what explains variation in the skills of central european...
TRANSCRIPT
What Explains
Variation in the
Skills of Central
European
Adults?
Kata Orosz
Presentation prepared for the 2nd Central European
Higher Education Cooperation Conference
Budapest, June 17, 2016
Definition of Skill
1) Productive: using skills at work are productive of value
2) Expendable: skills are enhanced by training and development
3) Social: skills are socially determined
Green (2013)
Data source: The Survey of
Adult Skills (PIAAC)
– Survey of adults aged 16 to 65
– Samples min. 5000 individuals in participating countries
– Nationally representative when sample weights are applied
– Skill domains: literacy, numeracy, and problem-solving in technology-rich
environments
– Valid cross-culturally and cross-nationally
– Administered in national languages
– Will be repeated over time
Source: OECD (2016)
Cross-country variation in adult
skills
Sample sizes refer to analytic samples, which exclude adults younger than 20 years old, adults with no paid work experience, and observations with missing
values on independent variables used in the regression models. Sample sizes reported refer to analytic sample sizes for the literacy and numeracy domains;
analytic sample sizes are smaller in the problem-solving domain. Data: PIAAC 2012.
274
277
281
266
261
272
276
279 280
250
255
260
265
270
275
280
285
Literacy Numeracy Problem-solving
Czech Republic (n=4632) Poland (n=6169) Slovak Republic (n=4581)
Empirical approach
– Present study modelled after Mellander (2014); relationship between adult skills,
education and work experience in Nordic countries
– Analytic samples: Adults age 20-65 w/ min. 1 year of paid work experience in CZ, PL, SK
with no missing values on variables included in the model
– Operationalization of skills: Proficiency score in literacy, numeracy, problem-solving
– Path analysis: Recursive equations to account for endogeneity of work experience
x1
x2
y
x1 : Educational attainment
x2 : Work experience
y : Skill
Model specifications
Equation #1:
Work Experience = g(Age + Age2 + Educational Attainment + Gender + Number of children + Gender x Nu er of hildre ) + ε1
Equation #2:
Skill = h(Age + Age2 + Parental Education + Educational Attainment + Labor Force Participation +
[Predi ted] Work E perie e + O upatio T pe) + ε2
– Parental Education (Below upper secondary; Upper secondary; Tertiary)
– Educational Attainment ( Below upper secondary, Upper secondary; Ba helor s / Short- le tertiar ; Master s / Lo g-cycle tertiary)
– Labor Force Participation (Employed; Unemployed; Out of the labor force; Not known)
– Occupation Type (Skilled; Semi-skilled white collar; Semi-skilled blue collar; Elementary; Not employed in past 5 years)
Sample weights and bootstrap replications used in all analyses for variance estimation.
Cross-country variation in
relationship of education & work
Regression results from a nationally representative sample of adults 20-65 who had at least one year of work experience. Sample sizes denote unweighted
sample sizes. Regression results are from the first equation of the recursive equation models; control variables in the first equation include age, the quadratic
term of age, gender, number of children, and the interaction of gender and number of children. βBachelor denotes the difference between upper secondary
education versus a short-cycle tertiary credential. βMaster denotes the difference between upper secondary education versus a long-cycle tertiary credential.
*** indicates significant association at the alpha < 0.001 level. R2 can be interpreted as the proportion of variance in work experience that is explained by the
model. Data: PIAAC 2012.
Model Country Sample
size βBachelor βMaster R2
Work Experience = Age + Age2 +
Educational Attainment +
Gender + Number of children +
(Gender x Number of children)
CZ 4632 -0.70*** -3.10*** 0.88
PL 6169 0.88*** -0.36*** 0.75
SK 4581 -0.77*** -2.08*** 0.85
Predictors of adult skills in
Central Europe
Regression results from a nationally representative sample of adults 20-65 who had at least one year of work experience. Regression results are from the second
equation of the recursive equation models. *** indicates significant association at the alpha < 0.001 level. R2 can be interpreted as the proportion of variance in
skills that is explained by the model. Data: PIAAC 2012.
Literacy Numeracy Problem-solving
Coefficient P > t Sign. Lev. Coefficient P > t Sign. Lev. Coefficient P > t Sign. Lev.
Age -0.792 0.240 * -2.359 0.000 *** -5.371 0.000 ***
Age2 0.004 0.188 -0.007 0.128 -0.021 0.000 ***
Parental education
Upper secondary 9.855 0.000 *** 10.489 0.000 *** 9.948 0.000 ***
Tertiary 16.294 0.000 *** 21.222 0.000 *** 23.422 0.000 ***
Educational attainment
Less than upper secondary -16.73 0.000 *** -18.81 0.000 *** -5.646 0.014 *
Post-secondary, non-tertiary 7.911 0.000 *** 4.978 0.001 ** 3.700 0.043 *
Professional 17.198 0.000 *** 13.246 0.010 ** 8.755 0.000 ***
Bachelor / Short-cycle tertiary 15.022 0.000 *** 15.678 0.000 *** 11.710 0.000 ***
Master / Long-cycle tertiary 23.749 0.000 *** 24.899 0.000 *** 25.975 0.000 ***
Employment status
Unemployed 0.178 0.845 1.226 0.524 7.129 0.000 ***
Out of the labor force -1.347 0.129 -0.181 0.909 6.177 0.001 **
Not known -4.454 0.779 -2.572 0.767 6.278 0.700
Work experience (predicted) 0.419 0.233 2.495 0.000 *** 5.032 0.000 ***
Occupational type
Semi-skilled white collar -9.163 0.000 *** -9.716 0.000 *** -9.057 0.000 ***
Semi-skilled blue collar -17.783 0.000 *** -17.558 0.000 *** -19.874 0.000 ***
Elementary -18.877 0.000 *** -18.502 0.000 *** -17.219 0.000 ***
Not employed in past 5 years -12.961 0.000 *** -3.818 0.261 16.842 0.001 **
Cons. 274.634 0.000 *** 297.764 0.000 ***
n (unweighted) 15382 15382 9718
R2 0.24 0.20 0.03
Between-country variation in
predictors of adult skills
Skill domain
βBachelor
βMaster
βWorkExp
CZ (n=4632) 18.41*** 27.36*** 1.01*
Literacy PL (n=6169) 17.92*** 27.44***
SK (n=4581) 8.42*** 15.93*** 1.20***
CZ (n=4632) 25.29*** 38.09*** 2.59***
Numeracy PL (n=6169) 19.38*** 27.60*** 2.75***
SK (n=4581) 6.33** 21.93*** 1.49***
CZ (n=3401) 11.61+ 31.07*** 3.09***
Problem-solving PL (n=3596) 15.97*** 26.46*** 3.68***
SK (n=2721) 11.28** 23.19*** 3.00***
Regression results from a nationally representative sample of adults 20-65 who had at least one year of work experience. Sample sizes denote unweighted
sample sizes. Regression results are from the second equation of the recursive equation models; control variables in the second include age, the quadratic term
of age, gender, parental education, labor force participation, and occupational type. Educational attainment denotes the difference between up to upper
secondary education versus postsecondary education up to bachelor degree. Work experience denotes an additional year of paid work experience during the
i di idual s lifeti e. *** i di ates sig ifi a t asso iatio at the alpha < . le el, ** at the alpha < . le el, a d * at the alpha < 0.05 level, and + at the alpha
< 0.1 level. Data: PIAAC 2012.
Skill trade-off between
educational attainment & work
Regression results from a nationally representative sample of adults 20-65 who had at least one year of work experience. Sample sizes denote unweighted
sample sizes. Regression results are from the second equation of the recursive equation models; control variables in the second include age, the quadratic term
of age, gender, parental education, labor force participation, and occupational type. Magnitude of skill trade-off is calculated by dividing βBachelor and βMaster with
βWorkExp. Data: PIAAC 2012.
Skill domain Country Skill
trade-off -
Bachelor
Skill
trade-off -
Master
Literacy
CZ (n=4632) 18 27
PL (n=6169)
SK (n=4581) 7 13
Numeracy
CZ (n=4632) 10 15
PL (n=6169) 7 10
SK (n=4581) 4 15
Problem-solving
CZ (n=3401) 4 10
PL (n=3596) 4 7
SK (n=2721) 4 8
Discussion
– Typical skill proficiency levels are the same in CE nations
– Higher education credential is negatively linked to work experience; relationship
varies across CE nations
– Both higher education credential and work experience positively linked to adult
skills
– Positive relationship between higher education and skills is more substantive
than positive relationship between work experience and skills
– Strength of positive association between higher education and skills varies
across CE nations
Implications for future research
and policy
– Higher education and skill: Causal and selection mechanisms
– Variation in skill predictors: Role of contextual forces
– Societal relevance of postsecondary education: Higher education credential a
strong predictor of adult skill proficiency
References
Green, F. (2013). Skills and skilled work: An economic and social analysis (1st ed.). Oxford: Oxford
University Press.
Mellander, E. (2014). The role of work experience for skills: Findings for the Nordic countries based on
the PIAAC survey. In A. Malin (Ed.), Associations between age and cognitive foundation skills in the
Nordic countries (pp. 131-170). Jyväskylä: University of Jyväskylä.
OECD (2013). OECD Skills Outlook 2013: First results from the survey of adult skills. Paris: Author.
OECD (2016). The Survey of Adult Skills (PIAAC) [Website]. Retrieved from
https://www.oecd.org/site/piaac/surveyofadultskills.htm
Appendix
Example of a literacy proficiency
item from PIAAC
– Item name: Library search
– Difficulty: Level 4
The test-taker is asked to identify a book suggesting that the claims made both for and against genetically modified foods are unreliable. He or she needs to read the title and the description of each book in each of the entries reporting the results of the bibliographic search in order to identify the correct book. Many pieces of distracting information are present. The information that the relevant book suggests that the claims for and against genetically modified foods are unreliable must be inferred from the state e t that the author des ri es ho oth sides i this hotl o tested de ate ha e a ufa tured propaga da, tried to dupe the pu li a d…[te t e ds].
Source: OECD, 2013, p. 66
Proficiency by skill domains
Skill domain Proficiency at Level 3 (scores from 276 to less than 326 points)
Literacy Adults performing at Level 3 can understand and respond appropriately to dense or lengthy texts,
including continuous, non-continuous, mixed, or multiple pages. They understand text structures and
rhetorical devices and can identify, interpret, or evaluate one or more pieces of information and make
appropriate inferences. They can also perform multi-step operations and select relevant data from
competing information in order to identify and formulate responses.
Numeracy Adults at Level 3 can successfully complete tasks that require an understanding of mathematical
information that may be less explicit, embedded in contexts that are not always familiar, and
represented in more complex ways. They can perform tasks requiring several steps and that may involve
a choice of problem-solving strategies and relevant processes. They have a good sense of number and
space; can recognize and work with mathematical relationships, patterns, and proportions expressed in
verbal and numerical form; and can interpret and perform basic analyses of data and statistics in texts,
tables, and graphs.
Skill domain Proficiency at Level 1 (scores from 241 to less than 291 points)
Problem-solving At Level 1, adults can complete tasks in which the goal is explicitly states and for which the necessary
operations are performed in a single and familiar environment. They can solve problems whose solutions
involve a relatively small number of steps, the use of a restricted range of operators, and a limited
amount of monitoring across a large number of actions.
Source: OECD, 2013
Variation in adult skills across
selected Central European nations
Source: OECD (2013)
274 274
267
273
276 276
260
269
250
255
260
265
270
275
280
Czech Republic Slovak Republic Poland OECD average
Literacy skill (mean) Numeracy skill (mean)
Variation in skills of tertiary-educated adults
across selected Central European nations
Source: OECD (2013)
301
295
297 297
310
305
290
297
280
285
290
295
300
305
310
315
Czech Republic Slovak Republic Poland OECD average
Literacy skill (mean) Numeracy skill (mean)
Research Questions
1. How do adult skills vary across selected Central European nations?
2. What are the relationships between educational attainment, work experience,
and adult skills in the selected Central European nations, after controlling for
differences in individual background characteristics and labor market
experiences?
3. How do the relationships between educational attainment, work experience,
and adult skills vary across the selected Central European nations?