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“Exploring social barriers in Chile: The influence of Parental Educational Capital and Gender on Salary in People with University Degree”



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Exploring social barriers in Chile:
The influence of Parental Educational Capital and Gender
on Salary in People with University Degree
Msc Sociology (Research)
Candidate Number: 61888
Date: 30th August 2012
Word Count: 9,922
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I. Introduction............................................................................................3
II. Literature Review....................................................................................6
III. Methods................................................................................................15
IV. Results..................................................................................................23
V. Discussion..............................................................................................47
VI. Conclusions ...........................................................................................51
VII. Appendix A.............................................................................................52
VIII. Appendix B...........................................................................................54
IX. References .............................................................................................58
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I. Introduction
According to Torche (2010a and 2010b) and Nuñez and Tartakowsky (2007, 2011), in
societies with high socioeconomic inequality, such as the Chilean one, benefits and
earnings are distributed unevenly from the beginning of people’s lives, allowing some
to make use of the meritocratic principles, while others cannot. In this context it is not
surprising that both the individuals receiving these benefits or disadvantages, and the
society as a whole, forget that "personal stories unfold in a context that provides
different opportunities to its members and that when they reach adulthood, the
cumulative result of this process appears as essentially personal attribute" (Torche,
2010a, pp.28).
Following that statement I would like to argue, that in the privileged groups such as
professionals with a University degree, we can expect unequal performance in their
professions expressed by the salary that they received as the most notable indicator
which represents that social exclusion and classist system that there is in Chile.
Indeed, these aspects tell us about inequalities or barriers that come primarily from
origin and gender, but we want to explore in this thesis other sociological variables
available in our data set.
To support that, I would like to do an analysis on a special and paradoxically branded
group, considered a more successful group of people in Chile. I am referring to
practitioners who are finish theirs University`s studies and could be considered the
“elite” in knowledge, cultural, and social positional terms. Based on this elite group I
would like to show that independently of academic attainment, theoretically, there are
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some (many social and cultural) factors, that I would like to consider as barriers, which
do not allow the equity in their professional life. We will discuss that standpoint
referring to the relevant sociological theories.
Two points support the idea that will be carried out as a main dissertation topic. On
the one hand, Torche (2005 and 2010a) and Nuñez and Miranda (2009) focus their
main analysis mixing on poor, vulnerable, middle and upper class people as a whole. In
fact, they have tried to understand social mobility and inherited mobility as entire
social phenomena considering all the social strata. Therefore, these researchers have
not focuses on an important piece of the population that represented around 70% of
professional people whose family of origin came from a low or intermediate
Educational Background. I am referring to people that constitute a new middle class, a
middle class more educated and highly qualified than others in the past decades
(Bazoret,2010; Espinoza,2011). Furthermore, they have not revealed intensively the
impact of gender in that group of people that conjoin perversely with the family´s
social origin. That fact seems to be associated with a highly structured view about
mobility and inequality in Chile.
On the other hand, research on higher education has tended to stress the role of
educational attainment has had in the last 30 years in the Chilean society. There are
more people in the Higher Educational system and more Universities than ever in the
history of the country. Can this social inequality context and their barriers be the
starting point in undermining the power of the Higher Education in the society?
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I will do a statistical analysis based on “Principal Occupational Salary” monthly earning
considered as the main response variable and using as explanatory variables the Social
Origin operationalized as Parental Educational Capital and gender. However, during
this thesis I would like to explore some other explanatory variables associated with the
response variable “Principal Occupational Salary” available in the CASEN data sets
analysed, such as changes in job between 1996 to 2001, the city where someone was
born, how they got a job, ethnicity and age. As will be shown these variables are
reflexive of some theoretical issues that will be developed through process of
conceptualization and operationalization in this thesis. That last has been doing in the
times and conditions that a short thesis allows.
I would like to use the Caracterización Socioeconomica (CASEN) Survey´s data sets
from the Ministry of Social Development of Chile. In order to respond to my statement
I will use three similar but different surveys. First, the Panel Casen data set (wave 1 and
2), and the cross sectional surveys CASEN 2006.
Research Questions
The research questions are as follows:
Do people with university degrees whose parental educational backgrounds are low
and intermediate obtain a similar monthly salary than peers from high parental
educational backgrounds in Chile?
1) Are these gaps in salary evident in males and females in the cases analysed?
2) Which other sociological variables could be associated with that gap in the available
data sets?
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II. Literature Review
The Chilean Higher Educational System
During the last forty years Chile has reinvented itself as a country in many regards. This has for
instance been highlighted by Undurraga (2011), who has pointed out major transformations in
the political, economic, social and cultural spheres, which have triggered profound changes in
the direction and destination of the country.
One of these areas has been the educational system and specially the Higher Educational
System. According to Urzúa (2012) and Torche (2005), as in rest of the world, the interest in
getting a University degree in the last 30 years has expanded at rates that at the beginning of
the 90’s were not expected. On the one hand, in Chile this growth could be explained by
changes in the Higher Education System , which resulted from a fast increase in the enrolment
of young people at private university that started in the 80’s.
On the other hand, that system has been supported by different political centreleft-wing
coalitions (Concertación 1990-2010) and it has also been continually supported by the new
right-wing coalition government (Alianza 2010-2014) both based on the strong belief that a
system like this could open more significant opportunities and generate social mobility. For
that reason, it seems to be that all the governments have been introducing certain reforms
that focused on access more than on the quality of the system and changing the free-market
fundamentalism of the entire educational system. Therefore, the idea of better education is a
strong cultural belief.
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Graph 1
Evolution of people enrolled at Tertiary Education in Chile by
Sort of Institution
Technical Training Centres
Source : Information Service for Higher Education (2012). MInistry of Education Chile.
From a very positive position, supporting this increasing educational openness, some
researchers have encouraged the idea that this process has been incarnated by "Subject type",
young people who have parents who were unable to access tertiary education achieve this
(Castillo,2010). They have been called the "first generation in higher education". According to
Castillo, "Seven out of ten young people studying a degree come from families in which no
member had had the opportunity to do it” (Armanet, 2005 in Castillo,2010). This reinforces the
idea of a growing trend in our educational system.
However, according to Caballing (2012), during the years 2006 and 2011 thousands of students
filled the streets to demand better public education, more social justice and equal
opportunities. They rejected the free-market fundamentalism in the entire educational system
that has generated segregation, stratification and inequalities. Does the Chilean society have
to more to move to a fairer and more just system? Are there more actual opportunities in the
social and labour scenario for people independent of their social background?
Educational and Income mobility: Social Inequality similarities in Chile and UK
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As academics responding to the latest questions, Núñez and Tartakowsky (2007) pointed out
that despite the range of educational changes, openness of higher education and more young
people doing a degree career, Chile’s social inequality-levels have remained virtually
unchanged over this period. Based on an analysis of intergenerational income mobility, Núñez
and Miranda (2009) deepen the analysis by Torche (2005) by providing evidence of increased
intergenerational mobility in education (years of schooling) in the last decades. However,
these authors have not found any significant evidence of increases in intergenerational income
mobility. For Núñez and Miranda (2009), this indicates that increases in educational mobility
that have apparently existed in Chile did not translate into intergenerational income mobility
to the same extent, at least for the reported period of cohorts between 1960 and 1983. This
seems to indicate that there are factors which are limiting the transformation of increased
educational mobility to increased income mobility.
According to Núñez and Miranda (2009), these could include differences in quality of primary,
secondary and tertiary education to which different socioeconomic segments of the
population have access to, as well as the role of social networks and the existence of
discrimination in employment based on social origin.
Overall, according to Torche (2010), there has been an increase in socioeconomic inequality at
the secondary and postsecondary in cohorts that experienced the economic crises, such as the
Asiatic Crisis in 1998 and the Mexican Tequilazo in 2000. However, there is a decline in
inequality in elementary and secondary level transitions due to their universalization. In
addition, this author pointed out the reduced advantage of males, and in some cases a
growing advantage of females at the same levels of education.
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Similar to the analyses of Chile, sociologic and economic research from the UK recognizes the
importance of education in determining the positions in the labour market, including
subsequent rewards; however, it also recognizes that other factors play an important role
(Jackson, Onwuegbuzie and Turner, 2007). In addition, Jackson et al. (2005) state that "the
association between education and class position is found to be declining, this is likely to be
because education is of reduced value to employers in making their personnel decisions in one
or other, or both, of its two main roles: i.e. as serving to certify relevant competencies or to
signal desirable attributes of employees that are not in themselves directly observable."(p.26)
As shown, there is evidence supporting the idea that more education is a necessity but not a
sufficient condition for increased social mobility (Jackson et al,2007; Goldthorpe and Jackson,
2008 and Boliver,2011). This is because there are barriers to a full realization of education as
the catalyst for social mobility, i.e. there are obstacles that prevent social subjects to enter
meritocratic positions (for a discussion of education-based meritocracy see Young,1958;
Sanders,1995, Themelis,2008). It can be shown that in highly industrialized societies,
characterised by a segmented capitalist economy such as the UK, education is increasingly
losing strength as a trigger of mobility and distinction in the labour market.
To sum up, one important point can be noted based on the UK and European experience:
“There is little sign of economic growth, rising levels of consumption or educational expansion
per se having any connection with greater educational equality” (Goldthorpe, 2010, pp.231).
Therefore, from a theoretical point of view, the assumed role of education as a source of
achievement in the meritocratic ideal has been questioned in the last years (Goldthorpe and
Jackson, 2008). The crisis is not only embodied by Chile, but seems to be global.
Applying the concepts and analyses of Torche (2005, 2010a), the meritocratic ideal would lose
its foundation as an effective agent of change. Contrary to meritocratic assumptions, what we
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witness in Chile is an example of inherited meritocracy, or in the words of Núñez and Miranda
(2009), a reproduction of Chile’s unequal social structure. Therefore, the interpretation of the
social conditions and aspects of the actual life, such as social origin, and being part of a
vulnerable family, are vital to reveal insights into some apparent consequences that the social
system produces in its subjects. These characteristics have had an important impact on
people’s life, and in most of the cases they are conditioning people’s experience, life and
death. Some of the concepts that could be pinpointed here refer to theories about social
reproduction and aspects that leave unchanged the social condition of people, such as gender
and racial-ethnicity issues that could be associated in this scenario.
The Role of Social “Class” Origin: Bourdieu’s Social and cultural Capital
The theory of cultural reproduction (Bourdieu , 1992, 1993; Bourdieu and Passeron, 1990)
explains the connections between social class of origin and social class of destination in terms
of the impact of cultural capital on educational attainment. According to Bourdieu, cultural
capital consists of familiarity with the dominant culture in a society. He argues that the
possession of cultural capital varies with social class, and it is likely that parents’ transferred to
their children their understanding, information (knowledge) and beliefs about the society, the
educational system and their expected result in real life.
“This consists mainly of linguistic and cultural competence and that relationship of familiarity
with culture which can only be produced by family upbringing when it transmits the dominant
culture” (Bourdieu, 1977 in Zimdars, Sullivan & Heath, 2009 pp.650)
At the same time, according to Portes (1998), Bourdieu emphasizes the intangible character of
social and cultural capital relative to other forms. Whereas economic capital is in people’s bank
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accounts and social and cultural capital is inside their heads, social capital is inherent in the
structure of their relationships and the cultural inheritance of sophistication and distinction.
Ultimately, to possess social and cultural capital, people must be related to others, and it is
those others, not himself, who are the actual source of his or her advantage. It can be noticed
that in the concept of social capital, the motivation of others to make resources available on
concessionary terms is not uniform. At the broadest level, one may distinguish between
cultural-talented versus instrumental motivations to do so, depending on the level of
sophistication and distinction which social influence is added to in that relation.
Therefore, the possession of social and cultural capital varies with social class and thus, it is
very difficult for people from a low parental cultural capital to succeed in the educational
system. Again and from a socioestructuralist standpoint, a conditioning establishment is
revealed. One system, where the symbols and codes (Bernstein, 1996) are reproducing
inequalities in a system that some authors call, sharing the Bourdieusian concept,
`resemblance within a difference' (Bourdieu and Wacquant, 1992, pp. 106, in Naidoo, 2004,
p.459), consists of cognitive and structural mechanisms that mediate socio-political and
economic forces while simultaneously reproducing fundamental principles of social
From a different standpoint but connected with this resemblance system, Lucas (2001) shows
that among the numbers of students entering higher education, students from the most
advantaged social backgrounds look for new strategies to try to maintain their advantage.
According to this theory of Effectively Maintained Inequality (EMI), ‘the socioeconomically
advantaged seek out whatever qualitative differences there are at level and use their
advantages to secure quantitatively similar but qualitatively better education’ (Lucas, 2001,
pp.1652). Therefore, as Torche (2005) points out, the “quantitative inequality” may be
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replaced by “qualitative inequality,” that is, the advantaged classes will be able to obtain
educational credentials that provide them with enhanced opportunities for further attainment.
In Bourdieu’s terms, they are looking for more and more distinction.
However, according to Zimdars et al (2009), Bourdieu’s analyses of cultural reproduction focus
just on social class, and ignores issues related to gender and ethnicity. Nevertheless, it may be
that differences in particular cultural resources and parental educational attainment can be
explained in conjunction with gender and ethnic differentials in educational outcome. In other
words, those aspects could be genuinely connected to each other rather than just the social
class differentials as a sole and main factor. This is an open theoretical assumption that will be
explored through this dissertation.
The Role of Gender: More Discrimination in the social system.
According to Chang (2000), the long-standing belief has been that occupations are the core
aspect of the class stratification system and social reproduction aspects. It means that for
some occupations, despite women entering into the formal economy in ever-increasing
numbers, the occupational structure becomes the main locus of gender stratification as well.
“That is, occupational segregation affects the gender gap in earnings, the likelihood of career
mobility, and the possibility of having some degree of autonomy over one’s work”
(Chang,2000, p.1658). However, those gaps are better shown in salary differences between
males and females.
At that point, from a sociological and theoretical standpoint, salary differences between males
and females caused by discrimination can result from several processes. The first is where
women are differentially allocated to occupations and establishments that pay lower wages.
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According to Petersen and Morgan (1995) and Castilla (2010), that primary mode can be
conceptualized as "allocative discrimination". The second is where occupations held by women
are paid at a lower salary than those held primarily by men, although skill requirements and
other salary-relevant factors are the same; this is called "valuative discrimination." The third is
where women receive a lower salary than men within a given occupation within a given
establishment, combining the allocative and valuative discrimination that involves the
segregation of men and women into different occupations, establishments, or both, and may
occur without within-job wage discrimination. Thus, it may be the case that where men and
women share the same jobs they receives the same pay but that in most cases they simply do
not share the same jobs.
Some studies carried out by Hogan, R. Perrucci., C. , and Behringer. A. (2005) and Hogan, R.
(2001 and 2011), considering the USA context, have asserted that females, particularly
professional married women, do not face the same disadvantage in family wealth or in
earnings that low-income self-employed women face, but that self-employment, in general,
seems to predict lower earnings for women, while in less developed countries such as Chile the
evidence does not share the same diagnosis. According to Bravo, Sanhueza & Urzúa (2008)
“Women are effectively discriminated against in the labour market, with the largest gender
gaps observed among the less educated groups” (pp.19)
Ethnic assumptions: more and more discrimination
According to Hogan, R. (2001 and 2011), the “competition versus exploitation” distinction
between labour market and class theories is not sufficiently nuanced to capture ethnicity and
gender barriers that are either independent of class or that interact with class and labour
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market, as in the racial, ethnic and gender barriers to and effects of self-employment,
professional credentials, and managerial and supervisorial authority.
According to Padilla (1985), ethnic identity develops from the cultural and structural
similarities of two or more groups and often in response to common experiences of social
inequality. He describes how such conditions as poverty and racial discrimination have
necessitated the assertion of a broader ethnic consciousness and behaviour, often more
successful in social action than individual, cultural or national associations. Nevertheless,
those issues seem to be no related with the life of some ethnic group in Chile. This country is
less ethnic differentiated in South America; we will like to know if that is real.
Chile: A brief history of its culture and geography
However, to put forward those theoretical concepts it is necessary to contextualize some
aspects of the Chilean culture. Chile, as has been pointed out, has been a kind of “laboratory”
of neoliberal policies (Harvey D., 2005 and 2010) that have been systematically carried out by
different governments from 1990. It is in fact a small country of around 16 million people
where 5% percent is said to belong to ethnic groups. However this could be rapidly increased
by the massive migration of Peruvians, Colombians, and Ecuadorians in the last ten years
(Cano and Soffia 2009). Geographically, it is a long country in physical and spatial terms,
where the political, economical and administrative power is controlled from the capital. For
those reasons, topics such as the region where people live or were born, or whether they live
in the capital, are concepts that can differentiate certain life conditions of the Chilean people.
Finally, as in other small countries, Chile has been so influenced by the economic crisis,
especially in 1998 and 2000. Both years can help to explain differences and contradiction in the
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III. Methods
The purpose of this research was to carry out a quantitative study to analyse if there
are differences in the main occupational salary given as a result of Parental
Educational Capital and Gender to professionals with a higher educational degree in
Chile. At the same time, this research explores other sociological variables that can
additionally explain these differences. The analysis of the information collected was
carried out with a t-test of differences of means to non-paired, paired samples and
multiple linear regression analysis.
The chapter is divided as follows. First, a brief description of data and sources utilised
in the current research. Second, a description of the number of cases and the
definition of the cases selected to carry out this dissertation. Third, the analytical
techniques are explained. Fourth, the main concepts are conceptualized and
operationalized, as well as considering measurement issues, and defining response and
explanatory variables. Finally, the chapter states some pitfalls, as well as, strengths,
weaknesses and limitations of this study.
Data and sources
This research was carried out using available data sets on the proper social realty of
Chile. For that reason, it was decided to use an available secondary data set used by
the government and social research institutions in that country. The main data source
was the CASEN surveys panel data set 1996 to 2001 (waves 1 and 2) and the Cross
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sectional CASEN 2006 (Ministry of Social Development, 2012; Social Observatory
Alberto Hurtado University, 2007).
I used these three data sets to better understand and infer my statement. On the one
hand, the panel data set allows the consideration of a longitudinal view about
practitioners in a period of time from 1996 to 2001. On the other hand, the cross-
sectional survey of CASEN, as will explain further, allows us to undertake a deeper
analysis to better understand those professionals. Additionally, the CASEN 2006 allows
for a more explanatory model to better understand and included some different or
unused variables in the panel CASEN 1996-2001.
Overall, the data sets allow us to have a general view based on reliable and
probabilistic information collected during a period of time from 1996 to 2006 in Chile.
Sample framework
The group of people that is at the core of this research are professionals having
finished their careers at the moment of response to both surveys. However,
information about Parent's Education and their jobs was just asked to Households
(jefes de hogar) and his/her Couples (Cónyuge o Pareja). Additionally, for analytical
purposes and to have cases with the most similar labour conditions, the cases were
selected considering males and females that at that time had been working at least 30
hours per week in the labour market, and whose ages there were between 26 and 65.
Likewise, for similar purposes, the sample did not consider professional males and
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females working at the FFAA (Army, Police etc.) and personal or domestic service. On
the other hand, for statistical purposes, it has to take into account to the omission of
extreme cases, called outliers, in the “Main Occupational Salary” variable.
Therefore, not all the people involved , especially, in the CASEN surveys in panel 1996-
2001 and in the Cross sectionals surveys CASEN 2006, passed those requirements on
the key issues to be carried out in this research. As a result the dataset was filtered.
Kind of Analytical Techniques
The main analysis that I will carry out is T test and multiple regression analysis. In the
case of the multiple linear regression analysis, the main response variable was “Main
Occupational salary” and the explanatory variables were “Parental Educational
Capital”, “Gender”, “Head or Manager job Father”, “To become a Postgraduate”,
among other things.
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Five separate regressions models are built to explore how available sociological
variables explain predicted outcomes on the “Main Occupational Salary” and to
establish which are statistically significant and have, therefore, more influence
depending on the model created.
The software STATA 12.0 was utilized as statistical tool, using a mix of descriptive and
inferential analysis.
Variables and Measurement
As noted, the main response variable here is “Main Occupational Salary”, and this
variable has a continued measure level.
Response Variable
Brief Definition
Level of
Income earned by
employed in their main
occupation, either by
way of dependent
work in the case of
employees or
independent work
concept in the case of
employers or
employers and self-
employee (Ministry of
Social Development,
2012; Social
Observatory Alberto
Hurtado University,
Income module
question: Last month
How much your salary in
his/her main
occupation/job was?
Summary Statistics see Appendix A.
The panel the income was corrected by current money at 2001 to be equal and comparable
measurement with 1996. That was doing by “IPC” an inflationary index from Chile.
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The explanatory ones referred to consider more sociological aspects than economic
Regarding our main definition about some sociological variables that can be used from
the data sets available, an important point has to be clarified and explained to
understand better our research analysis.
We are not able to consider embodied aspects of social class and socio-cultural
distinction as some cultural sociologists have pointed out (i.e. habitus”).
Unfortunately, for concrete reasons and available information, this is outside of the
scope of the current study. Indeed, according to Zimdars et al (2009), measures of
cultural , educational and social capital are limited, and cannot properly capture each
dimension of those concepts. In order to operationalize a concept, the researcher is
forced to define clearly and reliably the concept in question.
in surveys
Capital Family
Represents the
quality of
attainment that
father and mother
got it.
Which level of
education of father got
Recoding and
matching both
variables. In case of
differences the father
educational level
was considerate first
Low: elementary levels
complete/non complete
Secondary and technical
secondary education
High: University,
professionals technique
institutes levels
Which level of
education of mother
got it?
Occupation as
Head or
Had Interviewee’s
Father a top
Occupation at
labour market
Which main occupation
your father got it?
Head or Manager
Recoding the variable
into a dummy
0==Other occupation
1==Head or Manager
Are you Male or
Recoding the variable
into a dummy
How old are you?
Recoding the variable
into 4 categories
Variables was used as
continue and
categorical (Age’s
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in surveys
Live in the
current living
District or Commune’
where are you living?
Recoding the variable
into a dummy
0==Living in Other
1==Living in Capital
To get a
receive a
title recently
Which level of
education you got it?
Recoding the variable
into a dummy
0== other else
Changes in
between 1996
and 2001
changes in their
main occupation
from 1996 to
Which is his/her
main occupation
Which is his/her
main occupation
Recoding both
variables into a one
0== Did not changes
1==Did changes
Kind of
main occupation
Which is his/her
main occupation
Recoding both
categories into
several dummy
variables which base
line was to be Head
and Manager
Head or Manager==0
Worker or Employee
at Public Sector==1
Worker or Employee
at Private==1
How do
generally got
a Job?:
Other way
Modes that
people generally
get a job in Chile
Independently of
your own effort: Who
helps you to get
his/her Cuurent Job
Recoding the variable
into a dummy
ivate institutions
1==Other ways to get a
Born in a
district to
where they
Changes in the
original district
interviewee born
Are you living in the
same district where
your mother lived?
Recoding the variable
into a dummy
Belong to some
original ethnic in
Do you belong to the
follow Ethnics?
Recoding the variable
into a dummy
Strengths, weaknesses and pitfalls
As has been pointed out through this chapter, some of the strengths of the current
research have been based on the use of reliable and valid information on the statistical
characteristics of professionals in Chile. Indeed, this strategy to carry out the research
allowed the saving of time and money.
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However, that point has been a weakness as well. Due to constraints the
conceptualization and theoretical assumption have been based on secondary sources.
In fact, the current data sets sometimes generated a change of research focus and a
lack of information in related topics. In particular, this occurred in the analysis of the
Panel data set 1996-2001, where the level of attrition reached 48% between 1996 and
2001 and some variables were just considered in one wave, but not in another. With
regard to this, it has been convenient for this dissertation to construct and use the
CASMIN (Comparative Study of Social Mobility in Industrial Nations) and the
Goldthorpe, EriksonGoldthorpe class scheme based on employment status and
occupation of parents ( Erickson and Goldthorpe, 1993; Goldthorpe J, Withllewellyn, C.
and Payne, 1987). However, the level of attrition was so high in consideration of the
parental occupation in the panel data set that it was not possible used it.
On the other hand, it can be commented that based on statistical techniques, all the
data were analyzed applying econometrics principles, such as tests of hesteroscadicity
and graphs of residuals, as well as testing and assessing linearity and graphical tools to
see the distribution of the data available (see appendix C).
It is necessary to keep in mind that the use of the Panel Data Set (1996-2001) was just
referential information and just represents a sample with some attrition problems.
However, the information and cases provided by the same one has high quality in
terms of the reliability of the information collected to each case considered. For that
reason, an analysis of the Cross-sectional CASEN was carried out to follow up the
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consistency and trends with the 1996-2001 sample analysed in this dissertation. The
cross-sectional 2006 data set was a relievable and truthful data set and was very useful
to show the main statistical result in this dissertation.
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IV. Results
In this chapter, the main differences in occupational salary during the last years (1996 -
2006) will be provided. The analysis will draw on different models, whose main aim is
to reveal which variables could be associated with our explanatory variables “Main
Occupational Salary”. Certainly, our focus will be around some vital variables that
seem to be the core of our research, that is, “Parental Educational Capital” and
“Gender”. Using those variables as initial explanatory variables, different and available
variables will be analysed using descriptive and inference statistical analysis.
As can be noticed, there are some significant differences in “Principal Occupational
Salary”, contrasting mainly with Parental Educational Capital, gender and others sort of
variables. The evidence and previous research reveals that these gaps have remained
stable during the last years with some differentiations depending on social strata,
principally those which are supported by research into the social mobility and
inequality that underly the land of flux and opportunity land that is Chile. However, as
this dissertation claims, these gaps are very strong and deep-rooted in the Chilean
middle and upper middle class. What is more, it already co-exists in the lives of those
who have had educational attainment as well as having received a degree at
University. I will reveal this fact based on information coming from the longitudinal
Panel CASEN 1996 to 2001 and CASEN 2006 data sets.
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In order to present in a simple way some differences based on means analysis, a t
statistical test (t-test) has been used primarily to draw a general and open view. Then
testing based on multiple linear regressions analysis has been added and more
relevant models will then be shown.
What do panel data show in 1996 and 2001?
Apparently, there was no change in “Main Occupational Salary” if you had a university
certificate between 1996 and 2001. In fact, doing a T test analysis it is possible to show
that there are no differences between “principal occupation’s salaries” (by 153 people)
in 1996 compared to 2001.
Table 1
Comparison of Principal Occupational Salary 1996 and 2001 (n=153)
(t=0.086, DF=152, p<0.932)
The T statistic test to paired samples shows that there is not a significant statistical
difference in the mean of “Principal occupational salary” for both years. That means
that there is not enough statistical evidence to reject the null hypothesis (Ho) = mean
(MOS 96 MOS 01)=0 at the 0.05 level.
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However, if we do a deeper analysis crossing that paired mean for both years, it is
possible to show that there is just one significant difference depending on the “Parent
Educational Capital”. Table 2 shows the main significant difference that appears for
people whose Parent Educational Capital is Low.
Table 2
Comparison of Principal Occupational Salary 1996 and 2001 by Low Parent Educational Capital (n=55)
(t=-4.630, DF=54, p<0.001)
The table reveals a significant difference between “Principal Occupational Salary”
between 1996 and 2001. This analysis allows us to reject the null hypothesis assuming
the alternative one that there is a significant difference between both years when the
mean of “Principal Occupational Salary” crosses “Low Parent Educational Capital”. On
the contrary, for people whose father’s educational background is intermediate and
high there are no significant differences. For that reason, it is not possible to reject the
null hypothesis for those groups.
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Regression analysis based on Monthly Principal Occupational Salary” panel survey
year 1996
Another way to examine that trend is by adding regression models which include
variables that seem to be associated with the logarithm
of “Monthly Principal
Occupational Salary” on 1996. Table 3 shows the models that explore the partial
association between the response variable and a set of explanatory variables in 1996.
These explanatory variables have been selected considering the theoretic and
pragmatic criteria that the literature and the dataset review have provided.
Table 3: Multiple linear regression Monthly Principal Occupational Income of Professionals in
1996 on selected explanatory variables
Parental Educational Capital Intermediate
Parental Educational Capital High
Age 1996
Live in the Capital’s Country 1996
To receive a Postgraduate degree 1996
adj. R-sq
Prob > F
= *p<0.05; **p<0.01; ***p<0.001
Source: Own elaboration based on Casen Panel 1996-2001
Please refer to all further confidence intervals in appendix B
Here the specialized literature (Tarling, 2009; Bravo and Vasquez, 2008; Agresti and Finlay, 2009)
recommend use of the natural logarithm of the salary because the empirical log of income is closer to a
normal distribution, thus solving statistical and linear problems with applied econometrics techniques.
27 | P a g e
It can be seen that the model is highly significant, as is indicated by the F test
(p<0.001). Additionally, the R2 indicates that close to 22% of the variance of “Principal
Occupational Salary” is explained by the model added.
Additionally, the model indicates a significant partial association between the
dependent Dummy variables “Parental Educational Capital of Family: Intermediate”
(t=2.82; p<0.01) and “High” (t=2.10; 0,05) and the dummy variable Female (t=-
The interpretation of the estimated statistical significant coefficients is as follows:
The coefficient of dummy Parental Educational Capital: Intermediate is 0.47. This
shows that, controlling for the other explanatory variables, the predicted “Main
Occupational Income” is 47% higher for people whose Parental Educational Capital is
intermediate, compared to those whose Parental Educational Capital is Low. Similarly,
the coefficient of dummy Parental Educational Capital: High is 0.660. This reveals
that, controlling for the other explanatory variables, the expected “Monthly Principal
Occupational Income” here is 66% higher for people whose Parental Educational
Capital is High than for those whose Parental Educational Capital is Low.
On the other hand, the coefficient of dummy variable “Female is -0.65, suggesting
that after controlling for other explanatory variables, the predicted monthly payment
to females could be 65% lower than to Males.
The other explanatory variables whose coefficients do not result in significant statistics
associated with the monthly salary seem to be a bit historically unexpected. The most
noticeable cases are the dummy variables “To become a Post Graduate” and Age. As
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the statistical model suggests, maybe in 1996 the mode to “get ahead” for
professionals was not highly associated with continuing studies on a master’s course.
Additionally, the variable Age that could be associated with an increment in the
expected monthly “principal occupational salary” per year, does not present a
statistically significant prediction about the salary in the cases analysed in 1996. It is
important to take into account that an interaction and logarithmic transformation was
carried out on the variables without positive results being statistically associated with
the explanatory variables.
However, the question about the statistically significant differences expected in the
monthly occupational salary between the main categories of Educational Capital of
Parents and females/males in 1996 are highly connected with the theoretical evidence
and the previous studies considered for this thesis. Indeed, the analysis implemented
is coincidently connected with the literature about Chile supported by Torche (2005,
2010a), Nuñez and Miranda (2009), Espinoza (2011), (Bravo et al, 2008), who support
the idea about the important levels of differentiation and inequality in Chile. However,
the focus in this research has been on narrowing the analysis on Professionals with a
higher degree working in the labour market.
Are these conditions in 1996 fair or understandable to someone that reached a certain
level of educational attainment? This statement will be explored through the analysis
of the main results.
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The next analysis looks to carry out a different analysis considering the same number
of professionals workers in the labour market with a higher degree (n=147), but five
years later. The question here is what happened to the same people five years later?
However, it is important to take into account that it is not a statistical longitudinal
study, given the limitations of time and knowledge gain for the researcher developing
this thesis. This Issue was discussed in the limitations of the current dissertation.
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Regression analysis based on “Main Occupational Salary” panel survey Year 2001
In order to explore the state of the explanatory variable in the same sample (n=147), a
second regression was fitted. In the current analysis, it was carried out considering an
explanatory variable available and with less attrition problem. The most reliable
variable with the research variables were ‘Changes in the occupation’, considering the
actual changes of the cases between 1996 and 2001. The model was fitted presented
as follows.
Table 4: Multiple linear regression Principal Occupational Salary 2001 on selected independent
Parental Educational Capital Intermediated
Parental Educational Capital High
Age 1996
Live in the Capital’s Country 1996
To receive a Postgraduate degree 1996
Changes in Occupation between 1996 and
adj. R-sq
Prob > F
=~ p < 0.10; *p<0.05; **p<0.01; ***p<0.001
Source: Own elaboration based on Casen Panel 1996-2001
The predicted model on the monthly average of “Principal Occupational Salary 2001” is
highly significant, as is indicated by the F test (p<0.001). Additionally, the R2 indicates
that close to 26% of the variance of dependent variables is explained by the model
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The model indicates a significant partial linear association between the monthly log of
“Main Occupational Salary 2001” and the dummy variable Female (t=-3.79; p<0.001).
In addition, the model suggests a significant partial association with the dummy
variable “Changes Occupation between 1996 and 2001 (t=-3.08).
The interpretation of the estimated statistical significant coefficient is as follows:
The coefficient of dummy variable “Female” is -0.378, suggesting that even after
accounting for the control variables the predicted monthly pay for women decreased
by 38% compared to that for men.
Interestingly, the coefficient of dummy variable “Changes Occupation between 1996
and 2001” is -0.343. That indicates that controlling for all the exploratory variables, the
expected salary for people who changed their work between 1996 and 2001
decreased by 34% more than for people who remained in their jobs.
One interesting issue from the 2001 selected model indicates that coming from an
Educational Capital of Family intermediate or High, compared with coming from
Educational Capital of Family Low, does not significantly affect the expected (log of)
Monthly Main occupational salary average. It can be underlined that the null
32 | P a g e
hypothesis of association between those dummy variables and the explanatory one
cannot be rejected at a significant level of 5%. That fact could indicate a continuity of
the differentiation in the expected main occupational salary after five years, from
people coming from a more educated family. However, this fact must be taken
cautiously because the research was not carried out using a proper longitudinal
However, from a other statistical standpoint it can be argued that the null hypothesis
can be rejected at a significant level of 10%. Therefore, assuming that level of
significance, the predicted (log of) main occupational salary coming from a family with
a High Educational Capital (t=1.77; p<0.079) increased by 36% compared to those
coming from a Low Educated Family. That fact could indicate a continuity of the
differentiation in the expected main occupational salary after five years for people
coming from a more educated family. Unfortunately, in order to retain enough cases
to fit a multiple linear regression, it was not possible to analyse the sample doing a
cross tabulation by the same type of Work because the bases of cases involved
decreased so much.
From another contextual and historical standpoint, the statistical association indicated
in the model with the dummy variable “Changes Occupation between 1996 and 2001
reveals an historical Issue. During the years 1998-2000 Chile and others countries from
Latin-America suffered the effect of the “Asiatic Crisis” (Chang and Velasco, 2000).
That can explain the statistical significance that this variable had in the predicted main
occupational salary for professionals at that time.
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In spite of there being two totally different statistical models (different R2, residuals,
coefficients, statistical level of acceptance of null hypothesis, etc.) and conceptual
terms, there is a noticeable figure here. On the one hand, the models show that after 5
years, coming from a family with a Higher Educational capital is found to have a
positive effect on the predicted main occupational salary compared to people coming
from a family with a Low educational Capital, holding constant all the explanatory
variables in the models. On the other hand, the gap between those people coming
from intermediate Educational Capital and people coming from families with a low
Educational Capital presents an apparent convergence through the years. However,
this result considers a general sample without considering differences by age, kind of
job, or occupation. The sample size and the attrition problems created difficulties for
that task.
At the same time, result was important for noticing that females five later keep
significant differences theoretically connected with the theoretical framework and the
research evidence shown earlier. However, it is possible to notice with the current
evidence that in our sample the predicted salary of females is reduced compared with
males, but that those predicted outcomes or differences after the five year result are
no lower than in 1996. In other words, in 2001 the female salary is lower but not lower
than in 1996.
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What does a cross sectional view shows?
In this section I would like to show figures from the cross sectional survey CASEN 2006.
This analysis will be carried out using the same variables presented in the previous
section. However, as was demonstrated in the last section, some variables have been
presented in different categories, changing a little the meaning of the interpretation.
The idea is to deepen the analysis using a larger sample size and complementing some
explanatory aspects related to our response variable “Principal Occupational Salary”.
This perspective allows us to follow some general patterns and underline some other
variables that were not present in the panel data set because some of them were
In sum, this different view can be more helpful to support our statement that has
been followed through this thesis.
On the other hand, graph 2 shows from a general viewpoint, that there are significant
statistical differences in the means of the main occupational salary by age groups.
The one way ANOVA reveals that there are significant differences between those
groups called Low, Intermediate and High Parental Educational Capital”. Therefore,
there is significant evidence against the null hypothesis which indicates that the means
are not different for the population (p-value <0.001) in the ANOVA analysis and
Bartlett's test for equal variances.
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Graph 2
Princip al Occupational Income (Chilean Pesos)
Principal Occupational Income by Parenta l Educational Capital and Age
Groups (2006)
Professionals with University Degree Finished (n=3243)
Source: Own Elaboration based on Casen 2009. Ministry of Planification of Chile
Analysis of Variances: Pr ob>F="*p<0 .05 ** p<0.01 *** p<0.0 01"
*** *** *** ***
Considering these significant statistical differences and characteristics
two linear
regression models were fitted.
Regression analysis based on “Principal Occupational Salary” Cross sectional Survey
CASEN 2006
The table 5 shows the two models fitted. The first model was designed considering just
three dummy variables and one continuer. The second model included more
explanatory variables that seem to be statistically significant to predict the log of
“Main Occupational Salary”. Some of these variables have been suggested by the
A normality test it was carried out, and is presented in the appendix B.
36 | P a g e
theoretical or contextual assumptions guiding our research questions. Likewise, the
two models complement each other to present how much variance of the explanatory
variable is explained in conjunction with the explanatory variables considered by the
models. Therefore, R2 reflects the variation around the fitted regression line.
One of the main guiding hypotheses is that there are statistically significant differences
in the predicted log of “Main occupational salary” by Educational Capital of families
and gender in the Chilean professionals. Now with a data set with a wide sample of
cases we would like to fit a more reliable prediction. However, the analysis on the
Cross sectional CASEN 2006 does not have a view after 5 years as has been previously
indicated by the panel CASEN 1996-2001.
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Table 5: Multiple linear regression6 Log Principal Occupational Salary of Professionals in 2006
on selected independent variables
Model 1
Model 2
Educational Capital Family
Educational Capital Family High
Female 2006
Age 2006
Father Occupation as Head or
Live in the Capital’s Country
To receive a Postgraduate degree
Occupation: as Self-employed
person 2001
Occupation: as Worker or
Employee at Public Sector 2001
Occupation: as Worker or
Employee at Private Sector 2001
How do generally got a Job?:
Other way
Born in a different district to
where they live
Was born between 1980-1976
Was born between 1975-1966
Was born between 1967-1955
Was born between 1954-1946
adj. R-sq
Prob > F
= *p<0.05; **p<0.01; ***p<0.001
Source: Own elaboration based on Casen 2006
Consulting on linearly and heteroskedasticity assessment - please see in the appendix B for fitted
38 | P a g e
Model 1 2006
Model 1 indicates statistically significant evidence that there is a linear association
between the logarithm of “Main Occupational Salary” of professionals and the
explanatory variables in the population. The R2=0.14 demonstrates that 14% of the
observed variation of “Main Occupational Salary” by professionals is explained by
variation in the variables included in the model.
The table 5 also show statistically significant association between the predicted log of
monthly “main occupation salary” by the dummy variables “Educational Capital of
family” Intermediate (t=4.38; p<0.001) and high (t=10.70; p<0.001), as well as with the
dummy variables of “Females” (t=-17.93; p<0.001) and the continue variable of age
(t=7.67; p<0.001), holding constant the correspondent explanatory variables. Each of
these variables, as in the previous analysis on the years 1996 and 2001, revealed a
statistically significant association with the response variables log of Main occupational
Interestingly, the variable Age in this model’s result was positively associated with the
predicted Monthly “Main occupation Salary”. However, it can be seen that the effect in
the prediction of the salary is very small. The coefficient to this exploratory variable in
the model 1 is 0,009, suggesting that holding Educational Capital of Family
Intermediate and High, gender constant, and increasing age by one year, increases the
expected Monthly “Main Occupational Salary” by almost 1%.
It can be noticed that an interaction was fitted between the dummy variable female
and age but this was not shown to be statistically significant associated with the
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response variable in this model. In addition, there was an interaction between the
dummy variables, Parental Educational Capital Low, Intermediate, High and females,
but this did not provide a statistically significant result fitting a regression model with
those interactions.
Model 2 2006
Model 2 indicates statistically significant evidence that there is a linear association
between the average “Main Occupational Salary” of professionals and the explanatory
variables in the population. The R2=0.28 demonstrates that 28% of the observed
variation of “Main Occupational Salary” by professionals is explained by variation in
the variables included in the model.
The current model contrasts with mode 1 and shows that the dummy variables
referred to as Parent Educational Capital Intermediate leave off being partially
associated with the explanatory variables. However, the explanatory dummy variables
referred to as Parent Educational Capital High (t= 7.07; p<0.001), the dummy variables
female (t=-16.48; p<0.001) and the continue Age (t=3.78; p<0.001),still have almost the
same statistical significant pattern that indicates a partial association with the
predicted value of the response variable.
A particular note takes the dummy variables about kinds of Occupations that are
compared with the baseline Head and Managers. In fact, every single dummy variable
of kinds of occupations results are partially linearly associated with the predicted value
of log of main occupational salary, suggesting that for their coefficient of being a
professional Self-employed (t=-6.74; p<0.001), Worker or Employee at Public Sector
(t=-11.60; p<0.001) or Private Sector(t=-11.78; p<0.001), the predicted salary could be
40 | P a g e
between 39% or 58% lower than being a Head or Manager, holding constant each
single dummy variable one by one, respectively.
Likewise, the model indicates that other explanatory variables can be partially
associated with Monthly Main Occupational Salary. Such is the case of the dummy
variable “Live in the Capital’s Country 2006” (t=11.96; p<0,001), that its coefficient is
0.290. This suggests that for a professional living in Santiago, Chile (Administrative
Capital) the expected average pay increases by 29% compared to some professionals
living outside the Capital, holding constant all the variables.
On the other hand, the dummy variable “Getting a Postgraduate degree 2006” (t=3.70)
is indicated in the model as having a statistical association with Monthly “Main
Occupational Salary”, with its coefficient being 0.122. Therefore, the expected salary of
professionals that have a Postgraduate degree is 12% higher than those who do not,
holding all the explanatory variables constant, respectively to each single dummy
The other interesting finding indicated by the model is the partial statistical association
of the dummy variable referred to as “Live in a different district to where you were
born” (t=3.39; p<0.001) with the predicted monthly salary, whose coefficient is 0.080.
That Suggests that the expected monthly salary increased by 8% in those professionals
who leave their birthplace compared to those who do not leave the district where they
they were born.
In addition, the model indicates a statistically significant association with the response
variables in the dummy “How do generally got a Job?: Other way“(t=4.27; p<0.001).
The coefficient of this dummy variable is 0.097. In other words, the expected monthly
41 | P a g e
salary increases by 10% for those who got jobs based on “Other modes” compared to
those who get jobs based on contacts such as relatives and friends.
In addition to this is the fact that model 2 indicates and confirms an interesting natural
process in the labour market, which is how the salary is logarithmically distributed
during the course of life in Chile. The dummy variables indicate that those born during
the years 1975-1966 (t=2.24; p<0.05) and between 1967-1955 (t= 2.54; p<0.05) are
statistically associated with the explanatory variable. The statistically significant
coefficients in both variables indicate that those that were born in those years increase
their predicted salary by 27% and 23% respectively, compared to the rest of the people
in the sample, all other variables remaining constant. Indeed, these differences are the
product of the natural process that people in the labour market are more likely to
receive better salaries when they are in a more productive phase (31-51 years old).
After this year, the curve of salaries could be reduced. Hence, the fact that the other
dummy variables related with group of those years have not shown a statistically
significant association with the predicted salary compared to people that were not
born during those years.
Contrary to our expectations, some variables were not statistically significant in the
model. That is the case of the dummy variable ethnicity. That dummy variable
compared people who get jobs based on public information and by creating their own
jobs with the baseline of those who get jobs based on contacts with relatives and
friends. Unfortunately, for a research proposal that seems to be theoretically
associated with the prediction of main occupational salary, that model does not fit. In
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contrast, the dummy variable compared the “Other way” of how people get a job with
getting jobs based on contacts with relatives and friends.
The dummy variables ethnicity is not associated statistically with the predicted
monthly main occupational income average in the model. Of course, those variables
were just used instrumentally to give an account that at a professional level there is
not an issue if people belong to some ethnic group. From my point of view,
additionally, there is just a small group of people that can achieve the educational
attainment necessary to be a professional in Chile. Unfortunately, that is not reflected
in the results that the model indicates.
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Analysing more deeply the predicted Logs of Main Occupational Salary controlled by
Table 6 shows multiple linear regressions analysis predicting the log of Main
Occupational Salary controlled by gender on selected exploratory variables. In fact,
these variables are the same that model 3 presented in the table 6. The idea is to draw
out which explanatory variables are statistically associated with the predicted monthly
salary for males and females.
The model that refers to males as well as females indicates statistically significant
evidence that there is partial linear association between the logarithm of “Main
Occupational Salary” and the explanatory variables in the population referring to male
professionals in Chile. The R2 demonstrates that controlling for males and females a
19% and 13%, respectively, of the predicted variation of the Log of Main Occupational
Salary is explained by variation in the variables included in each of the fitted models.
According to the male and female regression models the dummy variables Parental
Educational Capital High (t=7.20; p<0,001), the dummy variable female (t=-16.2;
p<0,001), Age(t=7.20; p<0,001), Living at the Capital(t=7.20; p<0,001) and all the
dummy variables about occupation are statistically significant in predicting the log
monthly main occupational salary.
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Table 6: Multiple linear regression Log Principal Occupational Salary of Professionals in 2006 Controlled by
Gender on selected independent variables
Educational Capital Family Intermediate
Educational Capital Family High
Father Occupation as Head or Manager
Age 2006
Live in the Capital’s Country 2006
To receive a Postgraduate degree 2006
Occupation: as Self-employed person 2001
Occupation: as Worker or Employee at
Public Sector 2001
Occupation: as Worker or Employee at
Private Sector 2001
How do generally got a Job?:
Other way
Born in a different district that live
Was born between 1980-1976
Was born between 1975-1966
Was born between 1967-1955
Was born between 1954-1946
adj. R-sq
Prob > F
= *p<0.05; **p<0.01; ***p<0.001
Source: Own elaboration based on Casen 2006
One of the main differences is the partial association that a postgraduate has in the
predicted salary for males. In fact, the predicted log of main income for a male
postgraduate (t=4.09; p<0,001) increases by 20.5% more than for a females with a
degree, holding constant all the others variables . Likewise, males that have changed
in 2006 the district were they born (t=3.38; p>0, 01) increase their predicted main
occupational salary by 12% more than those who do not change until 2006 the district
where they were born, holding constant the rest of the explanatory variables.
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Additionally, it can be seen that for males belonging to an ethnic group in Chile there is
a significant negative association with the predicted monthly salary. For males that
belong to an ethnic group their predicted pay is 18% lower than for men who do not
Meanwhile, females present other kinds of differences when the explanatory variables
and coefficient are compared to males. One of the most relevant is the concentration
of statistically significant coefficients predicting the monthly occupational salary
referring to dummy variables of groups of ages. For instance, females that were born
during the years 1967-1955 (t=2.62; p>0.001) receive a monthly salary 35% higher than
those not born in those years, holding constant all other explanatory variables. Similar
statistically significant and positive effects on the predicted salary occur from 1954 to
1945 (t=2.32; p>0.01), between 1980 and 1976 (t=2.18; p>0.05) and through 1975-
1966 (t=2.46; p>0.05), with an increment in the predicted monthly salary by 24%, 43%,
46%, respectively, compared with those who were not born in that period.
On the other hand, it is important to take into account that the effect in the predicted
main salary is different for males and females. In fact, the predicted salary for females
that have a postgraduate degree increases by 4% compared to females without one,
holding constant all variables. While the predicted salary for males that have a
postgraduate degree increases by 21% compared to those that do not have a
postgraduate degree title.
Overall, these results provide support for the view that Parental Educational Capital
plays an influential role in the differences in the predicted average of salary, in spite of
46 | P a g e
the differences between males and females that the different models reveal. Not
surprisingly, the results show that there are continuing and systematically differences
in the social system for professionals in Chile, which confirms recent research that has
evidenced a similar trend in other countries in Latin America and in some other
industrialized countries. However this difference has been revealed in a preferential,
or what we would call an “elite”, group.
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V. Discussion
We now return to the questions proposed in the introduction. One of the main results
that can be drawn is the constant differences that have been found between the
predicted occupational salaries by Parental Educational Capital. Those facts are
coincident with the theoretical and research general approaches to the social
inequality system in Chile and Latin America (Torche; 2005, 2010a). However, it is not
related to evidence coming from industrialized and western countries that points out a
significant decline in the parental origin with regard to educational attainment and
labour market analyzing those effects by income or salary.
At that point, an interesting and debatable issue comes from the analysis of the period
1996 to 2001, not only in statistical terms, but also in conceptual and theoretical ones.
As it has been shown, assuming a 10% level of significance the predicted (log of) main
occupational salary in 2001, coming from a family with a High Educational Capital
increased by it 36% compared to those coming from a Low Educated Family. That fact
statistically indicates a continuity of the differentiation in the expected main
occupational salary after five years for people coming from a more educated family.
However, this fact must be taken cautiously, not only in assuming that level of
significance, but because the research has been not carried out using a proper
longitudinal analysis. More accurately, it has been a sort of differentiated analysis in
two times on two equal samples, but by carrying a cross-sectional analysis.
In spite of those facts, and from a theoretical standpoint, the statistical difference
between people coming from a high and a lower educational background reveals that
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after 5 years the social systems seem to be working properly in classifying structural
differences in these groups of professional in Chile. This fact is in contrast to the
evidence provided by Torche (2007) that Chile is a more flux society in middle class
groups. How can we understand that claim if at a high educational level differences
from social origin are revealed?.
In that respect, two main ideas can be followed to respond to those differences. On
the one hand, it can be a product of the personal and individual selection of
particularly higher educational careers (such as lawyer, doctor, engineer etc.) when
these people were young. That issue, is moreover, one of the main weaknesses and
limitations of the current research. On the other hand, from a conceptual stand point
the definition of social class, origin and the entire concept created in the rest of the
research, has been operationalized and conceptualized in our case from just two
available variables, the Level of education of father and mother. That, of course, is due
to the availability of the data sets. However, independent of this fact our research has
pointed out some interesting similarities and differences that can open new questions
about this topic.
Additionally, the current model 1996-2001, indicates a kind of convergence in the
predicted salary between those coming from an intermediate and those coming from a
low educational capital family. Again, independent of the conceptual and empirical
differences in how the research was carried out, that is in fact a finding that all the
research into social inequality and mobility in Latin America and Chile has concluded.
Maybe this important convergence from people with a low Parental Educational
Capital has been approaching their transitions to people that have an intermediate
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Parental Educational Capital. From our point of view, it could be this gap and the
approaches to flux and faster change that those others studies support, that is the final
conclusion of the research carried out.
The overall research also raises evidence supported by different studies and research
about the income inequalities for gender, which is our second question. In fact, the
evidence in this research suggests that females are likely to be the group of people
that need more attention, especially if they come from a less educated background.
The data base from 2006 gives us the opportunity to account for the association of
other variables on the predicted main occupational income, and provides insight
regarding the importance of the main occupational income that the father has,
whether they live in the capital or have received a professionals’ postgraduate degree
that year. All these variables give us important and robust evidence to understand the
social context in which professionals from different origin are actually living. However,
the analysis of the predicted salary by gender is fruitful and reveals that it is men who
more likely to use some “strategies” to reach a kind of differentiation, specifically to
get a postgraduate degree. Theoretically, that fact can be supported by the theory of
effectively maintained inequality (Lucas, 2001) that points out the utilization of
strategies by more advantaged groups to maintain their position. However, specifically
how this qualitative difference is actually used by males in the labour market cannot
be totally revealed by the current research. Rather, it can just be suggested that there
is a special movement in the higher educational system that seems to be associated
more with males than with females. Is this a pragmatic strategy from those more
advantaged or a strategy from those less socio-cultural advantaged?
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Overall, according to Bourdieu’s (1995, 1993, 1978) standpoint, we are witness of a
`resemblance within a difference' Chilean social system where entry to the higher
educational system seems to be very open, but with social inequalities that are
reproduced by generations. However, the structural mechanism that generates this
system is very difficult to reveal in this kind of research. Therefore, more complex
research models and research strategies using longitudinal statistical tools as well as a
deep use of qualitative techniques could help reveal those aspects. For the impact
that this study may have, it is necessary to focus research and public policy resources
on seeing the reality of the women in the labour market, especially if they come from a
disadvantaged social and cultural origin.
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VI. Conclusions
Assuming the main points of the current dissertation, the conclusions can be divided as
First, the importance of new and more relevant applied research that looks for a
strong connection between public policy and academia. Concretely, the information
available about into the higher educational system has to generate useful research
that seeks to study the consequences of more and more people leaving the
educational system to find a job in the labour market. At the same time, the challenge
to academia is the outcomes that can be considered by the government and by public
policy in education. In short, the challenge will be how academia can translate properly
and easily the theoretical and applied findings.
Second, connected with the previous point, it is necessary to expand more and more
the research focus in actual strategies that people coming from low, intermediate and
high origins have used during their course of life. We need to study aspects of social
strategies that EMI has pointed out as interesting theoretical insights to develop
theoretically and empirically those sociological topics in Chile. For instance, research
about the educational and labour market and how people coming from middle and
lower class are currently obtaining position.
52 | P a g e
VII. Appendix A
Summary Statistics
Casen Panel 1996
Casen Panel 2001
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Casen 2006
54 | P a g e
VIII. Appendix B
PANEL CASEN 1996-2001 Regressions Models and other test applied
Model 1
errores 3.9e+03 0.0 046 0.00 00 177.42 0.0000
Variable Obs Pr(Ske wness) Pr(Kurt osis) chi2( 2) Prob>chi2
Skewness/K urtosis tests fo r Normality
. sktest errore s, noad
errores 4.857196 -.111 0446
variable kurtosis skew ness
. tabstat error es, stats(kurto sis skewness)
(22970 missing values generate d)
. predict error es, resid
Prob > chi2 = 0.00 00
chi2(1 ) = 32. 75
Variab les: fitted val ues of logyo96
Ho: Co nstant variance
Breusch-Pagan / Cook-Weisberg test for heteros kedasticity
010 20 30
-8 -6 -4 -2 0 2
55 | P a g e
Model 2 Regressions Models and other test applied
errore s 208 0.0000 0.0000 139.07 0.0 000
Variabl e Obs Pr(Skewness) Pr(Kurtos is) chi2 (2) Prob> chi2
Skew ness/Kurtosi s tests for Normality
. sktest er rores, noad
errore s 11.17724 2.72534
variabl e kurtosis skewness
. tabstat e rrores, stats (kurtosis sk ewness)
(26674 miss ing values ge nerated)
. predict e rrores, resid
Pr ob > chi2 = 0.4691
ch i2(1) = 0.52
Va riables: fitt ed values of logyo01
Ho : Constant va riance
Breusch-Pag an / Cook-Wei sberg test f or heteroske dasticity
-2 0246
-2 0 2 4 6
56 | P a g e
CASEN 2006 Regressions Models Regressions Models and other test applied
Model 1
_cons 12.85778 .0614876 209.11 0.000 12.73722 12.97834
edad .0089835 .001171 7.67 0.000 .0066875 .0112796
_Isexo_2 -.4143171 .0231051 -17.93 0.000 -.4596192 -.3690151
_Icapfam3_2 .3267688 .0305467 10.70 0.000 .266876 .3866617
_Icapfam3_1 .1308008 .0298705 4.38 0.000 .0722337 .1893678
logyopraj Coef. Std. Err. t P>|t| [95% Conf. Interval]
Total 1611.4559 3242 .497056107 Root MS E = .65404
Adj R-squared = 0.1394
Residual 1385.097 3238 .427763124 R-squar ed = 0.1405
Model 226.358903 4 56.5897258 Prob > F = 0.0000
F( 4, 3238) = 132.29
Source SS df MS Number of obs = 3243
i.sexo _Isexo_1-2 (naturally coded; _Isexo_1 omitted)
Model 2
57 | P a g e
errores 4.0e+04 0. 000 0.00 0 1631.50 0.0000
Variable Obs Pr(Skewn ess) Pr(Kurtosi s) chi2(2) Prob>chi2
j oint
Skewness/Kurtosi s tests for Norma lity
. sktest errores , noad
errores 4.760454 .0554 801
variable kurtosis skewne ss
. tabstat errore s, stats(kurtosi s skewness)
(228581 missing values generated )
. predict errore s, resid
Prob > chi2 = 0.0000
chi2(1) = 39.5 2
Variabl es: fitted value s of logyopraj
Ho: Con stant variance
Breusch-Pagan / Cook-Weisberg te st for heterosked asticity
-20 020 40 60
-6 -4 -2 0 2 4
58 | P a g e
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ResearchGate has not been able to resolve any citations for this publication.
The idea of an education-based meritocracy (EBM) has evident political attractions. It suggests a basis on which the objectives of social efficiency and social justice might be reconciled. However, the question is raised of its sociological viability. Three processes of change are identified - concerning the relationships between individuals' social origins, their educational attainments and the level of employment they achieve - each of which should be observed in any society that is moving towards an EBM. Results of analyses based on several different data-sets are then presented which indicate that in Britain these changes are not in fact in train; and similar results from other modern societies are noted. It is further argued that the barriers to the realisation of an EBM to which these findings point are of more than a transient kind. Some might be reduced through political intervention, though perhaps of a more radical kind than proponents of an EBM would wish to contemplate; but others would appear quite integral to modern market-based economies and societies.
Neoliberalism--the doctrine that market exchange is an ethic in itself, capable of acting as a guide for all human action--has become dominant in both thought and practice throughout much of the world since 1970 or so. Writing for a wide audience, David Harvey, author of The New Imperialism and The Condition of Postmodernity, here tells the political-economic story of where neoliberalization came from and how it proliferated on the world stage. Through critical engagement with this history, he constructs a framework, not only for analyzing the political and economic dangers that now surround us, but also for assessing the prospects for the more socially just alternatives being advocated by many oppositional movements.