DOCUMENT DE TREBALL
XREAP2017-11

EFFICIENCY IN THE TRANSFORMATION OF
SCHOOLING INTO COMPETENCES: A
CROSS-COUNTRY ANALYSIS USING PIAAC
DATA
Inés P. Murillo
José L. Raymond (GEAP, XREAP)
Jorge Calero (IEB, XREAP)

Efficiency in the transformation of schooling into competences:
A cross-country analysis using PIAAC data

Inés P. Murillo (Universidad de Extremadura: ihuertas@unex.es), José L. Raymond (Universidad
Autónoma de Barcelona & IEB: josep.raymond@uab.edu) and Jorge Calero (Universidad de
Barcelona & IEB: jorge.calero@ub.edu)

Abstract

This study (i) compares the competence levels of the adult population in a set of OECD
countries; (ii) assesses the comparative efficiency with which the education system in each
country transforms schooling into competences, distinguishing by educational level, and (iii)
tracks the evolution of this efficiency by birth cohorts. Using PIAAC data, the paper applies
standard parametric frontier techniques under two alternative specifications. The results
obtained under both specifications are similar and identify Finland, Sweden, Denmark and
Japan as being the most efficient and Spain, the United Kingdom, Italy, Ireland and Poland as
the least efficient. The evolution of the efficiency levels by age cohorts shows that higher
education is more efficient for younger cohorts, while lower and upper secondary education
present a stable trend over cohorts.
Key words: adult population competences; efficiency; PIAAC; parametric frontier techniques.
JEL codes: I21, C13.

1. Introduction
The consideration of human capital as a key factor both in the economic growth of countries
and in the labor outcomes of individuals represents a long-standing tradition in the literature.
Similarly, the limitations researchers face as they seek to measure this human capital –
typically by resorting to the number of years of schooling (or, alternatively, the level of
education attained) on the basis of Mincer’s (1970; 1974) proposal – have been well
documented. More recently, various studies have recommended considering the cognitive
skills or competences acquired by individuals – as well as the number of years of schooling –
when measuring human capital. Borghans et al. (2001) discuss the advantages of such an
approach, stressing that the level of education achieved by an individual is an imperfect
indicator of their human capital at any one point in time. Indeed, several studies provide
empirical support for such arguments and show that cognitive competences can account for a
large part of a country’s growth in productivity (Hanushek and Kimko 2000; Barro 2001;
Hanushek and Woessmann 2008) and for a part of an individual’s labor achievements that

1

cannot be explained by their educational attainments (McIntosh and Vignoles 2001; Green,
and Riddell 2003).
If, therefore, we assume that an individual’s skills are defined not only by the quantity
of education they have received (measured in terms of the number of years of schooling), but
also by the quality of that education (measured in terms of the cognitive competences
acquired), it is of great interest to researchers to (i) determine which factors account for the
acquisition of competences throughout an individual’s life cycle and (ii) identify the greater
performance that some individuals derive from their schooling in terms of competences than is
obtained by others. The first of these issues has been broadly analyzed by estimating
education production functions (Hanushek 1979; 1997). It has been concluded that not only
the number of years of formal education received but other relevant variables, including an
individual’s personal characteristics and his/her socio-economic environment, can determine
the acquisition of cognitive competences (Björklund and Salvanes 2011; Mazzona 2014). When
estimating education production functions, however, it is assumed that all the units included in
the sample obtain the same benefit from each of the explanatory variables considered. In
international comparisons, this means, for example, assuming that an additional year of
schooling in two countries with different institutional environments – and, more specifically,
with different education systems – is equally effective, on average, in translating higher levels
of schooling into competences for their populations. In order to refine this assumption, we
need to determine whether the efficiency in the transformation of the number of years of
schooling received into competences varies by country. The estimation of production frontiers
is useful for this purpose since it indicates, for a given reference unit, the distance from that
unit to the frontier, estimated using the most efficient units in the sample. For a given set of
countries, this technique would provide a sorting of countries as a function of their distance
from the frontier, or what is the same, as a function of the efficiency with which their
education systems transform an additional year of schooling into competences 1.
The importance attached to the analysis of efficiency in education has grown notably
in recent years (see De Witte and López-Torres 2015, for an exhaustive review of the
literature). The bulk of the work in this regard has focused on estimating the efficiency of
different units (districts, schools or students) operating within the same country, with far
fewer studies comparing the efficiency of education systems across countries. However,
among the latter, the most relevant draw on information provided by the OECD’s PISA
program as they compare from different perspectives the efficiency with which the education
systems of different countries operate. For example, Afonso and Aubyn (2005; 2006) and
Sutherland et al. (2009) analyze the efficiency of public spending on education for a group of
OECD countries, and emphasize the role played by the institutions of each country in
accounting for the disparity in the results reported. The influential role played by a country’s
institutions is similarly stressed by De Jorge and Santín (2010), who, like Deutsch et al. (2013),
consider an analysis of efficiency at the student level as the best approach to optimize the use
of available information. Agasisti and Zoido (2015) assess efficiency at both the national and
school level for a broad set of OECD countries. They document a notable heterogeneity both
1

A review of papers using parametric boundary techniques to analyze various issues related to education can be
found in Worthington (2001).

2

between and within countries in terms of the degree of efficiency achieved by their respective
education systems and schools. Giambona et al. (2011), in contrast, focus on the role played by
the students’ socio-economic characteristics in the determination of their competences. The
authors assess the efficiency of the education systems of several EU countries with particular
regard to their ability to help students from a poor family background achieve optimal
development of their cognitive competences. The importance of the socio-economic
environment is similarly stressed in Thieme et al. (2012). The authors compare the efficiency of
a broad set of countries taking into account not only the results obtained by the students but
also the degree of dispersion in the distribution of those results as an indicator of the equity of
the system. Other studies use several waves of cross-sectional data in order to evaluate the
evolution of a given output over time. This is the case of Agasisti (2014) when comparing the
efficiency of public expenditure on education in twenty European countries between 2006 and
2009. In a similar vein, Giménez et al. (2017) examine student progress in terms of
competences between 2003 and 2009, as they assess the extent to which their progress can be
accounted for by the availability of better resources and/or the enhanced efficiency of their
respective education systems. Other databases that have been used to evaluate the efficiency
of education systems in an international setting include the Third International Mathematics
and Science Study (TIMSS) – see Clements (2002) and Giménez et al. (2007); and the Progress
in International Reading Literacy Study (PIRLS) – see Cordero et al. (2017).
The aforementioned papers adopt different methodologies (mainly non-parametric,
but also semi-parametric and parametric) to calculate the efficiency with which different
inputs are combined (at the country, school and/or student levels) in the production of various
outputs related to student competences. However, despite this multiplicity of tools and
results, they share a common limitation derived from their use of cross-sectional data that
refer to individuals belonging to the same birth cohort. This means that we can only evaluate
the efficiency of the education system for a given academic year (as in the case of TIMSS or
PIRLS) or for a specific age (as in the case of PISA). In contrast, to the best of our knowledge,
this paper is among the first that seeks to undertake an efficiency analysis for the education
system as a whole, distinguishing by country and by level of education 2. This is possible as we
draw on data from the Program for the International Assessment of Adult Competencies
(PIAAC), a survey conducted by the OECD among individuals aged 16-65 that have received a
varied number of years of schooling. By estimating standard stochastic frontier functions, our
objectives are as follows: (i) to compare the competence levels of the adult population in a set
of OECD countries; (ii) to assess the comparative efficiency with which the education system in
each country transforms schooling into competences, distinguishing by educational level, and
(iii) to track the evolution of this efficiency by birth cohorts.

2

Gupta and Verhoeven (2001) use information on adult population competences to make international
comparisons of efficiency indicators. However, the aim of their study is not to evaluate the efficiency with which
schooling is translated into competences, as is the case in our paper, but rather to compare the efficiency with
which public expenditure on education and health improve a series of social development indicators, for some
thirty African countries. For the specific case of education, the outputs assessed are school attendance rates in
primary and secondary education and adult population competences.

3

The rest of the paper is structured in four sections: sections 2 and 3 outline the
methodology and the database used, section 4 reviews the main results obtained and, finally,
section 5 presents the study’s main conclusions.

2. Methodology
Here we propose an education production function and employ standard stochastic
frontier techniques to calculate the distance from each country to the frontier. In this way, a
classification of the countries is obtained as a function of the (in)efficiency with which they
transform schooling into competences.
The education production function can be expressed as follows:

YiJ= β ′ X iJ + wiJ= β ′ X iJ + ( viJ − uiJ )
E [uiJ / wiJ = δ iJ= hi + θ J
]

(1)

in which the competences of individual “i” living in country “J” are accounted for by the
variables included in “X iJ ” plus a term of inefficiency or of distance with respect to the frontier,
“ uiJ ”. The expected value of this distance from the frontier, for individual “i”, is given by
“ δ iJ ”, which is the result of the standard calculation of frontier distances when using stochastic
frontiers.
The distance to the frontier for individual “i” living in country “J” has two components:
the individual component “ hi ”, which gathers the innate ability of individual “i”, and “ θ J ”, a
component of the country that includes the average efficiency with which the country’s
education system transforms schooling into competences. When calculating the average of the
individuals living in country “J”, we obtain:
M

∑δ

M

∑h

iJ
= 1= 1
i
i

M

=

M

i

+ θJ → θJ

(2)

In other words, for individuals from country “J”, insofar as the innate ability of the individuals
within the same country tends to be compensated for, the average of the individual distances
to the frontier will come closest to the average distance from the component country’s
derived frontier, which may represent a way to approach the efficiency of that country’s
education system.
The functional specification for the education production function suggests that using
a linear, as opposed to a semi-logarithmic model, provides the best fit for the available data.
Moreover, it appears that age and experience – two of the explanatory variables included in
the model – have a free effect on the competences when creating dummy specific variables for
age (i.e. a dummy for each age in years) and experience (i.e. a dummy for each experience in
4

years), compared to a more standard specification that suggests a linear effect for age and a
quadratic effect for experience. We estimate both options with the available data and
conclude that the latter gives the better outcomes (see Annex 1).
Standard stochastic frontier techniques are applied to Equation 1 under two different
specifications. In the first, the influence of the explanatory variables is accounted for, which
means the equation is estimated using the standard frontier function technique and that the
estimated coefficients are common to all the countries considered. In the second, the frontier
functions methodology is adapted so as to allow the coefficients (other than formal education)
that affect the transformation of inputs into competences (including, for example, number of
years of experience or type of occupation) to vary from country to country. This approach,
which can be consulted in Annex 2, means we can isolate more precisely the (in)efficiency of
the formal education system in transforming years of education received into competences.
This said, both approaches in fact give very similar results.

3. Data
The data used in the present paper are drawn from the first wave of the PIAAC
(corresponding to 2012), an OECD initiative aimed at assessing the competences of the
population aged 16-65. This database follows in the wake of others that have measured the
competences of the adult population (including IALS and ALL), although the number of
participating countries is in this case greater and the competences evaluated refer not only to
language skills, but also to mathematical skills and the use of new technologies. All these
competences are measured using specific tests, the results of which are presented in terms of
plausible values (ten for each skill). These plausible values indicate the performance of each
individual on a scale of 0 to 500 points and are grouped into six levels. The survey, designed to
facilitate a comparative analysis of the participating countries, also offers harmonized
information on the use of the competences assessed in the workplace and in daily life; on the
socio-demographic characteristics of the individuals surveyed (e.g. gender, age, nationality,
level of education of parents); and on their training and job characteristics (e.g. education
level, work experience throughout their working life, work situation: employed, inactive,
unemployed, salary and other characteristics of the job: type of contract and working day,
performance of supervision tasks, and even variables that allow for the identification of
eventual educational or skill mismatches).
We have excluded from the sample those countries that give rise to any kind of
concern regarding the reliability of the data they provide and those which fail to provide
information on some of the variables considered in our study. Our model’s dependent variable
is numeracy competences rescaled to 1000 so as to facilitate the interpretation of the results 3.
The explanatory variables provide information about age, number of years of schooling, work
experience (in quadratic terms), gender, first or second generation immigrant status, (the

All of the study’s estimations have been replicated using literacy skills as the dependent variable. The
results obtained (available upon request) are, to a large extent, quantitatively and qualitatively similar to
those presented here for numeracy.
3

5

absence of) coincidence between the mother tongue and the language in which the survey is
carried out, the level of studies of the parents, type of occupation and possible attendance on
non-regulated training courses.
Table 1 presents the descriptive statistics for the variables in the overall sample
(excluding observations without information regarding any of the variables considered in the
analysis, which limits the sample to around 79,000 observations). The average value of the
numeracy competence is c. 542 points, with a marked standard deviation of around 96 points.
The average number of years of schooling stands at 12.73 for individuals whose average age is
40 years old and who have an average work experience of 18.21 years. The proportion of first
generation immigrants is 7.9% (falling to 1.7% for second generation immigrants), most
individuals (92%) respond to the survey in their mother tongue and 38% (22%) have at least
one ascendant with post-compulsory (higher) secondary education. Roughly two-thirds of the
individuals in the sample work in a skilled occupation and, finally, around 40% reported
participating on non-regulated training courses in the 12 months prior to the survey.
Table 1. Descriptive statistics
Variable

Average

Standard dev.

Min

Max

Mathematics Comp.

542.0646

96.1126

49.6917

888.2642

Schooling

12.7323

3.0259

3.0000

22.0000

Age

39.9555

14.4749

16.0000

65.0000

Experience

18.2143

13.1439

0.0000

55.0000

Man

0.4783

0.4995

0.0000

1.0000

Immigrant 1st gen.

0.0796

0.2707

0.0000

1.0000

Immigrant 2nd gen.

0.0173

0.1304

0.0000

1.0000

Mother tongue

0.9233

0.2660

0.0000

1.0000

Parents higher secondary ed.

0.3815

0.4858

0.0000

1.0000

Parents higher ed.

0.2232

0.4164

0.0000

1.0000

Qualified occupation

0.6122

0.4873

0.0000

1.0000

Non-regulated training

0.3919

0.4882

0.0000

1.0000

Table 2 shows the average competences by country, with values ranging from 491 for
Spain to 576 points for Japan. Table 3 ranks the countries by competences, with Japan and the
Nordic countries heading the classification and Ireland, Spain and Italy finding themselves at
the bottom of the ranking.

6

Table 2. Average competences by country

Table 3. Ranking of countries by competences

Average Competences

Country

Belgium

560.7724

Japan

Czech. Rep

551.4677

Finland

Denmark

556.5568

Belgium

Estonia

546.239

Holland

Finland

564.4532

Sweden

Ireland

511.1808

Norway

Italy

494.2578

Denmark

Japan

576.3407

Slovak Rep.

Korea

526.7724

Czech Rep.

Holland

560.6922

Estonia

Norway

556.5957

Korea

Poland

519.5378

United Kingdom

Slovak Rep.

551.6152

Poland

Spain

491.6435

Ireland

Sweden

558.1049

Italy

United
Kingdom

523.4517

Spain

Country

Finally, and given that throughout this study the efficiency indices are estimated
distinguishing by level of education, Graph 1 presents average numeracy scores for each level
of education contemplated. Note that the rankings of countries according to their average
competences per level of study (see Table 4) present considerable similarities to those
obtained as a function of the efficiency indices (see Graphs 2 and 3).

7

Graph 1. Average competences by country and level of studies

Numeracy
600
550
500
450
400

Lower secondary

Higher secondary

Higher education

Table 4. Classification of countries as a function of their competence level, by level of studies
Lower Secondary

Higher Secondary

Higher education

Finland

Holland

Belgium

Japan

Japan

Holland

Norway

Sweden

Sweden

Czech Rep.

Slovak Rep.

Japan

Holland

Denmark

Slovak Rep.

Estonia

Finland

Finland

Denmark

Norway

Norway

Sweden

Belgium

Denmark

Belgium

Czech Rep.

Estonia

Slovak Republic

Estonia

Poland

Poland

Italia

United Kingdom

Korea

Korea

Korea

Italia

United Kingdom

Ireland

United Kingdom

Spain

Italy

Ireland

Ireland

Spain

Spain

Poland

Belgium

4. Results
Graph set 2 shows the results of the estimation of the efficiency indices for
specification 1 (see methodology, section 2), in which the influence of the explanatory
variables is taken into account. Graph set 3 corresponds to specification 2, which also
incorporates a frontier function but in which the coefficients (with the exception of formal
education) that affect the transformation of inputs into outputs are allowed to vary from
8

country to country 4. In each case, the results are broken down into the three educational
levels completed by the individuals: up to lower secondary; higher secondary, and higher
education. Note that the results obtained from the two specifications are largely similar, with
only minor differences.
Focusing on Graph set 2, similar patterns are found for the three levels of education
(Graphs 2a, 2b and 2c). The efficiency in the transformation of the number of years of
schooling into competences is greatest in three of the Nordic countries analyzed (Finland,
Sweden and Denmark), Japan and Belgium. In contrast, the lowest levels of efficiency are
recorded in Spain, Italy, Ireland, Poland, Korea and the United Kingdom. This pattern is
repeated with only minor differences across the three levels of education: the order of the
countries is largely similar, with some notable differences, (for example, in the case of higher
education Italy presents an especially low level of efficiency and Poland presents a slightly
higher level of efficiency).
Graph set 3 (Graphs 3a, 3b and 3c) presents the efficiency indices using specification 2
(in which the coefficients that affect the transformation of inputs into outputs vary from
country to country). As in Graph set 2, the Nordic countries present the highest rates of
efficiency, these indices being slightly higher than those reported for specification 1. Japan and
Belgium present very similar levels of efficiency to those obtained with specification 1, but
they fall in the overall ranking of countries by rates of efficiency. The United Kingdom and Italy
present the lowest levels of efficiency, while Spain, Ireland and Poland present indices that are
similarly low for both specifications. Here the differences in the efficiency indices between the
three levels of education (which are small in the case of specification 1) are even smaller. All in
all, the positions occupied by the countries in the rankings are very similar across the three
levels of education.
Graph set 4 tracks the evolution of the efficiency levels over the different age cohorts
for the three levels of education considered. In the case of higher education, it can be seen
that in most of the countries considered the levels of efficiency in generating competences are
higher among the younger cohorts. This increase in the index is most significant in Spain and
Italy, but is also appreciable in the Nordic countries (with the exception of Denmark), Belgium,
Holland and Korea. In the cases of the United Kingdom and Ireland, the increase is less
pronounced. However, there are hardly any changes in the levels of efficiency in the remaining
countries: Japan, Denmark and the four Eastern European countries considered (i.e. Czech
Republic, Estonia, Poland and the Slovak Republic).
In the case of higher secondary education, the pattern presented is one of general
stability across all the cohorts. The only deviations from this trend are recorded in the cases of
Italy and the United Kingdom, where there has been a fall in efficiency among the youngest
cohorts, and in that of Finland, where there has been an increase in efficiency.
Likewise, in the case of lower secondary education, efficiency levels in most countries
remain stable across all the cohorts. There are exceptions to this general pattern. For example,
Table A.3.1 of Annex 3 gathers the numerical indices calculated according to specification 1, and Table
A.3.2 the numerical indices according to specification 2.

4

9

in Spain and Korea efficiency levels have increased among younger cohorts, whereas in Italy
and the United Kingdom there has been a fall in efficiency levels for these same cohorts. In the
Eastern European countries, the pattern of stability is interrupted in the cohort aged between
46 and 55 (26-45 in Slovakia) with marked declines in efficiency, associated in all probability
with the historic evolution of the education systems in these countries

5. Conclusions
The aim of this study has been to compare the degree of efficiency with which the
OECD countries produce competences from the schooling provided and from other inputs and,
also, to monitor how this efficiency has evolved over different age cohorts. To do so, we have
estimated standard stochastic frontier functions applied to OECD data from the PIAAC. In
order to estimate this frontier we used two specifications so as to verify the robustness of our
results. In the first specification, the influence of the explanatory variables has been taken into
account and a function was estimated whose coefficients are common to all of the countries
considered; in the second, the frontier functions methodology has been generalized to allow
the coefficients (other than formal education) that affect the transformation of inputs into
competences (including, years of experience and type of occupation) to vary from country to
country.
The levels of efficiency reported by the analyses were similar for both specifications.
Furthermore, the results by level of education show that in most cases the efficiency indices
are similar for all three levels of education. However, efficiency in the transformation of
schooling into competences is greatest in Finland, Sweden, Denmark, Japan and Belgium, while
the lowest levels of efficiency are to be found in Spain, the United Kingdom, Italy, Ireland and
Poland.
Finally, as regards the evolution in the levels of efficiency associated with different age
cohorts, we found that in the case of higher education, levels are higher among younger
cohorts, whereas in the cases of lower and upper secondary education, the general pattern,
albeit with some exceptions, is one of stability for all the cohorts considered.

10

Graph set 2. Efficiency indices for competence in mathematics. Specification 1.
Graph 2.a. Lower secondary
100
95
90
85
80
75
70

Graph 2.b. Higher secondary
100
95
90
85
80
75
70

Graph 2.c. Higher education
100
95
90
85
80
75
70

11

Graph set 3. Efficiency indices for competences in mathematics. Specification 2.
Graph 3.a. Lower secondary
100
95
90
85
80
75
70

Graph 3.b. Higher secondary
100
95
90
85
80
75
70

Graph 3.c. Higher education
100
95
90
85
80
75
70

12

Graph set 4. Efficiency indices for competence in mathematics according to level of education and cohort
Continental countries

Belgium

Holland

100

100

95

95

90

90

85

85

80

16-25

26-45

46-55

56-65

80

16-25

26-45

46-55

56-65

Mediterranean countries

Italy

Spain

100

100

95

95

90

90

85

85

80

16-25

26-45

46-55

56-65

80

16-25

26-45

46-55

56-65

13

Nordic countries

Denmark

Finland

100

100

95

95

90

90

85

85

80

16-25

26-45

46-55

56-65

80

16-25

26-45

46-55

56-65

46-55

56-65

Sweden

Norway
100

100

95

95

90

90

85

85

80

16-25

26-45

46-55

56-65

80

16-25

26-45

14

Eastern European countries

Estonia

Poland

100

100

95

95

90

90

85

85

80

16-25

26-45

46-55

56-65

80

16-25

Czech Republic

26-45

46-55

56-65

Slovak Republic

100

100

95

95

90

90

85

85

80

80

16-25

26-45

46-55

56-65

16-25

26-45

46-55

56-65

15

Anglo-Saxon countries

United Kingdom

Ireland
100

100

95

95

90

90

85

85

80

16-25

26-45

46-55

56-65

80

16-25

26-45

46-55

56-65

46-55

56-65

Asian countries

Corea

Japan

100

100

95

95

90

90

85

85

80

16-25

26-45

46-55

56-65

80

16-25

26-45

16

References
Afonso, A. and Aubyn, M. S. (2005): Non-parametric approaches to education and health
efficiency in OECD countries, Journal of Applied Economics, 8 (2): 227-246.
Afonso, A. and Aubyn, M. S. (2006): Cross-country efficiency of secondary education provision:
A semi-parametric analysis with non-discretionary inputs, Economic Modelling, 23: 476-491.
Agasisti, T. (2014): The efficiency of public spending on education: an international comparison
of EU countries, European Journal of Education, 49 (9): 543-557.
Agasisti, T. and Zoido, P. (2015): The efficiency of secondary schools in an international
perspective: preliminary results from PISA 2012, OECD Education Working Papers, 117, OECD,
Paris.
Barro, R. J. (2001): Human Capital and Growth, American Economic Review, 91: 12-17.
Björklund, A. and Salvanes, K.G. (2011): Education and family background: mechanisms and
policies, in Hanushek, E.A., Machin, S. and Woessmann, L. (Eds.): Handbook of the Economics
of Education, North-Holland, Amsterdam.
Borghans, L.; Green, F. and Mayhew, K. (2001): Skills Measurement and Economic Analysis: An
Introduction, Oxford Economics Papers, 53: 375-384.
Clements, B. (2002): How efficient is education spending in Europe?, European Review of
Economic Finance, 1 (1): 3-26.
Cordero, J.M., Santín, D. and Simancas, R. (forthcoming): Assessing European primary school
performance through a conditional nonparametric model, Journal of the Operational Research
Society.
Deutsch, J., Dumas, A. and Silber, J. (2013): Estimating an educational production function for
five countries of Latin America on the basis of PISA data, Economics of Education Review, 36:
245-262.
De Jorge, J. and Santín, D. (2010): Determinantes de la eficiencia educativa en la Unión
Europea, Hacienda Pública Española, 193 (2), 131-156.
De Witte, K. and López-Torres, L. (2015): Efficiency in education: A review of literature and a
way forward, Journal of the Operational Research Society, doi:10.1057/jors.2015.92.
Giambona, F., Vassallo, E. and Vassiliadis, E. (2011): Educational systems efficiency in
European Union countries, Studies in Educational Evaluation, 37: 108-122.
Giménez, V., Prior, D. and Thieme, C. (2007): Technical efficiency, managerial efficiency
and objective-setting in the educational system: An international comparison, Journal of
the Operational Research Society, 58: 996-1007.
Giménez, V., Thieme, C., Prior, D. and Tortosa-Ausina, E. (2017): An international
comparison of educational systems: a temporal analysis in presence of bad outputs,
Journal of Productivity Analysis, 47 (1): 83-101.
Green, D. and Riddell, W. C. (2003): Literacy and Earnings: An Investigation of the Interaction
of Cognitive and Unobserved Skills in Earnings Generation, Labour Economics, 10: 165-184.

17

Gupta, S. and Verhoeven, M. (2001): The efficiency of government expenditure. Experiences
from Africa, Journal of Policy Modeling, 23: 433-467.
Hanushek, E.A. (1997): Assessing the effects of school resources on student performance: an
update, Educational Evaluation and Policy Analysis, 19(2), 141-164.
Hanushek, E.A. (1979): Conceptual and empirical issues in the estimation of educational
production functions, Journal of Human Resources, 14 (3), 351-388.
Hanushek, E. and Kimko, D. (2000): Schooling, Labor Force Quality, and Economic Growth,
American Economic Review, 90: 1184-1208.
Hanushek, E. and Woessmann, L. (2008): The Role of Cognitive Skills in Economic
Development, Journal of Economic Literature, 46: 607-668.
Mazzona, F. (2014): The long-lasting effects of family background: a European cross-country
comparison, Economics of Education Review, 40, 25-42.
McIntosh, S. and Vignoles, A. (2001): Measuring and Assessing the Impact of Basic Skills on
Labour Market Outcomes, Oxford Economic Papers, 53: 453-481.
Mincer, J. (1970): The distribution of labor incomes: A survey with special reference to the
human capital approach, Journal of Economic Literature, 8 (1): 1-26.
Mincer, J. (1974): Schooling, experience and earnings, Columbia University Press, New York.
Sutherland, D., Price, R. and Gonand, F. (2009): Improving public spending efficiency in primary
and secondary education, OECD Economic Studies, 4: 1-30.
Thieme, C., Giménez, V. and Prior, D. (2012): A comparative analysis of the efficiency of
national education systems, Asian Pacific Education Review, 13 (1): 1-15.
Worthington, A.C. (2001): An empirical survey of frontier efficiency measurement techniques
in education, Education Economics, 9(3): 245-268.

18

ANNEX 1. Selection of the functional form of age and experience in education production
Table A.1.1. Free Effect of age and experience
Variables
Schooling
Age dummies: see Graph 1.A
Experience dummies: see Graph 2.A
Woman

Immigrant 1st gen.

Immigrant 2nd gen.

Mother tongue

Parents basic ed.

Parents secondary ed.

Unqualified occupation

Without non-regulated training

Constant

Schwarz Statistic
Observations

Estimated coefficients
9.93***
87.65

-21.31***
-39.57
-33.73***
-23.93
-7.42***
-3.65
-19.92***
-13.64
-30.98***
-40.98
-15.13***
-22.42
-27.94***
-43.79
-12.84***
-22.56
585.52***
155.09
907087.7
78,825

19

Graph 1.A. Effect of age

20
0
-20
-40
-60
-80
-100 1 3 5 7 9 1113151719212325272931333537394143454749
Age
Graph 2.A. Effect of experience

40
30
20
10
0
-10
-20

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45
Experience

Table A.1.2. Linear effect of age and quadratic effect of experience
Variables
Schooling
Age
Experience
Squared experience

Estimated coefficients
9.70***
87.39
-1.50***
-31.94
1.85***
20.42
-0.02***
-11.88

Woman

-21.23***

Immigrant 1st gen.

-34.24***

Immigrant 2nd gen.

-7.51***

Mother tongue

-39.38
-24.25
-3.68
-20.05***
-13.69

20

Parents basic ed.

-31.87***

Parents secondary ed.

-15.82***

Unqualified occupation

-28.31***

Without non-regulated
training

-12.33***

Constant

590.32***

Schwarz Statistic
Observations

-42.28
-23.44
-44.37

-21.78
275.42
906532.7
78,825

ANNEX 2. A proposal to generalize the frontier production function (Approach 2)
The starting point is the competence production functions at the country level:

′
µ
(1) YiJ =J + β J X iJ + α J SiJ + wiJ
where “i” is the individual and “J” the country. X iJ are the characteristics of the individual and

SiJ are the number of years of education. From this, we obtain:
′
(2) YiJ − β J X iJ =J + α J SiJ + wiJ
µ

µ
(3) YiJ * =J + α J SiJ + wiJ
If “ Yij * ” of equation (3) were directly observable, this equation could be estimated using the
standard frontier function technique and assuming a common
proposal is:

α J . As this is not the case, the

a) Estimate (1) by OLS for the different countries. This enables us to obtain a consistent
estimation of the “ β ” coefficients.

ˆ
ˆ=
b) From this consistent estimation of “ β ”, we obtain an estimation of Yij* Yij − β ′ X ij .
j

ˆ
This variable “ Yij* ” is an estimation of the competences of individual “i” living in
country “J” after excluding the effects of experience, age, sex, and all the other
variables on the competences acquired.

ˆ*
c) Given that “ YiJ ” is the net of the contribution of the remaining variables of education,
a frontier function can be estimated for this variable using the number of years of
schooling as the only explanatory variable. Following this approach, the efficiency term
estimated from this frontier will also refer uniquely to the years of schooling.

21

ANNEX 3. Efficiency indices calculated according to specifications 1 and 2
Table A.3.1. Efficiency indices by levels of study. Specification 1
Lower
Secondary

Higher
Secondary

Higher
education

Belgium

87.02281

88.66001

91.17441

Czech Republic

85.46297

88.39928

90.40039

Denmark

86.66442

89.10681

89.91032

Estonia

86.17697

88.31044

88.65252

Finland

88.54883

89.32155

90.65049

Ireland

82.19496

85.06034

86.77006

Italy

84.83895

87.80328

85.25765

Japan

86.71206

90.28149

90.83987

Korea

83.38552

87.29479

87.14777

Netherlands

86.68916

90.00864

90.84135

Norway

85.86949

88.17571

89.82951

Poland

83.86766

85.2742

87.23417

Slovak Republic

85.85493

89.64773

89.68038

Spain

82.65196

86.29001

86.30075

Sweden

86.9926

89.72025

91.01138

United Kingdom

83.01533

86.13918

87.20603

Table A.3.2. Efficiency indices by levels of study. Specification 2
Lower
Secondary

Higher
Secondary

Higher
education

87.359

89.06649

91.17212

84.65757

88.20512

90.12897

87.862

90.36086

90.93721

Estonia

86.20949

88.62979

88.8939

Finland

90.68295

91.31189

92.48883

Ireland

81.61584

85.55253

86.69101

Italy

82.47282

86.56282

84.66655

Japan

85.05509

89.02824

89.74224

Korea

85.04414

88.91232

89.33719

Netherlands

87.77496

91.26699

91.95099

Norway

87.44143

89.83485

90.65718

Poland

83.49704

85.52269

87.78531

Belgium
Czech Republic
Denmark

Slovak Republic

85.24813

89.32839

89.73741

Spain

82.47431

86.91864

87.1491

Sweden

88.99646

91.47664

92.45048

United Kingdom

82.14009

85.82652

86.2135

22

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2006
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Raymond, J. Ll. (GEAP); Roig, J. Ll. (GEAP)
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Álvarez-Albelo, C. (CREB); Hernández-Martín, R.
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Duch, N. (IEB); Montolio, D. (IEB); Mediavilla, M.
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“Innovation sources and productivity: a quantile regression analysis”
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“Shifting death to their Alternatives: The case of Toll Motorways”
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Segarra-Blasco, A. (GRIT); Garcia-Quevedo, J. (IEB); Teruel-Carrizosa, M. (GRIT)
“Barriers to innovation and public policy in catalonia”
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Bel, G. (PPRE-IREA); Foote, J.
“Comparison of recent toll road concession transactions in the United States and France”
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Segarra-Blasco, A. (GRIT);
“Innovation, R&D spillovers and productivity: the role of knowledge-intensive services”
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Bermúdez Morata, Ll. (RFA-IREA); Guillén Estany, M. (RFA-IREA), Solé Auró, A. (RFA-IREA)
“Impacto de la inmigración sobre la esperanza de vida en salud y en discapacidad de la población española”
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Calaeys, P. (AQR-IREA); Ramos, R. (AQR-IREA), Suriñach, J. (AQR-IREA)
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Sánchez Hugalbe, A. (IEB)
“Influencia de la inmigración en la elección escolar”
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“The Causal Relationship between Individual’s Choice Behavior and Self-Reported Satisfaction: the Case of Residential Mobility in the EU”
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Matas, A. (GEAP); Raymond, J. L. (GEAP); Roig, J. L. (GEAP)
“Car ownership and access to jobs in Spain”
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Bel, G. (PPRE-IREA) ; Fageda, X. (PPRE-IREA)
“Privatization and competition in the delivery of local services: An empirical examination of the dual market hypothesis”
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Deconstructing Shocks and Persistence in OECD Real Exchange Rates
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Basher, S. A.; Carrión, J. Ll. (AQR-IREA)
Price level convergence, purchasing power parity and multiple structural breaks: An application to US cities
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A priori ratemaking using bivariate poisson regression models
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Solé-Ollé, A. (IEB), Hortas Rico, M. (IEB)
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Duch-Brown, N. (IEB), García-Quevedo, J. (IEB), Montolio, D. (IEB)
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Bilotkach, V., Fageda, X. (PPRE-IREA), Flores-Fillol, R.
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Albalate, D. (PPRE-IREA), Gel, G. (PPRE-IREA)
Tourism and urban transport: Holding demand pressure under supply constraints
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Calonge, S. (CREB); Tejada, O.
“A theoretical and practical study on linear reforms of dual taxes”
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Albalate, D. (PPRE-IREA); Fernández-Villadangos, L. (PPRE-IREA)
“Exploring Determinants of Urban Motorcycle Accident Severity: The Case of Barcelona”
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Borrell, J. R. (PPRE-IREA); Fernández-Villadangos, L. (PPRE-IREA)
“Assessing excess profits from different entry regulations”
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Sanromá, E. (IEB); Ramos, R. (AQR-IREA), Simon, H.
“Los salarios de los inmigrantes en el mercado de trabajo español. ¿Importa el origen del capital humano?”
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Jiménez, J. L.; Perdiguero, J. (PPRE-IREA)
“(No)competition in the Spanish retailing gasoline market: a variance filter approach”
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Álvarez-Albelo,C. D. (CREB), Manresa, A. (CREB), Pigem-Vigo, M. (CREB)
“International trade as the sole engine of growth for an economy”
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Callejón, M. (PPRE-IREA), Ortún V, M.
“The Black Box of Business Dynamics”
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Domènech Campmajó, L. (PPRE-IREA)
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Álvarez-Albelo, C. D. (CREB), Hernández-Martín, R.
“The commons and anti-commons problems in the tourism economy”
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García-López, M. A. (GEAP)
“The Accessibility City. When Transport Infrastructure Matters in Urban Spatial Structure”
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García-Quevedo, J. (IEB), Mas-Verdú, F. (IEB), Polo-Otero, J. (IEB)
“Which firms want PhDs? The effect of the university-industry relationship on the PhD labour market”
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Pitt, D., Guillén, M. (RFA-IREA)
“An introduction to parametric and non-parametric models for bivariate positive insurance claim severity distributions”
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Bermúdez, Ll. (RFA-IREA), Karlis, D.
“Modelling dependence in a ratemaking procedure with multivariate Poisson regression models”
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Di Paolo, A. (IEB)
“Parental education and family characteristics: educational opportunities across cohorts in Italy and Spain”
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Simón, H. (IEB), Ramos, R. (AQR-IREA), Sanromá, E. (IEB)

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Di Paolo, A. (GEAP & IEB), Raymond, J. Ll. (GEAP & IEB)
“Language knowledge and earnings in Catalonia”
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Bolancé, C. (RFA-IREA), Alemany, R. (RFA-IREA), Guillén, M. (RFA-IREA)
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Di Paolo, A. (GEAP & IEB)
“Knowledge of catalan, public/private sector choice and earnings: Evidence from a double sample selection model”
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Coad, A., Segarra, A. (GRIT), Teruel, M. (GRIT)
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Di Paolo, A. (GEAP & IEB), Raymond, J. Ll. (GEAP & IEB), Calero, J. (IEB)
“Exploring educational mobility in Europe”
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Borrell, A. (GiM-IREA), Fernández-Villadangos, L. (GiM-IREA)
“Clustering or scattering: the underlying reason for regulating distance among retail outlets”
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Di Paolo, A. (GEAP & IEB)
“School composition effects in Spain”
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Fageda, X. (GiM-IREA), Flores-Fillol, R.
“Technology, Business Models and Network Structure in the Airline Industry”
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Albalate, D. (GiM-IREA), Bel, G. (GiM-IREA), Fageda, X. (GiM-IREA)
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Oppedisano, V., Turati, G.
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Canova, L., Vaglio, A.
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2011
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Fageda, X. (GiM-IREA), Perdiguero, J. (GiM-IREA)
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Moreno-Torres, I. (ACCO, CRES & GiM-IREA)
“What if there was a stronger pharmaceutical price competition in Spain? When regulation has a similar effect to collusion”
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Miguélez, E. (AQR-IREA); Gómez-Miguélez, I.
“Singling out individual inventors from patent data”
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Moreno-Torres, I. (ACCO, CRES & GiM-IREA)
“Generic drugs in Spain: price competition vs. moral hazard”
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Nieto, S. (AQR-IREA), Ramos, R. (AQR-IREA)
“¿Afecta la sobreeducación de los padres al rendimiento académico de sus hijos?”
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Pitt, D., Guillén, M. (RFA-IREA), Bolancé, C. (RFA-IREA)
“Estimation of Parametric and Nonparametric Models for Univariate Claim Severity Distributions - an approach using R”
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Guillén, M. (RFA-IREA), Comas-Herrera, A.
“How much risk is mitigated by LTC Insurance? A case study of the public system in Spain”
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Ayuso, M. (RFA-IREA), Guillén, M. (RFA-IREA), Bolancé, C. (RFA-IREA)
“Loss risk through fraud in car insurance”
(Juny 2011)
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Duch-Brown, N. (IEB), García-Quevedo, J. (IEB), Montolio, D. (IEB)
“The link between public support and private R&D effort: What is the optimal subsidy?”
(Juny 2011)
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Bermúdez, Ll. (RFA-IREA), Karlis, D.
“Mixture of bivariate Poisson regression models with an application to insurance”
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Varela-Irimia, X-L. (GRIT)
“Age effects, unobserved characteristics and hedonic price indexes: The Spanish car market in the 1990s”
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Bermúdez, Ll. (RFA-IREA), Ferri, A. (RFA-IREA), Guillén, M. (RFA-IREA)
“A correlation sensitivity analysis of non-life underwriting risk in solvency capital requirement estimation”
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Guillén, M. (RFA-IREA), Pérez-Marín, A. (RFA-IREA), Alcañiz, M. (RFA-IREA)
“A logistic regression approach to estimating customer profit loss due to lapses in insurance”
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Jiménez, J. L., Perdiguero, J. (GiM-IREA), García, C.
“Evaluation of subsidies programs to sell green cars: Impact on prices, quantities and efficiency”
(Octubre 2011)

SÈRIE DE DOCUMENTS DE TREBALL DE LA XREAP
XREAP2011-15
Arespa, M. (CREB)
“A New Open Economy Macroeconomic Model with Endogenous Portfolio Diversification and Firms Entry”
(Octubre 2011)
XREAP2011-16
Matas, A. (GEAP), Raymond, J. L. (GEAP), Roig, J.L. (GEAP)
“The impact of agglomeration effects and accessibility on wages”
(Novembre 2011)
XREAP2011-17
Segarra, A. (GRIT)
“R&D cooperation between Spanish firms and scientific partners: what is the role of tertiary education?”
(Novembre 2011)
XREAP2011-18
García-Pérez, J. I.; Hidalgo-Hidalgo, M.; Robles-Zurita, J. A.
“Does grade retention affect achievement? Some evidence from PISA”
(Novembre 2011)
XREAP2011-19
Arespa, M. (CREB)
“Macroeconomics of extensive margins: a simple model”
(Novembre 2011)
XREAP2011-20
García-Quevedo, J. (IEB), Pellegrino, G. (IEB), Vivarelli, M.
“The determinants of YICs’ R&D activity”
(Desembre 2011)
XREAP2011-21
González-Val, R. (IEB), Olmo, J.
“Growth in a Cross-Section of Cities: Location, Increasing Returns or Random Growth?”
(Desembre 2011)
XREAP2011-22
Gombau, V. (GRIT), Segarra, A. (GRIT)
“The Innovation and Imitation Dichotomy in Spanish firms: do absorptive capacity and the technological frontier matter?”
(Desembre 2011)
2012
XREAP2012-01
Borrell, J. R. (GiM-IREA), Jiménez, J. L., García, C.
“Evaluating Antitrust Leniency Programs”
(Gener 2012)
XREAP2012-02
Ferri, A. (RFA-IREA), Guillén, M. (RFA-IREA), Bermúdez, Ll. (RFA-IREA)
“Solvency capital estimation and risk measures”
(Gener 2012)
XREAP2012-03
Ferri, A. (RFA-IREA), Bermúdez, Ll. (RFA-IREA), Guillén, M. (RFA-IREA)
“How to use the standard model with own data”
(Febrer 2012)
XREAP2012-04
Perdiguero, J. (GiM-IREA), Borrell, J.R. (GiM-IREA)
“Driving competition in local gasoline markets”
(Març 2012)
XREAP2012-05
D’Amico, G., Guillen, M. (RFA-IREA), Manca, R.
“Discrete time Non-homogeneous Semi-Markov Processes applied to Models for Disability Insurance”
(Març 2012)

SÈRIE DE DOCUMENTS DE TREBALL DE LA XREAP
XREAP2012-06
Bové-Sans, M. A. (GRIT), Laguado-Ramírez, R.
“Quantitative analysis of image factors in a cultural heritage tourist destination”
(Abril 2012)
XREAP2012-07
Tello, C. (AQR-IREA), Ramos, R. (AQR-IREA), Artís, M. (AQR-IREA)
“Changes in wage structure in Mexico going beyond the mean: An analysis of differences in distribution, 1987-2008”
(Maig 2012)
XREAP2012-08
Jofre-Monseny, J. (IEB), Marín-López, R. (IEB), Viladecans-Marsal, E. (IEB)
“What underlies localization and urbanization economies? Evidence from the location of new firms”
(Maig 2012)
XREAP2012-09
Muñiz, I. (GEAP), Calatayud, D., Dobaño, R.
“Los límites de la compacidad urbana como instrumento a favor de la sostenibilidad. La hipótesis de la compensación en Barcelona medida a través
de la huella ecológica de la movilidad y la vivienda”
(Maig 2012)
XREAP2012-10
Arqué-Castells, P. (GEAP), Mohnen, P.
“Sunk costs, extensive R&D subsidies and permanent inducement effects”
(Maig 2012)
XREAP2012-11
Boj, E. (CREB), Delicado, P., Fortiana, J., Esteve, A., Caballé, A.
“Local Distance-Based Generalized Linear Models using the dbstats package for R”
(Maig 2012)
XREAP2012-12
Royuela, V. (AQR-IREA)
“What about people in European Regional Science?”
(Maig 2012)
XREAP2012-13
Osorio A. M. (RFA-IREA), Bolancé, C. (RFA-IREA), Madise, N.
“Intermediary and structural determinants of early childhood health in Colombia: exploring the role of communities”
(Juny 2012)
XREAP2012-14
Miguelez. E. (AQR-IREA), Moreno, R. (AQR-IREA)
“Do labour mobility and networks foster geographical knowledge diffusion? The case of European regions”
(Juliol 2012)
XREAP2012-15
Teixidó-Figueras, J. (GRIT), Duró, J. A. (GRIT)
“Ecological Footprint Inequality: A methodological review and some results”
(Setembre 2012)
XREAP2012-16
Varela-Irimia, X-L. (GRIT)
“Profitability, uncertainty and multi-product firm product proliferation: The Spanish car industry”
(Setembre 2012)
XREAP2012-17
Duró, J. A. (GRIT), Teixidó-Figueras, J. (GRIT)
“Ecological Footprint Inequality across countries: the role of environment intensity, income and interaction effects”
(Octubre 2012)
XREAP2012-18
Manresa, A. (CREB), Sancho, F.
“Leontief versus Ghosh: two faces of the same coin”
(Octubre 2012)

SÈRIE DE DOCUMENTS DE TREBALL DE LA XREAP
XREAP2012-19
Alemany, R. (RFA-IREA), Bolancé, C. (RFA-IREA), Guillén, M. (RFA-IREA)
“Nonparametric estimation of Value-at-Risk”
(Octubre 2012)
XREAP2012-20
Herrera-Idárraga, P. (AQR-IREA), López-Bazo, E. (AQR-IREA), Motellón, E. (AQR-IREA)
“Informality and overeducation in the labor market of a developing country”
(Novembre 2012)
XREAP2012-21
Di Paolo, A. (AQR-IREA)
“(Endogenous) occupational choices and job satisfaction among recent PhD recipients: evidence from Catalonia”
(Desembre 2012)
2013
XREAP2013-01
Segarra, A. (GRIT), García-Quevedo, J. (IEB), Teruel, M. (GRIT)
“Financial constraints and the failure of innovation projects”
(Març 2013)
XREAP2013-02
Osorio, A. M. (RFA-IREA), Bolancé, C. (RFA-IREA), Madise, N., Rathmann, K.
“Social Determinants of Child Health in Colombia: Can Community Education Moderate the Effect of Family Characteristics?”
(Març 2013)
XREAP2013-03
Teixidó-Figueras, J. (GRIT), Duró, J. A. (GRIT)
“The building blocks of international ecological footprint inequality: a regression-based decomposition”
(Abril 2013)
XREAP2013-04
Salcedo-Sanz, S., Carro-Calvo, L., Claramunt, M. (CREB), Castañer, A. (CREB), Marmol, M. (CREB)
“An Analysis of Black-box Optimization Problems in Reinsurance: Evolutionary-based Approaches”
(Maig 2013)
XREAP2013-05
Alcañiz, M. (RFA), Guillén, M. (RFA), Sánchez-Moscona, D. (RFA), Santolino, M. (RFA), Llatje, O., Ramon, Ll.
“Prevalence of alcohol-impaired drivers based on random breath tests in a roadside survey”
(Juliol 2013)
XREAP2013-06
Matas, A. (GEAP & IEB), Raymond, J. Ll. (GEAP & IEB), Roig, J. L. (GEAP)
“How market access shapes human capital investment in a peripheral country”
(Octubre 2013)
XREAP2013-07
Di Paolo, A. (AQR-IREA), Tansel, A.
“Returns to Foreign Language Skills in a Developing Country: The Case of Turkey”
(Novembre 2013)
XREAP2013-08
Fernández Gual, V. (GRIT), Segarra, A. (GRIT)
“The Impact of Cooperation on R&D, Innovation andProductivity: an Analysis of Spanish Manufacturing and Services Firms”
(Novembre 2013)
XREAP2013-09
Bahraoui, Z. (RFA); Bolancé, C. (RFA); Pérez-Marín. A. M. (RFA)
“Testing extreme value copulas to estimate the quantile”
(Novembre 2013)
2014
XREAP2014-01
Solé-Auró, A. (RFA), Alcañiz, M. (RFA)
“Are we living longer but less healthy? Trends in mortality and morbidity in Catalonia (Spain), 1994-2011”
(Gener 2014)
XREAP2014-02

SÈRIE DE DOCUMENTS DE TREBALL DE LA XREAP
Teixidó-Figueres, J. (GRIT), Duro, J. A. (GRIT)
“Spatial Polarization of the Ecological Footprint distribution”
(Febrer 2014)
XREAP2014-03
Cristobal-Cebolla, A.; Gil Lafuente, A. M. (RFA), Merigó Lindhal, J. M. (RFA)
“La importancia del control de los costes de la no-calidad en la empresa”
(Febrer 2014)
XREAP2014-04
Castañer, A. (CREB); Claramunt, M.M. (CREB)
“Optimal stop-loss reinsurance: a dependence analysis”
(Abril 2014)
XREAP2014-05
Di Paolo, A. (AQR-IREA); Matas, A. (GEAP); Raymond, J. Ll. (GEAP)
“Job accessibility, employment and job-education mismatch in the metropolitan area of Barcelona”
(Maig 2014)
XREAP2014-06
Di Paolo, A. (AQR-IREA); Mañé, F.
“Are we wasting our talent? Overqualification and overskilling among PhD graduates”
(Juny 2014)
XREAP2014-07
Segarra, A. (GRIT); Teruel, M. (GRIT); Bové, M. A. (GRIT)
“A territorial approach to R&D subsidies: Empirical evidence for Catalonian firms”
(Setembre 2014)
XREAP2014-08
Ramos, R. (AQR-IREA); Sanromá, E. (IEB); Simón, H.
“Public-private sector wage differentials by type of contract: evidence from Spain”
(Octubre 2014)
XREAP2014-09
Bel, G. (GiM-IREA); Bolancé, C. (Riskcenter-IREA); Guillén, M. (Riskcenter-IREA); Rosell, J. (GiM-IREA)
“The environmental effects of changing speed limits: a quantile regression approach”
(Desembre 2014)

2015
XREAP2015-01
Bolance, C. (Riskcenter-IREA); Bahraoui, Z. (Riskcenter-IREA), Alemany, R. (Risckcenter-IREA)
“Estimating extreme value cumulative distribution functions using bias-corrected kernel approaches”
(Gener 2015)
XREAP2015-02
Ramos, R. (AQR-IREA); Sanromá, E. (IEB), Simón, H.
“An analysis of wage differentials between full- and part-time workers in Spain”
(Agost 2015)
XREAP2015-03
Cappellari, L.; Di Paolo, A. (AQR-IREA)
“Bilingual Schooling and Earnings: Evidence from a Language-in-Education Reform”
(Setembre 2015)
XREAP2015-04
Álvarez-Albelo, C. D., Manresa, A. (CREB), Pigem-Vigo, M. (CREB)
“Growing through trade: The role of foreign growth and domestic tariffs”
(Novembre 2015)
XREAP2015-05
Caminal, R., Di Paolo, A. (AQR-IREA)
Your language or mine?
(Novembre 2015)
XREAP2015-06
Choi, H. (AQR-IREA), Choi, A. (IEB)
When one door closes: the impact of the hagwon curfew on the consumption of private tutoring in the Republic of Korea

SÈRIE DE DOCUMENTS DE TREBALL DE LA XREAP
(Novembre 2015)

2016
XREAP2016-01
Castañer, A. (CREB, XREAP); Claramunt, M M. (CREB, XREAP), Tadeo, A., Varea, J. (CREB, XREAP)
Modelización de la dependencia del número de siniestros. Aplicación a Solvencia II
(Setembre 2016)
XREAP2016-02
García-Quevedo, J. (IEB, XREAP); Segarra-Blasco, A. (GRIT, XREAP), Teruel, M. (GRIT, XREAP)
Financial constraints and the failure of innovation projects
(Setembre 2016)
XREAP2016-03
Jové-Llopis, E. (GRIT, XREAP); Segarra-Blasco, A. (GRIT, XREAP)
What is the role of innovation strategies? Evidence from Spanish firms
(Setembre 2016)
XREAP2016-04
Albalate, D. (GiM-IREA, XREAP); Rosell, J. (GiM-IREA, XREAP)
Persistent and transient efficiency on the stochastic production and cost frontiers – an application to the motorway sector
(Octubre 2016)
XREAP2016-05
Jofre-Monseny, J. (IEB, XREAP), Silva, J. I., Vázquez-Grenno, J. (IEB, XREAP)
Local labor market effects of public employment
(Novembre 2016)
XREAP2016-06
Garcia-López, M. A. (IEB, XREAP), Hemet, C., Viladecans-Marsal, E. (IEB, XREAP)
Next train to the polycentric city: The effect of railroads on subcenter formation
(Novembre 2016)
XREAP2016-07
Vayá, E. (AQR-IREA, XREAP), García, J. R. (AQR-IREA, XREAP), Murillo, J. (AQR-IREA, XREAP), Romaní, J. (AQR-IREA, XREAP),
Suriñach, J. (AQR-IREA, XREAP),
Economic impact of cruise activity: the port of Barcelona
(Desembre 2016)
XREAP2016-08
Ayuso, M. (Riskcenter, XREAP), Guillen, M. (Riskcenter, XREAP), Nielsen, J. P.
Improving automobile insurance ratemaking using telematics: incorporating mileage and driver behaviour data
(Desembre 2016)
XREAP2016-09
Ruíz, A. (GEAP, XREAP), Matas, A. (GEAP, XREAP), Raymond, J. Ll.
How do road infrastructure investments affect the regional economy? Evidence from Spain
(Desembre 2016)

2017
XREAP2017-01
Bernardo, V. (GiM-IREA, XREAP); Fageda, X. (GiM-IREA, XREAP)
Globalization, long-haul flights and inter-city connections
(Octubre 2017)
XREAP2017-02
Di Paolo, A. (AQR-IREA, XREAP); Tansel, A.
Analyzing Wage Differentials by Fields of Study: Evidence from Turkey
(Octubre 2017)

XREAP2017-03
Melguizo, C. (AQR-IREA, XREAP); Royuela, V. (AQR-IREA, XREAP)
What drives migration moves across urban areas in Spain? Evidence from the great recession
(Octubre 2017)

SÈRIE DE DOCUMENTS DE TREBALL DE LA XREAP
XREAP2017-04
Boonen, T.J., Guillén, M. (RISKCENTER, XREAP); Santolino, M. (RISKCENTER, XREAP)
Forecasting compositional risk allocations
(Octubre 2017)
XREAP2017-05
Curto‐Grau, M. (IEB, XREAP), Solé‐Ollé, A. (IEB, XREAP), Sorribas‐Navarro, P. (IEB, XREAP)
Does electoral competition curb party favoritism?
(Novembre 2017)
XREAP2017-06
Esteller, A. (IEB, XREAP), Piolatto, A. (IEB, XREAP), Rablen, M. D.
Taxing high-income earners: tax avoidance and mobility
(Novembre 2017)
XREAP2017-07
Bolancé, C. (RISKCENTER, XREAP), Vernic, R
Multivariate count data generalized linear models: Three approaches based on the Sarmanov distribution
(Novembre 2017)
XREAP2017-08
Albalate, D. (GiM-IREA, XREAP), Bel-Piñana, P. (GiM-IREA, XREAP)
Public Private Partnership management effects on road safety outcomes
(Novembre 2017)
XREAP2017-09
Teruel, M. (GRIT, XREAP), Segarra, A. (GRIT, XREAP)
Gender diversity, R&D teams and patents: An application to Spanish firms
(Novembre 2017)
XREAP2017-10
Cuberes, D., Teignier, M. (CREB, XREAP)
How Costly Are Labor Gender Gaps? Estimates by Age Group for the Balkans and Turkey
(Novembre 2017)
XREAP2017-11
Murilló, I. P., Raymond, J. L. (GEAP, XREAP), Calero, J. (IEB, XREAP),
Efficiency in the transformation of schooling into competences: A cross-country analysis using PIAAC data
(Novembre 2017)

xarxa.xreap@gmail.com