we deem most comparable across countries, but we show below that our
results do not depend on the choice of a particular measure of cognitive skills.
Before the skill assessment, all participants responded to a background questionnaire thatgathered information about labor-market status, earnings, education, experience, and demographic
characteristics of the respondents. The measure of experience refers to actual work experienceand was collected as the number of years where at least six months were spent in paid work.
The wage measure in our baseline model refers to gross hourly earnings of wage and salary
workers. In the Public Use File, earnings data for Austria, Canada, Germany, Sweden, and theUnited States are reported only in deciles. For Germany and the United States, we obtained
access to continuous wage measures at the national data centers. For the other three countries,
we obtained information on the median wage of each decile, which allows us to assign the decile
median to each survey participant belonging to the respective decile of the country-specific wage
distribution. In robustness specifications based on countries with continuous wage data, weshow that using decile medians has no substantive impact on our results. The models that poolall countries have to rely on the Public Use File, so that the German and U.S. earnings data arealso measured in deciles in the pooled regressions. In each country, we trim the bottom and top
one percent of the wage distribution to limit the influence of outliers.19
To obtain a homogeneous sample of workers with strong labor-force commitment, in ourbaseline model we limit the estimation sample to survey respondents who work full-time at thetime of the survey, where full-time employees are defined as those working at least 30 hours per
week.20 This sample decision implies that we are underestimating the impact of skills on worker
18

Throughout, we use the first plausible value of the PIAAC scores in each domain.
Preliminary investigation of outliers suggests that these largely represent data entry or coding errors.
20
In Austria, the full-time working status is based on a question of whether a respondent works full-time. The
Canadian sample includes full-time and part-time workers because the available data do not report working hours orwork status. Since returns to skills tend to be slightly smaller when including part-time workers (17.1 vs. 17.7
percent on average across countries), this sample difference may lead to slightly conservative estimates for Canada.
19

10earnings to the extent that higher skills also lead to higher employment probabilities.21
Nonetheless, we wish to isolate the direct labor-market effects of skills and to avoid otherinfluences such as family demands, health limitations, or the like that might affect labor-force
attachment. To get the best estimates of long-run returns to skills, we further restrict the baseline
sample to prime-age workers, defined as those between 35 and 54 years of age. Self-employedworkers are also excluded from the baseline analysis. Robustness analyses show the relevanceof these sample restrictions.
Table 1 provides summary statistics for the baseline samples of prime-aged, full-time
employees in the 22 countries. Sample sizes range from 816 in Poland to 1,875 in Denmark and7,178 in Canada (which oversampled to obtain regionally reliable estimates), with a total of
34,447 observations in the pooled sample.22 Respondents in Japan achieve the highest average
numeracy score, and respondents in Italy the lowest, with a difference in average achievement
between these two countries amounting to 89 percent of a standard deviation in test scores in theinternational sample. There is also considerable variation across countries in average years ofschooling, actual work experience, and the share of females in the population of prime-aged,
full-time employees.
Wage inequality, measured by the log wage differential between the 90th and 10th percentileof the wage distribution, is largest in Estonia, Korea, and the United States at around 1.5. In
these countries, a worker at the 90th percentile of the wage distribution earns 4.5 times as muchas a worker at the 10th percentile. In Sweden, which is the other extreme, workers at the 90th
percentile earn only twice as much as workers at the 10th percentile.

4. Returns to Skills around the World
Other things equal, how do earnings vary with different skills? In addressing this question,
we begin with a set of baseline estimates and then investigate how these results change withmodifications of the model specification or sample. Our starting point is equation (4) which
21

From estimating simple linear employment probability models (available from the authors), we find that ourmeasured skills are systematically related to higher employment.
22
Table A-1 in the Appendix reports equivalent summary statistics for the full PIAAC sample (including thefull age range 16-65, part-time and self-employed workers, and the non-employed), which leads to a total sample
size of 153,675 observations in the pooled international sample. The main reduction to our sample comes fromrestricting the age range from fifty years to twenty years (age range 35-54, reduction to 62,981), with further
reductions from restricting to individuals with positive income (45,790), as well as exclusion of part-time and selfemployed workers and trimming of the bottom and top one percent of the wage distribution.

11

expresses log hourly wages as a function of numeracy skills, gender, and a quadratic polynomialin actual work experience for the sample of full-time employees aged 35-54. Since our skill
measure is standardized to (0,1), the parameter of interest, β1, can be interpreted as the percentage
increase in income associated with a one-standard-deviation increase in measured skills.
4.1 Baseline Results
Our baseline estimates consistently indicate that better skills are significantly related tohigher labor-market earnings, as seen in Table 2. The coefficient on numeracy in the pooled
estimation suggests that a one-standard-deviation increase in numeracy skills is associated withan average increase in hourly wages of 17.8 percent across the 22 countries.
But perhaps the clearest finding from our baseline estimation across countries, shown also in
Figure 1, is that the wage impact of skills varies significantly across our sample of countries. Sixof the countries indicate returns to skills that exceed 21 percent: the United States with almost
28 percent, Ireland and Germany with 24 percent, as well as Spain, the United Kingdom, andKorea. These high returns differ noticeably from those in another set of eight countries withreturns to skills falling below 15 percent: Belgium, Cyprus, Czech Republic, Italy, and all theparticipating Nordic countries – Denmark, Finland, Norway, and Sweden. While a number ofthese countries do have significant income redistribution programs, it is important to rememberthat pre-tax and pre-transfer wages are being analyzed.
On average, the experience terms suggest a concave earnings-experience relationship andthe gender term a male-female earnings gap in these samples of prime-aged, full-time
employees. But again, there are substantial differences across countries, with no significant
gender difference in earnings in four countries (Belgium, Ireland, the Netherlands, and Spain)
and an earnings disadvantage of women of 20 percent and more in six countries (Cyprus, theCzech Republic, Estonia, Japan, Korea, and the Slovak Republic).
4.2 Age Varying Returns to Skills
As discussed, a prime motivation for this study is that most prior evidence on the return toskills has come from samples of workers that are likely to give misleading and biased estimates
for the range of world economies – i.e., relying on just U.S. samples of young workers. Figure 1
shows clearly that returns to skills in the U.S. labor market might not adequately characterize the12labor-market situation in other countries. But additionally, there are multiple reasons to believethat these estimates understate the true impact of skills across the life cycle.
To test for age varying returns to skills, Table 3 uses the extended sample of employees aged
25-65 and allows the returns to skills to differ between entry-age (25-34), prime-age (35-54), andexit-age (55-65) workers. Results suggest that there are indeed significant age differences in
returns to skills: Across all countries, returns to numeracy are only 14 percent in the baseline
category of workers aged 25-34 and are 4 percentage points (some 30 percent) higher among
workers aged 35-54 or 55-65. In the aggregate, there are no significant differences between
prime-age and exit-age workers. In fact, estimates of returns to skills increase steadily with age
until age 35 and are reasonably flat from there on, getting only slightly smaller beyond age 55(Figure 2).23 Thus, the focus on early-career earnings in the existing literature is likely to
downward bias estimated returns to skills. As earnings of prime-age workers tend to be goodproxies for lifetime earnings (Haider and Solon (2006)), returns estimated in the prime-age
category will more likely capture lifetime returns to skills.
Looking at individual countries, returns to skills are significantly higher for prime-age
workers than for entry-age workers in 15 out of the 22 countries. In all but one country (theSlovak Republic), the interaction between skills and the dummy variable indicating prime-age
workers is positive. Insignificant age interactions are observed primarily in the transition
economies in Eastern Europe (the Czech Republic, Estonia, Poland, and the Slovak Republic),
where transition dynamics are likely to be important in explaining earnings across workers ofdifferent ages. These transition economies experienced a radical change in their labor markets
after the fall of the Iron Curtain. In the process of restructuring after privatization (e.g., Frydman
et al. (1999); Mertaugh and Hanushek (2005)), workers who are in their prime age or exit age
today may have lost much of their specific human capital during the early 1990s and may face ajob-skill mismatch, whereas workers in their entry age today may have acquired skills that are ofcloser labor-market relevance. Without the four Eastern European transition countries, returns toskills of prime-age and exit-age workers are even 5.0 percentage points (or 37 percent) higher
than among entry-age workers (13.5 percent) in the pooled sample.

23

The aggregate picture in Figure 2 might suggest including older workers in our estimates. However, past age
50, variations in retirement and labor-force participation across countries come into play (as seen by variations inreturns to exit-age workers in Table 3). Thus, we focus just on the prime-age worker population.

13

4.3 Returns to Skills and Interaction with Years of Schooling
As discussed, our focus on measured skills differs from the standard Mincer approach ofusing years of schooling as the sole measure of human capital. When adding years of schoolingto our baseline model, as shown in Table 4, both numeracy skills and years of schooling entersignificantly in the wage equation. The point estimate on numeracy skills goes down by 43
percent (from 17.8 percent to 10.1 percent in the pooled sample), indicating that a large part ofthe skills-earnings relation is related to the fact that people with higher skills are more likely toadvance further in the formal education system.24 In our estimation sample, the correlation
between numeracy skills and years of schooling is 0.45. Across countries, the reduction in the
point estimate on numeracy skills from the inclusion of years of schooling is particularly strong(over one half) in Cyprus, Korea, Poland, the Slovak Republic, Spain, and the United States,
which are the countries where the point estimates on years of schooling are particularly large.
Years of schooling enters significantly in each country, with point estimates ranging from
2.6 percent in Sweden to 9.0 percent in Poland. On average, one additional year of schooling is
associated with 5.9 percent higher earnings, conditional on numeracy skills. As discussed, theindependent impact of years of schooling implies either an impact of schooling through channels
other than cognitive skills or through measurement errors in cognitive skills. While it is difficult
to assess these possibilities, these results show the empirical importance of school attainment.
Note that, compared to a standard Mincer equation without the skill measure (shown in Table
A-2 in the Appendix), conditioning on numeracy skills lowers by some 20 percent the estimatedreturns to years of schooling from its unconditional average of 7.5 percent. Again, the drop
varies considerably across countries, with the largest reduction in returns to years of schooling
due to the inclusion of numeracy scores being almost one-third in Germany.
While it is difficult to compare the coefficient magnitude of our PIAAC results to estimatesof returns to skill from the IALS test in the mid-1990s because previous studies have useddifferent specifications and samples, the studies by Leuven, Oosterbeek, and Ophem (2004) andHanushek and Zhang (2009) report somewhat similar specifications that also include both skill
measures and years of schooling in one model. For the ten countries that are both in our PIAAC
analysis and in their IALS analyses, the average estimated return to skills is somewhat larger in
24

Results are very similar when we drop the assumption of linearity in years of schooling and instead include
separate indicator variables for six levels of school attainment (see bottom row of Table A-5 in the Appendix).

14

PIAAC than in IALS (9.1 percent here, 6.2 percent in Leuven, Oosterbeek, and Ophem (2004),
and 8.1 percent in Hanushek and Zhang (2009)).25 A few particular differences to the IALS
results stand out. On the one hand, our PIAAC estimates are substantially larger in Germany andin Poland (where IALS did not show any significant return to skills). On the other hand, our
PIAAC estimates are smaller in the United States and (depending on the comparison study) in
the Netherlands, the two countries that had the largest estimates in IALS. Closer investigation of
the extent to which these comparisons are affected by differences in model specifications andsamples warrants additional future research.
4.4 Returns to Different Dimensions of Skills
A particular innovation of the PIAAC survey is a richer measurement of various skills, bothin the sense that a wider range of competencies is assessed within each skill domain and that a
new skill domain was added. As noted, in contrast to the previous IALS assessments, thecorrelation between numeracy and literacy skills in PIAAC is somewhat lower at 0.85,
potentially allowing for an identification of the associations of each skill domain with earnings
separately. Furthermore, PIAAC for the first time tested the new skill domain of problem
solving in technology-rich environments (“problem solving” for short). The correlations ofproblem-solving skills with numeracy and literacy skills are 0.76 and 0.80, respectively.
Table 5 reports estimates of returns to the alternative skill measures for the pooled sample,
first included separately and then jointly in the model.26 By themselves, average estimated returns
to literacy skills are very similar to those for numeracy skills (17.1 compared to 17.8 percent).
By contrast, estimated returns to problem-solving skills are systematically smaller, at 14.3
percent on average.27 This pattern tends to be similar within most countries (see Table A-3 in the
Appendix). The only partial exception to the pattern is found in Estonia, where the problemsolving estimate is larger than the literacy estimate (but not than the numeracy estimate).
Our general focus on numeracy skills comes from the joint consideration of the separate
skill domains. When numeracy and literacy skills are entered together in column (4), both enter
25

These comparisons refer to Table 3 in Leuven, Oosterbeek, and Ophem (2004), together with information on
the standard deviation in skills from their Table A2, and to Model 4 of Table 5 in Hanushek and Zhang (2009).
26
All pooled models include country fixed effects so that all estimates rely just on within-country variation.
27
The number of observations in the specifications that include the problem-solving skill domain is smaller
than in the baseline model because Cyprus, France, Italy, and Spain did not participate in the test of problem-solving
skills and because individual respondents were allowed to opt out of the problem-solving test.

15

significantly, but the numeracy estimates tends to dominate the literacy estimate at 12.0 percent
compared to 6.8 percent on average. However, there is also some variation in this pattern acrosscountries. In all countries except Austria, the Netherlands, and Poland, the point estimate on
numeracy is larger than on literacy. While the coefficient on numeracy is statistically significant
in all countries, the coefficient on literacy becomes statistically insignificant in five countries(Estonia, France, Ireland, Norway, and the Slovak Republic) and even turns negative in Japan.
Of course, models that contain several skill measures at once have to be interpreted cautiously,as they are identified only from the limited part of the skill variation that is independent ofvariation in the other skill.
The assessment of problem solving is an innovative attempt in PIAAC to measure skills
needed to succeed in an information-based economy where information and communication
skills are required.28 Interestingly, these skills, at least as assessed by PIAAC, are systematicallyless strongly associated with individual earnings than more traditional cognitive skills. In
conjunction with numeracy skills, problem solving has half the estimated return: 6.2 percent on
average versus 12.1 percent for numeracy. This aggregate result holds across all countries
except the Czech and Slovak Republics. Further, the point estimate on problem solving is
insignificant in Japan, Korea, and Poland.
With all three skill domains together, each enters statistically significantly in the pooledmodel, but the point estimates on numeracy and literacy of 7.8 and 7.6 percent, respectively, aretwice as large as the point estimate on problem-solving skills of 3.7 percent. Identification of
separate effects is challenging in this model, particularly in the smaller within-country samples,so we do not want to over-interpret the country results. Nonetheless, while the coefficient on
problem-solving skills gets small and statistically insignificant in nine of the eighteen countries,
it actually dominates the numeracy estimate in three countries (the Czech and Slovak Republics
and the United Kingdom) and the literacy estimate in six countries (Denmark, Estonia, Norway,
the Slovak Republic, Sweden, and the United States).
The final column of Table 5 standardizes the numeracy skill measure to have mean zero andstandard deviation one for the entire international sample, rather than within each country
separately. This normalizes the numeracy scores to have comparable absolute returns acrosscountries. Overall, the estimated average return to skills is slightly larger with the international
28

See, for example, the description at: http://nces.ed.gov/surveys/piaac/problem-solving.asp.

16

standardization (18.3 percent compared to 17.8 percent). In fact, in most countries this makes
little difference (Table A-3), simply because within-country standard deviations in test scores are
similar in many countries. But in Japan and Korea, two countries with relatively narrow withincountry skill distributions, return estimates are 3.8 and 3.1 percentage points larger, respectively,reflecting the fact that any given movement on the international distributions represents a wider
within-country change. The opposite holds in the United States, the country with the widest
within-country skill distribution, where return estimates are 1.7 percentage points smaller.
4.5 Heterogeneous Returns in Different Subgroups
In discussions of wage determination, a variety of subgroups such as females, public-sector
workers, or immigrants are frequently singled out as facing different labor-market challenges.
Thus, it is useful to trace through the returns to skills in such identifiable groups. To address thisheterogeneity in wage determination, we added interaction terms of important subgroup indicatorswith the skill measure (and with all control variables) to our baseline model. Figure 3 depicts theresults for the pooled estimation, with country details given in Table A-4 in the Appendix.
There are some significant differences in estimated returns to skills in the overall
international sample, but the quantitative magnitude of the differences tends to be relatively
modest. First, estimated returns to numeracy are nearly identical for men and women in thesample of prime-age, full-time employees. Second, estimated returns tend to slightly increase
with the education level of the workers’ parents (measured in three categories), but the difference
ranges only from 14.8 percent for workers with low-educated parents to 18.0 percent for workers
with high-educated parents. Third, estimated returns are significantly higher in the private sector(at 18.3 percent) than in the public sector, but even in the latter, returns are at 14.7 percent.
Fourth, estimated returns are slightly higher in the native population (19.8 percent) than among
immigrants (15.9 percent).29 The small difference indicates, among others, that the fact thatinterviews were performed in the country’s language does not affect the overall results. Fifth,
while the baseline model refers to full-time employees only, part-time employees also have aconsiderable return to skills, albeit significantly smaller at 12.2 percent. In the sample of fulltime and part-time employees together (not shown), the estimated return to skills is 17.1 percent.

29

On average across countries, there are roughly 11 percent first-generation migrants in the sample of fulltime, prime-age employees.

17

There are some differences for individual countries (see Table A-4). For example, returns
for women exceed returns for men by more than 4 percentage points in Austria and the Czech
Republic, while the opposite is true in Korea. While the advantage of private-sector returns overpublic-sector returns is most pronounced in Germany, Ireland, Italy, Korea, and the United
Kingdom, returns are in fact slightly (albeit not significantly) higher in the public sector in
Cyprus, the Czech and Slovak Republics, and Finland. Exceptions to the general rule that parttime workers have lower returns to skills than full-time workers are Belgium, Finland, and Italy.
In the Czech and Slovak Republics, Denmark, Poland, and Sweden, skill returns for part-timeworkers are in fact very small and statistically insignificant.
4.6 Robustness to Additional Controls and Alternative Earnings Measures
Another way to assess the robustness of our baseline results is to include additional control
variables in the model and to look at alternative measures of economic performance. As isevident from Table A-5 in the Appendix, which reports returns to skills with alternative sets ofcontrol variables, controlling for parental education reduces the estimated returns to skills only
from 17.8 to 16.2 percent on average. This suggests that any direct influences of families on
earnings are unlikely to be a major driver of our baseline result, easing this omitted-variable
concern. Similarly, adding a migrant indicator to the baseline model hardly affects the average
estimated return to skills.30
Our baseline model controls for a quadratic polynomial in actual work experience. It might
be argued, though, that actual experience is endogenous to skill levels, thereby capturing achannel of the effect of skills on earnings. However, as the next model in Table A-5 shows,results are hardly different when we control for potential experience (age minus years ofschooling minus six) instead of actual experience. Although estimates tend to be slightly
smaller, the cross-country pattern is very similar to that obtained with actual experience.
To investigate possible channels of the effect of skills on earnings, the next model adds
controls for 10 one-digit occupation (ISCO) categories. The reduction in the coefficient on skills
indicates that part of the aggregate return to skills materializes through selection into higher-

30

In an extended model shown in Table A-6 in the Appendix, the additional control variables are
simultaneously included in the sample including part-time employees. Estimated returns to skills are smaller in all
countries, but the aggregate magnitude remains large: 15.0 compared to 17.8 percent on average. The table also
shows that the associations of some of the control variables with earnings differ markedly across countries.

18

paying occupations. Still, even within occupations and ignoring any cross-occupation variation,a one-standard-deviation increase in numeracy skills is associated with a 9.6 percent wage
increase on average. Controlling for 21 one-digit industry (ISIC) categories also lowers thecoefficient on skills, but much less so than controlling for occupations. Even within industries,
the return to skills is 15.0 percent on average, leading to the important conclusion that selection
into occupations or industries explains only part of the returns to skills. Looking just within such“micro labor markets,” those workers with higher skills systematically have higher wages.
Finally, the results prove highly robust in models with alternative earnings measures thanthe baseline specification with log gross hourly wages (Table A-7). First, in Austria, Canada,
and Sweden, we do not have continuous wage information, but only the median wage of thecountry-specific wage decile for each participant. When we use decile medians instead ofcontinuous wages in the other countries, estimated returns to skills are hardly different. In thepooled sample, the point estimate is 16.4 percent instead of 17.0 percent.31 Also within
individual countries, results do not differ significantly. Only in the Czech Republic, Ireland, andthe United States are the decile-median estimates smaller than the continuous estimates to anoteworthy extent (between 2.1 and 2.7 percentage points), whereas in several other countries,
the decile-median estimates are in fact larger than the continuous estimates. The overall patternsuggests that estimates based on decile medians may be reasonable proxies for the true estimates.
Second, while our baseline wage measure does not include bonuses, models where bonuses
are included in the hourly wage yield very similar estimates of skill returns that are slightly
larger (17.6 instead of 17.0 percent on average). Third, considering monthly rather than hourly
wages, so that hours worked come into play, hardly changes the picture (either with or withoutbonuses), although there is some variation for individual countries. Finally, extending thesample to the self-employed – 9 percent of the labor force on average in our estimation sample –also has little effect on the estimated returns across countries.

5. Accounting for Cross-Country Differences in Returns to Skills
In our assessment of wage determination, we have found that returns to skills differ
markedly across the 22 countries in our sample. This raises the question whether there are31

All pooled models in the table exclude Austria, Canada, Germany, Sweden, and the United States, wherecontinuous earnings data are not available in the Public Use File.

19

features of country economies such as labor and product market regulations that aresystematically related to differences in skill returns. Thus, in this final section, we turn toestablishing a set of stylized facts about country characteristics that are systematically related todifferences in the returns to skills across countries.
Over the past two decades, there have been significant changes in employment protection
policies, unionism, and other policies across Europe. These changes have been largely motivatedby a desire to improve the functioning of the economies and, by implication, the economic
wellbeing of individuals. Here we investigate whether these commonly identified aspects ofdifferent countries interact with the returns to skills. For example, unions have been argued tofavor wage schedules that reduce skill returns (see Frandsen (2012) and the references therein).
We begin with a baseline skill-earnings model (equation (2)) pooled across all countries andincluding country fixed effects μn, for n=1, …, 22 countries. We then include interaction terms
between the individual skill measure C and various measures of country characteristics N:
ln yin   n  1Cin   2 Cin N n    3 Ein   4 Ein2   5Gin   in(5)

Note that the main effects of the country-level features are absorbed in the country fixed effects
μn. The coefficient of interest is β2, which shows how returns to skills vary with different
country characteristics. As the latter vary only at the country level, standard errors in this model
are clustered at the country level. While degrees of freedom are limited in such a cross-country
analysis, the extended PIAAC sample provides substantially more analytical potential thanprevious studies. Note that any bias in estimated returns to skills that is the same acrosscountries would not affect this analysis, which is identified from the relative returns acrosscountries. To facilitate interpretation, all variables in the interacted model are de-meaned.
For this analysis, we incorporate aggregate data about differences in the structure of theinternational economies.32 Union density measures the share of wage and salary earners who aretrade union members. Employment protection legislation indicates the strictness of employment
32

Data are provided by the OECD and refer to 2011 unless otherwise noted. Union density refers to 2009 in
the Czech Republic and to 2010 in Denmark, Estonia, Poland, and Spain. The employment-protection indicator is
the weighted sum of sub-indicators concerning the regulations for individual dismissals (weight of 5/7) andadditional provisions for collective dismissals (2/7), incorporating 13 data items (see Venn (2009) for details). The
product-market regulation indicator refers to 2008 and covers formal regulations in state control of businessenterprises, legal and administrative barriers to entrepreneurship, and barriers to international trade and investment(see Wölfl et al. (2009) for details). Results are similar when using other measures of employment protection
legislation or sub-indices of product-market regulation. The public-sector share is calculated from the PIAAC data.

20

protection against individual and collective dismissal of employees on regular contracts.
Existence of minimum wages is measured by a binary variable that takes the value 1 if a country
has a statutory minimum wage and 0 otherwise. Product-market regulation is a composite
indicator measuring the degree to which policies promote or inhibit competition in areas of the
product market where competition is viable. Public-sector share is the share of wage and salary
workers employed in the public sector.
Analysis of the various aggregate institutional factors produces an interesting pattern ofresults (Table 6). When entered separately, union density, employment protection, and the
public-sector share have significant negative interactions with the returns to skill. That is,countries with a larger share of unionized workers, stricter employment protection, and larger
public sectors have systematically lower returns to skills on the labor market. For example, theestimate in column (1) suggests that a 25 percentage-point increase in union density (thedifference, for example, between Belgium and the United Kingdom) is associated with a 3.5
percentage points lower wage increase for each one-standard-deviation increase in numeracy
skills. By contrast, product-market regulations are not significantly related to differences in
returns to skills across countries, whereas countries with a minimum wage in fact appear to havelarger returns to skill in this simple model. However, when all five country-level measures arejointly included in column (6), only the interactions with union density and employment
protection legislation retain significance.
To ensure that results are not driven by country differences in the level or dispersion ofskills, column (7) adds interactions with the mean numeracy score of the country and with skill
inequality (the differential between the 90th and 10th percentile of the numeracy skill
distribution). The skill level may capture the supply of skills within a country, while skill
inequality reflects how compressed the distribution of skills is. It turns out that neither countries’
skill means nor their skill inequalities are significantly related to differences in returns to skills,
and the previous results remain intact.
Finally, column (8) adds a full set of 185 occupation-by-industry fixed effects to check
whether results are driven by differences in the occupation or industry structures of countries.
Identification in this model comes from variation within rather detailed industry-occupation
cells. Even in this quite demanding specification, the results on the interactions of returns to skill
with union density and with employment protection remain intact. In addition, the interaction

21

with the public-sector share recovers significance in this model. We conclude that returns toskills are systematically lower in countries with higher union density, stricter employment
protection legislation, and larger public sectors.

6. Conclusions
While human capital is virtually always identified as the key factor determining systematicdifferences in individual wages, existing empirical evidence has rested on crude and (almost
certainly) biased estimates of its importance. The ubiquitous Mincer earnings function assumes
that skill differences can be encapsulated in measures of school attainment, neglecting any otherfactors that might systematically affect skills. When direct measures of skill differences are
introduced, the bulk of evidence on the returns to skills is restricted to selective samples – namely,
young workers in the United States. These samples are, as we demonstrate, unrepresentative
both of the labor-market experiences in other countries and of older, prime-age workers. We
investigate the returns associated with a series of measures of cognitive skills across a large setof countries. The new international PIAAC study provides insights into the extent to which
modern, knowledge-based labor markets reward skills.
Higher cognitive skills – measured across numeracy, literacy, and problem-solving domains– are systematically related to higher wages in all 22 participating countries. The effect sizes ofour returns to skills estimates (best thought of as lower bounds because of measurement errors inskills) are economically meaningful. Among prime-age workers, a one-standard-deviation
increase in numeracy skills is associated with increased hourly wages averaging some 18 percent
across countries. But perhaps the most striking finding from the international analysis is thesubstantial heterogeneity in returns to skills across countries. Estimated returns to skills in the
countries with the highest returns (the United States, Ireland, and Germany) are roughly twice aslarge as in the countries with the lowest returns (Sweden, the Czech Republic, and Norway).
In addition, the age pattern of returns underscores the problem of relying just on theexperiences of younger workers in evaluating the economic role of skill differences. Prime-age
workers (age 35-54) quite consistently show greater returns to skills than labor-market entrants(age 25-34), averaging a 4 percentage-point difference. Only the participating Eastern European
transition economies fail to show this age pattern, probably reflecting the loss of human capitalto older workers when their economies changed.

22

The skill-earnings associations prove immensely robust in a variety of dimensions whenlooking at the labor-market earnings of prime-age workers. Perhaps surprisingly, though, returns
to general literacy and particularly numeracy are quite consistently higher than returns to problem
solving, a construct developed to measure skill demands in technologically-based economies.
Differences in returns to skills across subsets of workers also present interesting patterns. On
average, women and men have identical returns, while observed skills make somewhat lessdifference to immigrants, part-time workers, and public-sector workers. Again, however, there
are country differences that warrant more investigation.
Having established the pattern of returns to skills across countries, the cross-country
dimension of our analysis allows us to provide stylized facts about what accounts for differencesin returns to skills across countries. Intriguingly, returns to skills are systematically lower incountries with higher union density, stricter employment protection legislation, and larger public
sectors, while minimum wages, product-market regulations, average skill levels, and skill
inequality are not systematically related to differences in skill returns. While this is just a
descriptive depiction of which countries have higher skill returns than others, the consistency
with standard conceptual models is striking.
The regularity of the relationship between cognitive skills and higher earnings in all
countries documents the extent to which modern knowledge-based economies value skills. Weview the results presented here as a building block toward better understanding the sources andmechanisms that relate individuals’ skills to productivity and labor-market outcomes. For
example, by quantifying this association, the new results may have immediate application inparameterizing development-accounting studies that try to quantify the extent to which crosscountry differences in skills can account for cross-country differences in economic prosperity(Caselli (2005); Hanushek and Woessmann (2012)). Additionally, understanding individual
productivity differences is a necessary ingredient into projecting how the average level andinequality of economic outcomes of countries may move with future technological change.
Importantly, almost all international discussion of educational policy is centered on school
quality and student achievement. To understand the full economic implications of these
discussions, it is necessary to move the analysis of labor-market outcomes to match the analysis
of school outcomes.

23

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26

Table A-1: Descriptive Statistics in the Full Sample
PooledGross hourly wage
(national currency)
Wage inequality
NumeracyYrs schoolingExperience (years)
Female (share)
Observations
Gross hourly wage
(national currency)
Wage inequality
NumeracyYrs schoolingExperience (years)
Female (share)
Observations

1.12
268.5
(52.5)
12.5
(3.1)
18.7
(12.8)
0.50
153675Austria
13.5
(5.9)
1.07
274.9
(49.1)
11.8
(2.7)
20.0
(12.6)
0.50
5130
Italy
11.5
(6.0)
1.02
247.0
(50.2)
10.5
(3.9)
18.1
(12.3)
0.50
4621

Belgium
17.0
(6.3)
0.85
280.2
(51.4)
12.3
(2.9)
20.8
(12.5)
0.50
5463
Japan
1.6†
(1.1)
1.31
288.6
(43.7)
12.9
(2.4)
18.8
(12.7)
0.50
5278

Canada24.1(11.9)
1.25
265.7(55.9)
13.2
(2.7)
19.3
(12.7)
0.50
26683
Korea
13.6†
(11.5)
1.50
262.7(45.3)
12.6
(3.2)
13.2
(10.8)
0.50
6667

Cyprus
11.0
(6.6)
1.26
263.8
(46.7)
12.3
(3.1)
17.5
(12.3)
0.52
5053
Netherl.
16.5
(7.6)
1.06
280.1
(51.4)
13.1
(2.8)
18.7
(12.4)
0.50
5170

Czech R.
120.4
(57.6)
0.96
275.3(43.8)
13.0
(2.7)
20.0
(12.9)
0.50
6102
Norway211.9(78.1)
0.80
278.6(55.1)
13.9
(2.6)
18.7
(12.8)
0.49
5128

Denmark
179.7(68.0)
0.78
277.9(51.2)
12.5
(2.7)
21.0
(13.7)
0.50
7328
Poland
16.6(10.0)
1.25
258.9(50.9)
12.5
(3.1)
17.0
(12.5)
0.51
9366

Estonia
5.1
(3.2)
1.51
272.8
(45.2)
12.1
(2.7)
19.0
(13.0)
0.52
7632
Slovak R.
4.4
(3.2)
1.22
275.6
(47.2)
12.9
(2.8)
19.1
(12.5)
0.50
5723

Finland
17.0
(6.4)
0.88
282.4
(52.2)
12.2
(3.0)
19.0
(13.6)
0.50
5464
Spain
9.8
(5.1)
1.21
245.6
(51.4)
11.3
(3.5)
17.4
(12.1)
0.50
6055

France
12.9
(5.6)
0.86
254.0
(56.5)
11.2
(3.6)
19.4
(13.2)
0.51
6993
Sweden162.3(48.8)
0.69
278.1(54.9)
12.0
(2.5)
19.3
(13.8)
0.49
4469

Germany
14.3
(7.2)
1.35
272.4
(52.7)
13.0
(3.0)
19.2
(13.0)
0.50
5465
U.K.
12.1
(7.3)
1.20
262.2
(54.8)
13.0
(2.2)
19.6
(13.0)
0.50
8892

Ireland
17.7
(9.6)
1.20
255.5
(53.7)
14.5
(3.2)
17.0
(12.0)
0.51
5983
U.S.
21.0
(13.8)
1.49
253.0(57.1)
13.3
(3.1)
19.4
(13.2)
0.51
5010

Notes: Means, standard deviations (in parentheses), and numbers of observations for selected variables by country. Sample: full PIAAC sample(ages 16-65). Wage inequality: log wage diﬀerential between 90th and 10th percentile of wage distribution. Pooled speciﬁcation gives same
weight to each country. † wages divided by 1000. Data source: PIAAC.

Table A-2: Standard Mincerian Wage Regressions
Yrs schoolingExperience
Experience2
FemaleR2
Observations
Yrs schoolingExperience
Experience2
FemaleR2
Observations

Pooled.075∗∗∗(.001).027∗∗∗(.001)–.042∗∗∗(.003)–.200∗∗∗(.005).272
34161

Austria.077∗∗∗(.004).027∗∗∗(.007)–.040∗∗∗(.014)–.140∗∗∗(.023)
.295
1115
Italy
.053∗∗∗(.004).014∗∗(.007).001(.017)–.174∗∗∗(.026).264
1018

Belgium.062∗∗∗(.003).018∗∗∗(.006)–.020(.013)–.078∗∗∗(.017)
.238
1219
Japan
.088∗∗∗(.005).028∗∗∗(.008)–.025(.018)–.337∗∗∗(.025).345
1322

Canada.078∗∗∗(.003).029∗∗∗(.004)–.041∗∗∗(.008)–.160∗∗∗(.015)
.243
7155
Korea
.093∗∗∗(.005).027∗∗∗(.005)–.025∗(.015)–.322∗∗∗(.028).419
1441

Cyprus.089∗∗∗(.006).023∗∗∗(.008)–.014(.019)–.204∗∗∗(.033).306
938
Netherl.
.082∗∗∗(.005).036∗∗∗(.007)–.064∗∗∗(.015)–.058∗∗(.024).279
1013∗

Czech R.
.059∗∗∗(.008).014(.009)–.028(.020)–.216∗∗∗(.032).236
1065
Norway.056∗∗∗(.003).029∗∗∗(.005)–.055∗∗∗(.012)–.160∗∗∗(.014).250
1519

Denmark.055∗∗∗(.003).011∗∗∗(.004)–.014(.009)–.135∗∗∗(.013).236
1877
Poland.101∗∗∗(.005).028∗∗∗(.007)–.046∗∗∗(.017)–.228∗∗∗(.028).394
816

Estonia.074∗∗∗(.005).025∗∗∗(.009)–.072∗∗∗(.020)–.470∗∗∗(.024).277
1767
Slovak R.
.095∗∗∗(.006).017(.012)–.031(.027)–.290∗∗∗(.028).305
1198

Finland.068∗∗∗(.003).019∗∗∗(.005)–.026∗∗(.012)–.228∗∗∗(.015)
.375
1478
Spain
.079∗∗∗(.003).027∗∗∗(.007)–.038∗∗(.015)–.145∗∗∗(.024).365
1190

France.055∗∗∗(.002).021∗∗∗(.005)–.026∗∗(.011)–.136∗∗∗(.016).293
1707
Sweden.042∗∗∗(.003).020∗∗∗(.005)–.036∗∗∗(.010)–.132∗∗∗(.014).189
1316

Germany.095∗∗∗(.005).015∗(.008)–.016(.018)–.163∗∗∗(.025)
.274
1296
U.K.
.085∗∗∗(.006).029∗∗∗(.010)–.052∗∗(.021)–.158∗∗∗(.028).198
1671

Ireland.085∗∗∗(.007).040∗∗∗(.010)–.061∗∗∗(.023)–.077∗∗(.033)
.233
1031
U.S.
.111∗∗∗(.005).022∗∗∗(.008)–.039∗∗(.017)–.265∗∗∗(.024).384983

p<0.10, ∗∗ p<0.05, ∗∗∗ p<0.01

Notes: Least squares regressions weighted by sampling weights. Dependent variable: log gross hourly wage. Sample: full-time employees aged
35-54 (Canada includes part-time employees). Experience2 divided by 1000. Pooled speciﬁcation includes country ﬁxed eﬀects and gives same
weight to each country; R2 refers to within-country R2 . Robust standard errors in parentheses. Data source: PIAAC.

Table A-3: Returns to Skills for Alternative Skill Measures: Numeracy, Literacy, and Problem Solving
Numeracy
Literacy
Problem solv.
Numeracy
Literacy
Numeracy
Problem solv.
Numeracy
Literacy
Problem solv.
Numeracy(int. stand.)Pooled.178∗∗∗(.003).171∗∗∗(.003).143∗∗∗(.003).120∗∗∗(.005).068∗∗∗(.005).121∗∗∗(.005).062∗∗∗(.005).078∗∗∗(.007).076∗∗∗(.007).037∗∗∗(.005).183∗∗∗(.003)

Austria.179∗∗∗(.010).186∗∗∗(.010).143∗∗∗(.013).079∗∗∗(.020).117∗∗∗(.020).117∗∗∗(.021).068∗∗∗(.018).042(.026).136∗∗∗(.026).019(.020).193∗∗∗(.011)

Belgium.149∗∗∗(.009).150∗∗∗(.009).118∗∗∗(.010).081∗∗∗(.018).078∗∗∗(.019).119∗∗∗(.015).035∗∗(.015).069∗∗∗(.020).090∗∗∗(.023).004(.017).151∗∗∗(.009)

Canada.193∗∗∗(.008).192∗∗∗(.007).140∗∗∗(.008).101∗∗∗(.016).106∗∗∗(.016).134∗∗∗(.014).051∗∗∗(.012).077∗∗∗(.018).113∗∗∗(.020).007(.014).182∗∗∗(.007)

Cyprus.138∗∗∗(.019).126∗∗∗(.017).089∗∗∗(.027).061∗∗(.024).153∗∗∗(.021)∗

Czech R.
.124∗∗∗(.015).123∗∗∗(.015).110∗∗∗(.016).078∗∗∗(.024).062∗∗(.025).038(.025).085∗∗∗(.021)–.002(.027).091∗∗∗(.028).046∗∗(.022).150∗∗∗(.019)

Denmark.137∗∗∗(.008).139∗∗∗(.008).119∗∗∗(.008).082∗∗∗(.016).066∗∗∗(.016).078∗∗∗(.015).064∗∗∗(.014).058∗∗∗(.018).040∗∗(.019).050∗∗∗(.016).135∗∗∗(.008)

p<0.10, ∗∗ p<0.05, ∗∗∗ p<0.01

Estonia.179∗∗∗(.012).138∗∗∗(.013).165∗∗∗(.015).201∗∗∗(.021)–.027(.021).098∗∗∗(.026).088∗∗∗(.026).088∗∗∗(.029).017(.029).082∗∗∗(.029).208∗∗∗(.014)

Finland.142∗∗∗(.010).133∗∗∗(.011).123∗∗∗(.009).107∗∗∗(.016).041∗∗(.018).122∗∗∗(.014).036∗∗∗(.014).088∗∗∗(.017).060∗∗∗(.019).015(.016).149∗∗∗(.011)

France.174∗∗∗(.008).151∗∗∗(.009).163∗∗∗(.017).012(.017).160∗∗∗(.008)

Germany.235∗∗∗(.013).233∗∗∗(.013).185∗∗∗(.013).130∗∗∗(.026).121∗∗∗(.026).184∗∗∗(.024).055∗∗(.021).106∗∗∗(.030).128∗∗∗(.028).014(.022).240∗∗∗(.013)

Ireland.241∗∗∗(.020).215∗∗∗(.021).167∗∗∗(.019).185∗∗∗(.040).062(.040).167∗∗∗(.033).058∗∗(.029).101∗∗(.046).098∗∗(.048).029(.030).238∗∗∗(.020)

Table A-3: Returns to Skills for Alternative Skill Measures: Numeracy, Literacy, and Problem Solving (continued)
Numeracy
Literacy

Italy.132∗∗∗(.016).127∗∗∗(.016)

Problem solv.
Numeracy
Literacy.084∗∗∗(.023).058∗∗(.024)

Numeracy
Problem solv.
Numeracy
Literacy
Problem solv.
Numeracy(int. stand.).138∗∗∗(.017)

Japan.184∗∗∗(.014).149∗∗∗(.015).119∗∗∗(.015).233∗∗∗(.024)–.062∗∗(.026).164∗∗∗(.022).013(.020).185∗∗∗(.027)–.033(.029).020(.021).223∗∗∗(.016)

Korea.217∗∗∗(.014).206∗∗∗(.015).153∗∗∗(.017).162∗∗∗(.028).064∗∗(.029).186∗∗∗(.029).037(.026).131∗∗∗(.037).093∗∗(.040).008(.029).249∗∗∗(.016)

Netherl.
.183∗∗∗(.011).191∗∗∗(.012).171∗∗∗(.012).075∗∗∗(.023).126∗∗∗(.025).110∗∗∗(.019).092∗∗∗(.018).054∗∗(.025).105∗∗∗(.030).049∗∗(.022).195∗∗∗(.012)∗

Norway.127∗∗∗(.007).116∗∗∗(.008).110∗∗∗(.007).110∗∗∗(.017).019(.018).067∗∗∗(.015).064∗∗∗(.013).059∗∗∗(.019).013(.020).060∗∗∗(.014).124∗∗∗(.007)

Poland.191∗∗∗(.017).201∗∗∗(.017).135∗∗∗(.022).069∗∗(.028).145∗∗∗(.028).163∗∗∗(.041).044(.032).058(.044).180∗∗∗(.043)–.013(.036).203∗∗∗(.018)

Slovak R.
.179∗∗∗(.019).139∗∗∗(.019).135∗∗∗(.021).180∗∗∗(.029)–.001(.028).077∗∗(.030).095∗∗∗(.025).099∗∗∗(.037)–.040(.040).106∗∗∗(.029).197∗∗∗(.021)

Spain.228∗∗∗(.017).220∗∗∗(.016).137∗∗∗(.029).105∗∗∗(.028).224∗∗∗(.017)

Sweden.121∗∗∗(.009).123∗∗∗(.008).104∗∗∗(.007).071∗∗∗(.018).058∗∗∗(.017).071∗∗∗(.014).056∗∗∗(.012).051∗∗∗(.018).033∗(.019).048∗∗∗(.013).112∗∗∗(.008)

U.K.
.225∗∗∗(.013).215∗∗∗(.013).191∗∗∗(.013).124∗∗∗(.025).113∗∗∗(.025).109∗∗∗(.024).116∗∗∗(.021).047(.030).098∗∗∗(.029).085∗∗∗(.023).222∗∗∗(.013)

U.S.
.279∗∗∗(.017).271∗∗∗(.016).217∗∗∗(.015).192∗∗∗(.034).099∗∗∗(.032).175∗∗∗(.031).097∗∗∗(.026).173∗∗∗(.036).005(.039).095∗∗∗(.039).262∗∗∗(.016)

p<0.10, ∗∗ p<0.05, ∗∗∗ p<0.01

Notes: Horizontal lines divide separately estimated models. First row replicates baseline model from Table 2. Least squares regressions
weighted by sampling weights. Dependent variable: log gross hourly wage. Sample: full-time employees aged 35-54 (Canada includes part-time
employees). All regressions control for gender and a quadratic polynomial in actual work experience. Test scores standardized to std. dev. 1
within each country, except for last speciﬁcation at the bottom where numeracy score is standardized to std. dev. 1 in the entire international
sample. All models with problem solving do not include Cyprus, France, Italy, and Spain, where problem-solving skills were not tested, as
well as individuals who opted out of the problem-solving test. Pooled speciﬁcation includes country ﬁxed eﬀects and gives same weight to eachcountry. Robust standard errors in parentheses. Data source: PIAAC.

Table A-4: Returns to Skills in Alternative Subgroups
Subsample
Baseline
Males∆ Females
Low parent ed.
∆ Medium par. ed.
∆ High parent ed.
Private sector∆ Public sector
Natives∆ Migrants
Part-time∆ Full-time

Pooled.178∗∗∗(.003).178∗∗∗(.004)–.002(.006).148∗∗∗(.005).014∗∗(.007).033∗∗∗(.009).183∗∗∗(.004)–.036∗∗∗(.006).180∗∗∗(.003)–.022∗∗(.008).122∗∗∗(.008).057∗∗∗(.009)

Austria.179∗∗∗(.010).164∗∗∗(.011).045∗∗(.022).157∗∗∗(.017)–.005(.023).068∗∗(.034).180∗∗∗(.012)–.012(.024).177∗∗∗(.013)–.010(.024).123∗∗∗(.019).056∗∗∗(.021)

Belgium.149∗∗∗(.009).150∗∗∗(.011)–.004(.018).141∗∗∗(.014)–.008(.022)–.013(.026).152∗∗∗(.011)–.002(.019).149∗∗∗(.010)–.020(.029).182∗∗∗(.024)–.034(.025)

Canada.193∗∗∗(.008).191∗∗∗(.010).005(.015).188∗∗∗(.015)–.014(.020).001(.021).190∗∗∗(.010)–.042∗∗∗(.016).196∗∗∗(.009)–.013(.017)∗

Cyprus.138∗∗∗(.019).148∗∗∗(.027)–.022(.037).104∗∗∗(.023).070(.043).129∗(.072).116∗∗∗(.024).037(.039).132∗∗∗(.020).061(.064).132∗(.068).006(.070)

Czech R.
.124∗∗∗(.015).089∗∗∗(.025).065∗∗(.030).089∗∗∗(.029).019(.035).071(.061).112∗∗∗(.018).043(.036).126∗∗∗(.017)–.033(.044)–.005(.103).129(.104)

Denmark.137∗∗∗(.008).131∗∗∗(.012).012(.016).121∗∗∗(.013).014(.019).005(.022).151∗∗∗(.011)–.034∗∗(.016).137∗∗∗(.009)–.020(.021).027(.039).110∗∗∗(.040)

p<0.10, ∗∗ p<0.05, ∗∗∗ p<0.01

Estonia.179∗∗∗(.012).177∗∗∗(.019).004(.024).097∗∗∗(.024).058∗(.032).129∗∗∗(.035).185∗∗∗(.015)–.009(.025).177∗∗∗(.013)–.028(.044).096∗(.053).083(.054)

Finland.142∗∗∗(.010).130∗∗∗(.017).023(.020).170∗∗∗(.012)–.044∗∗(.018)–.077∗(.045).139∗∗∗(.016).010(.019).155∗∗∗(.009)–.103∗∗(.042).218∗∗∗(.058)–.077(.059)

France.174∗∗∗(.008).172∗∗∗(.011).004(.017).143∗∗∗(.012).001(.021).108∗∗∗(.034).188∗∗∗(.010)–.038∗∗(.018).178∗∗∗(.009)–.030(.029).159∗∗∗(.023).015(.025)

Germany.235∗∗∗(.013).241∗∗∗(.016)–.024(.021).232∗∗∗(.046).004(.020).066(.053).249∗∗∗(.016)–.080∗∗∗(.028).239∗∗∗(.015)–.048(.039).122∗∗∗(.030).113∗∗∗(.032)

Ireland.241∗∗∗(.020).241∗∗∗(.027)–.002(.039).200∗∗∗(.034).039(.049)–.010(.049).270∗∗∗(.023)–.115∗∗∗(.040).258∗∗∗(.023)–.086∗∗(.043).151∗∗∗(.031).091∗∗(.036)

Table A-4: Returns to Skills in Alternative Subgroups (continued)
Subsample
Baseline
Males∆ Females
Low parent ed.
∆ Medium par. ed.
∆ High parent ed.
Private sector∆ Public sector
Natives∆ Migrants
Part-time∆ Full-time

Italy.132∗∗∗(.016).124∗∗∗(.022).026(.030).116∗∗∗(.018).011(.035)–.154(.126).148∗∗∗(.018)–.080∗∗(.033).123∗∗∗(.017).007(.059).155∗∗∗(.031)–.023(.035)

Japan.184∗∗∗(.014).195∗∗∗(.017)–.032(.028).149∗∗∗(.026).032(.034).025(.040).180∗∗∗(.015)–.038(.032)

Korea.217∗∗∗(.014).240∗∗∗(.018)–.062∗∗(.030).176∗∗∗(.018).037(.038).089∗∗(.044).205∗∗∗(.017)–.093∗∗∗(.036).067∗(.036).117∗∗∗(.039).109(.078).108(.080)

Netherl.
.183∗∗∗(.011).179∗∗∗(.013).012(.025).166∗∗∗(.014).007(.030).050(.032).187∗∗∗(.014)–.025(.024).176∗∗∗(.012)–.016(.034).097∗∗∗(.019).086∗∗∗(.022)∗

Norway.127∗∗∗(.007).125∗∗∗(.010).005(.014).122∗∗∗(.011)–.000(.017)–.026(.018).143∗∗∗(.009)–.041∗∗∗(.015).119∗∗∗(.009).008(.016).113∗∗∗(.026).013(.027)

Poland.191∗∗∗(.017).197∗∗∗(.021)–.022(.034).082∗∗∗(.025).128∗∗∗(.034).163∗∗∗(.062).193∗∗∗(.023)–.030(.034)

Slovak R.
.179∗∗∗(.019).178∗∗∗(.029)–.001(.038).124∗∗∗(.029).031(.039)–.016(.083).174∗∗∗(.026).012(.036)–.013(.087).203∗∗(.089).044(.101).135(.102)

Spain.228∗∗∗(.017).220∗∗∗(.023).022(.033).216∗∗∗(.017)–.092(.062).080(.060).199∗∗∗(.021)–.008(.033).233∗∗∗(.016)–.123∗(.072).178∗∗∗(.041).050(.044)

Sweden.121∗∗∗(.009).124∗∗∗(.013)–.006(.017).121∗∗∗(.011).011(.020)–.014(.021).139∗∗∗(.010)–.054∗∗∗(.019).128∗∗∗(.009)–.028(.022).021(.036).099∗∗∗(.037)

U.K.
.225∗∗∗(.013).231∗∗∗(.017)–.021(.025).191∗∗∗(.032).015(.040)–.002(.045).246∗∗∗(.016)–.084∗∗∗(.025).224∗∗∗(.014).053(.043).166∗∗∗(.030).059∗(.032)

U.S.
.279∗∗∗(.017).284∗∗∗(.028)–.019(.033).266∗∗∗(.045)–.043(.054)–.031(.054).288∗∗∗(.021)–.037(.040).270∗∗∗(.021).032(.039).152∗∗(.059).127∗∗(.062)

p<0.10, ∗∗ p<0.05, ∗∗∗ p<0.01

Notes: Horizontal lines divide separately estimated models. Each row without a ∆ reports the coeﬃcient on numeracy for the respective
subgroup. Each row with a ∆ reports the marginal diﬀerence for the identiﬁed subgroup, estimated with an interaction term between numeracy
and the speciﬁed subgroup indicator. (The total gradient for this subgroup is the sum of the coeﬃcient of the top group and this marginal
eﬀect.) Least squares regressions weighted by sampling weights. Dependent variable: log gross hourly wage. Sample: full-time employees aged
35-54 (Canada includes part-time employees), except for bottom panel which additionally includes part-time employees. All regressions control
for gender, a quadratic polynomial in actual work experience, the respective subgroup indicator, and interactions of the covariates with therespective subgroup indicator. Baseline: replicates Table 2. Parental education: low = neither parent attained upper secondary education;
medium = at least one parent attained upper secondary education; high = at least one parent attained tertiary education. Migrants: 1 =workers born abroad (with at least one parent also born abroad). Full-time: 1 = working more than 30 hours per week. Numeracy scorestandardized to std. dev. 1 within each country. Pooled speciﬁcation includes country ﬁxed eﬀects and gives same weight to each country.
Robust standard errors in parentheses. Data source: PIAAC.

Table A-5: Returns to Skills with Alternative Sets of Control Variables
Controls
Baseline
Parental
education [3]Migrant [1]Potential
experience [2]ISCO [10]ISIC [22]Educational
attainment [6]Controls
Baseline
Parental
education [3]Migrant [1]Potential
experience [2]ISCO [10]ISIC [22]Educational
attainment [6]Pooled.178∗∗∗(.003).162∗∗∗(.003).175∗∗∗(.003).169∗∗∗(.003).096∗∗∗(.003).150∗∗∗(.003).103∗∗∗(.003)

Austria.179∗∗∗(.010).164∗∗∗(.011).172∗∗∗(.011).175∗∗∗(.010).095∗∗∗(.010).150∗∗∗(.010).114∗∗∗(.011)
Italy.132∗∗∗(.016).112∗∗∗(.017).123∗∗∗(.017).129∗∗∗(.017).066∗∗∗(.016).100∗∗∗(.016).071∗∗∗(.016)

Belgium.149∗∗∗(.009).135∗∗∗(.010).147∗∗∗(.009).150∗∗∗(.009).088∗∗∗(.010).133∗∗∗(.009).085∗∗∗(.010)
Japan.184∗∗∗(.014).170∗∗∗(.014).184∗∗∗(.014).184∗∗∗(.014).118∗∗∗(.014).158∗∗∗(.014).111∗∗∗(.015)

Canada.193∗∗∗(.008).182∗∗∗(.008).192∗∗∗(.008).191∗∗∗(.008).109∗∗∗(.008).145∗∗∗(.008).127∗∗∗(.009)
Korea.217∗∗∗(.014).195∗∗∗(.015).217∗∗∗(.014).198∗∗∗(.016).113∗∗∗(.015).170∗∗∗(.015).092∗∗∗(.015)

Cyprus.138∗∗∗(.019).127∗∗∗(.019).137∗∗∗(.019).136∗∗∗(.019).080∗∗∗(.017).096∗∗∗(.018).062∗∗∗(.019)
Netherl.
.183∗∗∗(.011).176∗∗∗(.012).175∗∗∗(.012).179∗∗∗(.012).100∗∗∗(.012).158∗∗∗(.012).105∗∗∗(.012)∗

Czech R.
.124∗∗∗(.015).115∗∗∗(.016).123∗∗∗(.016).114∗∗∗(.017).055∗∗∗(.016).107∗∗∗(.015).088∗∗∗(.020)
Norway.127∗∗∗(.007).114∗∗∗(.008).122∗∗∗(.008).125∗∗∗(.008).075∗∗∗(.008).106∗∗∗(.007).073∗∗∗(.008)

Denmark.137∗∗∗(.008).129∗∗∗(.008).134∗∗∗(.008).128∗∗∗(.008).082∗∗∗(.008).121∗∗∗(.008).084∗∗∗(.008)
Poland.191∗∗∗(.017).170∗∗∗(.017).191∗∗∗(.017).173∗∗∗(.017).088∗∗∗(.015).155∗∗∗(.017).083∗∗∗(.015)

Estonia.179∗∗∗(.012).156∗∗∗(.013).175∗∗∗(.012).153∗∗∗(.012).097∗∗∗(.012).160∗∗∗(.013).118∗∗∗(.014)
Slovak R.
.179∗∗∗(.019).140∗∗∗(.019).179∗∗∗(.019).158∗∗∗(.019).099∗∗∗(.018).152∗∗∗(.017).101∗∗∗(.018)

Finland.142∗∗∗(.010).139∗∗∗(.011).140∗∗∗(.010).134∗∗∗(.010).054∗∗∗(.010).120∗∗∗(.010).075∗∗∗(.011)
Spain.228∗∗∗(.017).210∗∗∗(.017).216∗∗∗(.018).212∗∗∗(.018).115∗∗∗(.017).152∗∗∗(.017).098∗∗∗(.018)

France.174∗∗∗(.008).155∗∗∗(.010).175∗∗∗(.009).167∗∗∗(.009).086∗∗∗(.008).154∗∗∗(.008).094∗∗∗(.009)
Sweden.121∗∗∗(.009).120∗∗∗(.008).119∗∗∗(.009).124∗∗∗(.009).061∗∗∗(.008).102∗∗∗(.008).086∗∗∗(.010)

Germany.235∗∗∗(.013).225∗∗∗(.014).231∗∗∗(.014).224∗∗∗(.014).125∗∗∗(.013).200∗∗∗(.013).144∗∗∗(.014)
U.K.
.225∗∗∗(.013).198∗∗∗(.016).229∗∗∗(.013).209∗∗∗(.014).129∗∗∗(.013).166∗∗∗(.013).158∗∗∗(.015)

Ireland.241∗∗∗(.020).220∗∗∗(.022).242∗∗∗(.020).238∗∗∗(.021).161∗∗∗(.022).194∗∗∗(.022).134∗∗∗(.024)
U.S.
.279∗∗∗(.017).236∗∗∗(.019).278∗∗∗(.018).269∗∗∗(.018).158∗∗∗(.018).234∗∗∗(.017).149∗∗∗(.020)

p<0.10, ∗∗ p<0.05, ∗∗∗ p<0.01

Notes: Each cell reports the coeﬃcient on numeracy skills from a separate regression. Least squares regressions weighted by sampling weights.
Dependent variable: log gross hourly wage. Sample: full-time employees aged 35-54 (Canada includes part-time employees). All regressions
control for gender, a quadratic polynomial in actual work experience (unless otherwise noted), and respective additional control variables as
indicated in ﬁrst column. Numbers in square brackets report the number of dummy variables included for the respective measure. Baseline:
replicates Table 2. Parental education: adds controls for low/medium/high parental education (see Table A-4). Migrant: adds control forindicator variable for ﬁrst-generation migrants. Potential experience: replaces the quadratic polynomial in actual work experience by a quadratic
polynomial in potential work experience (age minus years of schooling minus 6). ISCO: adds controls for one-digit occupation categories. ISIC:
adds controls for one-digit industry categories. Educational attainment: adds controls for school attainment, measured by 6 ISCED dummies.
Numeracy score standardized to std. dev. 1 within each country. Pooled speciﬁcation includes country ﬁxed eﬀects and gives same weight toeach country. Robust standard errors in parentheses. Data source: PIAAC.

Table A-6: Extended Model with Part-Time Workers and Additional Control Variables
Numeracy
Experience
Experience2
FemaleMed. par. ed.
High par. ed.
Migrant
Full-time
R2
Observations
Numeracy
Experience
Experience2
FemaleMed. par. ed.
High par. ed.
Migrant
Full-time
R2
Observations

Pooled.150∗∗∗(.003).022∗∗∗(.001)–.036∗∗∗(.003)–.157∗∗∗(.005).079∗∗∗(.006).174∗∗∗(.007)–.052∗∗∗(.009)–.031∗∗∗(.009).206
37935

Austria.145∗∗∗(.010).006(.007)–.007(.013)–.111∗∗∗(.023).081∗∗∗(.021).165∗∗∗(.030)–.055∗(.032).078∗∗∗(.024)
.247
1568
Italy
.104∗∗∗(.015).016∗∗(.006)–.012(.016)–.101∗∗∗(.026).145∗∗∗(.030).397∗∗∗(.096)–.225∗∗∗(.044)–.210∗∗∗(.038).235
1207

Belgium.137∗∗∗(.009).027∗∗∗(.006)–.041∗∗∗(.012)–.026(.017).032∗(.019).145∗∗∗(.023)–.049(.036)–.066∗∗∗(.025)
.232
1431
Japan
.155∗∗∗(.013).017∗∗∗(.006)–.004(.016)–.351∗∗∗(.029).136∗∗∗(.028).237∗∗∗(.034)

Canada.179∗∗∗(.008).021∗∗∗(.004)–.031∗∗∗(.008)–.112∗∗∗(.015).044∗∗(.019).102∗∗∗(.020)–.019(.019).158∗∗∗(.040).386
1510–.769∗∗∗(.069).358
1608

Cyprus.113∗∗∗(.019).020∗∗(.010)–.027(.021)–.236∗∗∗(.034).172∗∗∗(.042).307∗∗∗(.059)–.330∗∗∗(.063)–.347∗∗∗(.074).213
1024
Netherl.
.144∗∗∗(.011).024∗∗∗(.006)–.041∗∗∗(.013).002(.023).074∗∗∗(.022).155∗∗∗(.026)–.057(.037).116∗∗∗(.025)
.229
1510

.233
7331
Korea
.180∗∗∗(.016).034∗∗∗(.006)–.060∗∗∗(.018)–.427∗∗∗(.030).151∗∗∗(.035).226∗∗∗(.045)∗

Czech R.
.110∗∗∗(.017).017∗∗(.008)–.039∗∗(.020)–.199∗∗∗(.034).048(.046).156∗∗(.064)–.074(.109)–.076(.099).205
1071
Norway.111∗∗∗(.008).020∗∗∗(.005)–.041∗∗∗(.010)–.118∗∗∗(.014).042∗∗(.017).080∗∗∗(.019)–.040∗(.023).022(.025).232
1700

Denmark.112∗∗∗(.009).004(.004)–.005(.008)–.089∗∗∗(.014).009(.016).066∗∗∗(.019)–.083∗∗∗(.021)–.066∗∗(.032).168
2045
Poland.149∗∗∗(.019).044∗∗∗(.008)–.086∗∗∗(.020)–.100∗∗∗(.034).148∗∗∗(.036).408∗∗∗(.059)

Estonia.150∗∗∗(.013).030∗∗∗(.009)–.075∗∗∗(.020)–.381∗∗∗(.025).118∗∗∗(.029).206∗∗∗(.034)–.183∗∗∗(.037)–.142∗∗(.063).265
1779
Slovak R.
.133∗∗∗(.019).015(.011)–.029(.025)–.247∗∗∗(.029).141∗∗∗(.035).468∗∗∗(.063)–.458∗∗∗(.083)
.267
859

–.108
(.117).207
1261

Finland.137∗∗∗(.010).016∗∗∗(.005)–.031∗∗∗(.012)–.157∗∗∗(.016).021(.017).106∗∗∗(.027)–.051(.048).045(.046)
.258
1521
Spain
.197∗∗∗(.017).020∗∗∗(.007)–.029∗(.016)–.055∗∗(.026).208∗∗∗(.042).165∗∗∗(.042)–.115∗∗(.050)–.080∗(.043).236
1335

France.157∗∗∗(.010).022∗∗∗(.005)–.030∗∗∗(.010)–.098∗∗∗(.018).047∗∗∗(.018).160∗∗∗(.028).020(.031)–.069∗∗(.030).248
1668
Sweden.106∗∗∗(.009).017∗∗∗(.005)–.034∗∗∗(.010)–.088∗∗∗(.014).030(.018).049∗∗∗(.017)–.011(.021).013(.035).212
1355

Germany.188∗∗∗(.015).004(.007).003(.016)–.096∗∗∗(.027).013(.040).089∗(.043)–.063(.040).157∗∗∗(.034)
.239
1583
U.K.
.185∗∗∗(.016).014∗(.007)–.022(.016)–.101∗∗∗(.030).056∗(.031).199∗∗∗(.041).030(.047).215∗∗∗(.040).272
1990

Ireland.194∗∗∗(.019).033∗∗∗(.009)–.051∗∗(.020).005(.031).078∗∗(.034).230∗∗∗(.039)–.131∗∗∗(.041).069∗(.038)
.247
1340
U.S.
.229∗∗∗(.019).002(.006).002(.012)–.176∗∗∗(.031).189∗∗∗(.049).298∗∗∗(.053).004(.048).286∗∗∗(.031).3301227

p<0.10, ∗∗ p<0.05, ∗∗∗ p<0.01

Notes: Least squares regressions weighted by sampling weights. Dependent variable: log gross hourly wage. Sample: employees aged 35-54.
Numeracy score standardized to std. dev. 1 within each country. Experience2 divided by 1000. See Table A-4 for variable deﬁnitions ofparental education categories, migrant, and full-time work status. Pooled speciﬁcation includes country ﬁxed eﬀects and gives same weight toeach country; R2 refers to within-country R2 . Robust standard errors in parentheses. Data source: PIAAC.

Table A-7: Returns to Skills for Alternative Earnings Measures
Log wage
Hourly (baseline)
Hourly deciles
Hourly+bonus
MonthlyMonthly+bonus
Monthly+bonusincl. self-emp.
Log wage
Hourly (baseline)
Hourly deciles
Hourly+bonus
MonthlyMonthly+bonus
Monthly+bonusincl. self-emp.

Pooled.170∗∗∗(.003).164∗∗∗(.003).176∗∗∗(.003).172∗∗∗(.003).178∗∗∗(.004).177∗∗∗(.004)

Austria†.179∗∗∗(.010).179∗∗∗(.010)

Canada†.193∗∗∗(.008).193∗∗∗(.008).196∗∗∗(.014)

Belgium.149∗∗∗(.009).149∗∗∗(.009).151∗∗∗(.009).166∗∗∗(.009).170∗∗∗(.010).164∗∗∗(.012)

Italy.132∗∗∗(.016).122∗∗∗(.016).144∗∗∗(.018).123∗∗∗(.017).140∗∗∗(.019).145∗∗∗(.020)

Japan.184∗∗∗(.014).182∗∗∗(.013).195∗∗∗(.014).180∗∗∗(.013).190∗∗∗(.014).184∗∗∗(.014).215∗∗∗(.011)

Cyprus.138∗∗∗(.019).140∗∗∗(.018).139∗∗∗(.019).133∗∗∗(.018).134∗∗∗(.018).134∗∗∗(.019)

Czech R.
.124∗∗∗(.015).097∗∗∗(.017).129∗∗∗(.017).128∗∗∗(.015).126∗∗∗(.015).109∗∗∗(.017)

Denmark.137∗∗∗(.008).146∗∗∗(.009).136∗∗∗(.009).164∗∗∗(.011).163∗∗∗(.011).166∗∗∗(.013)

Estonia.179∗∗∗(.012).174∗∗∗(.012).179∗∗∗(.012).175∗∗∗(.013).177∗∗∗(.013).171∗∗∗(.015)

Finland.142∗∗∗(.010).144∗∗∗(.009).145∗∗∗(.010).149∗∗∗(.011).155∗∗∗(.009).163∗∗∗(.014)

France.174∗∗∗(.008).167∗∗∗(.008).180∗∗∗(.009).202∗∗∗(.009).211∗∗∗(.010).203∗∗∗(.011)

Germany.235∗∗∗(.013).243∗∗∗(.013).243∗∗∗(.014).266∗∗∗(.016).268∗∗∗(.017).275∗∗∗(.018)

Ireland.241∗∗∗(.020).216∗∗∗(.014).249∗∗∗(.020).244∗∗∗(.020).251∗∗∗(.020).257∗∗∗(.032)

Korea.217∗∗∗(.014).205∗∗∗(.014).226∗∗∗(.015).166∗∗∗(.013).181∗∗∗(.013).187∗∗∗(.016)

Netherl.
.183∗∗∗(.011).174∗∗∗(.011).190∗∗∗(.012).193∗∗∗(.012).198∗∗∗(.013).189∗∗∗(.013)

Norway.127∗∗∗(.007).133∗∗∗(.007).134∗∗∗(.008).151∗∗∗(.010).154∗∗∗(.010).161∗∗∗(.011)

Poland.191∗∗∗(.017).192∗∗∗(.017).193∗∗∗(.017).173∗∗∗(.017).180∗∗∗(.017).195∗∗∗(.020)

Slovak R.
.179∗∗∗(.019).169∗∗∗(.017).192∗∗∗(.020).182∗∗∗(.020).195∗∗∗(.020).177∗∗∗(.022)

Spain.228∗∗∗(.017).222∗∗∗(.015).243∗∗∗(.017).203∗∗∗(.016).218∗∗∗(.016).204∗∗∗(.016)

Sweden†.121∗∗∗(.009).121∗∗∗(.009)

U.K.
.225∗∗∗(.013).206∗∗∗(.013).230∗∗∗(.013).227∗∗∗(.014).225∗∗∗(.016).230∗∗∗(.021)

U.S.
.279∗∗∗(.017).258∗∗∗(.021).285∗∗∗(.018).315∗∗∗(.018).321∗∗∗(.019).306∗∗∗(.019)∗.127∗∗∗(.012)

p<0.10, ∗∗ p<0.05, ∗∗∗ p<0.01

Notes: Each cell reports the coeﬃcient on numeracy skills from a separate regression. Least squares regressions weighted by sampling weights.
Dependent variable: log gross earnings, diﬀerent measures as indicated in ﬁrst column. Hourly (baseline): log gross hourly wage excluding
bonuses (measured continuously, replicates Table 2). Hourly deciles: log gross hourly wage measured as decile medians. Hourly+bonuses: log
gross hourly wage including bonuses. Monthly: log gross monthly wage excluding bonuses. Monthly+bonuses: log gross monthly wage including
bonuses. Monthly+bonuses incl. self-employed: log gross monthly wage including bonuses for sample including self-employed. Sample: full-time
employees aged 35-54 (Canada includes part-time employees). All regressions control for gender and a quadratic polynomial in actual work
experience. Numeracy score standardized to std. dev. 1 within each country. Pooled speciﬁcation includes country ﬁxed eﬀects and gives
same weight to each country. All pooled models exclude Austria, Canada, Germany, Sweden, and U.S., where continuous earnings data are
not available in the Public Use File that pools all countries. Robust standard errors in parentheses. † Models in Austria, Canada, and Swedenrefer to earnings measured as decile medians. Data source: PIAAC.

U
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Returns to numeracy skills
.05
.1
.15
.2
.25

.3

Figure 1: Returns to Skills around the World

Notes: Coeﬃcient estimates on numeracy score (standardized to std. dev. 1 within each country) in a regression of log gross hourly wage on
numeracy, gender, and a quadratic polynomial in actual work experience, sample of full-time employees aged 35-54. Replicates baseline modelfrom Table 2. Data source: PIAAC.

0

Returns to numeracy skills
.05
.1
.15

.2

Figure 2: Returns to Skills by Age Group16−19 20−24 25−29 30−34 35−39 40−44 45−49 50−54 55−59 60−65
Age group
Notes: Coeﬃcient estimates on numeracy score (standardized to std. dev. 1 within each country) for indicated 5-year age groups in a regressionof log gross hourly wage on numeracy, gender, a quadratic polynomial in actual work experience, and country ﬁxed eﬀects, sample of full-time
employees pooling all countries. Slopes of solid lines reﬂect average change in returns by age groups (separately estimated for ages 16-34 and35-54). Data source: PIAAC.

ll
Pa −tim
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0

Returns to numeracy skills
.05
.1
.15

.2

Figure 3: Returns to Skills in Alternative Subgroups

Notes: Coeﬃcient estimates on numeracy score (standardized to std. dev. 1 within each country) for indicated subgroup in a regression oflog gross hourly wage on numeracy, gender, a quadratic polynomial in actual work experience, and country ﬁxed eﬀects, sample of full-time
employees aged 35-54 pooling all countries. Replicates fully interacted model from Table A-4. With the exception of gender, all subgroupdiﬀerences are statistically signiﬁcant at the 5 percent level. Data source: PIAAC.

Table 1: Descriptive Statistics
PooledGross hourly wage
(national currency)
Wage inequality
NumeracyYrs schoolingExperience (years)
Female (share)
Observations
Gross hourly wage
(national currency)
Wage inequality
NumeracyYrs schoolingExperience (years)
Female (share)
Observations

1.11
279.0
(49.8)
13.3
(2.9)
22.3
(7.8)
0.44
34447

Austria
15.3
(6.1)
1.07
282.1
(49.2)
12.4
(2.6)
24.9
(7.4)
0.34
1115
Italy
11.7
(5.5)
1.02
256.1
(47.7)
11.1
(3.6)
20.8
(8.4)
0.36
1018

Belgium
18.0
(6.1)
0.85
290.4
(49.6)
13.0
(2.6)
23.2
(7.0)
0.39
1220
Japan
1.9†
(1.1)
1.31
300.2
(42.1)
13.6
(2.4)
21.3
(7.0)
0.35
1322

Canada27.4(11.9)
1.25
271.9(53.5)
13.9
(2.5)
23.5
(8.0)
0.49
7178
Korea
13.0†
(8.7)
1.50
265.9
(43.5)
13.4
(3.0)
16.2
(7.9)
0.40
1441

Cyprus
11.9
(6.7)
1.26
273.8
(44.5)
13.1
(2.8)
22.0
(7.5)
0.46
938
Netherl.
19.7
(7.7)
1.06
292.7
(47.0)
14.0
(2.5)
22.9
(7.3)
0.27
1013

Czech R.
117.0
(50.8)
0.96
274.2(43.1)
13.2
(2.4)
22.4
(7.4)
0.50
1066
Norway235.8(75.2)
0.80
294.4(48.9)
14.9
(2.3)
22.2
(7.4)
0.44
1520

Denmark
198.5(62.4)
0.78
291.9(45.3)
13.3
(2.5)
24.4
(7.8)
0.49
1875
Poland
16.4
(9.2)
1.25
261.9
(46.8)
13.3
(2.9)
20.7
(7.7)
0.47
816

Estonia
5.2
(3.3)
1.51
275.1
(44.0)
12.5
(2.6)
22.0
(7.2)
0.56
1767
Slovak R.
4.4
(2.7)
1.22
286.6
(40.3)
13.6
(2.5)
22.4
(7.0)
0.51
1198

Finland
18.6
(6.7)
0.88
293.5
(47.6)
13.4
(2.8)
21.0
(7.4)
0.51
1478
Spain
10.4
(5.2)
1.21
260.3
(46.7)
12.5
(3.5)
20.5
(8.0)
0.41
1191

France
13.7
(5.6)
0.86
262.7
(53.0)
11.8
(3.4)
22.5
(8.0)
0.44
1715
Sweden172.2(49.4)
0.69
288.0(50.6)
12.8
(2.4)
22.4
(7.7)
0.49
1316

Germany
17.2
(7.8)
1.35
285.4
(49.5)
14.3
(2.5)
23.3
(7.5)
0.39
1296
U.K.
14.3
(7.6)
1.20
274.0
(51.9)
13.2
(2.3)
24.5
(7.4)
0.39
1786

Ireland
21.3(10.3)
1.20
271.9(49.9)
15.7
(2.8)
22.0
(7.7)
0.41
1031
U.S.
24.3
(15.0)
1.49
266.0(54.7)
14.3
(2.9)
24.0
(8.1)
0.50
1105

Notes: Means, standard deviations (in parentheses), and numbers of observations for selected variables by country. Sample: full-time employees
aged 35-54 (Canada includes part-time employees). Full-time workers are deﬁned as those working at least 30 hours per week (except for Austria,
where classiﬁcation is based on respondents’ reports of whether they work full-time). Wage inequality: log wage diﬀerential between 90th and10th percentile of wage distribution. Pooled speciﬁcation gives same weight to each country. † wages divided by 1000. Data source: PIAAC.

Table 2: Returns to Skills around the World
Numeracy
Experience
Experience2
FemaleR2
Observations
Numeracy
Experience
Experience2
FemaleR2
Observations

Pooled.178∗∗∗(.003).023∗∗∗(.002)–.041∗∗∗(.003)–.150∗∗∗(.005).209
34447

Austria.179∗∗∗(.010).009(.007)–.012(.015)–.136∗∗∗(.024)
.260
1115
Italy
.132∗∗∗(.016).011(.008)–.005(.019)–.121∗∗∗(.028).162
1018

Belgium.149∗∗∗(.009).016∗∗(.006)–.024∗(.014)–.020(.018)
.202
1220
Japan
.184∗∗∗(.014).018∗∗(.007)–.012(.018)–.329∗∗∗(.027).309
1322

Canada.193∗∗∗(.008).020∗∗∗(.004)–.030∗∗∗(.009)–.115∗∗∗(.015)
.226
7178
Korea
.217∗∗∗(.014).033∗∗∗(.006)–.056∗∗∗(.016)–.384∗∗∗(.030).343
1441

Cyprus.138∗∗∗(.019).033∗∗∗(.008)–.054∗∗∗(.018)–.199∗∗∗(.036).154
938
Netherl.
.183∗∗∗(.011).019∗∗∗(.007)–.036∗∗(.015).013(.024).218
1013∗

Czech R.
.124∗∗∗(.015).016∗∗(.008)–.039∗∗(.019)–.213∗∗∗(.035).207
1066
Norway.127∗∗∗(.007).021∗∗∗(.005)–.045∗∗∗(.011)–.119∗∗∗(.014).220
1520

Denmark.137∗∗∗(.008).001(.004)–.000(.008)–.088∗∗∗(.014).185
1875
Poland.191∗∗∗(.017).046∗∗∗(.008)–.095∗∗∗(.020)–.122∗∗∗(.033).201
816

Estonia.179∗∗∗(.012).028∗∗∗(.009)–.079∗∗∗(.021)–.399∗∗∗(.024).259
1767
Slovak R.
.179∗∗∗(.019).011(.012)–.030(.027)–.250∗∗∗(.030).169
1198

Finland.142∗∗∗(.010).015∗∗∗(.006)–.032∗∗(.013)–.170∗∗∗(.017)
.255
1478
Spain
.228∗∗∗(.017).024∗∗∗(.008)–.037∗∗(.017)–.023(.027).214
1191

France.174∗∗∗(.008).021∗∗∗(.005)–.030∗∗∗(.011)–.092∗∗∗(.016).239
1715
Sweden.121∗∗∗(.009).017∗∗∗(.005)–.032∗∗∗(.010)–.090∗∗∗(.014).217
1316

Germany.235∗∗∗(.013).003(.008).008(.018)–.105∗∗∗(.026)
.258
1296
U.K.
.225∗∗∗(.013).000(.009)–.002(.018)–.081∗∗∗(.026).241
1786

Ireland.241∗∗∗(.020).028∗∗(.012)–.043(.027).031(.034)
.211
1031
U.S.
.279∗∗∗(.017).018∗∗(.009)–.035∗∗(.018)–.176∗∗∗(.032).308
1105

p<0.10, ∗∗ p<0.05, ∗∗∗ p<0.01

Notes: Least squares regressions weighted by sampling weights. Dependent variable: log gross hourly wage. Sample: full-time employees aged
35-54 (Canada includes part-time employees). Numeracy score standardized to std. dev. 1 within each country. Experience2 divided by 1000.
Pooled speciﬁcation includes country ﬁxed eﬀects and gives same weight to each country; R2 refers to within-country R2 . Robust standard
errors in parentheses. Data source: PIAAC.

Table 3: The Age Pattern of Returns to Skills
Numeracy× Prime Age× Exit Age
Prime Age
Exit Age
R2
Observations
Numeracy× Prime Age× Exit Age
Prime Age
Exit Age
R2
Observations

Pooled.140∗∗∗(.004).042∗∗∗(.005).038∗∗∗(.007).030∗∗∗(.007).034∗∗∗(.010).197
61384

Austria.107∗∗∗(.017).074∗∗∗(.020).131∗∗∗(.033).062∗∗(.027).186∗∗∗(.047)
.261
1799
Italy
.094∗∗∗(.021).037(.027).123∗∗∗(.039).099∗∗∗(.033).141∗∗(.057).195
1534

Belgium.116∗∗∗(.012).032∗∗(.015).088∗∗∗(.028).076∗∗∗(.022).166∗∗∗(.042)
.244
2026
Japan
.128∗∗∗(.017).055∗∗(.022).054∗(.031).024(.028)–.052(.047).298
2405

Canada.155∗∗∗(.014).039∗∗(.016).040∗∗(.020).065∗∗∗(.019)–.003(.027)
.216
12396
Korea
.176∗∗∗(.021).053∗∗(.025).058(.044)–.061∗∗(.028)–.210∗∗∗(.058).276
2490

Cyprus.111∗∗∗(.018).032(.026).087∗∗(.037).042(.036).069(.065).199
1769
Netherl.
.131∗∗∗(.019).054∗∗(.022).057∗∗(.024).177∗∗∗(.031).254∗∗∗(.044).265
1777∗

Czech R.
.121∗∗∗(.024).008(.028).007(.046)–.046(.042).071(.072).179
2053
Norway.104∗∗∗(.013).026∗(.015).012(.022).071∗∗∗(.020).108∗∗∗(.030).234
2596

Denmark.088∗∗∗(.012).050∗∗∗(.015).043∗∗∗(.015).084∗∗∗(.018).089∗∗∗(.025).193
3593
Poland.184∗∗∗(.022).010(.028)–.024(.043)–.133∗∗∗(.040)–.157∗∗(.066).176
2158

Estonia.170∗∗∗(.017).013(.021)–.041(.027)–.094∗∗∗(.035)–.097∗(.051).241
3259
Slovak R.
.183∗∗∗(.025)–.002(.031).090∗∗(.038)–.060(.042)–.016(.059).171
2105

Finland.105∗∗∗(.011).040∗∗∗(.015).028(.019).102∗∗∗(.022).130∗∗∗(.034)
.237
2705
Spain
.196∗∗∗(.022).035(.027).011(.036).054(.033).050(.056).234
1962

France.141∗∗∗(.013).034∗∗(.015).053∗∗(.021).054∗∗∗(.020).099∗∗∗(.031).260
2897
Sweden.046∗∗∗(.011).076∗∗∗(.015).073∗∗∗(.018).030(.020).047∗(.028).210
2381

Germany.189∗∗∗(.021).045∗(.025)–.015(.040).052(.035).103∗∗(.052)
.237
2211
U.K.
.188∗∗∗(.018).039∗(.022).010(.029).129∗∗∗(.032).034(.051).243
3200

Ireland.136∗∗∗(.017).105∗∗∗(.026).180∗∗∗(.061).071∗∗(.036).095(.062)
.246
1871
U.S.
.226∗∗∗(.018).057∗∗(.024).018(.034).148∗∗∗(.034).228∗∗∗(.054).296
2143

p<0.10, ∗∗ p<0.05, ∗∗∗ p<0.01

Notes: Least squares regressions weighted by sampling weights. Dependent variable: log gross hourly wage. Sample: full-time employees aged
25-65 (Canada includes part-time employees). Prime age: 35-54 years. Exit age: 55-65 years. Coeﬃcient on numeracy reﬂects returns tonumeracy skills for baseline category, i.e., entry-age workers (25-34 years). All regressions control for gender and a quadratic polynomial in
actual work experience. Numeracy score standardized to std. dev. 1 within each country. Pooled speciﬁcation includes country ﬁxed eﬀects
and gives same weight to each country; R2 refers to within-country R2 . Robust standard errors in parentheses. Data source: PIAAC.

Table 4: Skills, Years of Schooling, and Earnings
NumeracyYrs schoolingExperience
Experience2
FemaleR2
Observations
NumeracyYrs schoolingExperience
Experience2
FemaleR2
Observations

Pooled.101∗∗∗(.003).059∗∗∗(.001).022∗∗∗(.001)–.032∗∗∗(.003)–.176∗∗∗(.005).308
34159

Austria.120∗∗∗(.010).058∗∗∗(.004).016∗∗(.007)–.019(.013)–.119∗∗∗(.022)
.370
1115
Italy
.057∗∗∗(.016).046∗∗∗(.004).011(.007).007(.017)–.168∗∗∗(.026).278
1018

Belgium.089∗∗∗(.010).045∗∗∗(.004).011∗(.006)–.006(.013)–.050∗∗∗(.017)
.286
1219
Japan
.114∗∗∗(.015).067∗∗∗(.006).022∗∗∗(.007)–.009(.018)–.308∗∗∗(.026).381
1322

Canada.129∗∗∗(.008).057∗∗∗(.003).020∗∗∗(.004)–.024∗∗∗(.008)–.131∗∗∗(.014)
.303
7155
Korea
.102∗∗∗(.015).076∗∗∗(.005).025∗∗∗(.005)–.019(.015)–.314∗∗∗(.028).438
1441

Cyprus.057∗∗∗(.018).082∗∗∗(.007).020∗∗(.008)–.002(.019)–.193∗∗∗(.034).315
938
Netherl.
.111∗∗∗(.012).063∗∗∗(.006).027∗∗∗(.006)–.044∗∗∗(.014)–.022(.022).337
1013∗

Czech R.
.074∗∗∗(.020).045∗∗∗(.010).015∗(.008)–.029(.019)–.196∗∗∗(.033).262
1065
Norway.082∗∗∗(.008).042∗∗∗(.003).023∗∗∗(.005)–.042∗∗∗(.011)–.137∗∗∗(.014).297
1519

Denmark.085∗∗∗(.009).043∗∗∗(.003).007∗(.004)–.006(.008)–.116∗∗∗(.013).282
1875
Poland.071∗∗∗(.015).090∗∗∗(.006).027∗∗∗(.007)–.044∗∗(.017)–.216∗∗∗(.028).410
816

Estonia.116∗∗∗(.014).055∗∗∗(.005).024∗∗∗(.009)–.069∗∗∗(.020)–.442∗∗∗(.024).310
1767
Slovak R.
.086∗∗∗(.018).084∗∗∗(.006).013(.012)–.023(.026)–.282∗∗∗(.027).323
1198

Finland.079∗∗∗(.011).057∗∗∗(.003).014∗∗∗(.005)–.014(.012)–.205∗∗∗(.015)
.414
1478
Spain
.104∗∗∗(.018).066∗∗∗(.004).021∗∗∗(.007)–.024(.015)–.111∗∗∗(.025).392
1190

France.094∗∗∗(.009).041∗∗∗(.003).018∗∗∗(.005)–.018∗(.010)–.118∗∗∗(.015).331
1707
Sweden.089∗∗∗(.010).026∗∗∗(.004).017∗∗∗(.004)–.028∗∗∗(.010)–.109∗∗∗(.014).252
1316

Germany.148∗∗∗(.014).064∗∗∗(.005).009(.008)–.004(.017)–.125∗∗∗(.024)
.338
1296
U.K.
.173∗∗∗(.016).061∗∗∗(.007).009(.009)–.009(.019)–.107∗∗∗(.026).301
1671

Ireland.151∗∗∗(.021).060∗∗∗(.007).030∗∗∗(.011)–.040(.024)–.029(.033)
.286
1031
U.S.
.138∗∗∗(.022).081∗∗∗(.007).015∗(.008)–.028∗(.016)–.228∗∗∗(.032).420983

p<0.10, ∗∗ p<0.05, ∗∗∗ p<0.01

Notes: Least squares regressions weighted by sampling weights. Dependent variable: log gross hourly wage. Sample: full-time employees aged
35-54 (Canada includes part-time employees). Numeracy score standardized to std. dev. 1 within each country. Experience2 divided by 1000.
Pooled speciﬁcation includes country ﬁxed eﬀects and gives same weight to each country; R2 refers to within-country R2 . Robust standard
errors in parentheses. Data source: PIAAC.

Table 5: Returns to Skills for Alternative Skill Measures: Numeracy, Literacy, and Problem Solving
Numeracy(1).178∗∗∗(.003)(2).171∗∗∗(.003)

Literacy
Problem solving(3).143∗∗∗(.003)(4).120∗∗∗(.005).068∗∗∗(.005)(5).121∗∗∗(.005).062∗∗∗(.005)(6).078∗∗∗(.007).076∗∗∗(.007).037∗∗∗(.005)

.199
18
24475

.205
18
24475

Numeracy (international standardization)
R2 (within country)
Countries
Observations

.209
22
34447

.196
22
34447
∗

.173
18
24475

.215
22
34447

(7)

.183∗∗∗(.003)
.208
22
34447

p<0.10, ∗∗ p<0.05, ∗∗∗ p<0.01

Notes: Least squares regressions pooling all countries with country ﬁxed eﬀects, weighted by sampling weights (giving same weight to eachcountry). Dependent variable: log gross hourly wage. Sample: full-time employees aged 35-54 (Canada includes part-time employees). All
regressions control for gender, a quadratic polynomial in actual work experience, and country ﬁxed eﬀects. Test scores standardized to std.
dev. 1 within each country, except for column (7) where numeracy score is standardized to std. dev. 1 in the entire international sample.
All models with problem solving do not include Cyprus, France, Italy, and Spain, where problem-solving skills were not tested, as well as
individuals who opted out of the problem-solving test. Robust standard errors in parentheses. Data source: PIAAC.

Table 6: What Accounts for Diﬀerences in Returns to Skills across Countries?
Numeracy× Union density(1).180∗∗∗(.007)–.139∗∗∗(.026)

yes

yes

yes

yes

yes

yes

(7)
.184∗∗∗(.005)–.129∗∗(.046)–.032∗∗∗(.011)–.018(.020)–.024(.020)–.160∗(.084).000(.001)–.000(.001)
yes

.216
21
33509

.214
21
33509

.210
22
34447

.213
21
33509

.211
22
34447

.218
21
33509

.218
21
33509

× Empl. protection(2).186∗∗∗(.008)(3).181∗∗∗(.008)–.040∗∗(.016)(4).181∗∗∗(.009)(5).174∗∗∗(.008)–.278∗∗(.110)(6).184∗∗∗(.005)–.150∗∗∗(.042)–.032∗∗∗(.011)–.023(.020)–.033(.021)–.117(.093).039∗∗(.018)× Minimum wage
× PM regulation–.030(.031)

× Public sector
× Skill inequality
× Skill mean
Country ﬁxed eﬀects
Occupation × industry
ﬁxed eﬀects [185]R2 (within country)
Countries
Observations∗(8).089∗∗∗(.006)–.119∗∗(.043)–.034∗∗∗(.009)–.020(.019)–.011(.020)–.171∗∗(.067)–.000(.001)–.000(.000)
yes
yes
.408
21
32798

p<0.10, ∗∗ p<0.05, ∗∗∗ p<0.01

Notes: Least squares regressions pooling all countries with country ﬁxed eﬀects, weighted by sampling weights (giving same weight to each country).
Dependent variable: log gross hourly wage. Sample: full-time employees aged 35-54 (Canada includes part-time employees). All regressions control forgender, a quadratic polynomial in actual work experience, and country ﬁxed eﬀects. All variables are de-meaned. Union density: share of wage andsalary earners who are trade union members. Empl. protection: employment protection legislation (EPL), composite indicator measuring strictness of
employment protection for individual and collective dismissals. Minimum wage: binary variable indicating whether country has a statutory minimum
wage. PM regulation: composite indicator measuring product-market regulation (PMR). Public sector: share of workers employed in the public sector.
Skill inequality: numeracy score diﬀerential between 90th and 10th percentile of numeracy skill distribution. Skill mean: mean numeracy score of thecountry. Number in square brackets reports the number of occupation × industry ﬁxed eﬀects. Numeracy score standardized to std. dev. 1 withineach country. Due to missing data, Cyprus is only included in columns (3) and (5). Robust standard errors (adjusted for clustering at country level) in
parentheses. Data sources: PIAAC, OECD.