Cognitive and Socio-Emotional Abilities
Fernando Saltiel
University of Maryland
Miguel Sarzosa1
Sergio Urzúa2
Purdue University
University of Maryland, NBER
and IZA
February 17, 2017
Abstract
Abilities matter. This chapter reviews the nature of cognitive and socio-emotional abilities and examines their
importance in the development of successful lives. The text highlights the evidence documenting the causal
association between abilities and labor market outcomes. It introduces an occupational tasks framework and shows
how the interaction of abilities, skills and tasks is important for understanding labor market disparities. It
concludes with policy recommendations based on interventions aimed at improving skills and future avenues for
this research agenda
I. Introduction
The availability of new and rich individual-level longitudinal information has motivated some of the
most exciting new empirical findings in labor economics. The empirical examination of the causal
association between adult outcomes and early cognitive and non-cognitive abilities is a prime example.3
The evidence demonstrates independent and important roles for multiple types of abilities, documenting
they are of the highest importance in the development of successful lives.
Although early studies in economics examining the impact of ability had mostly focused on the impact
of cognition, recent research has recognized that abilities are multi-dimensional in nature. Many
disciplines converge to this corner of knowledge, and rightly so. In particular, a growing literature has
confirmed that cognitive and socio-emotional abilities determine schooling attainment, labor market
outcomes, social behavior, and physical and mental health status.4 For example, empirical results show
 [email protected]
1 [email protected]
2 [email protected]
3 We consider cognitive abilities to be “all forms of knowing and awareness such as perceiving, conceiving, remembering,
reasoning, judging, imagining, and problem solving” (APA, 2006). Non-cognitive or socio-emotional abilities—terms we will
use interchangeably—have been vaguely defined as personality and motivational traits that determine the way individuals
think, feel and behave (Borghans et al., 2008). These definitions, in particular the one related to non-cognitive abilities has no
shortage of critics, especially outside the economics discipline. We will return to this issue later in this chapter.
4 Papers on the relation between abilities and outcomes different to labor market ones include among many others Heckman
and Rubinstein (2001), Heckman et al. (2006), Urzua (2008) and Espinoza et al. (2016) on risky behaviors and illicit activity;
Duckworth and Seligman (2005) on academic performance; Heckman et al. (2006), Tough (2012), Prada and Urzua
(Forthcoming) and Espinoza et al. (2016) on schooling choices; and Sarzosa and Urzua (2015) and Sarzosa (2015) on school
victimization and mental health. Papers on labor market outcomes will be described throughout this chapter.
that the average economic returns to early abilities (that is, the percentage change in wages from a
change of one standard deviation in skill level) are important in magnitude and statistical significant
(Mulligan, 1999; Murnane, Duhaldeborde, and Tyler, 2000; Lazear 2003; Urzúa, 2008), though the
exact magnitude of the effect depends on the characteristics of the population (such as race and
education level). Moreover, while there is extensive evidence showing the importance of abilities on
labor market outcomes in the United States, expanding evidence suggests analogous patterns around
the world (OECD, 2015). For example, Figure 1 shows that in Chile, a developing economy, the highest
performers in the math section of the College admission exam—a proxy for cognitive ability—, and
those with the highest average GPA—a construct at least partially determined by socio-emotional
traits—, earn significantly higher monthly salaries by age 30 relative to their less skilled counterparts.5
Similarly, Figure 2 shows that in Norway, going from the first decile to the tenth decile in the cognitive
distribution—constructed from pre-labor market test scores in math and scholastic competence—triples
the probability of belonging to the top 25 percent of the income distribution while a similar move on a
particular non-cognitive construct—a measure of strong mindedness—increases the increases the
probability of belonging to the top 25 percent by 50 percent (Espinoza et al. 2016).
This chapter discusses recent developments made by the empirical literature on the analysis of the
determinants and consequences of cognitive and socio-emotional abilities, with special emphasis in labor
market outcomes. We separate our discussion into two main blocks that reflect the way literature has
developed. One exploring how cognitive and non-cognitive abilities affect later outcomes, and another
one studying why some people reach adulthood with greater stocks of skills than others. We also
explore the mechanism through which higher skill endowments lead to better labor market outcomes,
and therefore understand skill heterogeneity as a fundamental determinant of income inequality. We
posit that an important factor is the assignment of workers to occupations. In a nutshell, since
occupations can be conceptualized as bundles of tasks, workers who suffer from a mismatch between
their skill endowment and the tasks they perform in their job earn lower wages. As a result, an economy
with significant frictions in the assignment of workers to occupations will result in lower labor market
productivity. The implications for public policies are non-trivial. For example, countries with various
occupational licensing requirements reduce the quality of worker-task matches, whereas those with
high-quality job training and job matching programs will instead increase the quality of such matches.
This chapter is organized as follows. Section 2 presents evidence on the historical discussion of the
importance of skills in the labor market. Section 3 presents a methodological discussion of the difference
For Chile, the data comes from administrative records linking pre-labor market ability measures and adult labor market outcomes for
the universe of high school students (approximately 80,0000) taking the College Admission tests in 2001. For Norway, the data comes
from multiple waves of The Young in Norway (YiN) Study, a longitudinal project headed by the Program for Adolescent Research.
The first wave was collected in 1992, when 12,287 students attending 7th to 12th grades were interviewed.
5
between observed measures and unobserved abilities. Section 4 presents evidence on the nature of skill
development and section 5 thoroughly discusses the evidence of policies, which seek to improve skills
during childhood6. Section 6 discusses how the interaction of skills and tasks is important for
understanding labor market earnings. It also introduces a theoretical model. Section 7 presents the
evidence on active labor market policies and we suggest alternative policy solutions moving forward.
We conclude in Section 8 and discuss future avenues for this research agenda.
II. The interest of economists in abilities
Economists have had a long-standing interest in understanding the role of abilities as determinant of
socio-economic outcomes. A clear example emerges from the literature estimating the returns to
education. Early studies followed the empirical approach first proposed by Jacob Mincer (Mincer, 1958),
linearly regressing (log) wages (𝑊𝑗 ) on schooling (𝑆𝑗 ) and other variables (𝑋𝑗 )
𝑊𝑗 = 𝛼 + 𝛽𝑆𝑗 + 𝛾𝑋𝑗 + 𝑈𝑗
(1)
where 𝑈𝑗 is the error term. Such empirical exercises rely on the critical and not always plausible
assumption of conditional independence between education and the unobserved term (i.e., 𝐸[𝑈𝑗 𝑆𝑗 |𝑋𝑗 ] ≠
0). As Griliches (1977) noted, the omission of “ability” from the analysis should yield biased estimates of
the returns to schooling, 𝛽. Subsequent work highlighted this inherent problem in the Mincerian setup
(Willis and Rosen 1979, Card 2001, Heckman et. al., 2008).
The availability of rich longitudinal information, linking data on pre-labor market cognitive and socioemotional measures and adult outcomes, helped in overcoming this essential problem. In particular,
early cognitive measures (𝑇𝑗 ), incorporated as proxy measures of underlying abilities to models as
equation (1), allowing the identification of the causal effect of education and an assessment of the
economic return to cognitive ability.7 However, important questions remained.
First, it was important to determine whether ability should be defined as a trait, immutable due to its
genetically determined nature, or whether it should be conceived of as skills, which could instead be
shaped over the life-cycle. Recent evidence from the field of psychology has suggested that while skills
are partly determined by an individual’s initial traits, skills are in fact malleable and can be accumulated
In Sections I through V, we refer to abilities and skills interchangeably. In Section VI, we define the difference between
abilities and skills and use this definition through the rest of the paper.
7 Willis and Rosen (1979) use the NBER-Thorndike data to incorporate test scores on reading math, mechanical ability and
manual dexterity in their empirical model. They find that these abilities affect the selection into college and have important
return in the labor market. Murnane, Willett and Levy (1995) used test score and earnings data from two longitudinal surveys
and found an increasing return to cognitive skills across two cohorts. This approach has also been applied to examine the
importance of ability in explaining racial and gender wage gaps (e.g., Neal and Johnson 1996). Hernstein and Murray (1994)
used NLSY data to argue that early life Armed Services Vocational Aptitude Battery (ASVAB) test scores were important
predictors of employment and salary outcomes later in life.
6
throughout an individual’s lifecycle, thus offering room for policy interventions which foster skill
accumulation over time (for a complete literature review see Boghans et al., 2011).8
Second, despite the fact that this emerging literature was showing that cognitive abilities explained a
portion of the variation in earnings that had previously been ignored, a sizeable fraction of it remained
unexplained. Then, there ought to be other dimensions of abilities, which affect labor market
productivity.9 Subsequent research has acknowledged the limitations of the unidimensional ability
framework and emphasized the distinction between cognitive and non-cognitive abilities. In the United
States context, Heckman, Stixrud, and Urzua (2006), for example, find that cognitive and non-cognitive
abilities, linked to self-esteem and self-control, are positively associated with various employment
outcomes, including occupational decisions and experience, as well as with labor income, schooling
attainment, risky behaviors and social outcomes (fertility decisions and marital status). The economic
foundations for these findings are well grounded. A simple static labor supply model can be used as an
example.
Consider an individual deciding how to divide her time (T) between leisure (l) and work (h). Let 𝜃
denote her vector of endowments, which includes individual’s cognitive and socio-emotional abilities.
Optimal levels of leisure and consumption (c) result from the following maximization problem:
𝑀𝑎𝑥 𝑈(𝑙, 𝑐; 𝜌)
subject to 𝑇 = 𝑙 + ℎ (time constraint) and 𝐴 + 𝑊 × ℎ = 𝑐 (budget constraint), where 𝑈(∙) is a twicedifferentiable quasi-concave utility function characterized by preference parameters 𝜌, 𝐴 represents nonlabor income, and 𝑊 is the offered wage rate. The consumption good is the numeraire.
What is the role of abilities in this context? Despite the fact 𝜃 does not directly appear in this
conventional model, economic arguments suggest each of its components should be considered as key
underlying ingredients. Specifically, since non-labor market income (because of, for example, assortative
mating), offered wages (due to human capital theory), and preference parameters are, in one way or
another, determined by individual’s endowments; causal linkages from abilities to labor market and
social outcomes should be natural products of any rational decision process. Thus, vectors of abilities
should, for instance, directly affect hourly wages, but also indirectly through schooling decisions and
individual preferences. Likewise, ability endowments should affect a person’s labor force participation
(extensive margin), which is another mechanism through which ability could affect total income. In this
Carlsson et al. (2015) exploit random variation in the assigned test date for a number of cognitive tests given to all 18 year
old men in Sweden, such that individuals randomly take it at different ages. The authors find that ten more days of school
instruction raises cognitive scores by one percent of a standard deviation on two tests, which measure crystallized and fluid
intelligence, two common measures of cognitive skills. As a result, they argue that test scores like the AFQT should not be
compared across individuals of different ages since cognitive ability changes over time.
9 In the context of the United States, Heckman and Kautz (2012) show that test scores measures (AFQT) explain just 17
percent of the variance earnings at age 35 and IQ measures are only able to explain 7 percent of the variance.
8
context, it is easy to see how heterogeneity in the stock of abilities within a society should be easily
translated into income disparities in that community. We will revisit this issue in Section VI.
Multiple empirical studies have confirmed many of these causal channels (Heckman, Stixrud, and Urzua,
2006). In particular, researchers have documented positive effects of abilities (and skills) on various
labor market outcomes (Cawley, Heckman and Vytlacil, 2001; O’Neill, 1990; Neal and Johnson, 1996;
Herrnstein and Murray, 1994; Bowles, Gintis and Osborne, 2001; Farkas, 2003; Heckman, Stixrud and
Urzua, 2006; Urzua, 2008, among others), with an increasing number of papers highlighting the relative
importance of non-cognitive abilities around the world. For example, Lindqvist and Vestman (2011) use
data from the Swedish military enlistment and find that cognitive and non-cognitive skills have
differential effects on labor market outcomes. The authors measure non-cognitive ability using a
personal interview conducted by a psychologist, and find that it is a stronger predictor of labor force
participation than cognitive ability of earnings at the low end of the earnings distribution and wages of
unskilled workers. By contrast, they find that, cognitive ability is a stronger predictor of wages for
skilled workers and earnings above the fiftieth percentile. Lundin, Skans and Zetterberg (2016), on the
other hand, estimate the effect of leadership skills, developed through participation in student union
councils, on labor market careers in Sweden. They find that marginally elected students to the student
councils experience faster transitions into the labor market. Nyhus and Pons (2005) analyze the effect of
the Big Five personality traits for workers in the Netherlands and find that emotional stability is
positively associated with wages of both women and men. Similarly, for the U.S., Mueller and Plug
(2006) find that emotional stability increases earnings for males and conscientiousness and openness
increase female earnings, Kern et. al. (2013) find that facets of agreeableness (compliance) predict lower
risk of unemployment a decade later while Deming (2015) presents evidence of the growing demand for
social skills in recent decades. He finds that social skill intensive occupations have grown significantly as
a share of occupations in the labor force, coupled with strong wage growth in these occupations. While
occupations which require both social and math skills have done well in recent years, there has been a
decrease in the share of high-math, low-social skill jobs, including STEM occupations. Deming (2015)
argues this pattern can be explained by the fact that computers are unable to replicate human
interaction, but they are instead good substitutes for math-based jobs. As a result, in the face of the
computerization of the U.S. economy, workers who have social skills are well suited to complement
computers. Previous papers have also found survey-based evidence that employers value workers who
are endowed with non-cognitive skills (Holzer 1997, Zemsky 1997, NACE 2015, Heckman et al. 2014).
Finally, previous studies have also documented the growing importance of the complementarities
between different ability dimensions in the labor market. For instance, analyzing longitudinal data from
two cohorts of high schools (1972 and 1992), Weinberger (2014) presents important evidence on the
complementarity of cognitive and non-cognitive skills in the U.S. labor market. He finds that higher
level of math-related skills and social skills separately results in higher lifetime earnings, with
individuals who are highly endowed in both skills receiving the largest earnings. These results are
consistent with those presented in Figures 1 and 2, where high ability individuals along different
dimensions experience better labor market outcomes.
III. Test scores versus abilities
A substantive portion of the literature on abilities recognizes that the test scores we observe in the data
are not true measures of skills. Instead, abilities are considered to be latent constructs that affect the
observed test scores. This recognizes that test scores contain information about the levels of true latent
abilities, but they are not themselves measures of the true stock of skills of the respondent
(Bartholomew et al., 2011). In fact, besides abilities, test scores are also affected by observable
characteristics like family background or schooling completed at the time of the test (Hansen et al,
2004). This framework requires special tools and modeling for its empirical applications, as we no
longer observe ability values for each individual. The core of identification in this type of models is the
assumption of a linear production function of test scores with observable and unobserved inputs—from
the point of view of the econometrician. Namely, latent skills 𝜃, observable characteristics 𝑿 like
mother’ and father’s education, family income, type of school, characteristics of the schooling process,
age, gender, ethnicity, and random shocks 𝒆𝑻 collected in the following linear form.
𝑻 = 𝑿𝑻 𝛽 𝑇 + 𝛼𝜃 + 𝒆𝑻
(2)
where 𝑻 is an 𝐿 × 1 vector of scores (e.g., measures of reading proficiency, emotional stability, or grit).
Carneiro et al. (2003) show that this measurement system can be used to non-parametrically identify the
distributions of the latent abilities, 𝑓𝜃 (∙), as long as there are multiple tests per dimension of skills.10
Having identified the distributions of the latent abilities, we can move on to estimate the effect of skills
on labor market outcome 𝑌 by considering the following equation:
𝑌 = 𝑿𝒀 𝛽 𝑌 + 𝛼 𝑌 𝜃 + 𝑒 𝑌
where 𝛽 𝑌 and 𝛼 𝑌 are coefficients to estimate; 𝑿𝒀 is a matrix of observable controls (e.g., gender, age); and 𝑒 𝑌
is an iid error term orthogonal to 𝜃 and 𝒆𝑻 .
This approach to the analysis of the effect of skills in later outcomes recognizes that some portion of the
variation in the test scores cannot be directly linked to skills, which can bias the estimation of a regression of
The estimated distributions 𝑓𝜃 (∙) are not assumed to follow any particular distribution. The procedure uses a mixture of
normal distributions, which are known to be able to re-create a wide range of distributions (Judd, 1998). See all indeitfication
requirements in Carneiro et al. (2003) and Sarzosa and Urzua (2016).
10
𝑌 on 𝑻.11 Figure 3 illustrates the implications of this analysis when analyzing pre-labor market
cognitive and non-cognitive test scores in the U.S (see Prada and Urzua, 2017).
IV. Where do skills come from?
Given the importance of both types of abilities in the labor market and—through it—their strong
relation with income inequality, it is crucial to understand where they come from and when do they
develop, if they in fact do so. Cognitive and non-cognitive traits are partially heritable: Bergen, Gardner
and Kendler (2007) show that 40 percent of childhood IQ is heritable and Bouchard and Loehlin (2001)
estimate that non-cognitive skills are about 30-60 percent heritable. Nevertheless, there is extensive
evidence that these skills change throughout a person’s life. For instance, conscientiousness increases
throughout a person’s lifetime and interventions focused on cognitive development improve IQ in a
lasting way (Knudsen et al. 2006). In fact, cognitive and non-cognitive skills are more malleable at early
ages (Shonkoff and Phillips, 2000).
In this context, Cunha et al. (2010) present a theoretical framework in which cognitive and noncognitive skills are treated as any stock whose available quantity at time 𝑡 (𝜃𝑡 ) depends on the stock
available last period (𝜃𝑡−1 ) and investments between 𝑡 and 𝑡 − 1 (𝐼𝑡−1 ). Specifically, Cunha et al. (2010)
formalize the intuition by assuming the following production function:
𝜃𝑡 = 𝑔(𝜃𝑡−1 , 𝐼𝑡−1 ) + 𝑣𝑡
where 𝑣𝑡 ∼ 𝑓𝑣 (⋅) is an iid serially uncorrelated random variable. The recursive nature of the production
function guarantees that the stock of skills at a given moment in time is the result of all the investments
made at previous stages of the life cycle, plus an initial endowment 𝜃0 , itself given usually by pre-natal
investments and inherited traits. Although, it could also be a product of early-life investments whether
through parenting, social interactions or schooling, depending on when is the initial endowment
measured.
An important contribution made by this framework is that function 𝑔(∙) is assumed to be non-linear,
and therefore the efficiency of an investment in the production of next-period skills (i.e., 𝑔𝐼′𝑡−1 (∙)) will
depend on the current level of skills, reflecting a static complementarity. If the estimated parameters of
𝑔(∙) yield a positive static complementarity, the ability production process is such that children who are
highly skilled will benefit the most from additional investment. However, as current abilities and
present-day investments affect future ability endowments, the ability accumulation process will also
reflect dynamic complementarities, such that the returns to early life investments are higher because
11
Sarzosa and Urzua (2016) provide the routines needed to estimate these types of models in Stata.
these investments make future investments more productive. That is, 𝑔𝐼′′𝑡−1 ,𝐼𝑡−𝜏 (∙) > 0, where 𝜏 > 1.
This gives foundation to the call for early childhood development and preschool interventions (Knudsen
et al., 2006; Heckman, 2006). Furthermore, policy interventions at early ages have a longer time period
during which the returns can be accrued (Becker 1964; Cunha & Heckman, 2007).
The model can be augmented to incorporate the fact that the investment decisions themselves depend
on past levels of ability. Namely
𝐼𝑡 = 𝜄(𝜃𝑡 ) + 𝑢𝑡
where 𝑢𝑡 ∼ 𝑓𝑢 (⋅) is an iid variable uncorrelated with 𝑣𝑡 . This turns the framework into a fully dynamic
model in which the stocks of skills and investment decisions evolve over time.
We formalize the ability accumulation process by following Cunha et al. (2010). The authors choose a
CES function for 𝑔(∙) of the form:
𝜌
𝜌 1⁄𝜌
𝜃𝑡+1 = [𝛾𝜃 𝜃𝑡 + (1 − 𝛾𝜃 )𝐼𝑡 ]
This function provides the curvature required to inquire about possible non-linearities and
complementarities between the inputs. Besides the static and dynamic complementarities already
defined, we can use it to estimate self-productivity (i.e., how the productivity of one skill depends on the
level of that skill) and cross productivity (i.e., how the productivity of one skill is enhanced by the level
of other input).
The model considers abilities to be latent as described in Section III, and therefore data requirements
comprise the need of test scores in multiple moments in time such that 𝑓𝜃𝑡 (∙) and 𝑓𝜃𝑡+1 (∙) can be
identified through the estimation of multiple measurement systems like (2):
𝑻𝒕 = 𝑿𝑻 𝒕 𝛽𝒕𝑻 + 𝛼𝑡 𝜃𝑡 + 𝒆𝑻𝑡 for 𝑡 = 1, … , 𝜏 and 𝜏 > 1
Details on the estimation procedure of such models can be found in Sarzosa (2015). Cunha et al. (2010)
estimate this model using the NLSY79. Sarzosa (2015) and Espinoza et al. (2016) do so in the Korean
Youth Panel Survey. Cunha et al. (2010) find that non-cognitive abilities affect positively the production
of cognitive ability early in life. Not so much later. They also find that cognitive skills do not contribute
to the formation of next-period non-cognitive skills. Sarzosa (2015) and Espinoza et al. (2016) find
similar results to those of Cunha et al. (2010) in the Korean data. Figures 4 show that non-cognitive
skills matter in the production of cognitive abilities, but the converse does not hold. That is, they find
evidence of self-productivity on both skills, but positive cross-productivity only in the formation of
cognitive skill. Finally, they find that the productivity of investment in both dimensions is greater
among those who have higher previous levels of non-cognitive abilities. That is, they find evidence of
static complementarity.
V. Skill-Boosting Interventions
Following the framework presented above, interventions aimed at increasing cognitive and noncognitive skills should focus on early ages. Knudsen et al. (2006) review skill development policies and
find large impacts for programs targeted to preschoolers and primary school children. Moreover,
interventions at younger ages and targeted toward high-risk children from disadvantaged populations
tend to produce larger effects and significant benefit-to-cost ratios (Cunha et.al, 2006; Heckman, 2006).
With respect to their general characteristics, although they differ by duration and child age at entry,
these policies usually include some sort of parental involvement component. For instance, the NurseFamily Partnership program in the U.S. encourages mothers to engage in good health care practices,
helping them in finding employment, and includes visits from a public health nurse from pregnancy
until the child turns two years old. The program successfully developed cognitive and non-cognitive
skills, as participating children had higher IQ scores through age 6 (Olds et al. 2004). By age 12, treated
children had lower rates of substance abuse (Kitzman et al. 2010), and by age 19, children were less
likely to engage in criminal activities (Eckenrode et al. 2010). Noboa-Hidalgo and Urzua (2012) have
also found that early participation in child-care centers in Chile boosts an area of emotional regulations,
but the effects depend on the quality of the available care. Other policies have focused on skill
development by expanding access to schooling through pre-school programs. The Perry Preschool
Project provided high-quality preschool education to three- and four-year-old poor African-American in
1962-1967. Children enrolled in the program attended pre-school for 12.5 hours per week for two years,
and participants were taught to plan tasks, execute them and subsequently review them with fellow
students and teachers. While there is no evidence the program boosted IQ scores (Heckman and
Masterov 2007), it increased annual earnings by 7-10 percent. Heckman, Pinto, and Savelyev (2013)
argue that increased non-cognitive skills explain the earnings effect, as the program improved
externalizing behavior, and openness for experience for girls. Engle et al. (2007) present a meta-study
on the effects of early life interventions in various countries. They present the effects of the opening or
expansion of pre-schools in eight developing countries and find large effects on children’s cognitive
development as well as gains in non-cognitive skills, such as sociability, self-confidence, willingness to
talk to adults, and motivation.
Nevertheless, since both skills are malleable beyond early childhood, later life interventions (but still
targeting school age populations) could still affect skill endowments. Meghir et al. (2012) exploit an
exogenous increase in the amount of mandatory schooling in Sweden and find that an extra year of
schooling increases cognitive and non-cognitive skills, and the gains in non-cognitive skills reduce the
likelihood of long-term unemployment. Treated low-income students gain the most in terms of
cognitive skills and earn six percent more than comparable untreated students. Bassi et al. (2012)
examine the relationship between schooling attainment and different types of skills in Argentina and
Chile. The authors find a strong positive correlation between additional years of schooling and average
cognitive and non-cognitive skill levels, in particular for measures of self-efficacy and meta-cognitive
strategies.
Additional evidence has shown how non-cognitive skills can be shaped beyond infancy, highlighting the
potential of interventions focused on improving this specific skill dimension (Borghans et. al., 2008).
Martins (2010) examines the effects of the EPIS program in Portugal, which seeks to reduce dropout of
at-risk students by developing non-cognitive skills. The author finds that the program reduces the
likelihood of dropout by 10 percentage points. The Montreal Longitudinal Experimental Study, on the
other hand, targets seven-to-nine year old students who had behavioral issues in kindergarten and
includes of nineteen sessions focused on behavioral training and a parental component focused on
fostering similar skills. Algan et al. (2014) evaluate the program and find it boosts non-cognitive skills
during adolescence, yielding subsequent improved employment outcomes and reduced the probability of
having a criminal record. The xl-program in England is focused on improving confidence, self-esteem,
motivation and locus of control in 14 year olds. Browne and Evans (2007) evaluate the program and find
that participants experience an improvement in motivation, behavior, self-esteem and confidence, but
find no substantial effects on cognitive skills. There is further indication of the effectiveness of
interventions at later points in life, especially for those focused on non-cognitive skills. OECD (2015)
presents a useful summary of these interventions.
All in all, the evidence shows that both cognitive and non-cognitive abilities can be shaped in early ages
and that these abilities are positively correlated with various labor market outcomes. Nevertheless, less
is known about the mechanism driving this effect.
VI. Ability Heterogeneity, Tasks Mismatch and Labor Market Disparities
When considering the long-term impact of abilities on labor outcomes, it is important for policymakers
to incorporate other central insights from labor economics. For instance, information asymmetries may
lead individuals to incorrectly choose schooling attainment levels and this, jointly with search and
matching frictions, can result in the misallocation of workers to occupations (Jaimovich and Siu 2012).
In what follows, we define occupations as bundles of tasks. We argue this perspective is a building block
for a comprehensive understanding of labor market disparities. In particular, one way—somewhat
overlooked by the literature—in which abilities affect labor market outcomes is their differential relation
with the tasks each job requires.
Tasks can be broadly defined as occupation-specific pieces of work to be undertaken. They can be
characterized narrowly, distinguishing between connecting wires and repairing faulty equipment, or
broadly, such that these two activities are considered non-routine manual tasks (Acemoglu and Autor,
2011). Some abilities increase productivity across all tasks, but also through the quality of skill-task
matches, since some abilities are only useful if the worker is performing tasks, which make use of her
skill endowment (Prada and Urzua, 2017). For instance, a worker who is endowed with cognitive and
social abilities will make productive use of her cognitive ability across all occupations, but her social
skills will only affect productivity if she is employed in an occupation, which requires her to perform
interactive tasks.
While the difference between the tasks performed by an economist and a car repairman can be
understood intuitively, this distinction is less evident when comparing, for instance, the tasks performed
by electricians against those performed by carpenters. The comparison of tasks across different
occupations has thus relied on information included in various Dictionaries of Occupations (DOT),
which provide evidence on the types of tasks performed by workers across all occupations in the
economy.12 The current DOT in the US is the O*NET dictionary, which includes information on 239
dimensions of skills and job characteristics for over 1,000 different occupations, such as qualifications
required, necessary practical and technical skills, and detailed information of the tasks involved in the
job. For instance, an economist who is required to “compile, analyze, and report data to explain
economic phenomena” must be able to “use mathematics to solve problems.”13
Autor, Levy and Murnane (2003) first took advantage of DOT data to understand the effect of
computerization on wage inequality in the United States. The authors argued that computers are
complements to non-routine tasks, but substitutes to routine tasks, which could explain how the
introduction of computers led to an increase in wage inequality, through the change in the relative
demand for these two types of tasks. The authors found the relative increase in the demand for nonroutine tasks could account for 60 percent of the relative demand shift towards college-educated
workers from 1970 through 1988.14
The United States first developed its Dictionary of Occupational Titles in 1939. This dictionary included information on
tasks performed in manual occupations, such as the physical activities done in various blue-collar jobs.
13 Across different contexts, DOTs may include different information on occupations, such as the associated complexity with
each particular task. Other DOTs are less comprehensive, but they still provide useful information to understand the nature of
different jobs in different countries. While these dictionaries are prevalent in developed countries, to the best of our knowledge,
only Brazil has developed a similar document in a developing country context.
14 A number of papers have followed the approach put forth by Autor, Levy and Murnane (2003). For instance, in Germany
Black and Spitz-Oener (2010) and Spitz-Oener (2006) classified occupations by the share of time spent on each task group, and
similarly, Gathmann and Schonberg (2010) developed a measure of task-specific human capital.
12
In this context, we posit that task prices are positively correlated to the scarcity of the specific skill
required to successfully perform the task. For instance, O*NET indicates that economists are effective
problem solvers. On the other hand, a stock clerk requires a general endowment of manual skills. As a
result, if the economy is endowed with workers who are able to perform routine manual tasks but with
few who are able problem solve, the economy-wide equilibrium price for the “analysis of statistical
evidence” task will be higher than for tasks requiring stocking products. We can therefore use this and
the definition of occupations as bundles of tasks to understand how salaries vary across occupations.
And, in general, the simultaneous analysis of both the heterogeneity in skills and on the share of time
spent by a worker performing different types of tasks might lead to a better understanding of the labor
market disparities.
In light of the evidence, underlying heterogeneity in cognitive and non-cognitive traits can be thought
as potential determinants of inequalities, but differential rates of skill investments might also contribute
to the phenomenon. There are various avenues through which workers can improve their skills while
active in the labor force. For instance, “learning-by-doing” allows workers to do so by repeatedly
performing the same task in their job, affecting not only levels of labor market productivity but also its
dispersion.15
Most papers that examine the interaction between skills and tasks assume that workers are optimally
able to self-select into occupations which require them to perform tasks well suited to their skillendowments (Gibbons et al. 2005, Gervais et al. 2014; Sanders, 2014; and Antonovics and Golan 2012).
In Borghans et al. (2014), certain jobs require social skills whereas others do not, and workers sort into
these types of jobs given their skill endowments and relative wages. Deming (2015) estimates a model in
which workers with higher social skills self-select into non-routine and social skill-intensive
occupations. In what follows, as in Albrecht and Vroman (2002) and Buhrmann (2017), we assume that
heterogeneous workers—in terms of their stock of abilities—search for employment, and acknowledge
the existence of labor market frictions which prevent optimal assignment of workers to occupations.
Furthermore, we entertain the possibility that certain labor market policies, such as hiring and firing
restrictions or strict occupational licensing requirements, may limit the ability of workers to move along
the occupational ladder, rendering them unable to efficiently use their skill endowment in their
preferred occupation. Guvenen et al. (2015) incorporate the concept of skill-tasks mismatch in the
analysis of labor, empirically assessing dynamic occupational choice and human capital accumulation
and allowing for mismatches between skill endowments and occupations. They find that early career
Yamaguchi (2011) posits that workers who perform more complicated tasks will develop skills faster, such that the tasks
performed by an actuary, which require high levels of complicated quantitative analysis will lead her to acquire skills at a faster
rate than a cashier, who performs simple quantitative tasks. Skills in adulthood may also be acquired through on-the-job
training programs, which firms use to ensure workers acquire the relevant skills to successfully perform the tasks required in
their jobs.
15
mismatches have a negative effect on wages in future occupations. We build on this framework and
sketch our theoretical framework below.
Conceptual Framework. We build on the work of Guvenen et al. (2015) to explain how individuals
invest in skills throughout their lifetime and how these skills lead to different labor market outcomes,
which partly depend on the types of tasks they perform at work. In particular, consider a standard life
cycle model where individuals maximize their lifetime consumption and leisure given their preferences
and skills, subject to initial levels of human capital (s(0)) and assets (A(0)) as well as dynamic
constraints characterizing the human capital and asset accumulation processes. Thus, in the spirit of
Heckman, Stixrud, and Urzua (2006), the agent maximizes:
𝑇
∫ 𝑒𝑥𝑝(−𝜌𝑡) 𝑈(𝑐(𝑡), 𝑙(𝑡); 𝜂) 𝑑𝑡
0
and subject:
Ȧ(t) = W(t, κ) (1 − l(t) − h(t)) S(t) − P(t)’ c(t) + r A(t),
𝑆̇(t) = φ (h(t), S(t), 𝜏),
{A(0), 𝑠(0)}.
where 𝜂 is a vector of preference parameter, 𝜌 is the discount factor, κ determines labor income, and 𝜏
defines the human capital accumulation process. As in the static model in Section II, the individual
divides her time between leisure (l), and work, but we now allow for time invested in human capital
accumulation (h). For simplicity, we conceive of human capital as a time-varying multi-dimensional
vector, composed of J skills (𝑆(t) = {𝑆𝑗 (t)}𝑗∈𝐽 ).
Thus, what is the role of abilities in this context? As above, cognitive and non-cognitive traits affect
preference and technology parameters directly, which in turn affect different labor market outcomes and
shape skill levels. Formally, since:
A(0) = A0 (𝜃 𝐶 , 𝜃 𝑁 ), S(0) = S0 (𝜃 𝐶 , 𝜃 𝑁 ), η = η(𝜃 𝐶 , 𝜃 𝑁 ), 𝜌 = 𝜌(𝜃 𝐶 , 𝜃 𝑁 ), 𝜏 = 𝜏(𝜃 𝐶 , 𝜃 𝑁 ), κ = κ(𝜃 𝐶 , 𝜃 𝑁 ),
Hence, in equilibrium,
𝑆(t) = {𝑆𝑗 (𝜃𝐶 , 𝜃𝑁 , 𝑡)} , and ℎ(t) = {ℎ𝑗 (𝜃 𝐶 , 𝜃 𝑁 , 𝑡)}𝑗∈𝐽 .
𝑗∈𝐽
In summary, multi-dimensional skills, human capital investments, and hours of work should
endogenously evolve, at least partially influenced by individual’s endowments.
Occupations. Although the simple theoretical framework abstract from the optimal selection of
occupations, total labor income will depend on the set of tasks performed by a worker in a specific
occupation. Let 𝑇𝑘,𝑜 (t) denote the fraction of the time a worker in occupation o dedicates to task 𝑘 ∈ 𝐾,
the set of potential tasks. Workers are not necessarily employed in their optimal occupation, but they
are instead selected into occupations through a matching function m, which matches their skills to
different task allocations across occupations.16 Let 𝑀𝑜 (𝑡) indicates how productive the worker will be in
occupation o, which depends on the quality of the skill-task matching process:
𝑀𝑜 (t) = ∑ ∑ 𝑚(𝑆𝑗 (𝜃 𝐶 , 𝜃 𝑁 , 𝑡), 𝑇𝑘 (𝑡))
𝑗∈𝐽 𝑘∈𝐾
Thus, we can write total labor income as
𝑌(t) = W(t, κ) (1 − l(t) − h(t)) S(t) = 𝑌(𝜃 𝐶 , 𝜃 𝑁 , 𝑀𝑜 (𝜃 𝐶 , 𝜃 𝑁 , 𝑡); Ω)
where Ω denotes a set of parameters. This model not only suggests that labor market income is defined
by the tasks performed by the worker (Autor and Handel, 2013),17 but also that investing in skill
accumulation can increase it. In this context, policy interventions that foster skill development should
result in improved labor market outcomes. Moreover, as workers who find better occupational matches
are more productive at work, policies geared towards eliminating matching frictions in the labor market
should increase productivity. However, this framework also provides valuable insights for potential
policy interventions designed to reduce labor market inequalities.
Let 𝐺𝑡 be any inequality indicator constructed in period t from a sample of individuals with labor income
Y𝑖 (t) (i=1,..,N). That is,
𝐺𝑡 = G{Y𝑖 (t)}𝑁
𝑖=1
Given the direct impact of individual skills on labor market outcomes, policies focused on, for example,
skill development for individuals at the low-end of the income distribution could reduce inequality.
Similarly, if individuals with low skill endowments find worse occupational matches in the labor force
vis-à-vis their higher-skilled counterparts, policies focused on improving job match quality for lowskilled individuals should also boost equity.
VII. Policy implications and future agenda
Existing labor market policies have often focused on improving employment opportunities for
unemployed workers as well as increasing productivity for those already in the labor force. The first
group of policies often include job search courses, and assistance, vocational guidance and counseling
and monitoring. These policies are common in the United States and European countries. However,
It is possible to allow for optimal selection into occupations, but the search and matching framework allows for labor market
frictions.
17 See Agastisti, Johnes and Paccagnella (2016) for evidence on the impact of tasks on wages for OECD countries.
16
Card, Kluve and Weber (2010) conducted a meta-analysis of the impact of these policies, finding no
significant effects through the medium term.
Governments have also developed various job-training programs focused on employed workers, in the
face of market failures, which limit the ability of firms to train their workers in general skills (Becker
1964). These policies include wage and employment subsidies, classroom training programs and on-thejob programs. Heckman, Lalonde and Smith (1999) surveyed 75 papers examining the effect of these
programs in developed countries and found small or no impact. Card, Kluve and Weber (2010) examined
almost 200 program estimates and found subsidized public sector employment programs to be largely
ineffective and no impacts for on-the-job and classroom training programs in the short-run (see also
Card, Kluve and Weber 2015).
On the other hand, the evidence presented in Section V notes the difficulty of developing cognitive and
non-cognitive abilities for adults, which may explain the limited effectiveness of active labor market
policies. In Latin America, these policies have often focused on improving outcomes for young adults,
who are often faced with a high probability of unemployment but still have the ability to develop skills.
Attanasio et al. (2011) evaluated the impacts of the Jovenes en Accion program in Colombia, which trained
over 80,000 individuals between the ages of 18 and 25 for six months, and found that it increased
earnings, the probability of employment and the number of days worked for women. Ibarrarán and
Rosas Shady (2009) and Puentes and Urzua (2010) have reviewed the results of similar youth training
programs in Latin American countries, and found that the effects that program results vary
significantly, with the of impact on employment ranging from 0 to 5 percentage points. Similarly,
apprenticeships are common in European countries and focus on enhancing general and occupational
skills for workers in the early stages of their careers by combining academic courses with work-based
learning, often through internships for three-to-four years. There is extensive evidence showing the
success of these programs. Cooke (2003) estimates the returns to apprenticeship training in Germany
across two cohorts and finds that trained workers earn higher wages through the medium-run. Pischke
and von Wachter (2004) further find positive returns to participation in apprenticeships in Germany, in
the range of 7-8 percent per year. The evidence shows while the returns are not positive in every
context, policy interventions focused on younger workers are more likely to deliver improved labor
market outcomes. In particular, since apprenticeships focus on developing skills and delivering
improved occupational matches, our conceptual framework predicts the success of these policies and we
argue that future active labor market interventions should focus on these two dimensions.
Public policies could also help in delivering improved labor market outcomes by directly focusing on
reducing labor market frictions, which prevent the optimal allocation of workers to specific occupations.
For example, hiring and firing restrictions, which are prevalent in European and Latin American
countries, often impede the ability of high-skilled workers to move up the occupational ladder, as older
workers are too expensive to fire. Similarly, as occupational licensing requirements impose numerous
requirements for workers to practice certain occupations, they limit the access to various jobs and thus
limit labor market potential for some workers. In the United States, this has become a growing concern
in recent years as the share of workers who require a license to work has increased from less than 5
percent in the early 1950s to about 29 percent today (Kleiner and Krueger 2013). However, evidence
shows that eliminating institutional barriers to occupational entry, such as occupational licensing or
qualification credentials would significantly increase aggregate occupational mobility rates (Cortes and
Gallipoli, 2016).
Policymakers who are concerned about income inequality should also focus their attention on the role of
occupational matching frictions in the labor market. Dengler, Stops and Vicari (2016) estimate
occupational matching functions in Germany and found that the quality of matches differs significantly
by the degree of standardization in an occupation. These results indicate that the matching function
technology is important for labor market outcomes. For instance, since occupational licensing
requirements tend to affect middle-skilled occupations like hairdressers and truck drivers, workers who
would have self-selected into these occupations will end up in worse-quality matches, thereby increasing
high-to-middle inequality and lowering the rates of social mobility.
All in all, active labor market policies have focused on improving the outcomes of adult workers, often
yielding unsuccessful outcomes. This result should not be surprising given the difficulty in developing
skills during adulthood. Fortunately, there is suggestive evidence these policies deliver better outcomes
when focused on young workers, whose non-cognitive skills are malleable. Furthermore, the evidence
from the success of apprenticeships has highlighted the importance of improving occupational matches,
which allow workers to better use their skill endowments in occupations, which require them to use
these skills. This is consistent with the implications of our theoretical framework. In this context, there
is also room for governments to eliminate frictions, as they not only can reduce aggregate labor market
productivity, but they may also increase income inequality.
VII. Conclusion
Abilities are multi-dimensional in nature, and they have distinctive effects on various labor market
outcomes. We summarize the literature documenting the importance of cognitive and non-cognitive
traits, both in developed and developing country contexts. The economic theory suggests that
heterogeneity in these dimensions will be reflected in measures of income inequality. We also review
studies showing that skills are not fixed at birth, but are instead malleable, especially in early ages.
There is broad evidence that early interventions, such as early nursing programs and high-quality pre-
school, result in cognitive and non-cognitive skill development, which subsequently deliver improved
labor and non-labor market outcomes. This is consistent with the implications of a framework of
dynamic skill accumulation, which allows for static and dynamic complementarities: investment at early
ages will yield the highest pay-off in terms of skill development.
The empirical literature also shows that increased years of schooling are positively correlated with
increased skill endowments and that programs focused on developing non-cognitive skills can yield
successful outcomes even for late adolescents. An important lesson from theses studies is that skill
development policies should focus on young children, preferably before they enter school, but that
targeted interventions in later years can also have positive impacts.
Nonetheless, the nature of skill development makes it costly, or sometimes impossible, for adults to
improve their skills. Existing active labor market policies often seek to improve labor market outcomes
by increasing the stock of skills in adults. We have shown that these policies result, at best, in small
impacts in the labor market, while those focused on younger workers, including policies focused on
improving occupational matches have resulted in better labor market outcomes. These results fit with
our conceptual framework, which posits that the match between skills and occupational tasks is an
important mechanism through which skills deliver improved employment outcomes.
Lastly, we highlight that policymakers can deliver better labor market outcomes by reducing
occupational matching frictions. Policymakers who are interested in boosting aggregate productivity
and reducing inequality should strive to improve the quality of occupational matches, whether through
targeted job matching programs or by eliminating frictions from the economy.
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Figure 1. Average Monthly Salary by Age 30 in Chile, by High School GPA and Average Math
PSU Score
Source: Authors’ calculations. PSU is the score from mathematical section of the Chilean college admission exam.
GPA is the average of final grades obtained from 9th to 12th grades. The sample includes the universe of high
school grades in 2001 reporting formal employment during the next decade.
Figure 2. Probability of Belonging to Top 25 Percent of Income Distribution in Norway, by
Cognitive and Non-Cognitive Abilities
Source: Espinoza, Sarzosa, Urzua and Miyamoto (2016). Data from the Young in Norway (YiN), a longitudinal data
set product of a research project headed by the Program for Adolescent Research (Ungforsk) in 1990. Three scores
are used to identify “openness and ease to create social connections” (Openness): Extraversion, social acceptance,
close friendship. For “self-worth”: satisfaction with the way the person is, romantic appeal, and physical appearance.
For “strong-minded” (assertiveness): decided, independent dominant, and lead. For cognitive: grades and scholastic
competence.
Figure 2: Variance Decomposit ion
Figure 3. Variance Decomposition
of Cognitive and non-cognitive abilities in the United States
Reckless B.
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Source:
and Urzua
The figure
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he figure
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ionpresents
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erminant
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from the National Longitudinal Study of Youth 1979 (USA)
Figure 4: Average (log) Annual Earnings (ages 25-35) by Ability Levels (Deciles)
Figure 4. Estimation of the Dynamic Process of Ability Formation in South Korean MiddleSchool Students. Abilities at 𝑡 + 1 as a function of abilities at 𝑡 (by decile)
Non-Cognitive
Cognitive
11
10.8
10.6
10.4
10.2
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8
7
6
5
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Deciles of Cogni ve
3
2
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2
3
4
5
6
7
8
9 10
Deciles of Mechanical
Source: Espinoza, Sarzosa, Urzua and Miyamoto (2016). The empirical analysis is carried out using longitudinal information
from the Junior High School Panel (JHSP) of the Korean Youth Panel Survey (KYP). Non-cognitive ability is linked to two
Cognitability
ive andis Midentified
echanicalfrom academic test scores (math and science,
domains: responsibility and locus of control. (a)
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11
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