the influence of socioeconomic factors on cognitive and non...

SCHOOL DAY LENGTHENING AND ACADEMIC PERFORMANCE: AN ASSESSMENT OF THE MAIS EDUCAÇÃO PROGRAM IN MIDWESTERN BRAZIL

CAPÍTULO 3: PRODUCCIÓN EDUCATIVA Y EFICIENCIA 413

The influence of socioeconomic factors on cognitive and non-cognitive

educational outcomes

JOSÉ MANUEL CORDERO FERRERA ROSA SIMANCAS RODRÍGUEZ

Universidad de Extremadura

MANUEL MUÑIZ PÉREZ Universidad de Oviedo

Abstract: This paper aims to extending the literature about education production function by comparing the determinants of two different dimensions of educational outcomes, academic achievement and non-cognitive skills. For that purpose, we explore the information provided by self-report survey developed for the aim of this research, which allow us to obtain an innovative measure of non-cognitive skills based on questions about universal values such as democracy, tolerance or equality as well as others related to social and emotional skills like motivation or self-control. Using a Bayesian approach, we estimate the potential influence of

multiple individual and family variables on both dimensions of educational outcomes. Our results show that, although there are some similarities, it is possible to find some important divergences with regard to some socioeconomic variables that have been traditionally considered as the most influential determinants of academic achievement, which do not seem to have a significant impact on the non-cognitive outcomes or even have the opposite effect.

Key words: Education, non-cognitive skills, cognitive achievement, Bayesian approach

JEL Codes: I21, I29, I28

INVESTIGACIONES DE ECONOMÍA DE LA EDUCACIÓN NÚMERO 10

414 CHAPTER 3: EDUCATIONAL PRODUCTION AND EFFICIENCY

1. INTRODUCTION

The study of the education production function has a long tradition in the field since the

Coleman Report (Coleman et al., 1966) was released in the sixties. The basis for most of the

existing research is a basic function with a measure of educational performance on the left

side and factors related to school inputs and students´ characteristics on the right (Levin, 1974;

Hanushek, 1979)1. Until recently, most research in this field has been focused mainly on

measures of cognitive proficiencies represented by test scores as the main output of

evaluations, since those values provide an objective metric for evaluation and accountability

recognized by principals, teachers, policy makers and the general public. However, when using

test scores as a measure of educational output, other dimensions of learning such as social

skills, attitudes, personal maturity or moral values are not considered despite they are crucial

for individual development (Levin, 2012). The main argument usually claimed to ignore those

aspects in the education function is that it is extremely difficult to establish a standard

definition for these non-cognitive skills (Cohn & Geske, 1990), although there is a certain level

of agreement that those skills should be promoted at schools.

During the last two decades there has been a growing body of research in the field of

psychology and sociology focused on measuring and developing non-cognitive skills (Borghans

et al., 2008; Almlund et al., 2011). Likewise, some studies have analyzed what schools can do

to improve students´ non-cognitive learning (Dee & West, 2010; Durlak et al., 2011), although

their findings suggest that the effects of those policies are found to be rather small compared

to the influence of individual characteristics and family background (Opdenakker & Van

Damme, 2000).

Given the prominence of those individual and family variables, in this paper we are interested

in analyzing the potential influence of those factors on the acquisition of both types of skills

(cognitive and non-cognitive) and, more specifically, exploring the existence of potential

divergences when identifying those determinants for each dimension of the educational

outcome. This approach is unusual in the literature, since most studies are frequently devoted

to analyze one single aspect of educational performance or to explore whether the results in

one dimension is related to the other (Lleras, 2008; Cunha & Heckman, 2008; 2010). Knuver &

Brandsma (1993) were pioneers in recognizing those divergences, although their study was

focused on institutional factors while we are more interested in exploring the role of students´

activities, beliefs and behaviors.

For that purpose, we draw on cross-sectional data from an unusually large sample of Spanish

students in the final year of secondary education, including measures of both cognitive and

non-cognitive skills. Our definition of non-cognitive skills relies on contributions from

psychology to ensure the correspondence between the items included in a self-report survey

instrument and the personality traits we attempt to measure. In particular, the questionnaire

includes questions about universal values such as democracy, tolerance or equality as well as

1 An exhaustive theoretical exposition on how to model this relationship and the challenges that it entails can be

found in Todd & Wolpin (2003).

THE INFLUENCE OF SOCIOECONOMIC FACTORS ON COGNITIVE AND NON-COGNITIVE EDUCATIONAL OUTCOMES


others related to social and emotional skills like motivation or self-control. Moreover, we

retrieve data about previous academic achievement as well as multiple individual and family

variables that might have a relationship with both dimensions of educational outcomes. The

final dataset comprises around 5,500 15 year-old pupils during the academic year 2010-2011.

Some of the information provided by this dataset has been analyzed in a previous study

(García-Valiñas et al., 2014), in which the authors focused on identifying differences between

public and private schools regarding the influence of individual variables on non-cognitive

skills. Their main findings were that the relationship between the traditional explanatory

factors related to the socioeconomic factors and the non-cognitive outcomes is weak, while

other variables related to parents´ characteristics such as the age emerge as one of the most

influential determinants of non-cognitive results. However, this previous analysis did not

explore the potential influence of an interesting set of questions related to study habits or

parental involvement that might have an important role to explain non-cognitive results.

Moreover, in this paper we also discuss the potential influence of all these variables on the

cognitive component of performance, an issue that was not studied in that previous work

either.

In order to account for uncertainty surrounding model selection process among all the

potential explanatory variables that we could include in our model2, we adopt a Bayesian

Model Averaging (BMA) approach. This method allows us to estimate all the candidate models

and then compute a weighted average of all estimates for the parameters in the regression for

both competences (Moral‐Benito, 2013). Our results show that some factors affect the two

dimensions of the educational outcomes in the same direction such as gender, their peers´

study habits, practicing some leisure activities or parental involvement. However, our most

remarkable finding is that some of the socioeconomic variables that have been traditionally

considered as the most influential ones to explain academic achievement (e.g. mother´s level

of education or the volume of incomes) do not seem to have a significant impact on the non-

cognitive outcomes or even have the opposite effect (father´s occupation level). Finally, we

have identified some key factors associated exclusively with the non-cognitive component

such as the importance of parents´ age or the existence of co-habitation rules in the household

about student´s behaviors and habits.

The remainder of the paper is organised as follows. In Section 2 we review the existing

evidence about the main determinants of different types of outcomes, i.e., distinguishing

between the findings related to cognitive and non-cognitive skills. In Section 3, we describe our

data and variables. In section 4, we explain the empirical strategy and the methodology used

to perform our analysis. In Section 5, we present the main results. Finally, in Section 6 we

discuss those results and their main policy implications.

2 See Claeskens & Hjort (2008) for a detailed explanation of this problem.



2. LITERATURE REVIEW

The vast literature devoted to the analysis of determinants of educational outcomes

distinguishes mainly two blocks of factors. The first one is focused primarily on the role of

students´ family background, including socioeconomic status (SES), family structure, family

resources and parental involvement (Sirin, 2005), while the second block covers aspects

related to school factors and teaching practices (Reynolds et al., 2014). The scope of this paper

is mainly focused on the first block due to the aforementioned evidence that they seem to

have a more significant impact on both dimensions of performance. Moreover, our empirical

analysis is based on a dataset that mainly includes information about those aspects, while data

about schools only allow us to assign them some basic labels (public, private, urban or rural).

As we indicated previously, most of previous literature have been mainly focused on

identifying the factors related to cognitive achievement, although the study of the

determinants of non-cognitive skills has also received a growing interest in recent years

(Tough, 2012). In the following lines, we try to provide a brief summary of the main findings of

both streams of research from a comparative perspective.

Among all the potential determinants of both types of educational outcomes, probably the

most influential variable is the family socioeconomic background (Hanushek, 2002; Bornstein

& Bradley, 2003; Fuchs & Woessman, 2007). Although this factor can be measured by a variety

of different combinations of variables, there is a certain level of agreement about four main

indicators: parental income, home resources, parental education and parental occupation

(Hauser, 1994). Those variables should be incorporated into the analysis separately since they

represent different aspects (Entwisle & Astone, 1994), although it is a common practice to

include them as a synthetic index because they are usually highly correlated among

themselves.

The parental income has generally been found to be correlated with cognitive achievement,

although the extent to which it affects child development is mixed (Haveman & Wolfe, 1995;

Duncan & Brooks-Gunn, 1997). Regarding home resources, only some of them seem to have

an impact on educational outcomes. Among them, the most influential is the number of books

at home (Woessman, 2003; Schuetz et al., 2008), since they represent maybe the best tool to

foster learning in different areas (Evans et al., 2010). There is also a recognized positive

relationship between having a computer at home and the academic achievement (Schmidt &

Wadsworth, 2006, Beltran et al, 2008), while the evidence about the influence of using

computers and the acquisition of non-cognitive skills is still scarce (Subrahmanyam et al.,

2001).

Parental education and occupational status are also found to have positive and significant

effects on the children’s educational achievements (Hill & Duncan, 1987; Reynolds & Temple,

1998), although the educational level of the mother has often been identified as the most

influential variable among them (Korupp et al., 2002). However, the results are inconclusive

with regard to the occupation status of the mother and can vary depending on whether we

consider cognitive or non-cognitive outcomes. Hence, multiple studies have proven that

students whose mothers are working demonstrate to have better academic results. However,



the mother’s absence from home may have a negative impact on the acquisition of non-

cognitive skills. Other relevant issue is the family structure, since students who are raised in a

single-parent household seems to perform worse in cognitive tests (McLanahan, 1985).Those

negative effects have also been identified with regard to non-cognitive aspects, since those

students frequently show more antisocial or inappropriate behaviors (Dunifon & Kowaleski,

2002; Cho et al., 2010; Parent et al., 2013).

Apart from socioeconomic variables, there are multiple student characteristics linked (directly

or indirectly) with educational outcomes, including gender, religion or the immigrant condition

of the child. Firstly, the academic performance of students, it is well known that girls usually

outperform boys in reading tests, while the reverse occurs for maths and science. With regard

to the non-cognitive aspects, male students seem have more disciplinary problems at school

because they are more aggressive in social relationships, according to the results obtained by

Card et al. (2008) in their meta-analysis of almost 150 empirical studies. Likewise, boys are less

predisposed to collaborate with their classmates (Jacob, 2002) and consider that teachers pay

more attention to girls (Kleinfield, 1998). Secondly, the existing evidence about the effect of

individual and parental religion commitment on outcomes is not conclusive. Hence, some

studies conclude that the religious affiliation of parents did not explain differences in cognitive

and non-cognitive results (Driessen & Van Der Slik, 2001), while others claim that religious

involvement is associated with higher levels of educational attainments (Stokes, 2008; Eirich,

2012). Thirdly, immigrant students usually have lower cognitive results than native students in

most countries (Driesen, 2000; Schnepf, 2008). Some nations like Belgium and Canada have

been able to reduce this gap between the two groups (Entorf & Minoiu, 2005), but in other

countries those differences still persist as in Germany or Spain (Ammermuller, 2007; Zinovyeva

et al., 2013).

There has also been a growing interest in studying the link between educational outcomes and

the so-called cultural capital (Bourdieu 1993), which includes a wide variety of factors related

to an underlying cultural education, cultural possessions, motivation, parental activities and

the interaction and communication with them. In fact, some recent evidence reveals that

those factors have stronger effects on students´ cognitive outcome than the traditional fixed

variables such as the parental educational level or their occupation (Tramonte & Willms,

2010).

Within these variables representing this cultural capital, the involvement of parents in the

education of their children plays a major role (Wilder, 2014), although the existing evidence

about its influence is mixed when different types of outcomes are considered. Hence, it is

possible to find multiple studies claiming that parent involvement leads to improved academic

achievement (Chrispeels, 1996; Ho & Willms, 1996), while other research does not provide

support for this relationship (Keith, 1991; Fan, 2001). However, the relationship between

parental involvement and behavorial or non-cognitive outcomes seems to be consistently

positive (McNeal, 1999).

Differences in how researchers conceptualize parental involvement is one of the major reasons

for those inconsistent results, since this is extensive concept that comprises multiple practices

both at home and school (Epstein & Sanders, 2002; Hill & Tyson, 2009). Some researchers have



recognized the multidimensional character of parental involvement and have attempted to

capture the effect of different aspects of parental involvement on children’s´ education. The

findings revealed that high parental aspirations for their children (Singh et al., 1995), general

supervision and monitoring of their progress (McNeal, 1999; Dika & Singh, 2002) and

communication with them about their activities at school (Park, 2008) have a consistent

positive relationship with cognitive achievements. In contrast, the evidence supporting the

existence of a relationship between helping with homework or participating in school activities

and academic achievement is weakest (Ho & Willms, 1996; McNeal, 2012).

Finally, there are some habits or frequent activities that might affect both educational

outcomes, such as the practice of sports, playing video games or using social networks. Bailey

(2006) suggests that physical education and sport promote social skills and social behaviors,

and, in certain circumstances, academic and cognitive development. This evidence is even more

relevant for students participating in team sports, since this environment contributes to the

interrelationship with other students out of school (Weiss & Smith, 2002). The consumption of

electronic videogames has also a negative impact on academic achievement and the

development of social skills (Lin and Lepper, 1987)3, especially for those playing game with

high levels of violence which usually make children to become more aggressive (Griffiths,

1999; Anderson & Bushman, 2001). Finally, the study of the implications of the increasing

participation in virtual social media for educational outcomes has become very relevant in

recent years, since this activity has become one of the most common activities of children and

adolescents nowadays4. In this sense, although the use of those social instruments can

facilitate social relationships, the negative effect on the development of social skills are

frequent, since there is a risk for addiction or the development of compulsive behaviors (Van

den Ejden et al., 2008).

3. DATA AND VARIABLES

The possibility of performing an empirical assessment of the non-cognitive component of

educational outcome frequently depends on the development of valid and reliable measures

of performance (Brunello & Schlotter, 2010). Those measures are typically obtained using self-

report questionnaires. In our case, we use these instruments to collect data about the non-

cognitive skills, but also about academic achievement as well as multiple potential factors that

can be considered as determinants of performance. This process involved substantial

fieldwork, since we have designed and administered the survey in 126 high schools.

The surveyed population is composed of all students in the fourth year of Compulsory

Secondary School (Educación Secundaria Obligatoria) of the Spanish school system,

corresponding to an approximate age of 15 years, who were attending school in the period

2010-1011 in the Spanish region of Asturias. The population included students from both

3 This issue is a great concern, since the percentage of adolescent using videogames on a daily basis has increased

from 38% to 60% during the last decade (Rideout et al., 2010). 4 Okeefe & Claarke (2011) indicate that 22% of teenagers log on to their favorite social media site more than 10

times a day, and more than half of adolescents log on to a social media site more than once a day.



public schools and subsidized private schools. The potential survey population included 7,072

students and the survey response was remarkable, with 5,580 students being surveyed,

representing 78.9% of total potential individuals. The response rates were similar for both

categories of schools, with 76.1% responding from the public schools and 84.3% responding

from the subsidised private schools. After reviewing the questionnaires, we obtained a final

database consisting of 5,493 pupils enrolled in 126 centres, with 3,497 being from public

schools and 1,996 from subsidized private schools.

All students participating in our study completed a battery of questionnaires designed to

measure their non-cognitive skills along various dimensions. These questionnaires, which were

administered in students’ regular classrooms, included items related to different aspects of

educational performance that can be divided into two main conceptual categories with 5

aspects each. The first category, called "universal values", includes the concepts of Justice,

Equality, Democracy, Responsibility and Tolerance. The second, "Social, emotional and

intellectual skills", comprises the variables of Motivation, Empathy, Self-control, Effort and

Critical Thinking. Specifically, two situational questions were designed for each of these 10

concepts with 4 possible responses for each of the 20 questions, such that one alternative

could be clearly and undoubtedly judged as the most appropriate for each concept. An

additive result index was designed (NONCOG) and it is derived by assigning a unit value for

each of the 10 concepts to students who answer with the most positive response to both

questions representing this concept. This approach enables us to obtain an additive result

index with discrete values ranging from 0 to 10, although we have rescaled it by adding one

point to each value in order to avoid 0 values, thus the final index ranges from 1 to 11. The

tests used to contrast the consistency of the additive index offered satisfactory results. Thus,

Cronbach's alpha value reached a value of 0.665, and all item/total correlations yielded

statistically significant values greater than 0.45 for each of the 10 items considered.

As a measure of the cognitive or academic achievement we use the self-reported appraisal of

academic competences based on their records in the previous year6. The available data allow

us to construct an index with four categories according to the following equivalence: 4 as an

indicator that the student obtained excellent grades; 3 if the pupil was rated as good; 2 in the

case that the student only demonstrated a sufficient level and 1 if the pupil failed in the former

year7.

As determinants of educational results, we have available data about individual and family-

related characteristics that are commonly identified in the traditional literature about the

5 The literature recommends Cronbach's alpha values greater than 0.6 or 0.7, depending on the heterogeneity of

the overall concept to be measured relative to the partial items. In our case, where the overall concept (non-cognitive education) is highly heterogeneous, a value of 0.66 is interpreted as indicative of a suitable and sufficient consistency of the overall index.

6 After performing a survey of previous studies about the validity of self-reports, Assor & Connell (1992) conclude that there is no empirical justification for viewing this type of self-reported appraisals of academic competence as invalid measures of performance.

7 Obviously, we would have preferred to have an exogenous measure of this performance in a continuous scale based on an external general assessment, but there not exist this type of evaluation in the Spanish educational system at the end of the secondary school. The only existing standard test in compulsory lower secondary schools in Spain, the so-called General Diagnostic Evaluation (Evaluaciones Generales de Diagnóstico), is undertaken by students at the end of the second year.



education production function based on academic performance such as the gender, immigrant

status, family structure, parents´ education level and type of occupation, family incomes or

possessions in the household. The questionnaire also includes several questions related to

pupils´ study habits and their relationship with their friends as well as the level of involvement

demonstrated by parents in monitoring the activities or their children. Likewise, data provided

by the survey allow us to construct different variables related to other multiple aspects that

have received less attention in previous research such as the age of the parents, religious

beliefs or students´ hobbies, which might also have an impact on educational outcomes,

especially on the non-cognitive dimension.

Finally, we have also considered some basic indicators representing the characteristics of the

schools such as the type of ownership (public or private) or the location (urban or rural) as well

as the so-called peer effect, which we measure as the average of the results obtained in each

dimension of the outcome by students attending the same school. The detailed definition of all

variables is showed in Table 1.

Table 2 reports the descriptive statistics for individual and school variables. Given that most

variables are binary, mean values can be interpreted as proportions. According to this

information it is possible to observe that our sample is almost evenly distributed by gender

and includes a relatively low number on immigrants. The distribution of educational levels and

occupation status is very similar between mothers and fathers, while their average age is

clearly higher for fathers. Most students have a computer and two out of three connect to

social media every day, while the consumption of videogames is lower. Almost half of the

sample plays some federated sport (individual or in a team) and also a half devotes part of

leisure time to reading.

Table 1. Definition of explanatory variables

VARIABLE DEFINITION

STUDENT LEVEL

GENDER Male = 0, Female = 1

IMMIG1 Born in another country = 1, other = 0

IMMIG2 Born in Spain, both parents born in another country = 1, other = 0

REPEAT Student has repeated at least once = 1, other = 0

FATUNI Father’s Educational Level: College = 1, other = 0

MOTUNI Mother’s Educational Level: College = 1, other = 0

FATOCUP Father holds professional qualifications = 1, other = 0

MOTOCUP Mother holds professional qualifications = 1, other = 0

FATAGE35 Father's age under 35 = 1, other = 0

FATAGE45 Father’s age over 45 = 1, other = 0

MOTAGE35 Mother's age under 35 = 1, other = 0

MOTAGE45 Mother’s age over 45 = 1, other = 0

SINGLEPAR Single-parent family = 1, other = 0

RECONFAM Reconstructed family = 1, other = 0

HIGHINC Monthly family income higher than 2,000 euros = 1, other = 0



VARIABLE DEFINITION

RELIG Student is practicing Catholic = 1; other =0

PARELIG Parents are practicing Catholic =1; other =0

COMPROOM There is a computer with internet connection at their own room = 1; other = 0

ACTSOCMED Student connects to social media on a daily basis = 1; other = 0

VIDEOGAM Student plays with video games more than 2 hours every day =1; other = 0

READLEIS Student reads in his/her leisure time =1; other =0

SPINDIV Student practices an individual federated sport = 1; other =0

SPTEAM Student practices a team federated sport = 1; other =0

FRIMARKS Friends obtain good marks = 1; other =0

STHOUR3 Pupil devotes less than 3 hours a week to study =1; other =0

STHOUR9 Pupil devotes more than 9 hours a week to study =1; other =0

STDAILY Pupil devotes time to study every day =1; other =0

PHOMEWORK Parents monitor pupil´s homework on a daily basis =1; other =0

RULES There are clear rules at home and they are fulfilled = 1; other =0

PARFRIENDS Parents know pupil´s friends =1; no =0

PARLEIS Parents monitor pupil´s activities in leisure time =1; other =0

PARINV Parents devote time to the pupil every day =1; other =0

SCHOOL LEVEL

PRIVATE Type of school (0 = public, 1 = subsidized private)

URBAN Location of school (1 = urban, 0 = rural)

PGNONCOG Continuous variable reflecting the peer effect, defined as the average score in non-cognitive education obtained by students attending to the same school

PGCOGNIT Continuous variable reflecting the peer effect, defined as the average academic record obtained by students attending to the same school

Regarding the study habits and home environment, the proportion of pupils reporting that

study less than three hours a week is surprisingly high as well as the extremely small

percentage of students devoting more than 9 hours a week. However, the parents seem to be

involved in checking that homework is made. In addition, in most households parents establish

rules and control activities carried by students in their spare time, although the proportion of

parents devoting time to their children every day is quite low. Finally, the mean values of the

school variables allow us to detect that students are almost equitably distributed among rural

and urban schools, but not among public and private schools, since the former brings together

a higher proportion (almost 70%) of students.



Table 2. Descriptive statistics

VARIABLES

Minimum Maximum Mean SD Dependent variable

NONCOG 1 11 3.78 2.14

COGNIT 1 4 2.55 0.80

Regressors (student level)

GENDER 0 1 0.48 0.50

IMMIG1 0 1 0.08 0.28

IMMIG2 0 1 0.02 0.15

REPEAT 0 1 0.29 0.45

FATUNI 0 1 0.28 0.45

MOTUNI 0 1 0.30 0.46

FATOCUP 0 1 0.43 0.50

MOTOCUP 0 1 0.41 0.49

FATAGE35 0 1 0.07 0.26

FATAGE45 0 1 0.50 0.50

MOTAGE35 0 1 0.12 0.32

MOTAGE45 0 1 0.33 0.47

SINGLEPAR 0 1 0.25 0.43

RECONFAM 0 1 0.06 0.24

HIGHINC 0 1 0.38 0.49

RELIG 0 1 0.19 0.39

PARELIG 0 1 0.20 0.40

COMPROOM 0 1 0.53 0.50

ACTSOCMED 0 1 0.67 0.47

VIDEOGAM 0 1 0.12 0.32

READLEIS 0 1 0.56 0.50

SPINDIV 0 1 0.24 0.43

SPTEAM 0 1 0.29 0.46

FRIMARKS 0 1 0.26 0.44

STHOUR3 0 1 0.51 0.50

STHOUR9 0 1 0.06 0.25

STDAILY 0 1 0.33 0.47

PHOMEWORK 0 1 0.47 0.50

RULES 0 1 0.77 0.42

PARFRIENDS 0 1 0.88 0.32

PARLEIS 0 1 0.93 0.24

PARINV 0 1 0.24 0.42

Regressors (school level)

PRIVATE 0 1 0.31 0.50

URBAN 0 1 0.54 0.50

PGNONCOG 1.46 5.77 3.72 0.64

PGCOGNIT 1.86 3.08 2.51 0.19



4. METHODOLOGY

In empirical contexts, economic researchers concerned about the potential effects of a set of

variables on a particular outcome frequently select a single model from the variety of

statistically reasonable alternatives. However, given that there are other multiple model

options that might have been considered, conclusions based on a single statistical inference

will be biased because they ignore uncertainty about model form (Raftery, 1995). In order to

overcome this problem, the implementation of the Bayesian model averaging technique

proposed by Leamer (1978) has become very popular in economics because it outperforms

other strategies in terms of predictive ability8. This approach provides a model selection

criterion to account for this uncertainty in statistical inference focusing on identifying which

regressors should be included in the analysis9. The key idea of this approach is computing a

weighted average of the conditional estimates across all possible models because each of

them provides some information on the focus regression parameters.

Our statistical framework is a normal linear regression model with k potential explanatory

variables (X) in which model uncertainty comes from the selection of regressors to include in

the right hand side of the following equation:

(1)

where y is a vector representing the dependent variable, X is a matrix of regressors that may

or may not be included in the model and β is a vector that contains the parameters to be

estimated. Given the number of regressors, there are a total of 2k different models indexed by

Mj for j = 1,2,3,…,2k, which all seek to explain y. Model averaging refers to the process of

estimating some quantity under each model Mj and then averaging the estimates according to

how likely each model is:

∑ (2)

where represents the weight associated to model j. In the spirit of Bayesian inference, the

weight given to each model and the conditional estimates of its parameters are determined on

the basis of data (D) and priors. Hence, the posterior distribution for any coefficient of interest

(βh) given the data can be written as:

∑ ( | ) ( | ) (3)

8 For example in Fernández et al. (2001) or Doppelhofer et al. (2003). 9 See Hoeting et al. (1999) for a general discussion on this method.



Given a prior model probability , we can compute the posterior model probability of

each model Mj as the ratio of its marginal likelihood between the sum of marginal likelihoods

and the entire model space:

( | ) ( | )

( | )

∑

(4)

which can be transformed into the marginal (or integrated) likelihood:

( | ) ∫ ( | ) ( | ) (5)

where is the vector of parameters from model , ( | ) is a prior probability

distribution assigned to the parameters of model by the researcher and is the prior

probability that is the true model.

Following Leamer (1978) we can consider β a function of βj for each j = 1,...,2k and then

calculate the posterior density of the parameters for all the models under consideration by the

law of total probability:

∑ ( | ) ( | ) (6)

Therefore, the full posterior distribution of β in equation (2) is a weighted average of its

posterior distributions under each of the models, where the weights are given by

( | ). When applying BMA using equation (6), both estimation and inference are derived

from the posterior distribution taking into account model uncertainty. The estimated posterior

means of can be obtained considering the expected value in (6):

∑ ( | ) (7)

with the following associated posterior variance:

( | ) ∑ ( | ) ( | ) (8)

This variance incorporates the weighted average of the estimated variances of the individual

models as well as the weighted variance in estimates of the parameters across different

models. Although the ratio between and the standard deviation √ has not a

exact distribution like in traditional OLS models, we can consider that variables with a value

higher than 2 have a significant effect on the dependent variable, since it covers 95% of the

posterior distribution excluding 0.

In addition, once we have estimated the posterior probability of each model, we can also

compute the so-called inclusion probability (PIP) for a given variable k, i.e., the probability of a



parameter being different from zero. This PIP, which can be interpreted as a measure of the

importance of the variable in the model, is calculated as the sum of the posterior model

probabilities for all models including the variable k:

∑ ( | ) (9)

The variables with higher PIP values will be the main contributors to explain the variability of

the dependent variable, thus they will be considered as the most robust explanatory variables.

Standard rules of thumb for interpreting these values have been provided by Kass & Raftery

(1995). These authors establish the following effect thresholds: < 50% evidence against the

effect, 50-75% weak evidence for the effect, 75-95% positive evidence, 95-99% strong

evidence, and > 99% very strong evidence.

The correct implementation of BMA in practice requires handling two difficult tasks

represented by the computational problems that arise when the number of covariates under

consideration (2k) is too large and the elicitation of a specific structure of priors (on parameters

and models). The explanation of the multiple existing alternatives for those aspects is beyond

of the scope of this paper10, however in the next lines we provide a brief description of the

model sampling algorithms as well as the specific set of prior probabilities applied in our

empirical analysis conducted in the statistical computing language of R. First, we use the

commonly-used Markov Chain Monte Carlo Model Composition algorithm developed by

Madigan & York (1995) to reduce the computational burden of BMA. In particular, this method

takes draws from the model space focusing on models with high posterior probability. With

regard to priors, we use the uniform prior on the model space, which implies to assume that

every model has the same a priori probability, and the unit information prior (UIP) on the

parameter space, which attributes the same information to the prior as is contained in one

observation11.

5. RESULTS

In this section we present the results obtained by applying the BMA approach considering all

the potential explanatory variables (k=36) and two alternative dependent variables: COGNIT

and NONCOG. To perform the empirical analysis, we have used the BMS (an acronym for

Bayesian Model Selection) package in R (Feldkircher & Zeugner, 2009). The results of the

empirical analysis are reported in Table 3 for the cognitive dependent variable and Table 4 for

the non-cognitive component as dependent variable. The importance of the variables in

explaining the variability of each dimension of the educational output is represented by the

posterior inclusion probabilities (PIP) showed in the first column. The coefficients averaged

over all the models are displayed in the second column, including the models wherein the

variable was not contained (the coefficient is zero in this case), while the posterior standard

10 See Moral-Benito (2013) for details. 11 Eicher et al. (2011) demonstrate that this combination of priors provides better results than any other possible

combination.



deviations are presented in the third column. Finally, the ratio between the coefficient and the

standard deviation is presented in column 4 to facilitate the identification of significant

variables. In both tables the explanatory variables are sorted by the value of the PIP in order to

facilitate the identification of the main contributors.

The main explanatory factors positively correlated with the cognitive outcome are two

socioeconomic variables (incomes and mother´s level of education), three variables related to

activities at home (reading for pleasure, studying more than 9 hours a week and parents

monitoring homework), two representing the cognitive peer effect (friends and classmates)

and the gender (girls obtain better results). In contrast, variables representing retaken

students and those hooked on social media are considered as the main negative factors

associated with academic results. This evidence can be observed graphically in Figure 1, which

shows the cumulative marginal likelihoods of the best 300 models scaled by their posterior

model probability. On the vertical axis, explanatory variables are sorted by their PIP and the

color of the graph indicates whether the coefficient is positive (blue) or negative (red).

In the case of the non-cognitive dimension, some of the main explanatory factors with a

positive effect are the gender, the immigrant status (only first generation), reading in leisure

time and several variables related to the supervision of parents (RULES, STDAILY,

PHOMEWORK, PARLEIS and PARFRIENDS) and peer effects (classmates and friends). Among

the most important negative predictors, we identify two leisure activities (connecting to social

media and playing video games) in addition to other variables related to the age of parents

and the qualification of father´s job. The effect of all these variables can be seen in Figure 2,

which shows the cumulative marginal likelihoods of the best models for the non-cognitive

output.

Figure 1. Cumulative model probabilities for the best models (COGNIT)



Table 3. BMA estimates for the cognitive dimension of output

VARIABLES PIP Post Mean Post SD Ratio

(absolute value)

REPEAT 1.000 -0.489 0.023 21.495

MOTUNI 1.000 0.120 0.029 4.178

HIGHINC 1.000 0.107 0.022 4.779

ACTSOCMED 1.000 -0.091 0.021 4.300

READLEIS 1.000 0.158 0.020 7.772

PHOMEWORK 1.000 0.119 0.020 5.862

PGCOGNIT 1.000 0.625 0.060 10.436

GENDER 0.991 0.084 0.022 3.845

STHOUR9 0.978 0.144 0.045 3.174

FRIMARKS 0.958 0.085 0.028 2.989

PARFRIENDS 0.697 0.076 0.058 1.326

MOTOCUP 0.690 0.054 0.041 1.317

PRIVATE 0.601 -0.039 0.036 1.088

VIDEOGAM 0.405 -0.034 0.046 0.746

PARLEIS 0.309 0.034 0.056 0.606

RULES 0.261 0.016 0.030 0.542

URBAN 0.247 0.014 0.026 0.522

PARELIG 0.175 0.011 0.025 0.419

FATAGE35 0.105 0.008 0.028 0.305

PGNONCOG 0.084 -0.003 0.013 0.275

FATOCUP 0.080 0.004 0.014 0.261

SINGLEPAR 0.079 -0.003 0.013 0.255

SPINDIV 0.046 0.002 0.009 0.185

STDAILY 0.046 0.002 0.009 0.191

MOTAGE45 0.044 0.001 0.006 0.128

PARINV 0.043 -0.002 0.010 0.185

COMPROOM 0.035 0.000 0.004 0.088

IMMIG2 0.025 0.001 0.014 0.102

FATUNI 0.024 0.000 0.004 0.060

IMMIG1 0.022 0.000 0.006 0.048

RELIG 0.022 0.000 0.005 0.060

MOTAGE35 0.020 0.000 0.005 0.085

STHOUR3 0.016 0.000 0.003 0.072

SPTEAM 0.014 0.000 0.003 0.071

RECONFAM 0.012 0.000 0.006 0.072

FATAGE45 0.010 0.000 0.002 0.018



Figure 2. Cumulative model probabilities for the best models (NONCOG)

Table 4. BMA estimates for the non-cognitive dimension of output


(absolute value)

GENDER 1.000 0.671 0.054 12.339

IMMIG1 1.000 0.497 0.095 5.205

FATAGE35 1.000 -0.582 0.111 5.263

ACTSOCMED 1.000 -0.357 0.056 6.327

READLEIS 1.000 0.582 0.054 10.743

STDAILY 1.000 0.607 0.059 10.284

PHOMEWORK 1.000 0.634 0.054 11.701

RULES 1.000 0.260 0.064 4.065

PARLEIS 1.000 0.494 0.111 4.441

PGNONCOG 1.000 0.543 0.048 11.278

VIDEOGAM 0.976 -0.322 0.096 3.362

PARFRIENDS 0.971 0.343 0.107 3.211

FATOCUP 0.936 -0.184 0.072 2.558

FRIMARKS 0.751 0.147 0.100 1.476

MOTAGE45 0.608 0.107 0.097 1.104

SINGLEPAR 0.381 -0.061 0.087 0.706

RELIG 0.370 0.063 0.091 0.685

PARINV 0.256 0.042 0.078 0.537

MOTAGE35 0.145 -0.026 0.073 0.357

URBAN 0.114 0.009 0.030 0.286

RECONFAM 0.107 -0.022 0.074 0.299

STHOUR3 0.100 -0.012 0.039 0.300

FATUNI 0.075 -0.009 0.037 0.238




(absolute value)

HIGHINC 0.066 -0.009 0.037 0.241

FATAGE45 0.061 -0.001 0.019 0.059

SPINDIV 0.056 0.003 0.020 0.163

IMMIG2 0.047 -0.011 0.064 0.177

PARELIG 0.044 0.004 0.023 0.174

MOTOCUP 0.044 0.001 0.013 0.086

PRIVATE 0.043 -0.004 0.023 0.181

REPEAT 0.042 -0.003 0.020 0.164

COMPROOM 0.041 -0.001 0.012 0.099

PGCOGNIT 0.018 -0.002 0.024 0.064

MOTUNI 0.014 0.000 0.008 0.057

SPTEAM 0.012 -0.001 0.015 0.100

STHOUR9 0.009 -0.001 0.014 0.066

Since one of our main concerns is to find potential similarities and differences between both

dimensions, in Table 5 we show again the BMA estimates, but maintaining the same order of

variables to facilitate the comparison. In the following lines we analyze those divergences in

more depth trying to establish a link between our empirical results and previous evidence

about the determinants of both dimensions of the educational outcome.

Table 5. Comparison of BMA estimates for both dimensions of output

VARIABLES Dependent variable: COGNIT Dependent variable: NONCOG

PIP Ratio PIP Ratio

GENDER 0.991 3.845 1.000 12.339

IMMIG1 0.022 0.048 1.000 5.205

IMMIG2 0.025 0.102 0.047 0.177

REPEAT 1.000 21.495 0.042 0.164

FATUNI 0.024 0.060 0.075 0.238

MOTUNI 1.000 4.178 0.014 0.057

FATOCUP 0.080 0.261 0.936 2.558

MOTOCUP 0.690 1.317 0.044 0.086

FATAGE35 0.105 0.305 1.000 5.263

FATAGE45 0.010 0.018 0.061 0.059

MOTAGE35 0.020 0.085 0.145 0.357

MOTAGE45 0.044 0.128 0.608 1.104

SINGLEPAR 0.079 0.255 0.381 0.706

RECONFAM 0.012 0.072 0.107 0.299

HIGHINC 1.000 4.779 0.066 0.241

RELIG 0.022 0.060 0.370 0.685

PARELIG 0.175 0.419 0.044 0.174



VARIABLES Dependent variable: COGNIT Dependent variable: NONCOG

PIP Ratio PIP Ratio

COMPROOM 0.035 0.088 0.041 0.099

ACTSOCMED 1.000 4.300 1.000 6.327

VIDEOGAM 0.405 0.746 0.976 3.362

READLEIS 1.000 7.772 1.000 10.743

SPINDIV 0.046 0.185 0.056 0.163

SPTEAM 0.014 0.071 0.012 0.100

FRIMARKS 0.958 2.989 0.751 1.476

STHOUR3 0.016 0.072 0.100 0.300

STHOUR9 0.978 3.174 0.009 0.066

STDAILY 0.046 0.191 1.000 10.284

PHOMEWORK 1.000 5.862 1.000 11.701

RULES 0.261 0.542 1.000 4.065

PARFRIENDS 0.697 1.326 0.971 3.211

PARLEIS 0.309 0.606 1.000 4.441

PARINV 0.043 0.185 0.256 0.537

PRIVATE 0.601 1.088 0.043 0.181

URBAN 0.247 0.522 0.114 0.286

PGNONCOG 0.084 0.275 1.000 11.278

PGCOGNIT 1.000 10.436 0.018 0.064

The usual divergences in the results between boys and girls, which have been demonstrated in

many previous empirical works, also arise in our study in favor of girls for both types of

educational outcomes. In contrast, we do not find any differences between immigrants

(independently of the generation) and native students in curricular results as it is common in

most of empirical works carried out in the Spanish context (e.g. Zinovyeva et al., 2013),

although it is necessary to bear in mind that the specific characteristics of Asturias, the region

where the survey was administered, differs substantially from other regions in Spain in the

composition and number of immigrant students at schools12. Likewise, it is worth noting that

being a first generation immigrant is a key factor to explain better non-cognitive results,

although this influence disappear for the second generation.

The case of retained students deserves a special attention, since Spain is the country with the

highest rates in the European Union (Eurydice, 2011; Goos, 2013). As we expected, we find

that this factor has a relevant and negative role in explaining cognitive results. This evidence

had been already detected in some previous studies using Spanish data from international

cognitive tests like PISA (e.g. García-Pérez et al., 2014) as well as for other countries (see Xia &

Kirby, 2009 for an extensive review of literature on this topic). Nonetheless, we also obtain a

12 Specifically, Asturias is one of the Spanish regions with a lower percentage of immigrants (around 4%, which

represents almost one third of the national average). Moreover, the origin of those immigrants differs significantly from the national average (most of immigrants in the region come from South America, while in Spain there are a great proportion of immigrants coming from Morocco and Eastern Europe).



striking result regarding this issue since this variable is not relevant as a predictor of the non-

cognitive result.

Among the determinants of academic achievement, it is straightforward to corroborate the

existing previous evidence in the literature about the importance of socio economic variables.

In particular, the mother´s level of education and work qualification as well as the family

income appear to be significantly and positively correlated with this indicator, although the

socioeconomic characteristics of students´ fathers do not seem to have a significant influence

(apart from the family income). Nevertheless, some important divergences arise when we

analyze the association between those socioeconomic factors and the acquisition of non-

cognitive skills. First, there is no link between the non-cognitive output and the level of

education of both parents, nor with the family income. Hence, the empirical evidence does not

support the belief that parents with higher level of education and incomes are more capable of

embedding values in their children. Second, the professional qualification of the father has a

negative effect on the non-cognitive indicator, while this effect is inexistent for the mother.

From our viewpoint, these findings might be related to the lesser presence of parents at the

household when they have a highly-qualified job, which in practice implies devoting less time

to the education of their kids, although we would require having additional information about

different typologies of jobs to support this intuition.

We have also found some interesting divergences with regard to the relevance of another

factor that has been barely examined in previous studies like the age of the parents. In this

sense, although none of the variables considered is significantly associated with academic

performance; there is an evident influence of those variables on the non-cognitive results.

Hence, according to the value of the correspondent PIP, having younger fathers or older

mothers can be considered as influential factors affecting this type of outcomes, with a

negative and positive effect respectively.

With regard to variables related to the family structure as well as some possessions in the

household, although they have been identified as relevant factors in many previous empirical

studies estimating education production functions, our results indicate that those factors are

not considered among the most relevant ones to explain the results in any of the two

dimensions of the output. Likewise, the potential effects of variables related to religious beliefs

and practices of students and parents are not relevant either for any of the output dimensions.

This result could be awaited for the curricular performance, but it is somewhat unexpected for

the non-cognitive measure of educational output given that it embodies ethical and moral

values.

Another relevant issue is represented by the activities carried out by students in their leisure

time. In this sense, our findings indicate that the influence on both components of the

education outcome is decisive in most cases. Thus, the volunteer reading (positive) and the

compulsive use of virtual social media and videogames (negative) are significantly associated

with cognitive and non-cognitive proficiency. This evidence could be expected with regard to

the academic performance, but it is important to highlight that this influence is also exerted on

the acquisition of social values, for which the relationship is far from being evident. In contrast,

the practice of sports, identified as a relevant factor associated with better social relationships



in some previous studies (Rutten et al., 2007), does not seem to be relevant factor for the non-

cognitive component, nor to explain academic results.

Regarding the pupil´s study habits, we detect a predictable positive association between the

academic performance and devoting a lot of time to study (more than nine hours) during the

week, but this relationship is not significant for the non-cognitive measure. However, our most

remarkable finding arises from the fact that studying every day is not a relevant factor to

explain the cognitive results, but it is for the non-cognitive. Our interpretation of this result is

that the existence of a daily routine of study is a proxy that accounts for a more responsible

and mature behavior on the part of students. Finally, as we could expect, a good selection of

valuable friends, i.e., those who obtain good marks, makes a student more prone to also

obtain better academic and non-cognitive results.

The last set of individual variables examined is represented by indicators about the basic

routines in the household and the level of involvement demonstrated by parents in monitoring

different aspects of day-to-day students´ activities. Among them, we identify several variables

with a high (and positive) impact on both dimensions of the educational outcome such as

parents´ interest for meeting the friends of their children and controlling whether they are

doing their homework or not. In addition, there are other variables like the existence of co-

habitation rules and the supervision on leisure activities considered as very relevant factors to

explain solely the non-cognitive results.

Apart from the different sets of variables related to the individual and family background, in

our empirical we have also considered some basic indicators about the characteristics of the

schools were the student are enrolled. The main conclusion derived from the consideration of

these variables is the great relevance of the peer effect of classmates for both dimensions of

the output; although there are no cross-links between them (PGCOGNIT only has effect on

COGNIT and PGNONCOG on NONCOG). Moreover, our results support the existing previous

evidence concerning the Spanish educational systems about the scarce (or even negative13)

influence of attending private schools on academic results (Perelman & Santin, 2011;

Mancebon et al., 2012) and provide new insights about the inexistence of correlation between

this variable and the non-cognitive outcome14.

6. CONCLUDING REMARKS

The analysis of the potential factors associated with the educational outcomes has been one of

the main concerns of researchers in the field of economics of education during the last

decades. In this paper, we provide new insights about this topic of research by revealing the

existence of similarities, but also significant divergences, in the identification of those

determinants depending on whether the study is focused on academic proficiency or the

acquisition of non-cognitive skills, which in the context of this study are represented by social

and ethical values.

13 The value of the PIP associated with this variable only allows us to identify a weak effect for this variable. 14 A similar result was obtained by García-Valiñas et al. (2014) using a different methodological approach.



The unusual availability of an extensive dataset about a large sample of Spanish students

enrolled in the final year of secondary education have allowed us to perform an empirical

analysis using two alternative measures of outcomes (cognitive and non-cognitive) as our

dependent variable. In order to identify the main determinants of the two alternative models

we have adopted a Bayesian approach. This method allows us to estimate all candidate models

considering the potential explanatory variables available in our dataset, thus we avoid the

usual bias of the traditional statistical inference based on a single model that ignores

uncertainty about model form.

Our results indicate that some factors have a positive influence on both types of outputs like

being a girl, reading for pleasure, having parents supervising homework or friends with good

marks. In addition, we also identify the active participation in social media as a negative factor

associated with both dimensions. However, the most remarkable conclusions derived from this

evaluation are the existing divergences found concerning the family background indicators as

well as those related to their monitoring of their children. Specifically, our results suggest that

some of the socioeconomic variables identified as the main determinants of academic

achievement, such as the level of education of the mother or the family income, are not

significantly related to the non-cognitive dimension of the educational output. Indeed, this

latter component can be better explained by parents´ behavior at home, i.e, if they monitor

pupils´ activities during their leisure time, encourage them to study or establish co-habitation

rules. Likewise, parents´ age, a good proxy of their level of maturity, is also identified as a key

factor associated with fostering those non-cognitive skills.

Regarding school variables, we detect that the influence of classmates is also a relevant factor

for both indicators, although it is worth noting that we have not found cross relationships

among variables, i.e., the academic results of peers only have influence on the proficiency

demonstrated by each student, but not on her non-cognitive outcomes and vice versa. Finally,

our results reinforce some previous evidence about the inexistence of significant divergences

in the cognitive results of Spanish students between those attending public and private

schools. Moreover, we obtain similar conclusions for the less studied non-cognitive outcome,

which belies the widespread belief about the superiority of private schools to provide good

moral and ethical values to pupils.

To the best of our knowledge, this paper represents one of the first attempts to compare the

determinants of cognitive and non-cognitive educational outputs using real data about

students enrolled in secondary school. However, the results obtained and discussed above

should be examined with caution since they are based on cross-sectional data about one

specific region in Spain, thus they cannot be interpreted in general terms nor in a causal sense.

Even so, we believe that our results raise relevant issues about the determinants of both

dimensions of educational outcomes that deserve a further analysis in the future.

ACKNOWLEDGMENTS

The authors would like to thank financial support from the Gobierno de Extremadura (Project

IB13106) to conduct this research project.



REFERENCES

Almlund, M., Duckworth, A. L., Heckman, J. J., & Kautz, T. D. (2011). Personality psychology and economics. Handbook of the economics of education. Amsterdam: Elsevier.

Ammermuller, A. (2007). Poor Background or Low Returns? Why Immigrant Students in Germany perform so Poorly in the Programme for International Student Assessment, Education Economics, 15(2), 215-230.

Anderson, C. A., & Bushman, B. J. (2001). Effects of violent video games on aggressive behavior, aggressive cognition, aggressive affect, physiological arousal, and prosocial behavior: A meta-analytic review of the scientific literature. Psychological science, 12(5), 353-359.

Assor, A., & Connell, J. P. (1992). The validity of students’ self-reports as measures of performance affecting self-appraisals. Student perceptions in the classroom, 25-47.

Bailey, R. (2006). Physical education and sport in schools: A review of benefits and outcomes. Journal of School Health, 76(8), 397-401.

Bourdieu, P. (1993). The field of cultural production. New York: Columbia University Press.

Borghans, L., Duckworth, A. L., Heckman, J. J., & ter Weel, B. (2008). The Economics and Psychology of Personality Traits, Journal of Human Resources, 43(4), 972-1059.

Bornstein, M. C., & Bradley, R . H. (Eds.) ( 2003). Socioeconmic status, parenting, and child development. Mahwah, NJ: Lawrence Erlbaum.

Bowles, S., Gintis, H., & Osborne, M. (2001). The determinants of earnings: A behavioral approach. Journal of Economic Literature, 39(4), 137–1176.

Bronstein, P., Ginsburg, G.S., Herrera, I.S. (2005). Parental predictors of motivational orientation in early adolescence: A longitudinal study. Journal of Youth and Adolescence, 34, 559–575.

Brunello, G., & Schlotter, M. (2010).The effect of non cognitive skills and personality traits on labour market outcomes. Analytical report for the European Commission prepared by the European Expert Network on Economics of Education.

Chrispeels, J. (1996). Effective schools and home-school-community partnership roles: a framework for parent involvement, School Effectiveness and School Improvement, 7, 297-324.

Claeskens, G., & Hjort, N. L. (2008). Model selection and model averaging(Vol. 330). Cambridge: Cambridge University Press.

Cohn, E & Geske, T.E. (Eds.) (1990). The Economics of Education, Pergamon Press.

Coleman, J., Campbell, E.Q., Hobson, C.F. McPartland, J. & Mood, A.M. (1966). Equality of Educational Opportunity, Washington, U.S. Office of Education.

Cunha, F., & Heckman, J. J. (2008). Formulating, identifying and estimating the technology of cognitive and noncognitive skill formation. Journal of Human Resources, 43(4), 738-782.

Cunha, F., Heckman, J. J., & Schennach, S. M. (2010). Estimating the technology of cognitive and noncognitive skill formation. Econometrica, 78(3), 883-931.

De Graaf, N. D., De Graaf, P. M., & Kraaykamp, G. (2000). Parental cultural capital and educational attainment in the Netherlands: A refinement of the cultural capital perspective. Sociology of Education, 73, 92–111.

Dika, S. L. & Singh, K. (2002). Applications of Social Capital in Educational Literature: A Critical Synthesis. Review of Educational Research ,71 (1), 31-60.

Doppelhofer, G., Miller, R., & Sala-i-Martin, X. (2003). Determinants of Long-Term Growth: A Bayesian Averaging of Classical Estimates (BACE) Approach. American Economic Review.



Driesen, J. & Geert, M.W. (2000): Ethnicity, Forms of Capital and Educational Achievement, International Review of Education, 47(6), 513-538.

Driessen, G., & Van Der Slik, F. (2001). Religion, denomination, and education in the Netherlands: cognitive and noncognitive outcomes after an era of secularization. Journal for the Scientific Study of Religion, 40(4), 561-572.

Duncan, G. & Brooks-Gunn, J. (1997).Consequences of Growing Up Poor. Russell Sage Foundation, New York.

Durlak, J. A., Weissberg, R. P., Dymnicki, A. B., Taylor, R. D., & Schellinger, K. B. (2011). The impact of enhancing students’ social and emotional learning: A meta‐analysis of school‐based universal interventions. Child development,82(1), 405-432.

Eicher, T. S., Papageorgiou, C., & Raftery, A. E. (2011). Default priors and predictive performance in Bayesian model averaging, with application to growth determinants, Journal of Applied Econometrics, 26(1), 30-55.

Eirich, G. M. (2012). Parental religiosity and children's educational attainment in the United States, Research in the Sociology of Work, 23, 153-181.

Ensminger, M. E., Forrest, C. B., Riley, A. W., Kang, M., Green, B. F., Starfield, B., & Ryan, S. A. (2000).The validity of measures of socioeconomic status of adolescents. Journal of Adolescent Research, 15(3), 392-419.

Entwisle, D. R., & Astone, N. M. (1994). Some practical guidelines for measuring youth's race/ethnicity and socioeconomic status. Child Development, 65(6), 1521-1540.

Epstein, J.L. & Sanders, M.G. (2002). Family, school, and community partnerships. In: Bornstein, M.H. (ed.). Handbook of parenting: Vol. 5. Practical issues in parenting. Erlbaum; Mahwah, NJ, 507–437.

Eurydice (2011): Grade retention during compulsory education in Europe: Regulation and Statistics, Education, Audiovisual and Culture Executive Agency. Available at: http://eacea.ec.europa.eu/education/eurydice/documents/thematic_reports/126EN.pdf

Evans, M., Kelley, J., Sikora, J. & Treiman, D. (2010): “Family scholarly culture and educational success books and schooling in 27 nations”, Research in social stratification and mobility 28 (2), 171-197.

Fan, X.(2001). Parental Involvement and Students’Academic Achievement: A Growth Modeling Analysis, The Journal of Experimental Education, 70, 27–61.

Fernandez, C., Ley, E., & Steel, M. F. (2001). Model uncertainty in cross‐country growth regressions. Journal of Applied Econometrics, 16(5), 563-576.

Feldkircher, M. & Zeugner, S. (2009). Benchmark Priors Revisited: On Adaptive Shrinkage and the Supermodel Effect in Bayesian Model Averaging. IMF Working Paper, WP/09/202.

Fuchs, T. & Woessmann, L. (2007). What Accounts for International Differences in Student Performance? A Re-Examination Using PISA Data. Empirical Economics, 32(2-3), 433-464.

García-Pérez, J. I., Hidalgo-Hidalgo, M., Robles-Zurita, J. A. (2014). Does grade retention affect students’ achievement? Some evidence from Spain. Applied Economics, 46(12), 1373-1392.

García-Valiñas, M., Muñiz, M. & Suarez-Pandiello, J. (2014): The determinants of non-cognitive education: does the school matter? Empirical evidence from Spain, European Journal of Education, in press.

Goos, M., Schreier, B.M. Knipprath, H.M., De Fraine, B. Van Damme, J., Trauwein, U. (2013). How Can Cross-Country Differences in the Practice of Grade Retention Be Explained? A Closer Look at National Educational Policy Factors. Comparative Education Review, 57 (1), 54-84.

Griffiths, M. (1999). Violent video games and aggression: A review of the literature, Aggression and Violent Behavior, 4(2), 203-212.

http://eacea.ec.europa.eu/education/eurydice/documents/thematic_reports/126EN.pdf



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. A. (2002). Publicly provided education. In Handbook of Public Economics, edited by AlanJ. Auerbach and Martin Feldstein. Amsterdam: Elsevier: 2045-2141.

Hanushek, E. A., Kain, J. F., Markman, J. M., & Rivkin, S. G. (2003). Does peer ability affect student achievement?. Journal of Applied econometrics, 18(5), 527-544.

Hanushek, E. A., & Woessmann, L. (2008). The role of cognitive skills in economic development. Journal of Economic Literature, 607-668.

Hanushek & Woessman L (2011). The economics of international differences in educational achievement. In Hanushek EA, Machin S and Woessmann L (eds) Handbook of the Economics of Education, vol. 3, Amsterdam: North Holland, pp. 89-200.

Hauser, R. M. (1994). Measuring socio economic status in studies of child development. Child Development, 65(6), 1541-1545.

Haveman, R. & Wolfe, B. (1995). The Determinants of Children's Attainments: A Review of Methods and Findings. Journal of Economic Literature, 33(4), 1829-1878.

Hill, M.S. & Duncan, G. (1987). Parental family income and the socioeconomic attainment of children, Social Science Research 16(1), 39–73.

Ho, E. & Willms, D.J. (1996). Effects of parental involvement on eighth-grade achievement, Sociology of Education, 69, 126-141.

Hoeting, J. A., Madigan, D., Raftery, A. E., & Volinsky, C. T. (1999). Bayesian model averaging: a tutorial. Statistical science, 382-401.

Kass, R. E. & Raftery, A. E. (1995). Bayes Factors, Journal of the American Statistical Association, 90, 773–795.

Keith, T. Z. (1991). Parental Involvement and Achievement in High School, Advances in Reading/Language Research, 5, 125–41.

Knuver, A.W.M. & Bradsma, H.P. (1993). Cognitive and affective outcomes in school effectiveness research, School Effectiveness and School Improvement, 4, 189-204.

Korupp, S. E., Ganzeboom, H. B., & Van Der Lippe, T. (2002). Do mothers matter? A comparison of models of the influence of mothers' and fathers' educational and occupational status on children's educational attainment. Quality and Quantity, 36(1), 17-42.

Leamer, E. (1978). Specification searches: Ad hoc inference with non experimental data, Wiley, New York.

Levin, H. M. (1974). Measuring efficiency in educational production. Public Finance Quarterly, 2, 3–24.

Levin, H. M. (2012). More than just test scores. Prospects, 42(3), 269-284.

Lin, S., & Lepper, M. R. (1987). Correlates of Children's Usage of Videogames and Computers1. Journal of Applied Social Psychology, 17(1), 72-93.

Lleras, C. (2008). Do skills and behaviors in high school matter? The contribution of noncognitive factors in explaining differences in educational attainment and earnings. Social Science Research, 37(3), 888-902.

Madigan, D. & York, J. (1995): Bayesian Graphical Models for Discrete Data, International Statistical Review, 63, 215-232.

McLanahan, S. (1985). Family structure and the reproduction of poverty. American Journal of Sociology, 90(4), 873–901.



McNeal, R. B. (1999). Parental Involvement as Social Capital: Differential Effectiveness onScience Achievement, Truancy, and Dropping Out. Social Forces, 78, 117–44.

McNeal, R. B. (2012). Checking in or checking out? Investigating the parent involvement reactive hypothesis. The Journal of Educational Research, 105(2), 79-89.

Moral‐Benito, E. (2013). Model averaging in economics: An overview. Journal of Economic Surveys.

Murnane, R., Willett, J., & Levy, F. (1995).The growing importance of cognitive skills in wage determination. The Review of Economics and Statistics, 77(2), 251–266.

O'Keeffe, G. S., & Clarke-Pearson, K. (2011). The impact of social media on children, adolescents, and families. Pediatrics, 127(4), 800-804.

Opdenakker, M. C., & Van Damme, J. (2000). The importance of identifying levels in multilevel analysis: An illustration of the effects of ignoring the top or intermediate levels in school effectiveness research. School Effectiveness and School Improvement, 11(1), 103-130.

Park, H. (2008). The Varied Educational Effects of Parent-Child Communication: AComparative Study of Fourteen Countries. Comparative Education Review 52, 219-243.

Perelman, S. & Santín, D. (2011): Measuring educational efficiency at student level with parametric stochastic distance functions: an application to Spanish PISA results. Education Economics, 19(1), 29-49.

Raftery, A. E. (1995). Bayesian model selection in social research. Sociological Methodology, 25, 111-164.

Reynolds, A. J.& Temple, J. A. (1998). Extended Early Childhood Intervention and School Achievement: Age 13 findings from the Chicago Longitudinal Study. Child Development, 69, 231-246.

Reynolds, D., Sammons, P., De Fraine, B., Van Damme, J., Townsend, T., Teddlie, C., & Stringfield, S. (2014). Educational effectiveness research (EER): a state-of-the-art review. School Effectiveness and School Improvement, 25(2), 197-230.

Rideout, V. J., Foehr, U. G., & Roberts, D. F. (2010). Generation M: Media in the Lives of 8-to 18-Year-Olds. Henry J. Kaiser Family Foundation.

Rutten, E. A., Stams, G. J. J., Biesta, G. J., Schuengel, C., Dirks, E., & Hoeksma, J. B. (2007). The contribution of organized youth sport to antisocial and prosocial behavior in adolescent athletes. Journal of Youth and Adolescence, 36(3), 255-264.

Schnepf, V. S. (2008): Inequality of Learning amongst Immigrant Children in Industrialized Countries, IZA Discussion Paper 3337.

Schuetz, G., Ursprung, H.W. & Woessmann, L. (2008). Education policy and equality of opportunity, Kyklos, 61(2), 279-308.

Singh, K., Bickley, P.G., Trivete, P.S., Keith, T.Z., Keith,P.B. & Anderson, E. (1995). The effects of our components of parental involvement on eighth grade student achievement: Structural analysis of NELS-8 data, School Psychology Review, 24, 9-317.

Sirin, S. R. (2005). Socioeconomic status and academic achievement: A meta-analytic review of research. Review of Educational Research, 75,417–453.

Stokes, C. E. (2008). The role of parental religiosity in high school completion. Sociological Spectrum, 28(5), 531-555.

Subrahmanyam, K., Greenfield, P., Kraut, R., & Gross, E. (2001). The impact of computer use on children's and adolescents' development. Journal of Applied Developmental Psychology, 22(1), 7-30.

Todd, P. E., & Wolpin, K. I. (2003). On the specification and estimation of the production function for cognitive achievement. The Economic Journal, 113(485), 3-33.



Tough, P. (2012). How Children Succeed: Grit, Curiosity, and The Hidden Power of Character. New York: Houghton Mifflin Harcourt.

Tramonte, L. & Willms, D. (2010). Cultural capital and its effects on education outcomes. Economics of Education Review, 29(2), 200–213.

Van den Eijnden, R. J., Meerkerk, G. J., Vermulst, A. A., Spijkerman, R., & Engels, R. C. (2008). Online communication, compulsive Internet use, and psychosocial well-being among adolescents: a longitudinal study. Developmental Psychology, 44(3), 655.

Van Landeghem, G., Van Damme, J., Opdenakker, M. C., De Frairie, D. F., & Onghena, P. (2002). The effect of schools and classes on noncognitive outcomes. School Effectiveness and School Improvement, 13(4), 429-451.

Vignoles, A., De Coulon, A., & Marcenaro-Gutierrez, O. (2011). The value of basic skills in the British labour market. Oxford Economic Papers, 63(1), 27–48.

Weiss, M. R., Smith, A. L., & Stuntz, C. P. (2002). Moral development in sport and physical activity: Theory, research, and intervention. Advances in Sport Psychology, 2, 243-280.

West, M. R., Kraft, M. A., Finn, A. S., Martin, R., Duckworth, A. L., Gabrieli, C. F., & Gabrieli, J. D. (2014). Promise and Paradox: Measuring Students’ Non-cognitive Skills and the Impact of Schooling, unpublished paper.

Wilder, S. (2014). Effects of parental involvement on academic achievement: a meta-synthesis. Educational Review, 66(3), 377-397.

Woessmann, L. (2003): Schooling resources, educational institutions and student performance: the international evidence, Oxford Bulletin of Economics and Statistics, 65 (2): 117-170.

Xia, N., Kirby, S. N. (2009). Retaining students in grade: A literature review of the effects of retention on students' academic and nonacademic outcomes. (Technical Report No. 678). Available at RAND Education website:

http://www.rand.org/pubs/technical_reports/TR678/

Zinovyeva, N., Felgueroso, F. & Vázquez, P. (2013). Immigration and student achievement in Spain: evidence from PISA, Journal of the Spanish Economic Association, SERIEs.

http://www.rand.org/pubs/technical_reports/TR678/

the influence of socioeconomic factors on cognitive and non...

Documents