Wesleyan University
1
The Honors College
Unequal Equality:
The Effects of Socioeconomic Status on Academic
Achievement in Open and Closed Societies
by
Catherine Juliana Doren
Class of 2013
A thesis submitted to the
faculty of Wesleyan University
in partial fulfillment of the requirements for the
Degree of Bachelor of Arts
with Departmental Honors in Sociology
Middletown, Connecticut
April, 2013
2
Table of Contents
Abstract
7
Acknowledgements
8
Chapter 1: INTRODUCTION
Open and Closed Systems and the Socioeconomic Achievement Gap
The Role of Education
Schools as Equalizing Agents
Schools as Stratifying Agents
An Overview of the Thesis
Chapter 2: METHODOLOGY
Data
Rationale for Choosing PISA and the Quality of Government Dataset
Missing Data
Variables
Country-Level Covariates
School-Level Covariates
Student-Level Covariates
Additional Variables
Methods
Why not the Status Attainment Model?
Why not Log-Linear Models?
Limitations
Chapter 3: OPEN AND CLOSED SYSTEMS
Introduction
Closed Systems
Open Systems
A Review of Hypotheses from Case Studies
Models
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10
13
14
16
19
22
22
23
25
26
29
30
33
34
34
35
38
40
46
46
46
51
53
54
3
Results
Examining Predicted Values
Effectively Maintained Inequality and Methods of Exclusion and Adaptation
Discussion
Maintaining Inequality Outside of Academic Achievement
Maintaining Inequality Through Academic Achievement
Conclusions
57
59
62
64
65
66
67
Chapter 4: FORMAL BUREAUCRACY AND CORRUPTION
73
73
Introduction
Theories of Bureaucracy and Transmission of Advantage
The Rise of the New Class
Corruption and Methods of Transmitting Advantage
Variables and Models
Results
Examining Predicted Values
Discussion
Exploring the Possibility of a Meritocratic, Corrupt System
Meritocracy: Still a Myth
The Importance of Status Groups
The New Society and The Old Society
Conclusions
74
77
80
84
85
92
96
97
99
101
102
105
Chapter 5: MEASURING INEQUALITY
116
116
117
119
121
122
125
128
129
Introduction
Measures of Inequality
Identifying a more appropriate measure for my model
Variables and Models
Results
Examining Predicted Value
Discussion
Conclusions
Chapter 6: CONCLUSIONS
4
The Myth of the “Great Equalizer”
Expanding the Scope of my Model
Policy Implications, or the lack thereof
139
140
142
143
Works Cited
147
5
Table of Contents: Tables
Table 2.1: Descriptive Statistics
42
Table 3.1: The Effects of the Interaction
58
Table 3.2: Predicted Values of the Effect of SES on Achievement
60
Table 3.3: Full Table of the Effects of the Interaction
69
Table 4.1: The Effects of Corruption
89
Table 4.2: Removing Functioning of Government from the Model
93
Table 4.3: Predicted Values of the Effect of SES on Achievement
95
Table 4.4: Full Table of the Effects of Corruption
108
Table 4.5: Full Table, Removing Functioning of Government from the Model
112
Table 5.1: The Effects of the Richest 10%’s Share of Income and Consumption
124
Table 5.2: Predicted Values of the Effect of SES on Achievement
127
Table 5.3: Descriptive Statistics of the Smaller Sample
131
Table 5.4: Full Table of the Effects of the Richest 10%’s Share of Income and
Consumption
133
Table 5.5: Revisiting Models 3.2 and 4.5 with the Smaller Sample
136
6
Table of Contents: Figures
Figure 2.1: Graph of the Effect of SES on Achievement by Country
44
Figure 3.1: Models 3.1 and 3.2
56
Figure 3.2: Graph of Predicted Values of the Effect of SES on Achievement
60
Figure 4.1: Models 4.1, 4.2, 4.3, and 4.4
87
Figure 4.2: Models 4.4 and 4.5
91
Figure 4.3: Graph of Predicted Values of the Effect of SES on Achievement
95
Figure 5.1: Models 5.1 and 5.2
122
Figure 5.2: Graph of Predicted Values of the Effect of SES on Achievement
127
7
Abstract
From one country to the next, the role of socioeconomic status differs in its relation to
academic achievement (OECD 2010). In this study, I examine the role of countrylevel factors, specifically open and closed systems, in affecting this relationship. The
existing literature provides conflicting evidence, suggesting that open, democratic
characteristics may either decrease or increase the effect of socioeconomic status in a
country, as may closed, non-democratic characteristics. Using multi-level models to
analyze data from the Programme for International Student Assessment and the
Quality of Government datasets, I find that the relationship is more complex. Results
suggest that more closed, more corrupt countries have a smaller effect of
socioeconomic status on achievement than more open, less corrupt countries.
However, countries with a more unequal distribution of income also have smaller
socioeconomic achievement gaps than do more equal countries. I thus suggest that in
open societies with a more equitable distribution of income, where the effect of
socioeconomic status is larger, elites use to education as a path through which to
maintain their elite status. In closed societies with a less equitable distribution of
income, corrupt methods, such as bribery and nepotism, are used outside of the scope
of education to help elites maintain their status. Therefore, in closed and unequal
societies, the socioeconomic achievement gap is smaller, as elites have less reason to
hoard educational opportunity or to ensure the success of their children in school.
Only in more open and equal systems is this disparity in achievement is necessary for
maintaining existing social order.
8
Acknowledgements
As sociologists like to note, all individual actions are highly influenced by outside
factors. Social origin, as I explore in this thesis, has a large effect on one’s ultimate
trajectory. Because of this, I will begin by thanking my parents, Douglas Doren and
Deborah Ehrenthal, for the cultural capital that they have transferred to me
throughout my life. Without their help and their contributions of many forms of
capital, it is unlikely that I would be at Wesleyan or studying sociology at all. On a
less abstract note, I would also like to thank them for proofreading my thesis and
giving me feedback throughout the process. Beyond my parents, I have had many
friends help me throughout the process of writing my thesis. I would like to thank
Jeremy Fehr for walking the Cat, putting up with my ramblings about sociological
concepts, and encouraging me to put down my work from time to time. I would also
like to thank the siblings of the Alpha Delta Phi Society for their support and for the
wonderful conversations about my thesis that we have shared.
Of course, academic life does not exist in a vacuum either. I would like to thank
Professor Rob Rosenthal for introducing me to sociology and Manolis Kaparakis for
giving me numerous opportunities to improve my research skills in the Quantitative
Analysis Center. Collaboration with Rebecca Coven throughout this semester has
been invaluable and I would like to thank her for her insightful comments and
critiques of my thesis. I would finally like to thank Professor Joyce Jacobsen, given
that her comments at a December 2012 presentation of this project in inspired my
analyses in Chapter 5.
Most importantly, without the help of Professor Daniel Long, I am confident that my
thesis would be far inferior and that I would not have learned nearly as much as I did
while working on the project. From teaching me Unix to helping me apply to
graduate schools, Professor Long has given me more than I could ever expect from an
advisor. I cannot express my gratitude for his phenomenal advising, both on my
thesis and on other matters during my time at Wesleyan.
9
Chapter 1
INTRODUCTION
Since the founding of the first schools in the United States, differential
achievement along the lines of socioeconomic status has been central to American
education (Karabel 2006; Ravitch 2000). According to Diane Ravitch, “by 1890, 95
percent of children between the ages of five and thirteen were enrolled in school for
at least a few months of the year. Less than 5 percent of adolescents went to high
school, and even fewer entered college” (2000: 20). This disparity in access to
further education, she says, was tied to race, socioeconomic status, and location
within the country. Even after the secondary school system expanded and was
required to include all children, regardless of social origin, the children of the elite
still managed to succeed at higher rates than their less advantaged peers (Bowles and
Gintis 1976). 1
Today, the socioeconomic achievement gap continues to widen. In fact, it has
increased between 30 and 40 percent from the 1970s to the early 2000s (Reardon
2011). The existence and persistence of the effect of social origin on academic
achievement is, however, not a uniquely American problem. Numerous studies have
revealed that a socioeconomic achievement gap is also prevalent in many other
countries (Bourdieu 1989; Carnoy 2007; Shavit and Blossfeld 1993). Yet, the extent
1 Between
1883 and 1906, elite education became even further segregated from that
of the masses when seven elite boarding schools were founded, including Groton,
Hotchkiss, Choate, and Kent (Karabel 2006). These schools were seen as feeders to
Harvard, Yale, and Princeton, which were founded between 1636 and 1746. Public
schools, unlike these elite boarding schools, often failed to even offer courses
required by the Big Three for admission. 10
of socioeconomic status’s effect on achievement differs from one country to the next
(OECD 2010). This international variation in the size of this achievement gap
suggests that some characteristics at the country level account for this difference.
However, the factors causing this varying relationship between socioeconomic status
and academic achievement remain undetermined.
In this thesis, I will study this effect of socioeconomic status on academic
achievement and I will assess the underlying causes of the socioeconomic
achievement gap. Specifically, I will focus on factors at the country level that may
contribute to this gap. While limited research has been conducted to examine this
particular question, some scholars have noted some basic characteristics that may
differentiate countries in their studies. Bowles and Gintis (1976), for instance,
discuss the role of America’s capitalist system in shaping its school system.
Similarly, Carnoy (2007) notes the effects of Cuba’s egalitarian socialist context in
promoting high academic achievement among all students. Still, there is little
indication as to whether the effects of these systems stem from political, economic, or
social contexts, all of which differ greatly among countries. Because this question
remains unanswered, I will therefore focus my analysis on the effects of country-level
variables on this relationship.
Open and Closed Systems and the Socioeconomic Achievement Gap
I choose to begin with an examination of open and closed political systems
because I believe that the economic and social factors, such as welfare regime and
ideology, respectively, are influenced by this political factor. Max Weber’s
11
(2009[1920]) theory of open and closed systems will serve to frame my discussion.
According to this theory, there are primarily two sorts of systems. Weber writes that
a system is “‘open’ to outsiders if and insofar as its system of order does not deny
participation to anyone who wishes to join and is actually in a position to do so”
(2009[1920]: 94). On the other hand, a system is “‘closed’ against outsiders so far as,
according to its subjective meaning and its binding rules, participation of certain
persons is excluded, limited, or subjected to conditions” (2009[1920]: 94).2 Because
a system’s openness or closure influences the locus of control in decision-making
processes, it is also likely to influence decisions made regarding the welfare and
redistributive policies that differentiate countries. Ideology is likely to influence and
be influenced by openness as well, considering that legitimating mechanisms will
inherently differ in some ways along the lines of whether a system is open or closed.
Because the effects of openness and closure are linked to these other factors that are
also likely to affect the socioeconomic achievement gap, I believe that focusing on
open and closed systems will provide a comprehensive understanding of the types of
countries that experience the highest and lowest effects of socioeconomic status on
academic achievement.
A system’s openness or closure has not yet been directly linked to rates of
social mobility in the existing literature. However, many studies, particularly those
by Bowles and Gintis (1976), Carnoy (2007), and pieces included in the Shavit and
Blossfeld (1993) anthology, indicate that level of openness or closure may have an
effect on the relationship between socioeconomic status and academic achievement.
2
Weber’s theory of open and closed systems will be discussed in more detail in
Chapter 3.
12
As suggested by these findings, the amount of citizen involvement in governmental
processes and the democratic functioning of a government could affect the way in
which socioeconomic status and academic achievement are tied. However, the
existing studies provide conflicting evidence as to the particular effect that openness
or closure may have. These indications that the factors are related to social mobility,
accompanied by the contestation as to their effects, lead me to believe that the matter
deserves a closer examination.
My study of open and closed systems and their effect on the socioeconomic
achievement gap will use a large sample of countries to understand the differences in
the gap’s size among countries. Whereas as many past studies have examined ideal
types or compared analyses of a small number of countries (see Blau and Duncan
1967; Bowles and Gintis 1976; Carnoy 2007; Featherman, Jones and Hauser 1975;
Goldthorpe 1980; Shavit and Blossfeld 1993), I will instead analyze a dataset with
information with forty-nine countries. This larger sample of countries will allow me
to focus on international trends instead of traits of individual countries, which, to my
knowledge, has not previously been done.
Additionally, rather than using occupational status or educational attainment
to measure the final socioeconomic destination of children as is true for many
previous studies, (see Blau and Duncan 1967; Featherman, Jones, and Hauser 1975;
Sewell, Haller and Portes 1969) I use academic achievement at age fifteen. This
allows me to see what occurs in schools, particularly in the way that social
reproduction relates to the socioeconomic achievement gap. The measures typically
used, particularly occupational status, do not reflect this difference in academic
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achievement. Therefore, it is important to use an academic achievement score if this
gap is to be tied to the greater study of social reproduction. This shift in focus
provides a new perspective on the process of reproducing social status that has not
previously been given extensive attention.
To situate my analysis, I will discuss two critiques of the current sociology of
education literature that my study will address. I will initially provide a critique of
the discussion of meritocracy, which continues in spite of the persistent effect of
socioeconomic status on academic achievement (see Lipset and Bendix 1959; Blau
and Duncan 1967). I will then critique the focus on micro level factors that has
dominated much of the literature on the effects of social origin in schools (see Barr
and Dreeben 1983; Lareau 2011; Tough 2009).
The Role of Education
Closely tied to the debate about open and closed systems and their effect on
social mobility is the debate about whether education systems work to equalize or to
stratify. Functionalist and conflict theorist perspectives have distinctly different
views as to how the education system serves its students. These ideas generally stem
from greater debates about whether schools intend to allow for even distribution of
rewards across different social groups or whether societies systematically distribute
rewards unevenly, favoring certain groups over others. More specifically, the issues
tend to focus on whether or not schools provide equal opportunities for success to all
students.
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The notion of the “American Dream” suggests that anyone in America can
rise from the bottom of the socioeconomic spectrum to the top, provided that they
work hard enough (Ravitch 2010). A system following this functionalist theory
should allow anyone, regardless of social origin, to achieve high socioeconomic
status, given that they devote the necessary effort (Collins 1971). This path of social
mobility typically revolves around the education system, where effort and high
achievement are thought to provide substantial rewards.
In opposition to this concept of a meritocracy, the conflict theorist perspective
suggests that those who rise from rags to riches are simply outliers. Conflict theorists
propose that it is instead far more common to attain the social status held by one’s
parents rather than one that is attained through effort alone (Bowles and Gintis 1976).
Not only does hard work not contribute enough to help improve one’s social status,
but this theory also suggests that schools actually play a role in preventing social
mobility. In this section, I discuss these two opposing theories in more depth.
Schools as Equalizing Agents
The first of these two theories is the technical-function theory of schooling,
where schools are viewed as equalizing agents. This functionalist theory suggests
that the skills that one learns in school help the individual attain a better job (Collins
1971). This would suggest that extra years of schooling or the attendance of an elite
school provide new, valuable skills that promote better life outcomes. Thus, if all are
given an equal opportunity to attend high-quality schools, all should be able to attain
high social standing. This does, of course, require that individuals contribute the
15
necessary effort. In cases where a limited number of elite positions exist, the
positions would be given to those who had worked the hardest and achieved the
highest prestige among those in a society. Social status is thus determined by merit,
as opposed to external factors, such as social origin, that may otherwise give an unfair
advantage to some individuals. This suggests that if one does not manage to obtain a
position in the elite, it is simply due to their personal failure to do well enough to
attain the position, be it related to intellect or cultural disposition. For this reason,
Horace Mann referred to schools as the “great equalizer” (Ravitch 2010).
Blau and Duncan’s (1967) findings in The American Occupational Structure
support this theory, demonstrating that mobility does exist, particularly among the
middle class. Education serves as a moderating variable, so with more education, one
can attain a higher social status than they would have otherwise inherited from their
parents. Lipset and Bendix (1959) also support this finding, suggesting that upward
mobility is possible and even likely in industrial societies. Featherman, Jones, and
Hauser (1975) provide further evidence for this theory, noting that the degree to
which mobility occurs is reliant upon a society’s level of industrialization.
Meritocracy, they note, is most prevalent in situations where industrial development
is high, which renders the obtainment of a job ever more competitive. Based on these
studies, it appears as though meritocratic methods of selection determine who gains a
position in the elite of a society. If my analyses support these findings, I will see a
small effect, if any, of socioeconomic status on achievement.
16
Schools as Stratifying Agents
In contrast with the theories of education and inequality proposed in the
previous section, there is an alternate possibility that schools work to stratify. Along
with this theory is the idea that social stratification is not inherent and is instead
purposefully put in place by the dominant class. The conflict theory of education
discussed by Bowles and Gintis (1976) suggests that schools are instruments that are
perpetuating capitalist society. Thus, schools are a mechanism through which the
social status of parents is reproduced in their children. Because Bowles and Gintis
believe that there would not possibly be this degree of social reproduction if a
meritocracy were in place, they declare meritocracy to be a myth.
Previous studies demonstrate a wealth of support for this theory. As noted
above, Bowles and Gintis (1976) use their analyses to demonstrate that the rate of
social reproduction in the United States is too high for the education system to be
meritocratic. Additionally, Lucas (1999) shows that American students are often
separated into tracks at an early age, exposing students to different material and
ensuring that some are prevented from achieving the necessary levels of education.
His theory of Effectively Maintained Inequality (EMI) further supports this,
demonstrating that student background determines who is given the opportunity to
pursue higher levels of education that are not available to all (Lucas 2001). Beyond
this, student background also determines the kind of education that one receives
within the levels of education that are available to all. Hout, Raftery, and Bell (1993)
demonstrate this phenomenon as well. In their theory of Maximally Maintained
Inequality, they suggest that only when all of those of an elite social origin have
17
gained a position in the elite will elite positions be available to those of lower social
origin. This directly opposes the functionalist assumption that elite positions are
allocated based on merit, not social origin. The only way that the two would be
compatible is if socioeconomic status and merit were entirely correlated, which is
inherently unaligned with the functionalist view. If my findings support these studies
and the conflict theory hypothesis is accurate, my results will suggest that academic
achievement will be highly effected by socioeconomic status.
However, it is possible that there is no effect of socioeconomic status on
academic achievement, but that the elite still maintains their dominance through an
alternate route. This brings about the possibility that if advantage is not noticeably
transmitted through schooling, there may simply be a different way of transmitting
advantage. Lucas’s (2001) theory of EMI and Alon’s (2009) theory of adaptation and
exclusion suggest that regardless of the system’s characteristics, elites will find a way
to maintain their advantage. These theories of inequality suggest that stratification,
although problematic, is unlikely to be eradicated. If economic capital or
membership in a particular social circle ceases to be effective in guaranteeing their
position at the peak of the hierarchy, the elite will find another method. Elites will
continue hoarding opportunities, so as to ensure that they maintain their prized
position (Tilly 2009[1999]). This systematic and institutionalized opportunity
hoarding, which is often accompanied by exploitation, leads to social closure, in a
Weberian sense, strengthening the power of the elite even further (Weber
2009[1920]).
18
Extrapolating from this phenomenon, if education becomes fully equalized
and no person can possibly have a quantitatively or qualitatively superior education,
the elite will simply adapt their methods of exclusion as to guarantee that they
maintain a position at the peak of the hierarchy (Alon 2009). This would suggest that
the conflict theory of stratification may still hold, even if the socioeconomic
achievement gap were to close. If a high level of socioeconomic inequality remains
in spite of a small or nonexistent socioeconomic achievement gap, it detracts from the
elite’s need to focus on education, as it is not the path through which elite status is
gained. Alternate paths to the elite could include the use of nepotism, bribery, or
other similar methods. If this is universally true, the important distinction between
systems is the mechanism that is used to maintain advantage. Thus, even if education
is more equitable in some countries and does not act as the method of transmitting
advantage, other avenues for such transmission will arise, making it difficult, if not
impossible, to avoid such inequality in a society. If this hypothesis is true, I will find
a small effect of socioeconomic status on academic achievement, but there will still
necessarily be a high level of inequality in a society.
Shifting the Focus from the Individual to the Social Structure
Although the question of whether schools promote equality or stratification
has been addressed in the literature, it has been primarily approached from an
individual level, ignoring the social structure. Many policymakers since the 1970s
have declared that low academic performance is a result of failures of individuals,
focusing on IQ deficit theories, cultural deficit theories, and theories of lower teacher
19
expectations for students of lower status (Persell 1977). However, research has
demonstrated that these theories are not, in fact, able to explain this inequality in
achievement (Fischer, et al. 1996; Tough 2009; Harris 2011).
In contrast to these individualist explanations, Persell (1977) suggests that the
socioeconomic achievement gap is instead attributable to factors at societal,
institutional, interpersonal, and intrapsychic levels. Her theory necessitates a broader
examination of causes and consequences, shifting the responsibility for low
performance beyond the individual. Because the intrapsychic level is nested within
the interpersonal level, which is nested within the institutional level and finally all
within the societal level, it is important to start with an analysis of societal influences,
which serves as the focus of this piece. With all other layers nested within and
therefore impacted by the societal level, this overarching level must be understood in
order to analyze the other levels falling under its influence.
In this study, I reverse the direction of analysis in Barr and Dreeben’s (1983)
nested layers model, which begins at the student level and extrapolates outwards. In
acknowledging that schools are nested institutions, I choose to begin at the broadest
layer, the country level, and work toward smaller layers from there. Given the
direction of my analysis, I believe that this study will be able to reveal phenomena
that would go unnoticed were the individual level the focus.
An Overview of the Thesis
Expanding on the debates discussed above, this thesis will examine relevant
social and political contexts, investigating which country-level factors most
20
significantly alter the socioeconomic achievement gap. The analyses will focus
specifically on the role of open and closed systems in relation to the gap. As noted,
previous studies have tended to focus on a single country or a small group of
countries. My analyses use a large sample of countries, accounting for many
different sorts of contexts. These methods will allow me to reveal general trends that
were previously unrecognized, given the use of such small sample sizes. To further
broaden the existing literature, the current study also shifts the focus toward factors at
the country level that may universally create these differences in international trends,
rather than specific characteristics of individual countries.
Before delving into my analyses, I will provide more information about my
models and methods in Chapter 2. In this chapter, I will describe the data and
variables that I will use in my analyses. I also will provide justification for my choice
of data and models, as opposed to others, such as occupational data and status
attainment models, both of which are more commonly used in studies of mobility.
Following this methodological section, I will begin with an analysis of open
and closed systems in Chapter 3. This chapter will provide a comprehensive review
of the literature on these two types of systems. I will also discuss four case studies,
from which I provide two opposing hypotheses. My analysis in Chapter 3 gives rise
to two subsequent questions. The first relates to corruption as a possible explanatory
variable for an unanticipated finding. The second deals with alternate measures of
inequality that may more properly suit my model. These two questions will serve as
topics for exploration in the latter half of the thesis. Chapter 4 elaborates on the
question concerning corruption that was brought about in Chapter 3. In Chapter 4, I
21
use a theoretical and analytical description of corruption and bureaucratization to
further explore the issues that arose in my analyses of open and closed systems.
Chapter 5 then targets the second question brought up in Chapter 3, analyzing
differences among measures of inequality. Specifically, the chapter looks at
theoretical underpinnings of measures and determines how the most appropriate
measure affects the results of my analyses.
Finally, I end my thesis with Chapter 6, in which I provide some final
conclusions. Here, I address the theories introduced in the earlier chapters,
particularly those of open and closed systems and exclusion and adaptation. I then
discuss policy-related implications, or the lack thereof, brought about by my findings.
I ultimately propose further avenues for exploration, which would provide stronger
justification for my conclusions.
22
Chapter 2
METHODOLOGY
Data
The analyses in this thesis use data from the 2009 wave of the Programme for
International Student Assessment, referred to as PISA, accompanied by the Quality of
Government Social Policy Dataset. PISA, which provides the majority of variables
for analysis, includes information from sixty-five countries, 17,145 schools, and
475,460 fifteen-year-old students. However, due to missing data in both datasets, the
analyses in this thesis include data from forty-nine countries, 14,612 schools, and
393,743 students, unless otherwise noted. 3
PISA data were obtained when approximately 5,000-10,000 students in each
of the sixty-five countries were randomly selected to take a standardized test (OECD
2012). The test assessed their academic achievement in reading and mathematics,
focusing more strongly on critical thinking skills than the average standardized test in
the United States. Surveys were also given to the students who took the test and the
administrators of their schools. For students, surveys asked about personal
characteristics, including socioeconomic status and language spoken in the home.
The administrators answered questions about the corresponding schools. The
information obtained through the administrator surveys allows PISA to provide data
addressing a range of topics, such as educational resources available to the school, the
source of the school’s funding, and number of other schools in the area among which
students can choose.
3
Refer to Table 2.1 at the end of this chapter for all descriptive statistics. 23
Country-level variables are drawn primarily from the Quality of Government
Dataset, released by the University of Gothenburg (Svensson, et al. 2012). The
dataset is a compilation of a number of other datasets, all of which are freely
available. My study makes use of a subset of the dataset, the Social Policy Dataset,
which focuses particularly on social policy and its connections to government quality.
Rationale for Choosing PISA and the Quality of Government Dataset
I chose to use PISA because it provides me with a comprehensive set of
information about a large number of students in a wide range of countries. With data
obtained from students at age fifteen, I have information reflecting the effects of
much of the students’ academic careers and time in school. At this point in the
students’ academic trajectories, they have likely already been divided into different
academic tracks or ability groups, de facto or de jure, regardless of their country. If
students will never be tracked, the effects of this may be more evident at this point
than they would be in an analysis conducted at an age before students in countries
that do use tracking have been separated.
Another reason that I use PISA is simply due to its focus on academic
achievement. As noted in Chapter 1, this data lets me focus on the studying mobility
and reproduction through the lens of achievement in school, rather than focusing on
occupational attainment or years of education attained, as many other studies have
(Blau and Duncan 1967; Featherman, Jones, and Hauser 1975, Shavit and Blossfeld
1993). My shift of focus in relation this question of the effects of social origin
24
provides new information about what occurs within the schooling process, not only
the effect of the number of years spent in school.
The large sample of countries also gave me a wide range of data to work with,
allowing me to examine many different types of systems without using different
datasets that are not entirely parallel. Other studies, such as Treiman and Terrell’s
(1975) “The Process of Status Attainment in the United States and Great Britain,”
have used different datasets for different countries even when drawing comparisons.
In Treiman and Terrell’s comparative study, they used data from all white American
males, while using data from only white British heads of households. These groups
are quite likely systematically different, making it difficult to compare the two groups
studied. Among other problems with their data and study design (Burawoy 1977),
this problem with non-parallel datasets makes it not only difficult but also
problematic to compare directly between one country’s results and those of another.
Because I have the same information from each country in my sample, I do not need
to make assumptions that may wrongly equate two groups.
Additionally, the amount of data that PISA provides from administrator
surveys allowed me to closely study the school level as well as the student level.
Without access to such data, I would not have been able to control for differences
between schools, such as the average socioeconomic status of the students or the
resources available in the school. Having this information made it possible to more
adequately assess the role of the school, rather than wrongly attributing this
information to factors at the student or country level.
25
In order to supplement PISA with country-level variables, I used the Quality
of Government dataset, which provides a fairly comprehensive set of variables from a
number of sources, including the World Bank and the Luxemburg Income Study
(Svensson, et al. 2012). Compiled in 2012, much of the data was taken from surveys
conducted close to 2009, which let me use data that was closest to my ideal time
period as possible. The large amount of available data made it possible for me to
study the country level without eliminating a large number of countries from my
sample. Unfortunately, I did lose sixteen countries due to unavailable data from some
countries on certain measures, but the vast majority of the countries in my initial
sample could be used in my analyses.
Missing Data
In my analyses, I use multiple imputation to account for missing data at the
student and school levels. Countries with missing data at the country level are
dropped from the analysis.4 In performing multiple imputation, I make use of five
plausible values for math achievement scores provided in the PISA 2009 dataset. For
all other student- and school-level covariates, I used the SAS “proc mi”
implementation of the Monte Carlo Markov Chain (MCMC) algorithm to generate
4 I
choose to use listwise deletion for country-level data because in many cases, each
data point has been calculated through a formula and is based on information specific
to each country. For instance, Gini index, as described below, is calculated based on
the Lorenz curve. This makes it difficult, if not impossible, to predict the value
without properly calculating the index, as the formula suggests. Multiple imputation
is thus inappropriate to use.
26
five plausible values for each variable.5 I computed the imputed variables for each
country separately, so as to predict values based on values from the corresponding
country only, not values of other countries. For all dummy-coded variables, I round
the imputed values to either zero or one, maintaining the binary nature of the variable.
However, all other imputed values are left as they are after imputation, even where
values are not intuitively sensible, such as cases where the value for school size is
negative. The analyses are replicated for each of the five imputed data sets and the
final coefficients and standard errors are ultimately merged using Rubin’s Rules
(Allison 2002). Weights that were included in the PISA data set are used at the
student level to eliminate over- or under-sampling, which may lead to biased results
(OECD 2012).
Variables
The dependent variable used in this analysis is the mathematics achievement
score from the assessment administered by PISA. As previous studies have shown,
math and readings scores typically show the same results (Long 2013). For this
reason, I believe that it is unnecessary to conduct analyses with both math and
reading, so I choose to use math achievement score alone.
However, math score itself is not the focus of this study. Instead, the slope
when score is predicted by socioeconomic status serves as the dependent variable.
Country-level variables are studied in examining this slope, and I identify which
5
I use MCMC because it has been proven both unbiased and efficient, which has not
been proven for the Multiple Imputation with Chained Equations (MICE), an
alternate dominant method (Allison 2002).
27
factors may impact its magnitude. These analyses may reveal which factors have the
greatest effect on either increasing or decreasing the ability of socioeconomic status
to predict math score. As seen in Figure 2.1 at the end of this chapter, there is a lot of
variance in the effect of socioeconomic status on math achievement internationally.6
To assess socioeconomic status, I created a construct, which holds together
with a Cronbach’s alpha above .70. The construct measures socioeconomic status
through the presence of certain possessions in a child’s home; the highest parental
occupation status; and the highest parental education status, which uses years of
schooling as determined by ISCED standards. The construct is set to have a standard
deviation of approximately 1. When split into quartiles, the score at the first quartile
is -1.047, the median score is -0.235, and the score at the third quartile is 0.531.
Because the means and standard deviations vary by country, I may reach problems
due to the fact that I do not control for country-level socioeconomic status. However,
I include a proxy for this measure, which is seen in purchasing power parity, which I
discuss with my country-level covariates.
The main independent variable in my first round of analyses is level of
democracy. I use this variable to guide my understanding of open and closed
systems, as social closure and participation in government, both of which are central
to distinguishing open and closed systems, are addressed by the democracy measure.
To assess the effects of these systems, I use the factors comprising the Economist
6 This
figure shows coefficients from an OLS regression of SES on math score from
the first iteration alone, but my later analyses suggest that all five iterations provide
similar outcomes. The figure includes more countries than are in my sample, but I
choose to use the entire sample provided by PISA to further the illustrative example
in the figure.
28
Intelligence Unit’s index of democracy. Ranging from 0 (fully authoritarian
characteristic) to 10 (fully democratic characteristic), the index encompasses a
number of factors that are believed to assess a country’s level of democracy (Kekiç
2007). Of the factors contributing to democracy, the first, civil liberties, includes
freedom of speech, expression, religion, association, among other freedoms, noting
that human rights are ensured for all. The second factor, the presence of a democratic
political culture, measures the extent to which societal consensus supports democratic
principles and transitions smoothly occur corresponding with citizen votes. A
measure of electoral processes and pluralism serves as the third factor, indicating
whether elections are free, fair, and competitive. The fourth factor is the functioning
of government, which shows whether democratically based decisions are actually
carried out by the government and indicates the level of governmental corruption.
Political participation serves as the final factor and it examines adult literacy rate,
ability of citizens to freely choose to elect their representative or join political parties,
among other actions. Because this is a country-level variable, there is a single value
for all students and schools within a given country.
I chose to use this measure of democracy because it allows me to break down
the individual components that make up the index. The ability to separate these
components, participation in government and civil liberties, for instance, is
particularly important. Both political participation and civil liberties are often seen
as key indicators of democracy, yet the potential effect that the factors may have on
my study are incredibly different. For example, one may predict that low levels of
political participation, as are seen in Cuba, promote a smaller effect of socioeconomic
29
status on academic achievement. This less democratic option would, in an overall
index that measures democracy, be tied to low civil liberties. However, having low
civil liberties is not part of the theory that predicts that Cuba will have a smaller
socioeconomic achievement gap. Because I would like to be able to separate the
effects of different aspects that are conceptualized as defining democracy, I believe
that this separated index will most adequately help me achieve my goals.
Country-Level Covariates
At the country level, Gini index and Gross National Income per capita based
on purchasing power parity are also used as covariates. The Gini index represents a
country’s degree of inequality in family incomes (Central Intelligence Agency 2011).
Scores are derived from the Lorentz Curve, which plots total family income against
the number of families in a country, who are ordered from lowest to highest income.
Perfect equality would have an index of zero, whereas perfect inequality would have
an index of 100. In the most equitable countries, scores are lower, with the lowest
score being 23.0, reported by Sweden. More unequal countries receive higher scores
and the highest, although not included in this sample, is Namibia with a Gini index of
70.7. The United States, for instance, scores a 45.0 on this scale. Using this variable
will control for the potential that income inequality itself may predict the relationship
between socioeconomic status and educational achievement, as discussed above.
Gross National Income (GNI) per capita based on purchasing power parity, or
PPP, serves as another covariate. According to the World Bank, “GNI is the sum of
value added by all resident producers plus any product taxes (less subsidies) not
30
included in the valuation of output plus net receipts of primary income (compensation
of employees and property income) from abroad” (World Bank 2012). I use this
variable to help understand the effect of a country’s level of economic development
on the socioeconomic achievement gap. This also allows me to control for the
general socioeconomic status of countries. Because of the small size of the
coefficients of PPP, I multiply the values by 10,000 in my tables.
School-Level Covariates
At the school level, a variety of categories are taken into account. These
factors allow me to acknowledge both the differences in schools systems among
countries and the effect of certain school characteristics on students. The sector of
the school is among the covariates used at this level. School sector is recorded based
on administrator responses as to whether their school is public or private. I define
schools that are private and receive 50% or more of their funding from the
government as dependent private schools. Private schools receiving less than 50% of
funding from the government I consider independent private schools. The variable
for public schools serves as a referent group for each type of private school.
Size of the community in which the school is located also serves as a control.
Sizes of community are dummy coded into the following five categories: a village,
hamlet or rural area (fewer than 3,000 people), a small town (3,000 to about 15,000
people), a town (15,000 to about 100,000 people), a city (100,000 to about 1,000,000
people) and a large city (over 1,000,000 people) (OECD 2012). The category of city
serves as a baseline for comparison.
31
Another set of covariates measures amount of school choice. Using answers
provided by the school administrators, this amount of school choice is broken down
into three dummy variables. The first assesses whether there is a single alternate
school for students to choose in a given area. The second looks at whether two or
more schools of choice exist. Administrators also have the final option of indicating
that no other schools exist with which to compete. This third dummy variable is the
referent group for the other two school choice variables.
Measures of principal autonomy are included to account for the way in which
decisions are made at the school and how much of the decision-making power the
school’s administration holds. School administrators could respond either “yes” or
“no” to whether the principal had a considerable responsibility in determining budget
allocation, course content, and hiring and firing of teachers.
School selection policies are also taken into account. This is particularly
important, as gaining admission to certain schools is contingent upon test scores, or
the like, will provide selection bias in the test scores of students from certain schools.
Also, monetary or hereditary qualifiers for admission to elite schools may increase
the reliance of achievement on socioeconomic status. Reasons for admittance that are
controlled for include residence in a particular area, academic record,
recommendation of feeder schools, parents’ endorsement of the school’s instructional
or religious philosophy, requirement of or interest in a particular program, and
preference given to students whose parents or siblings previously attended the school.
Administrators responded either “never,” “sometimes,” or “always” to each policy.
32
Each of the three options is dummy coded and the option of never adhering to a
policy is left out of the analysis and thus captured by the constant.
Because educational background of teachers is highly likely to impact the
achievement of their students, the percentage of teachers with a university degree and
the percentage with proper certification as determined by their area also serve as
covariates. Number of students attending the school and the school’s student-teacher
ratio are included as well. To measure resources available in each school, I
constructed a variable from a series of questions in the administrator survey. This
index of school shortages accounts for shortages of science, math, and reading
teachers, as well as qualified teachers, library staff, and other personnel. Material
shortages are factored in as well, accounting for shortages of instructional material,
computers, Internet access, computer software, library materials, and audio-visual
materials. Scores noting the problems in effectively teaching due to amount of
resources range from 1, signifying “not at all” to 4, signifying “a lot.” This index has
a Cronbach’s alpha above .70. This resource construct was created separately for
each country. Together, these indicators will help assess whether a school has
adequate human and material capital to successfully teach students.
Overall socioeconomic status of the school is also taken into account, which is
calculated by taking the mean socioeconomic status of students attending the school.
Including this variable in the model ensures that socioeconomic inequalities between
schools will be noted, not only the inequalities between individual students or
particular countries. To assess the average socioeconomic status of a school, I use the
33
socioeconomic status construct that I described above and identify the average value
for each school.
Student-Level Covariates
At the student level, student opportunity to learn and personal characteristics
serve as covariates. In assessing opportunity to learn, minutes spent in math classes
each week are taken into account. Time spent in math classes out of school is also
reported and included in the model, but is measured on a scale where students may
indicate that they either do not attend out of school lessons or if they do attend such
lessons. If they do, they must note whether they spend less than two hours in class,
between two and four hours in class, between four and six hours in class, or over six
hours in class each week. In the analysis, each time bracket takes the value of its
midpoint, with the exception of less than 2 hours per week, which is coded as 1 and
more than six hours per week, which is coded as 7. To supplement these time-related
variables, there are dummy variables that note whether or not a student spends time in
remedial math classes and enrichment math classes.
Student characteristics are also factored into the model. Responses provided
by students about gender serve as covariates, as do responses to whether the same
language is spoken at home as is spoken in school. Additionally, a student-level
construct of socioeconomic status is used, which is described above.
34
Additional Variables
Based on my findings in the initial set of analyses in Chapter 3, I add some
other variables to my subsequent models. All of these variables address factors at the
country level. The variables as well as the sources of their data will be described as
they become relevant in Chapters 3 through 5.
Methods
In this study, I use three-level multi-level models to conduct my analyses. All
data management and preliminary analyses were done using Stata 12 and all multilevel analyses were run in HLM. Using these multi-level models allows me to nest
students within schools and subsequently schools within countries. In doing so, I
properly account for differential errors at each level as well as the correlation within
nested levels. Additionally, I am able to clearly look at the effect of country level
factors on the slope of socioeconomic status’s effect on achievement.
Before delving into my analyses, I will discuss some alternate methodologies
that are typically popular in social mobility research. First, I will look at the status
attainment model and note some critiques. I will then look at log-linear models,
which were used by many wishing to address some critiques of the classic status
attainment model. From here, I will further defend my choice of neither method, as
well as my decision to use hierarchical linear models as an alternative.
35
Why not the Status Attainment Model?
Among the most frequently used approaches to studying social mobility and
transmission of social status is the status attainment model. Popularized by Peter M.
Blau and Otis Dudley Duncan (1967), this model gained prevalence in sociological
discussions of mobility and transmission of advantage during the late 1960s and
through the 1970s. Blau and Duncan’s model uses social origin, measured by father’s
educational attainment and occupational status, to predict son’s ultimate social
standing, measured through his educational achievement and occupational status.
They find that educational attainment has a mediating effect that mobility thus does
occur within the middle class. There is, however a strong barrier along the borders
between classes, making inter-class mobility rare.
The Wisconsin Model, designed by Sewell, Haller and Portes (1969),
elaborated on the Blau and Duncan model by adding social psychological factors.
Specifically, this model takes into account educational aspirations and effects of a
student’s significant others in predicting their ultimate level of mobility. The authors
suggest that these social psychological factors do play a role, showing a potential path
through which interventions could be made. Although these two prominent models
and other offshoots of the initial model bring to light some interesting findings, they
have given rise to a number of critiques.
Primarily, the status attainment model has been critiqued as atheoretical
(Coser 1975; Burawoy 1977). Coser argues that the advanced statistical methods
implemented by Blau and Duncan and others are used without being backed up
substantively. Some of the measures used, particularly those of occupational status
36
are not ideal, thus leading to conclusions that do not accurately reflect the world. The
use of developed models is possibly counterproductive to the field if the data is not
adequate, so Coser argues that the models and methods should not be used if the
proper data for providing more accurate results is unavailable.
Additionally, their focus on the individual level misses many important
factors, especially because there is no concern for class, power differentials, or social
advantage. Coser also notes that the combination of this primary focus on the
individual level with the American ideology is very problematic, given that both lend
little room to consider structural constraints on mobility. When the structural level
serves as the focus, the status attainment model still typically omits these factors and
remains aligned with the American ideology as closely as is possible from the
structural level, as the focus is level of industrialization, which reflects capitalism and
the capitalist mindset (Featherman, Jones, and Hauser 1975).
As Mateju, Smith, Soukup and Basl (2007) note, the true impacts of
educational systems can only be fully understood when these structural factors are
noted, making international comparisons particularly important. For the most part,
these studies have focused on a single country, like Blau and Duncan’s (1967) The
American Occupational Structure or two similar countries, like Treiman and Terrell’s
(1975) study of the United States and Great Britain. This leaves little room to notice
the role of structural factors, as they are essentially held as a constant. Even
psychological and individual-level factors studied in status attainment models are
shaped by issues at the structural level of social and political systems. For this
37
reason, it is particularly important to analyze the structural level, especially if
stratification in education is to be targeted.
Adding to Coser’s critique, Burawoy (1977) focuses on the problem of
standardization based on the linear model used. This model, particularly that used by
Treiman and Terrell (1975), assumes that education is unidimensional, which is
inaccurate. With the linear model, many assumptions about patterns of mobility and
inequality are imposed, which Burawoy opposes. Burawoy stresses that this
overlooks the simple fact that education serves differentially for those in different
socioeconomic classes. Specifically, education may act as a source of mobility for
those of the middle class, but those of the rich and the poor experience education
instead as an avenue for maintaining status. Without acknowledging the possibility
of status maintenance or the structural will to maintain status, the findings will
inevitably be incomplete.
This attack against the status attainment model as atheoretical and ignorant of
structural factors spurred the development of some alternate theories and approaches.
One of these approaches is Kerckhoff’s (1976) proposed allocation model.
According to Kerckhoff, sociologists siding with a socialization model have
dominated the field. The socialization model considers the individual essentially free
to move about within the social system, essentially regardless of social constraints.
However, this model has proven insufficient for certain groups, like African
Americans, which suggest that a limitation to mobility may exist, particularly for
some groups. He notes that a measure of discrimination and its effects, which may
impact both aspirations and ultimate outcomes, would likely add a lot to the model.
38
Kerckhoff thus proposes the allocation model, in an attempt to account for
these other factors. In the allocation model, differences due to structural limits and
selection criteria are acknowledged and not all is attributed to an individual’s learned
skills and motives. The constraints on individuals based on their position within the
system are entirely ignored in the socialization model. In accounting for these
constraints, the structural and institutional levels are added to the theory and the
model, contextualizing the model as it was not previously.
Based on these critiques of the model, I am led believe that the status
attainment model is inadequate in revealing the true nature of international
differences. To address this, I account for the importance of the structural level in
both my theories and methods. Additionally, I use hierarchical linear models as
opposed to a simple linear regression, as to account for differential variation at each
level. This does not entirely alleviate the problems with assuming a linear model as
discussed by Coser (1975) and Burawoy (1977), but it brings me closer to arriving at
a method that fits my theories more soundly. With my strong reliance on sociological
theories to understand stratification and effects of the social structure, I believe that
my use of a more structural model as opposed to the status attainment model is
theoretically justified.
Why not Log-Linear Models?
Other previous studies of social mobility, particularly those critiquing the
traditional status attainment model, have used log linear models. Hoping to decrease
the theoretical and methodological problems with the status attainment model, log-
39
linear models were chosen to improve the analyses of social mobility. According to
Lucas, log-linear models “are appropriate for investigating the association between
categorical variables” (1999: 24). Additionally, these models account for a set of
variables within a given structure. If the greater structure changes, the model will
account for the change. The structure is thus acknowledged, but it is not studied
centrally.
For instance, Featherman, Jones, and Hauser (1975) used a log-linear model in
their famous study that generated the Featherman-Jones-Hauser Hypothesis, which
suggests that level of industrialization is the key determinant of mobility in a society.
Because they account for the effects of the structural level in their model, they are
able to come to such conclusions about the greater social structure. Beyond this
study, Lucas’s (1999) study of tracking and Goldthorpe’s (1980) study of British
mobility, among others, applied these methods.
In my study, I choose not to use log-linear models for two primary reasons.
First, the variables that I have chosen to work with are not categorical. This,
however, could have been possible to alter, had I wished to use log-linear models.
The more substantive reason that I do not use log-linear models is because structural
level is the focus of my study. I thus wish to understand the structural factors that
have the greatest effect, rather than simply studying the effect when these factors are
controlled. Because HLM lets me clearly identify and understand the effects of
structural factors while properly accounting for error at each level, I believe that
HLM is the best-suited method for my analyses. For this reason, I break with the
40
tradition of using log-linear models as an alternative to the basic status attainment
model and instead, I use an approach that directly examines structural factors.
Limitations
Although the data that I use provide much of the information that I need to
answer my research questions, I encounter a few limitations in my analyses.
Unfortunately, I do not have access to data from all countries in the world. This
limited my sample size at the country level, giving me few degrees of freedom.
Additionally, it limited the scope of the study, as many countries were omitted.
Because it is primarily only OECD countries that were used in PISA to begin with, I
immediately lost many countries, particularly those in Africa and many in Asia.
When I added variables from other sources, my sample size was further decreased. It
is possible that the countries with no data are systematically different from those
included in PISA, so their omission is could detract from an understanding of
worldwide systems. Additionally, due to problems with multiple imputations, I
needed to remove some other countries from my sample as well, including Great
Britain.
Available data from different data sources were also taken from different
years, presenting some problems for the validity of the analysis. However, this was
the best possible compromise, as many sources providing variables necessary for
analysis did not supply data taken from 2009, when PISA, the main data set, was
produced. Where data was not available from 2009, I chose the closest possible date.
Had I omitted data not obtained in 2009, it would have been very difficult, if not
41
impossible, for me to find country-level data to allow me to answer my research
questions.
Finally, the international nature of my study made it impossible for me to
account for racial or ethnic group. This data would have necessarily been tailored to
specifically fit each country, designating each student either as a racial or ethnic
minority or majority. I did not have access to general information about race, nor did
have access to data about which groups are dominant and which groups are minorities
in each country, so this could not be included in the model.
In spite of these limitations, I feel as though my models do a good job
acknowledging potential confounding variables. Additionally, I believe that I am still
able to address a wide range of countries with diverse social, political, and economic
systems. In the coming chapters, I will set out the models I use, specifying the
theories that underlie the variables in the model.
42
Table 2.1: Descriptive Statistics
Variable
Math Achievement Score
LEVEL 3 COVARIATES
Gini Index
PPP
Political Participation
Civil Liberties
Democratic Political Culture
Democratic Election Processes
Functioning of Government
Corruption Perception Index
LEVEL 2 COVARIATES
One School Choice
Two or More School Choices
Dependent Private School
Independent Private School
Village
Small Town
Town
Large City
Principal controls Hiring
Principal controls Firing
Principal controls Budget
Principal controls Curriculum
Always consider Residence
Sometimes consider Residence
Always consider Acad. Record
Sometimes consider Acad. Record
Always consider Feeder Schools
Sometimes consider Feeder Schools
Always consider Parental
Endorsement
Sometimes consider Parental
Endorsement
Always consider Legacy
Sometimes consider Legacy
Percent of Teachers with University
Degree
Standard
Mean
Deviation
465.29
104.224
35.65
23120.41
6.03
8.31
6.89
8.33
6.84
5.45
8.77
13611.22
1.79
1.92
1.65
2.59
2.2
2.36
0.15
0.61
0.1
0.06
0.14
0.21
0.29
0.12
0.56
0.45
0.64
0.23
0.36
0.18
0.29
0.22
0.16
0.32
0.35
0.48
0.3
0.24
0.34
0.41
0.46
0.33
0.49
0.5
0.48
0.42
0.48
0.38
0.45
0.41
0.36
0.46
0.19
0.39
0.174
0.15
0.27
0.38
0.36
0.44
0.78
0.33
43
Table 2.1, continued
Percent of Teachers with
Certification
Student-teacher Ratio
School Size
Resource Construct
School-Level Socioeconomic Status
LEVEL 1 COVARIATES
Time in Math Class Out of School
Time in Math Class In School
Attends Enrichment Math Class
Attends Remedial Math Class
Same Language Spoken at Home as
in School
Gender
Individual Socioeconomic Status
Level 1 N
Level 2 N
Level 3 N
Mean
Standard
Deviation
0.76
16.46
724.91
1.98
-0.41
0.366
15.134
640.266
0.64
0.86
1.244
228.028
0.23
0.22
1.9
106.494
0.42
0.41
0.9
0.51
-0.33
0.3
0.5
1.16
393743
14612
49
44
Figure 2.1: Graph of the Effect of SES on Achievement by Country
The Effect of SES on Achievement by
Country
Czech Republic
New Zealand
France
Singapore
Germany
Belgium
Slovakia
Australia
Bulgaria
Hungary
Israel
United Arab Emirates
Sweden
Country Name
United Kingdom
Taiwan
Switzerland
Poland
United States
Slovenia
Denmark
Netherlands
Austria
Japan
Luxembourg
Lithuania
Russia
Peru
Norway
Trinidad and Tobago
South Korea
Ireland
Kyrgyzstan
Uruguay
0
10
20
30
40
Effect Size
50
60
70
45
Figure 2.1, continued
The Effect of SES on Achievement by
Country
Turkey
Argentina
China
Portugal
Kazakhstan
Canada
Greece
Spain
Chile
Croatia
Romania
Latvia
Country Name
Liechtenstein
Serbia
Iceland
Thailand
Colombia
Estonia
Panama
Montenegro
Qatar
Jordan
Albania
Finland
Italy
Hong Kong
Brazil
Indonesia
Mexico
Tunisia
Macao
Azerbaijan
0
10
20
30
40
Effect Size
50
60
70
46
Chapter 3
OPEN AND CLOSED SYSTEMS
Introduction
In this exploration of the role of political and social structures in affecting the
socioeconomic achievement gap, I will begin with a study of open and closed
systems. Because this aspect of political systems certainly does vary by country,
differences in level of openness or closure may relate to the differing effects of social
origin on achievement in school across countries. As noted in Chapter 1, Weber
(2009[1920]) proposes this dichotomous classification for understanding the
involvement of citizens in decision-making and the achievement of social closure in
dominant political and social groups. Closed systems typically exclude the majority
of people from governmental processes and work toward social closure. Conversely,
open systems allow participation of those beyond the small elite, even if it detracts
from the social closure of the society. This division therefore provides a theoretical
distinction between two extremes, each of which may, based on a number of theories
discussed below, lead to dramatically different effects of socioeconomic status on
educational achievement.
Closed Systems
In closed systems, as Weber (2009[1920]) describes, only a small group of
people are able to participate in governmental and decision making processes. As a
result, a very exclusive elite group comes to dominate, making decisions that often
serve their own interests, ignoring or minimizing the importance of others’ needs.
47
Monopolies are therefore maintained, leading to heightened levels of social closure,
in which a small and insular group comes to dominate all proceedings. C. Wright
Mills (2007[1956]) terms this group a “power elite.” This power elite is composed of
men or women who are power holders in the intertwined spheres of the economy,
politics, and the military. The small group makes the most important decisions for a
society without consulting those outside of the power elite. This allows their interests
to be the focus of all legislation without contest. Charles Tilly (2009[1999]) also
notes that a good deal of opportunity hoarding takes place in closed systems, as those
in control do what they can to maintain their advantage. At times, this opportunity
hoarding may involve corrupt methods of nepotism, cronyism, or bribes to ensure a
maintained advantage. Closed systems, therefore, may be more likely to maintain
heightened levels of stratification, as none but the elite have a place to voice or carry
out actions that may fulfill their interests.
The state of Hungary’s education system during the one-party socialist
regime’s rule presents an example of a closed system and its impact on educational
equality (Szelenyi and Aschaffenburg 1993). Consistent with theories of socialism,
the government attempted to institute equal opportunity and increase access for those
of lower classes through quota systems. However, inequalities along class lines
remained strong. This lack of the reform’s success could have occurred due to the
fact that the quotas were abandoned too early for their effects to play out, but
Szelenyi and Aschaffenburg offer an alternate explanation.
The authors instead suggest that the interferences with the success of the quota
system occurred while it was still in place. Rather than adhering to the quotas, elites
48
often used monetary bribes to ensure they retained their cherished status at the top of
the educational hierarchy. Informal ties with politicians, bureaucrats, and teachers
were taken advantage of as well, as parents used such relationships to guarantee
positions for their children in elite schools, in spite of exceeding the quota. The
authors even discuss evidence of parents misrepresenting class position on school
forms. Thus, it appeared as their child’s admission would not, in fact, be violating the
quota’s guidelines. These methods of retaining advantage, even when regulations
have been put in place to alter existing relations, show that exclusive governments
can keep power in the hands of the few. As such, it is likely that controlling for social
capital or personal ties to the Hungarian socialist party would be a fairly reliable
measure of access to the upper crust of society. Opportunity hoarding is omnipresent,
as Tilly (2009[1999]) suggests, revealing that socioeconomic status likely remains a
large predictor of a student’s achievement in school. Even interventions meant to
improve equality are undermined by the will of the few to hold on to their advantage.
This scenario predicts that my findings will show that less equitable, less democratic
societies will have a heightened effect of socioeconomic status on academic
achievement.
However, closed systems may also have the potential to enforce equality in
ways that open systems cannot. As Karl Marx’s theory proposes, a dictatorship of the
proletariat is required during the transition from capitalism into communism (Marx
and Engels 1978[1848]). The purpose of this phase is to ensure that the working class
is able to deconstruct all bastions of bourgeois power and oppression. Using the force
of the government, they can effectively create an egalitarian society, in which class
49
and hierarchy are nonexistent. This theory therefore suggests that a more
authoritarian government is more capable of enforcing egalitarian principles than the
government of a more democratic, open society.
Cuba presents an example of a closed system in which educational
achievement is quite equitable across socioeconomic strata. Martin Carnoy’s (2007)
study of Latin American countries reveals that those with more government control
over resources and day-to-day activities can have a positive effect on lessening
disparities. In this study, Cuba, with its socialist context, is compared with the more
democratic free-market systems of Brazil and particularly Chile. Carnoy argues that
although Cubans have fewer choices, they are more likely to succeed, as the single
option is quite good. This single choice also excludes the option of failure, or
dropping out of the system. In working to improve the public schools as much as
possible, pedagogy is fine-tuned as to ensure the highest quality. Curriculum is childbased and teaching is a valued profession, giving teachers a very respectable level of
pay. Schools are quite successful in teaching all students, not only those in a
particular group. This is, of course, partially because socioeconomic differences are
so narrow. Also notable is that because there is minimal variation in school quality,
there is no reason or way for elites to undermine the system, as occurred in Hungary.
Outside of the school, Cuba’s system provides services to all families, which
allow all to have good health care and adequate income to live comfortably. There is
therefore little poverty and few school children need to work outside of the home to
bring in more income. These conditions allow all children to have reliable food and
shelter, letting them focus on school, not only survival. Perhaps most importantly,
50
these changes have been in place for long enough that all parents have enough
education to be able to provide adequate human capital in the home and expect their
children to succeed.
Carnoy ultimately concludes that this state-generated social capital, which
parallels individual or family social capital at the country level, is the origin of the
difference in levels of social reproduction. He believes that if states can generate
social capital, students from lower socioeconomic strata will be more likely to
succeed academically in countries with more state-generated social capital.
Therefore, governmental control is necessary for enabling those of low
socioeconomic class to succeed. More open systems may thus face less equitable
achievement in the school. If my analyses reveal that more equitable, less
democratic systems will have the smallest effect of socioeconomic status on academic
achievement, my results will be consistent with this theory.
Evidently, Cuba shows that closed systems do not always promote a
heightened correlation between socioeconomic status and achievement in school,
which did occur in Hungary. Although the two theories predict that there is an effect
of closed systems on the socioeconomic achievement gap, they suggest contradictory
evidence about international trends. It is thus necessary to further investigate the
matter, given that the literature provides conflicting evidence on the effect of closed
systems.
51
Open Systems
In contrast to these closed systems, open systems allow for the full
participation of a society’s constituents (Weber 2009[1920]). This more democratic
system would be much more likely to cater to the interests of the majority of the
people, as opposed to solely the interests of the elite. Due to the interests of the group
that holds the most power, it is likely that closed systems would have a greater
interest in perpetuating inequality than would open systems. With the ability to truly
participate in government and advocate for needs, it is likely that an open system
would promote equality in ways that closed systems would be reluctant to carry out.
This theory provides the possibility that open systems are more conducive to social
mobility, as it is in the interest of the common people to increase the range of social
mobility. The elite typically seek to limit this possibility, securing their own position,
so if the people rather than the elites held power, equality is more likely to be
realized.
In the realm of education, some, such as Charles Eliot, a former Harvard
president, suggest that democratic, open societies should bring about strong education
systems that will serve all equitably (Ravitch 2000). Because the future of a
participatory system relies on the voters and their choices, it is necessary for the
voting public, or the masses, to be educated. A well-functioning system dependent on
these votes should, therefore, be sure to provide high quality education to all of its
citizens, which would in turn lead the government to run more smoothly (Equity and
Excellence Commission 2013).
52
Supporting this theory, Sweden has a very open and equitable system, in
which transmission of advantage has actually decreased over time, making
socioeconomic status an ever-smaller predictor of educational achievement (Jonsson
1993). The Swedish education system was largely reorganized in 1927, moving away
from the old tracking system. Students are now separated at a later age, as to allow
students of lower socioeconomic standing to catch up to those with prior advantage.
Also, this lack of tracking in early years gives all students a more equal opportunity to
learn, putting all in the same classroom and letting all have the same high-quality
teachers. Since these reforms, the reproduction of cultural capital from one
generation to the next has decreased, and those from the most disadvantaged classes
faced the most improved situations. If the theory supported by this case study is
accurate, analyses will predict that systems with lower levels of inequality and higher
levels of democracy will have a smaller effect of socioeconomic status on academic
achievement. This would be consistent with the theory set out reflecting the
Hungarian system, but displaying the opposite end of the spectrum.
Still, fitting with Marx’s (1978[1848]) argument above, open societies also
have the potential to make education systems less equitable. With the power in the
hands of the people, it is possible that none will protect the idea of the greatest good
for the greatest number. Bowles and Gintis (1976) write that although America is
politically open, its capitalist economy is closed, preventing equality from being
realized. It could be that this closed economic system interferes with the open
political system, promoting inequality in education as a byproduct of the economic
system’s efforts to maintain the closed economic character of the society. Even
53
though there may be a fully democratic and open political system, where all can voice
their opinions, there would be little social mobility. The economic elite would be
very closed off, likely preventing the entry of those not born into the elite.
Fitting with this theory, the authors ultimately conclude that schools serve the
purpose of reproducing the hierarchies and inequalities that perpetuate the capitalist
system, as discussed above. Because America is a liberal democracy with a capitalist
economy, it is unlikely that equality will be realized in the school system, as it would
undermine the stratified nature of the economy itself. In order to ensure that the
advantage of the elite is not lessened, a myth of meritocracy is enforced, making
people believe that in the future, they may also be able to become rich (Apple 2004).
This ideology leads people not to vote in their best interests, favoring instead policies
that maintain the advantage of the elite. The ideology thus undermines the potential
equality of an open, democratic political system, demonstrating the possibility that
such systems may not promote equality. If this theory is most consistent with
existing trends, analyses will reveal that systems that are less equitable but more
democratic will have a larger effect of socioeconomic status on achievement,
providing the counterpart to the Cuban hypothesis.
A Review of Hypotheses from Case Studies
These four case studies imply two opposing hypotheses, each of which is
demonstrated by two case studies, with one case study representing each of the
extremes. The first hypothesis, supported by Hungary at one end and by Sweden at
the other, suggests that academic achievement in closed societies is more strongly
54
effected by socioeconomic status than is achievement in open societies. The second
hypothesis, which is based on the case studies of Cuba and the United States at the
poles of the dichotomy, opposes the first hypothesis. These findings instead suggest
that closed societies would have a smaller socioeconomic achievement gap than
would open societies. However, both hypotheses and all four case studies predict that
societies with lower levels of economic inequality will see a smaller socioeconomic
achievement gap than will societies with more economic inequality.
In the subsequent analyses, I will examine these theories and hypotheses,
determining whether closed systems or open systems promote a smaller
socioeconomic achievement gap, which would therefore provide more social
mobility. I will also give attention to the effect of overall inequality to see whether or
not these theories are correct. If necessary, I will explore the possibility that neither
system will promote equality, but that different systems simply have different
methods of maintaining inequality.
Models
In this chapter, I use two models, where the first serves as a baseline for the
second. These models are detailed below in Figure 3.1. The single difference
between the models is that the first does not include the interaction term. In the
second model, I include an interaction term, which allows me to look at the impact of
country-level variables on the slope of socioeconomic status on achievement.
As noted in Chapter 2, I use a set of five variables to assess open and closed
systems through measures of democracy. The theories set out above suggest that
55
certain measures are more important to the working definitions of open and closed
systems than others. Participation in government, as measured by the participation
variable, is clearly central to the distinction between open and closed systems.
Additionally, the quest for social closure is fairly important. The level of social
closure can be seen in the functioning of government variable, which may reveal
whether factors outside of the typical rules of governing, such as corruption, are used
to maintain this closure. Electoral processes, which note how democratic and free
elections are run, is tangentially relevant, but it is not nearly as central to the theory as
participation and functioning of government. Although typically considered a key
element of democratic systems, the theory of open and closed systems does not give
extensive attention to elections. Further supporting this, Weber (1978[1922]) notes
that studying appointments may actually show more about the level of social closure
to be achieved. Finally, civil liberties, which reflect rights of citizens, and democratic
political culture, which looks at cultural qualities often linked with democracy, are
less centrally important to Weber’s (2009[1920]) theory and will thus be given less of
a focus in the analyses. I do, however, include all variables in the model, as they are
all at least tangentially related to Weber’s theory and the case studies discussed
above, even if they are not of utmost importance.
56
Figure 3.1: Models 3.1 and 3.2
Model 3.1: No Interaction
Model 3.2: With Interaction
Level 1 Model
Level 1 Model
Y = P0 + P1*(SES) + P2*(V) + E
Level 2 Model
Y = P0 + P1*(SES) + P2*(V) + E
Level 2 Model
P0 = B00 + B01*(W) + R0
P0 = B00 + B01*(W) + R0
P1 = B10
P1 = B10
P2 = B20
P2 = B20
Level 3 Model
Level 3 Model
B00 = G000 + G001*(Z) + U00
B00 = G000 + G001*(Z) + U00
B01 = G010
B01 = G010
B10 = G100
B10 = G100 + G101*(Z)
B20 = G200
B20 = G200
Vector of each level:
Level 1: V=time out of school in math class, time in school in math class, language at
home matches language at school, female, attends enrichment math programs, attends
remedial math programs
Level 2: W=one alternate school choice, two or more alternate school choices,
dependent private school, independent private school, village, small town, town, large
city, principal can hire teachers, principal can fire teachers, principal controls budget,
principal controls curriculum, always consider residence for admission, sometimes
consider residence for admission, always consider academic record, sometimes
consider academic record, always consider attendance of feeder schools, sometimes
consider attendance of feeder schools, always consider parental endorsement,
sometimes consider parental endorsement, always consider legacy, sometimes
consider legacy, percentage of teachers with university degree, percentage of teachers
with proper certification, student-teacher ratio, school size, resource construct,
school-level socioeconomic status
Level 3: Z=Gini, PPP, political participation, civil liberties, democratic political
culture, electoral processes and pluralism, functioning of government
57
Results
The initial model, an abbreviated version of which is displayed in Table 3.1,
shows that socioeconomic status does have a significant effect on academic
achievement.7 However, when adding in the effect of the third-level variables on the
relationship between socioeconomic status and achievement in Model 3.2, also seen
in Table 3.1, the model has a lower deviance statistic, rendering it the model of best
fit. As revealed by the significance of the interaction terms and the lower deviance
statistic of the model, the country-level factors that I examine as interaction terms do
play a role in determining the magnitude of the effect of socioeconomic status on
academic achievement. This supports my overarching hypothesis that democracy and
other country level variables, specifically Gini and PPP, have a significant effect on
the relationship between socioeconomic status and academic achievement.
Because it is the model of best fit, the rest of the current discussion will refer
to Model 3.2. In this model, all country-level variables, aside from the civil liberties
measure, had statistically significant effects on the slope of socioeconomic status on
achievement. Both political participation and functioning of government, which, as
noted above, are the two measures most indicative of the type of system, have a
positive effect on the socioeconomic achievement gap with of coefficients of 1.48 and
0.62, respectively. This is to say that higher levels of participation in government and
democratic functioning of government increase the size of the gap between students
at the top and the bottom of the socioeconomic hierarchy.
7
See Table 2.1 at the end of Chapter 2 for descriptive statistics and see Table 3.3 at
the end of this chapter for the full results of all models in Table 3.1.
58
Table 3.1: The Effects of the Interaction
Model 3.1:
No
Interaction
Individual Socioeconomic Status
Parameters
Deviance
Model 3.2:
With
Interaction
Coefficient
[S.E.]
Coefficient
[S.E.]
14.33*
[1.87]
14.24*
[1.52]
Gini Index
-0.25*
[0.02]
PPP*10,000
2.13*
[0.22]
Participation
1.48*
[0.18]
Civil Liberties
0.38
[0.21]
Democratic
Political Culture
-0.52*
[0.17]
Democratic
Election Processes
-0.88*
[0.16]
Functioning of
Government
0.62*
[0.13]
46
53
4448945.805 4446625.845
Level 1 N
393743
393743
Level 2 N
14612
14612
Level 3 N
*p<.05
49
49
The democratic political culture and democratic elections variables,
conversely, had a negative effect, with the respective coefficients of -0.52 and -0.88.
This suggests that with a more democratic political culture or more democratic
59
elections, the effect of socioeconomic status on academic achievement will decrease.
However, as mentioned above, these variables are not central to my theory. Weber’s
theory of open and closed systems does not touch on these issues as strongly, so I
choose to orient my findings toward the directionality of the more relevant variables.
Beyond this, the combined effect of democratic political culture and democratic
elections, -1.4, is of smaller magnitude than the combined effect of the participation
and functioning of government variables, which is 2.1, leading the full model to
suggest that higher levels of democracy will ultimately promote a larger
socioeconomic achievement gap.
These findings would appear to support Carnoy’s (2007) hypothesis that a
closed system can most adequately produce equality academically because they have
a greater potential to produce state-generated social capital. However, due to the
direction of its effect, Gini index’s coefficient of -0.25 suggests somewhat
paradoxically that countries with lower levels of equality have a larger socioeconomic
disparity in academic achievement. Because of this surprising finding, neither of the
two hypotheses derived from the four proposed theories based on case studies is
supported by my findings.
Examining Predicted Values
As seen in Table 3.2, a table of predicted values, both low levels of
democracy and high levels of inequality promote a smaller effect of socioeconomic
status on academic achievement. Throughout the table, PPP is held constant at
25,000, which is approximately the average value of the sample. I use values at the
60
first quartile for the low democracy and low inequality measures and I use the values
at the third quartile for the high democracy and high inequality measures. Because
there are multiple democracy measures, I take an average of the significant variables’
values for the model and use this distribution of values when calculating the quartiles
for the appropriate predicted values.
Table 3.2: Predicted Values of the Effect of SES on Achievement
High Inequality:
Gini=40
Low Inequality:
Gini=29
High Democracy:
All measures=8
15.16
Low Democracy:
All measures=6
13.76
17.92
16.52
Figure 3.2: Graph of Predicted Values of the Effect of SES on Achievement
Low Democracy
Low Inequality
High Inequality
High Democracy
0
5
10
15
Effect of SES on Achievement
20
61
The varying effect size of socioeconomic status on academic achievement
demonstrates the differences between democratic and nondemocratic, equal and
unequal countries. As seen in Table 3.2 and Figure 3.2, the difference in the effect of
socioeconomic status on academic achievement is the largest between societies with
high democracy and low inequality and societies with low democracy and high
inequality. Recall that a standard deviation in math achievement score is
approximately 100, so each point accounts for about one percent of a standard
deviation. Additionally, socioeconomic status has a set standard deviation of
approximately 1, so coefficients in the model each show the effect of about one
standard deviation.
With these facts in mind, it can be understood that each standard deviation in
socioeconomic status accounts for a gain of about 13.76% of a standard deviation in
achievement score in societies with high levels of inequality and low levels of
democracy. Countries with a level of democracy that is below the average and a level
of inequality that is above the average include Argentina, Brazil and China. In
countries with low levels of inequality and high levels of democracy, on the other
hand, this gain due to one standard deviation of socioeconomic status is
approximately 17.92 points. Austria, Denmark, and Germany are among the group of
countries fitting in this category, with a higher than average democracy level and a
lower than average inequality level. Countries falling within this group have the
largest socioeconomic achievement gap, whereas those falling into the first group
mentioned, with high inequality and low democracy, have the smallest socioeconomic
achievement gap.
62
Although this group provides neither the largest nor smallest socioeconomic
achievement gap, countries with high democracy and high inequality see that each
standard deviation of socioeconomic status adds approximately 15.16 points, or
15.16% of a standard deviation, to achievement score. Within this grouping,
countries with a higher than average level of both democracy and inequality include
the Japan, Portugal and United States. In the final group, where there is both low
democracy and low inequality, each standard deviation of socioeconomic status has
an effect of about 16.52 points. This category includes Croatia, Slovakia, and
Kyrgyzstan, all of which have a lower level of both democracy and inequality than
average.
As discussed above and demonstrated through the study of predicted values,
the outcomes of this analysis fit with neither of the hypotheses proposed in the case
studies discussed above. Additionally, the results refute the element of the hypothesis
suggesting that lower inequality, as assessed by Gini, will decrease the
socioeconomic achievement gap. The proposed alternatives of Effectively
Maintained Inequality and exclusion and adaptation, as briefly discussed in Chapter
1, may be the only theories that account for this possibility (Lucas 2001; Alon 2009).
Using these two theories, I will further discuss how they may be able to explain these
results.
Effectively Maintained Inequality and Methods of Exclusion and Adaptation
Beyond the two hypotheses based on case studies of open and closed systems,
there is a third possibility that in different systems, inequality is maintained in
63
different ways. The results suggest that there is either a large socioeconomic
achievement gap or an unequal distribution of income, so all societies appear to
maintain inequality in some way. These different methods of maintaining inequality
result from the necessary adaptations made by elites in an attempt ensure the nonelite’s exclusion. Lucas’s (2001) theory of Effectively Maintained Inequality (EMI)
asserts that regardless of how it is accomplished, inequality will unquestionably be
maintained. It could be that only in some systems, such as the open, ideologically
meritocratic United States, elites use the education system to maintain their
advantage. However, in other countries, like the closed Hungary during its time
under Soviet rule, other methods of maintaining advantage, such as bribes or
nepotism, especially when used outside of but also when used within the education
system, secured advantage (Szelenyi and Aschaffenburg 1993). Alon’s (2009) theory
of exclusion and adaptation would suggest that these different methods of exclusion
came about due to the elite’s need to adapt in order to find new methods of
maintaining their position when the non-elite threatened to gain elite status. If these
two theories provide the most accurate analysis, countries in which inequality is
maintained through education will have a larger socioeconomic achievement gap than
those that have not used that sort of adaptation.
Evidently, a theory of maintaining inequality through adaptations would
identify a much more complex relationship than the theories proposed above. If this
theory is true, then open societies do not exist. However, there would still be
different types of systems, all of which are closed. The distinguishing factor would
be the method through which the elite is reproduced, be that method family ties or
64
academic achievement. As suggested by the functioning of government measure in
the above analyses, corruption may be a factor underlying this division. If this is the
case, it is possible that countries with more corrupt government systems will be able
to maintain advantage without necessarily relying on the education system. Instead,
they may use methods like the nepotistic and cronyistic hiring strategies used in
Russia (Aron 2009). Therefore, the relationship between a democracy and the
socioeconomic achievement gap may not show that either democratic or nondemocratic systems are particularly more effective in producing equality. They may
instead show that mechanisms for maintaining advantage simply differ. If this theory
is correct, outcomes will reveal that more authoritarian systems, particularly those
with characteristics of corruption, have a smaller effect of socioeconomic status on
achievement. This would not signify that they are more committed to equality, but
simply that education is not the means through which advantage is transferred. The
effect of a country’s overall inequality is less evident, as it would depend on the
effectiveness of the implemented method of maintaining advantage.
Discussion
Aligned with Lucas’s (2001) theory of EMI and Alon’s (2009) theory of
exclusion and adaptation, my analyses do not simply imply that societies with
governments that are less democratically functioning are more equitable, as it may
appear. Instead, I suggest that elite advantage in these less democratic countries is
not passed down through education. Corruption, suggested by the functioning of
government variable, may be generally higher in countries that have a smaller effect
65
of socioeconomic status on academic achievement. Thus, it is quite possible that
advantage is passed down using methods other than education, such as bribes or
nepotistic hiring, which guarantee the children of the elite a top position within the
hierarchy. In societies in which education is the method for passing down advantage,
the education system is likely to be more stratified, as it maps onto the ultimate level
of social stratification more closely than it does in societies not relying on education
to transfer status. For this reason, I suggest that there are two types of societies, both
of which are closed. One of the forms of societies reproduces social status through
the education system, whereas the other reproduces status through alternate, possibly
corrupt, methods.
Maintaining Inequality Outside of Academic Achievement
As was suggested in Szelenyi and Aschaffenburg’s (1993) examination of
Soviet Hungary, bribes were common among the elite. Present-day Russia shows that
nepotism and cronyism remain prevalent, as it was estimated in 2008 that those
heading the boards comprising approximately 40% of Russia’s economy were the
president’s “political and personal allies” (Aron 2009). However, it is important to
note that if this is the case, bribes in the school would not necessarily improve
achievement of those using the bribes. They may only lead to additional social
capital and interpersonal connections, which would help with future job placement,
but would not be captured in the results of the tests used in this study to assess
achievement. High achievement is thus not driven by bribes, because academic
66
achievement does not necessarily help people retain elite status, but ultimate
placement within the elite likely results from such practices.
Because of the possibility of using bribes or social connections, which often
do not require as much hard work in school and are more secure than ensuring a
child’s good performance in school, more corrupt and less democratic societies may
be more likely to have a smaller socioeconomic gap in academic achievement. Elite
jobs, as seen in Russia, may be guaranteed through nepotistic hiring, bribes, or other
sorts of mechanisms requiring little effort from the child aside from their parents’
money and elite social network. Because academic achievement is not the path
through which elite status is preserved, it is not essential that those of high class
distinguish themselves from their less advantaged peers in their academic
achievement. Therefore, there is little reason that the socioeconomic achievement
gap would even come about, as academic merit plays a small role, if any, in
maintaining inequality.
Maintaining Inequality Through Academic Achievement
It could be that only in more bureaucratic societies, where corruption is less
prevalent, based on the above measures, that effort must be put into education in
hopes of maintaining elite status. This reliance on education as a means of
transmitting advantage ensures that elites will do what they can in the education
sector to guarantee that their children maintain their advantage. As Lucas (2001)
writes, elites will quantitatively and qualitatively improve their chances when
possible, so where education is the only acceptable path through which to maintain
67
advantage, elites alter the educational paths of their children. Qualitatively, elites
would choose better schools and quantitatively, elites would stay in school for more
years. Because this study captures only information at age fifteen, before compulsory
schooling is generally ended, only assertions about qualitatively better education can
be made. My previous work has revealed this pattern as well, showing that students
of higher socioeconomic status tend to separate themselves into schools of choice,
leaving the public school system for children of the non-elite (Long and Doren 2012).
Demonstrating an adaptation made for the purpose of exclusion, Alon’s
(2009) analysis shows that American elites have used standardized tests, particularly
the SAT, to maintain advantage in education. By increasing the importance of a test
on which their scores consistently exceed those of the lower class, they adapt their
methods to exclude others from attaining high status through education. However,
this would only occur in a society, where, like the United States, education was the
method of transmitting advantage. Where education does not play as large a role, this
effort to adapt the education system’s functioning would be unnecessary, as their
maintenance of dominance is not reliant upon achievement. Instead, the adaptation to
use education as a means to transmit advantage may never need to be made.
Conclusions
Based on these results, it appears as though there are two separate methods
that can be used to transfer advantage from one generation to the next, both of which
result in closed systems. Therefore, although many claim that education is the
method through which people can be socially mobile, it may be that only societies
68
where education is the path through which advantage is gained that this would even
be plausible. These findings, however, suggest that even in those societies that do use
education as the path toward dominance, it is quite difficult, if not impossible, to
reach the ranks of the elite if one is not born into them. Most importantly, the results
demonstrate the pervasiveness of the elite’s mission to maintain inequality in all
cases.
In Chapter 4, I will work to identify the axis along which societies that use
education for reproduction and societies that use alternate methods for reproduction
are divided. Based on my findings relating to democracy, particularly the functioning
of government measure, I believe that further exploration of a more direct measure of
corruption will allow me to further substantiate my conclusions and assess their
accuracy. I will thus look more intensively at the role of corruption and formal
bureaucracy in these processes of transferring advantage.
In Chapter 5, I will further elaborate on the measure of inequality that I use in
my study. It could be that this seemingly paradoxical result of inequality’s effect is
simply due to the fact that Gini index is not ideal for measuring inequality, given the
theories that underlie this study. I will work to identify a more appropriate measure,
which will provide more conclusive results as to whether this finding is unique to
Gini. Through this chapter, I will try to gain a more comprehensive understanding of
the sensitivity of my findings.
69
Table 3.3: Full Table of the Effects of the Interaction
Intercept
Model 3.1:
No Interaction
Coefficient
[Standard
Error]
Model 3.2:
Interaction
Coefficient
[Standard
Error]
491.69*
506.41*
[55.70]
[47.74]
-0.21
-0.4
[0.62]
[0.67]
14.01*
14.68*
[5.46]
[7.05]
-9.47
-10.8*
[5.34]
[5.97]
LEVEL 3
Gini Index
PPP*10,000
Participation
Civil Liberties
Democratic Political Culture
Democratic Election Processes
Functioning of Government
-5.71
-6.57
[10.13]
[6.67]
-1.28
-1.38
[5.44]
[6.09]
-3.84
-2.92
[6.55]
[5.41]
12.87*
13.05*
[6.43]
[4.94]
0.39
0.14
[1.54]
[1.19]
LEVEL 2
One School Choice
Two or More School Choices
Dependent Private School
Independent Private School
Village
Small Town
Town
Large City
0.78
0.49
[1.37]
[0.99]
-0.26
-0.88
[3.15]
[1.50]
-13.84*
-12.78*
[5.71]
[1.82]
17.33*
16.59*
[3.59]
[1.45]
10.39*
9.94*
[2.53]
[1.14]
5.69*
5.41*
[2.03]
[0.98]
-1.34
-0.78
[2.59]
[1.32]
70
Table 3.3, continued
Principal controls Hiring
Principal controls Firing
Principal controls Budget
Principal controls Curriculum
Model 3.1
Model 3.2
1.54
1.90
[1.70]
[1.14]
0.70
0.64
[1.08]
[1.10]
2.58*
2.71*
[0.93]
[0.86]
-2.12*
-1.78
[1.03]
[0.93]
Always consider Residence
-5.34*
-4.99*
[1.80]
[0.93]
Sometimes consider Residence
-4.40*
-4.19*
[1.47]
[1.02]
14.08*
13.90*
[2.47]
[1.06]
5.43*
5.16*
[1.49]
[1.01]
-4.15
-4.15*
Always consider Acad. Record
Sometimes consider Acad. Record
Always consider Feeder Schools
Sometimes consider Feeder Schools
Always consider Parent Endorsement
Sometimes consider Parent Endorsement
Always consider Legacy
Sometimes consider Legacy
Percent of Teachers with Univ. Degree
Percent of Teachers with Certification
Student-teacher Ratio
School Size
Resource Construct
School-Level Socioeconomic Status
[2.36]
[1.24]
-2.54*
-2.4*
[0.98]
[0.88]
-2.21
-1.94
[1.96]
[1.08]
-2.99*
[1.15]
-0.96
-2.92*
[1.02]
-1.83
[1.53]
[1.16]
-1.98
-2.29*
[1.11]
[0.91]
19.78*
[5.94]
3.66
[3.58]
-0.05
18.49*
[1.71]
3.28*
[1.55]
-0.06*
[0.03]
[0.03]
0.01*
0.01*
[0.00]
[0.00]
-1.71
-2.48*
[1.07]
46.66*
[5.35]
[0.67]
48.23*
[0.72]
71
Table 3.3, continued
Model 3.1
Model 3.2
-3.13*
[0.50]
0.02*
-3.11*
[0.07]
0.03*
[0.01]
[0.00]
8.99*
7.54*
[3.28]
[0.44]
-8.6*
-8.57*
[2.16]
[0.30]
-25.16*
[1.69]
-15.81*
-25.09*
[0.30]
-15.9*
[1.34]
[0.22]
14.33*
[1.87]
14.24*
[1.52]
-0.25*
[0.02]
LEVEL 1
Time in Math Class Out of School
Time in Math Class In School
Attends Enrichment Math Class
Attends Remedial Math Class
Same Language Spoken at Home as in
School
Gender
Individual Socioeconomic Status
Gini Index
PPP*10,000
2.13*
[0.30]
Participation
1.48*
[0.18]
Civil Liberties
0.38
[0.21]
Democratic Political
Culture
-0.52*
[0.17]
Democratic Election
Processes
-0.88*
[0.16]
Functioning of
Government
0.62*
[0.13]
Parameters
Deviance
Level 1
Variance
Level 2
Variance
46
53
4448945.805
4446625.845
3477.20162
3459.932368
1245.79672
1203.316362
72
Table 3.3, continued
Model 3.1
Model 3.2
Level 3
Variance
1115.44085
1145.88136
Level 1 N
393743
393743
Level 2 N
14612
14612
Level 3 N
49
49
*p<.05
73
Chapter 4
FORMAL BUREAUCRACY AND CORRUPTION
Introduction
The results that I reached in my first round of analyses did not match either of
the opposing hypotheses developed from case studies. Instead, my findings in
Chapter 3 suggested that inequality is effectively maintained, regardless of the
openness or closure of the system. Because countries with more equitable education
systems generally had a more unequal distribution of wealth, as assessed by Gini
index, it is appropriate to note that having these equitable education systems does not
translate into egalitarian societies overall. This paradox suggests that inequality is
always maintained in some way, either using the education system or using alternate
methods to transfer advantage, which would ensure socioeconomic inequality.
As mentioned in Chapter 3, the models presented in the chapter spurred a
significant question about the way in which the method of transmitting advantage in a
society is determined. In this chapter, I will explore a potential answer. From my
analyses of open and closed systems, the effects of certain factors, particularly of
functioning of government, led me to believe that level of bureaucratization and state
corruption may play a role in determining whether academic methods of reproduction
or alternate methods dominate. This chapter will serve to theoretically examine and
analytically assess this hypothesis that bureaucracy and corruption factor into the
methods used to transfer advantage from parents to children.
74
Theories of Bureaucracy and Transmission of Advantage
Thus far, I have established that it appears as though inequality is always
effectively maintained, regardless of openness or closure. Chapter 3 suggests that
variation in its degree and methods of maintenance are linked to the country’s level of
democracy. Most notably, the analyses from Chapter 3 suggest that different methods
of social reproduction stem from the characteristics of a country’s government, which
influences the way that citizens attain positions within the elite.
In The State Nobility, Pierre Bourdieu (1989) proposes two primary methods
for reproducing social status, which he develops using a case study of the French elite
and their relation to schools, businesses, and government. He suggests that the path
that arose first and that is most common in private industries follows family lines.
One can directly inherit an occupational and therefore a social position from their
parents and then pass this status along to their offspring. As with titles of nobility, no
work is necessary to attain these positions. Academic qualifications are thus
unnecessary, leaving this mode of reproduction independent of the educational
sphere. Further, effort and qualifications in the absence of the proper lineage or
social ties can rarely lead one to gain such a position. Strategic marriages and close
friendships with those of high status alone make it possible to reach new levels within
the hierarchy. This type of system allows the elite to remain very insular, letting few
outsiders into their ranks. Because competence is often not a prerequisite for hire,
high academic achievement is not viewed as a necessity for attaining high status and
it is thus not a priority. It is likely that in such societies, the socioeconomic
75
achievement gap would be small, in spite of the large effect that social origin would
have on attained socioeconomic status.
The second path that Bourdieu describes is thought to have resulted from the
rise of formal bureaucracy. In societies reproducing status through this path,
academic qualifications are necessary for obtaining positions of a particular status.
Bourdieu suggests that this path is more frequently employed in the bureaucratic
public sector than in the private sector. As Max Weber notes, strictly bureaucratic
systems ensure that “only persons who qualify under general rules are employed”
(1978[1922]: 956). Fields of specialization are thus often required, necessitating
intensive training in the proper field of study. This educational credential not only
provides supposed qualifications, but it also places people into a particular status
group. Membership in this specifically credential-based status group is necessary for
appointment into the bureaucracy, so position is passed down through academic
status, not family name or family social status. Thus, one cannot be hired without the
credentials that are associated with a position, making it difficult to bypass these
requirements and instead hire children or friends lacking qualifications.
The strength of the rules associated with bureaucracy allow it to maintain its
form, only to have old bureaucrats replaced with new ones upon their retirement.
Given that both old and new bureaucrats must share the same qualifications and work
under the same rules, it would be quite difficult to allow one who was not
academically qualified into the system. Additionally, to use Weber’s words,
“bureaucracy develops the more perfectly, the more it is ‘dehumanized,’ the more
completely it succeeds in eliminating from official business love, hatred, and all
76
purely personal, irrational, and emotional elements which escape calculation”
(1978[1922]: 975). Such a system unquestionably gives preference to elements like
credentials rather than more personal traits, such as family background, name, or noncredential-based social circle.
With this more formalized structure, academic qualifications are more often
necessary for job placement, particularly in jobs of higher prestige within the
bureaucracy. Thus, Bourdieu (1989) suggests that in these systems, a parent cannot
directly pass their elite status to their children. Indirect methods, such as ensuring the
academic success of one’s children, must be used to preserve status in a particular
family. With this method of transmitting advantage, cultural capital becomes more
important than social capital and inherited titles. Parents can do little more than
socialize their children in a particular way in order to help them achieve their
academic goals. This inherited cultural capital proceeds to play a large role in
increasing academic success, often contributing to this reproduction of social status.
However, actual job placement is not as easy to manipulate in the absence of these
necessary qualifications, detracting from the possibility of using a family name or
connection to secure an elite social status. This distinct difference between this
system and systems using family-based methods of transmission is ultimately the
focus of efforts when working to reproduce social status from one generation to the
next.
77
The Rise of the New Class
Building on Bourdieu’s theories of social reproduction, Gouldner (1979)
suggests that not only are there these two different methods of status transmission, but
the rise of bureaucracy has promoted the development of the New Class. This New
Class is defined by their reliance on academic qualifications for attaining a highstatus occupational and social status. Although he uses the term “class,” Gouldner’s
New Class refers to more of a status group, where people identify with one another
based on similar affinities and lifestyles, as defined by Max Weber (1978[1922]).
Therefore, although it is related to socioeconomic status, the composition of
socioeconomic indicators, such as parental wealth and education, are likely to differ
from the elite of the old class and the New Class, although socioeconomic status may
look similar.
In recent years, the New Class has begun gaining power and prominence in
many societies. As Bourdieu’s (1989) theory would predict, Gouldner’s (1979) old
and New Classes demonstrate that different forms of capital dominate social relations
in each class. Thus, forms of capital that have the strongest effects in one society
may lack significant influence in another. Traditionally, in American society,
economic capital or the occupation of a traditionally prestigious social position
qualified as enough to be classified within the elite. Gouldner, however, suggests that
this New Class is characterized by the high importance of cultural capital, which has
begun to compete with the influence of economic capital in determining status.
A new elite has thus developed, in which education and a certain form of
cultural capital hold more importance than the inheritance of a particularly large sum
78
of money, which was once imperative for elite standing. Still, it is important to note
that cultural capital is inherited as well, passed down from parent to child, which
provides a large advantage in obtaining a position among the elite. These factors
clearly play a larger role in determining academic achievement, yet their indirect
relationship with determining ultimate job attainment and social status differentiates
the social reproduction of the New Class from that of the old class.
Accompanying this movement toward Gouldner’s New Class, the importance
of credentialism and the need to belong to a particular status group in order to find
success has risen dramatically. As Collins (1979) writes, educational attainment is
currently the most significant determinant of occupational attainment. However, he
suggests that rather than giving truth to Becker’s (1964) argument that schooling
provides students with the knowledge necessary to qualify them for the jobs that they
use their schooling to obtain, the credential itself provides the advantage. This
implies that it is only status that is necessary, and credentials obtained through
schooling provide status alone. The social and cultural capital that are gained in the
school are therefore more important in obtaining a high-paying, high-status job than
having the actual knowledge that should have been learned in the process of earning
the credential. This suggests that holding a degree provides evidence that one is a
member of a particular cultural group, simplifying the job selection process, as certain
people can immediately be accepted or rejected due to their membership or lack
thereof.
Membership in C. Wright Mills’ (2007[1956]) Power Elite, for instance, is
determined as such, given the importance of particular credentials and membership to
79
a particular status group. Without the necessary educational credentials and social
capital that are bred in particular institutions, it may be nearly impossible to access
the closed, elite group. Therefore, in a society in which Gouldner’s New Class holds
dominant positions, and credentials, but not always knowledge, serve as the basis for
hire and prestige. The forms of capital that most highly influence outcomes and the
outcomes that are most influenced by the possession of capital may differ from those
of the old classes.
The rising power of the New Class therefore leads to a break in the patterns of
social reproduction that previously existed, yet the old patterns are simply replaced
with a new hierarchy and new patterns of reproduction. In this new hierarchy,
education and possession of cultural capital, through embodied, objectified, and
institutionalized forms, are determinant of membership to a particular class or status
(Bourdieu 1989; Gouldner 1979). Social reproduction, therefore, remains essentially
unchanged, although characteristics of those in elite positions and the particular type
of capital that they possess may differ. This shift in emphasis on form of capital
exhibits yet another change in class structure, in which economic and social capital
are not the sole determinants of class membership, but education and the cultural
capital that it transmits play a large role as well. Therefore, although hierarchies have
simply been rearranged, it is important to note the change in the mechanisms through
which advantage is transmitted.
80
Corruption and Methods of Transmitting Advantage
In relation to bureaucracy and its effects on methods of social reproduction,
the sociological literature on corruption intersects with Bourdieu’s (1989) distinctions
and can also be tied to the rise of Gouldner’s (1979) New Class. These studies of
corruption, however, take the methods of reproduction that Bourdieu pairs with the
private and public sectors and that Gouldner pairs with old and New Classes and
extend them to apply to countries as a whole. They thus posit that some countries are
highly bureaucratic in the composition of their elite, whereas others have
governmental positions that are inherited or appointed based on friendship or family
ties. This difference in practice often proceeds to influence the actions of a country as
a whole, altering the composition of expenditures and the priorities of the
government. Merging the theories and putting them into the context of my previous
findings, it is possible that the importance of academic achievement in relation to
mobility varies along the lines of this dichotomy. Thus, I suggest that whether or not
the state has a formal bureaucracy, assessed by its level of corruption, may predict
which system of reproduction is used.
The literature on corruption suggests that systems prioritizing familial ties are
not simply working through a family-based system, as does Bourdieu, but they are
possibly corrupt. As defined by You and Khagram, corruption refers to the “use of
public power (or public office) for private gain” (2005: 137). This can apply to
something as simple as securing a job for a child or to something as significant as
requiring monetary bribes in order for certain laws to be passed. The first situation
unquestionably falls into the realm of Bourdieu’s family-based system or Gouldner’s
81
old class, while the second is less intimately tied, but may still be related. Beyond
simply providing gains for the individual in office, corruption often prevents national
resources from being fairly distributed, as those in power prioritize their own needs
and wishes over those of the masses. Thus, it is likely that the presence of a corrupt
state has distinctly negative effects on the poor while also promoting the well being of
the rich.
In further efforts to maintain dominance, corruption in the state is likely to
prevent the use of more academic methods of status inheritance, as bureaucratization
or hire based on credentials does not guarantee the reproduction of their advantage.
Instead, social reproduction in corrupt societies is most likely to occur almost solely
along the lines of family ties and elite social networks. In addition to limiting
reproduction through non-academic routes, corrupt states may even hold other
policies that also contribute to the elite group remaining small and exclusive.
In his case studies of developing states and industrial transition, Peter Evans
(1995) developed characterization of states and the use of corrupt methods in
governing. The first extreme, which describes corrupt states, is referred to as a
predatory state. This is contrasted with the more bureaucratized states with
“embedded autonomy.” Predatory states “extract at the expense of society,
undercutting development even in the narrow sense of capital accumulation” (1995:
12). In these states, individuals in office often pursue their own goals while
disregarding what is best for the collective. Evans describes the former Zaire as the
archetypical predatory state. Its political figures use their power for their own
benefit, only ever taking away from citizens and rarely, if ever, providing social
82
services. These practices create a large and growing division between those with
government affiliation and those without.
Not only do predatory states extract from their citizens, but they also detract
from the country’s budget, potentially changing allocations of expenditures to benefit
state leaders. This happens with particularly high frequency in industries that do not
provide the possibility of receiving bribes or favors. Education, specifically, falls into
this category. As seen in Mauro’s (1998) analysis, in more corrupt countries,
education expenditures are often among the most severely affected. The lack of
emphasis on academic qualification is made more severe by cutting these
expenditures, as an education-driven system could not easily be implemented when
education is available to none or very few. In systems that are perceived to be highly
corrupt, Smith (2010) finds that people even cease to believe in the ideology that tells
them that education will help them succeed in gaining a higher social status. Using
the example of the Czech Republic, he says that Czechs may come to accept the
dominance of alternative methods of getting ahead, given that many believe that
attainable capital, like an academic qualification, has less of effect on social status
than does a political connection. This, of course, differs from systems that are less
corrupt, in which it is possible, although often difficult, to attain the capital that
qualifies one for elite status.
States that do not act in a predatory manner tend to have a more formal
bureaucracy, which may even hold close ties with social networks outside of the state.
Thus, this system may be able to effectively work through meritocratic paths without
losing touch with public needs. Predatory states neither use meritocratic paths nor
83
remain cognizant of public needs. These systematic differences and their ties with
my previous findings, as well as Bourdieu’s (1989) theories, suggest that the
relationship between socioeconomic status and academic achievement will differ
between corrupt states and states that are not corrupt. Specifically, the effect of
socioeconomic status on academic achievement will differ along the lines of the
state’s level of corruption. In my analyses, I will explore these divergences, setting
out the following hypotheses.
My previous findings suggest that predatory states will not show as large an
effect of socioeconomic status on achievement as will states with formalized
bureaucracy, because more bureaucratic states allow for the possibility, and even a
high likelihood, of using credential-based methods for maintaining a position in the
elite. As discussed in Chapter 3, this may detract from the focus on education in
predatory states, given that it has little to do with ultimate status attainment.
Academic achievement may therefore be equalized, because it is not a central focus
of the elite’s maintenance of status.
However, as noted above, government education expenditures often suffer in
predatory states (Mauro 1998). This provides the alternate possibility that the quality
of public education would decline, while the children of the elite could find an
alternate system of education that would allow them to increase their academic
competence beyond that of the children of the non-elite. Although not essential to
transmitting advantage, this type of alternate system may be put in place to enforce
social closure and any superior academic achievement would be a convenient
coincidence. Thus, the socioeconomic achievement gap may expand, as the elite
84
alone would have access to high-quality education, if it were to exist. In the
subsequent analyses, I will examine these hypotheses and assess whether or not my
conclusions from above maintain their validity.
Variables and Models
To test these hypotheses and further explore arguments made in the previous
chapter, I add to my model the Corruption Perceptions Index (CPI), which was
created by Transparency International (Transparency International 2012). The CPI is
a measure of how corrupt residents perceive their country’s public sector to be.
Scores are based on a number of surveys and assessments about business and
governance climate analysis, which are collected by other institutions. Further
information about these other data sources can be found in the CPI sources
description document. The CPI ranges from 0 to 10, where 0 represents a state that is
perceived as highly corrupt and 10 represents a state that is perceived as having low
levels of corruption. Data was collected between 2000 and 2009, varying based on the
availability of data for each country in the sample.
Unlike many other measures of corruption, Transparency International uses
perceptions rather than reported incidents. This is particularly useful because it is
highly likely that the most severe instances of corruption are otherwise hidden,
rendering studies of bribes or court cases about corruption inadequate. This measure
thus demonstrates the experiences that people have with corruption without the
potentially confounding effect of the skill of courts or governments in hiding
evidence of corrupt actions from the aforementioned counts. For this reason, I
85
believe that this measure of perceptions will give a better reflection of what my
theories describe than would a direct measure of corruption itself.
To test the effect of CPI, I use a three level multi-level model to analyze my
data, as I did in previous analyses. For this analysis, I added CPI as a covariate at the
third level and as another interaction term with individual level socioeconomic status.
I also ran a simpler model with CPI but no variables targeting democracy, along with
models with Gini index and PPP. Aside from these changes at the third level and with
the interaction term, the model is otherwise unchanged. The models are shown in
Figure 4.1.
Results
When controlling for corruption, model 4.4, the full model, becomes the
model of best fit, based on deviance statistics (see Table 4.1 below and Table 4.4 at
the end of this chapter). Results remain similar to those in Chapter 3, with a few
differences that I will discuss below. However, the general argument set out in the
previous chapter still holds and is strengthened by this model. The smaller models in
this chapter support and provide more information about the effects as well. In fact,
when examining the effect of CPI’s interaction alone in model 4.1, the effect of
socioeconomic status on academic achievement becomes insignificant. This suggests
that the entire effect of socioeconomic status disappears in corrupt societies, showing
that corrupt systems either breed meritocracies or rely entirely on non-academic
methods for reproducing social status. I will further explore the implications of this
model later in this chapter. Additionally, there is a suppressor effect of PPP on Gini
86
index that can be seen in models 4.2 and 4.3. This could occur because it is necessary
to account for the overall socioeconomic status of a country for the amount of
inequality to play a role in the socioeconomic achievement gap. A further
explanation is perhaps needed, but it is beyond the scope of this thesis. For the rest of
the immediate discussion of results, I will refer to Model 4.4, the model of best fit.
In model 4.4, the most noticeable outcome is that the measure of corruption
has a significant positive effect of 3.23, a higher coefficient than is seen in models
4.1-4.3. From the models in Chapter 3, the biggest changes are that democratic
political culture’s effect increased in magnitude from -0.52 to -2.0, election processes
and PPP became spurious, and the directionality of functioning of government
changed. As noted in Chapter 3, democratic political culture’s negative effect raises
some important questions, but its discussion is beyond the scope of this thesis.
Regarding functioning of government, instead of maintaining its positive effect of
0.62 from model 3.2, the inclusion of the corruption perception index changed the
coefficient to -0.47. Whereas the spuriousness of election processes and PPP can be
attributed to the corruption perception index and the fact that the effects may, in fact,
be captured by the measure of corruption, the change in functioning of government’s
direction is less clear.
Figure 4.1: Models 4.1, 4.2, 4.3, and 4.4
Model 4.1: With CPI Interaction
Level 1 Model
Y = P0 + P1*(SES) + P2*(V) + E
Level 2 Model
Model 4.2: With Gini, CPI
Interaction
Level 1 Model
Y = P0 + P1*(SES) + P2*(V) + E
Level 2 Model
P0 = B00 + B01*(W) + R0
P0 = B00 + B01*(W) + R0
P1 = B10
P1 = B10
P2 = B20
P2 = B20
Level 3 Model
Level 3 Model
B00 = G000 + G001*(Z4.1)+U00
B00 = G000 + G001*(Z4.2)+U00
B01 = G010
B01 = G010
B10 = G100+G101*(Z4.1)
B10 = G100 + G101*(Z4.2)
B20 = G200
B20 = G200
Model 4.3: With Gini, PPP, CPI
Interaction
Model 4.4: With all Interaction
Terms
Level 1 Model
Level 1 Model
Y = P0 + P1*(SES) + P2*(V) + E
Level 2 Model
Y = P0 + P1*(SES) + P2*(V) + E
Level 2 Model
P0 = B00 + B01*(W) + R0
P0 = B00 + B01*(W) + R0
P1 = B10
P1 = B10
P2 = B20
P2 = B20
Level 3 Model
Level 3 Model
B00 = G000 + G001*(Z4.3)+U00
B00 = G000 + G001*(Z4.4)+U00
B01 = G010
B01 = G010
B10 = G100+G101*(Z4.3)
B10 = G100 + G101*(Z4.4)
B20 = G200
B20 = G200
Vector of each level:
For Levels 1 and 2, refer to the vectors used in Figure 3.1 in Chapter 3.
87
88
Vector of each level, continued:
Level 3 (Model 4.1): Z4.1= CPI
Level 3 (Model 4.2): Z4.2=Gini, CPI
Level 3 (Model 4.3): Z4.3= Gini, PPP, CPI
Level 3 (Model 4.4): Z4.4= Gini, PPP, political participation, civil liberties,
democratic political culture, electoral processes and pluralism, functioning of
government, CPI
Recall that in Chapter 3, I suggest that the functioning of government measure
is a rough indicator of corruption. This index, according to the Economist
Intelligence Unit, “is based on indicators relating to e.g. the extent to which control
over government is exercised by elected representatives, the capabilities of the civil
service to implement government policies, and the pervasiveness of corruption”
(Svensson, et al. 2012). This variable, given its focus on the ability of a government
to carry out political processes based on the bureaucratic rules of the state, was part of
what led me to ultimately study corruption. This reversal of direction seen in model
4.4 leaves the functioning of government indicator with the opposite directionality of
the corruption perception index, which is unexpected. However, the functioning of
government variable and the corruption perception index have fairly similar
definitions, measuring similar characteristics, so it is possible that the two are
collinear. If this is the case, the inclusion of the functioning of government variable
is not useful for improving the fit of the model and can thus be omitted.
89
Table 4.1: The Effects of Corruption
Model 4.3:
Individual Socioeconomic
Status
Model 4.1:
Model 4.2:
Gini, PPP,
Model 4.4:
CPI
Gini, CPI
CPI
Full Model
Coefficient
Coefficient
Coefficient
Coefficient
[S.E.]
[S.E.]
[S.E.]
[S.E.]
-0.08
15.28*
15.83*
24.26*
[2.77]
[6.10]
[0.82]
[1.58]
-0.31
-0.33*
-0.39*
[0.11]
[0.02]
[0.02]
2.98
-0.01
[1.90]
[0.24]
Gini Index
PPP*10,000
Participation
0.58*
[0.18]
Civil Liberties
-0.17
[0.21]
Democratic
Political
-2.00*
Culture
[0.18]
Democratic
Election
0.31
Processes
[0.17]
Functioning of
-0.47*
Government
[0.14]
Corruption
Perception
2.67*
2.09*
2.25*
3.23*
Index
[0.43]
[0.39]
[0.11]
[0.14]
41
43
45
55
Deviance
4446849.50
4446340.31
4446333.07
4446123.64
Level 1 N
393743
393743
393743
393743
Level 2 N
14612
14612
14612
14612
Level 3 N
49
49
49
49
Parameters
*p<.05
90
To test this, hypothesis, I run model 4.4 without the functioning of
government variable in model 4.5, which is detailed below in Figure 4.2. When I run
model 4.5, I find that the deviance statistic of Model 4.4 does remain the lowest, as is
seen in Table 4.2.8 However, the difference in the two deviance statistics is quite
small, which brings about the possibility that the difference is attributable to the
higher number of parameters in model 4.4, not better fit. To assess whether this
difference in fit was simply attributable to a change in the number of parameters, I ran
a BIC approximation to see whether there was truly a difference between the models.
I choose to use BIC because it both acknowledges this change in parameters and
accounts for sample size (Raftery 1995). Because my sample is so large, it is
particularly important to acknowledge its size. The BIC difference of 6 revealed that
model 4.5, the more parsimonious model, could be accepted as the model of best fit at
a .05 significance level.9 I can thus posit that the inclusion of the functioning of
government measure is unnecessary, as the traits that it assesses are similarly
measured by other variables in the model. From this point forward, I will refer to
model 4.5 in my discussion of results.
My findings in model 4.5 support those from the previous models, suggesting
that in corrupt or predatory states, there is a smaller effect of socioeconomic status on
academic achievement. Changes from model 4.4 include very slight changes in
magnitude for all variables. The effect of socioeconomic status rose from 24.26 to
25.8 and the effects of Gini index, participation, and democratic political culture
dropped from -0.39 to -0.40, from 0.58 to 0.49, and from -2.00 to -2.18, respectively.
8
For the full table of results, refer to Table 4.5 at the end of this chapter.
calculate BIC as follows: BIC=deviance+(ln(level 1 N)*parameters)
9 I
91
Corruption Perception Index dropped slightly as well, decreasing its effect from 3.23
to 3.06. Aside from these minor changes in coefficients, the absence of the
functioning of government measure does little to change the results.
Figure 4.2: Models 4.4 and 4.5
Model 4.4: With all Interaction
Terms
Model 4.5: Without Functioning of
Government Variable
Level 1 Model
Level 1 Model
Y = P0 + P1*(SES) + P2*(V) + E
Level 2 Model
Y = P0 + P1*(SES) + P2*(V) + E
Level 2 Model
P0 = B00 + B01*(W) + R0
P0 = B00 + B01*(W) + R0
P1 = B10
P1 = B10
P2 = B20
P2 = B20
Level 3 Model
Level 3 Model
B00 = G000 + G001*(Z4.4)+U00
B00 = G000 + G001*(Z4.5)+U00
B01 = G010
B01 = G010
B10 = G100 + G101*(Z4.4)
B10 = G100+G101*(Z4.5)
B20 = G200
B20 = G200
Vector of each level:
For Levels 1 and 2, refer to the vectors used in Figure 3.1 in Chapter 3.
Level 3 (Model 4.4): Z4.4= Gini, PPP, political participation, civil liberties,
democratic political culture, electoral processes and pluralism, functioning of
government, CPI
Level 3 (Model 4.5): Z4.5= Gini, PPP, political participation, civil liberties,
democratic political culture, electoral processes and pluralism, CPI
92
Table 4.2: Removing Functioning of Government from the Model
Individual Socioeconomic
Status
Gini Index
PPP*10,000
Participation
Civil Liberties
Democratic
Political
Culture
Democratic
Election
Processes
Functioning of
Government
Corruption
Perception
Index
Parameters
Deviance
BIC
Level 1 N
Level 2 N
Level 3 N
*p<.05
Model 4.4:
Full Model
Coefficient
[S.E.]
24.26*
[1.58]
-0.39*
[0.02]
-0.02
[0.24]
0.58*
[0.18]
-0.17
[0.21]
Model 4.5:
No
Functioning of
Government
Coefficient
[S.E.]
25.8*
[1.51]
-0.4*
[0.02]
0.12
[0.24]
0.49*
[0.18]
-0.32
[0.21]
-2.00*
[0.18]
-2.18*
[0.18]
0.31
[0.17]
-0.47*
[0.14]
0.24
[0.17]
3.23*
[0.14]
3.06*
[0.14]
55
4446123.64
4446832
393743
14612
49
53
4446143.385
4446826
393743
14612
49
Examining Predicted Values
In determining predicted values, I pair countries with low corruption with
those with high democracy and countries with high corruption with those with low
democracy because this is pairing suggested by Weber’s (2009[1920]) theory of open
93
and closed systems.10 Before examining the predicted values, which can be found in
Table 4.3 and Figure 4.3, recall once again that the standard deviation of math
achievement score is approximately 100 points. Each point thus accounts for about
one percent of a standard deviation in score. Also recall that each standard deviation
in socioeconomic status is approximately 1, so coefficients show the effect of one
standard deviation. Just as I did in Chapter 3, I use first and third quartile values for
each measure. To calculate democracy, I take an average of the values of all
significant measure in the model.
As seen in Table 4.3 and Figure 4.3, the predicted effect of socioeconomic
status on academic achievement is highest in systems with low corruption, high
democracy, and low levels of inequality. The model suggests that each standard
deviation of socioeconomic status raises a student’s score by 16.46 points in such
countries. The smallest predicted socioeconomic achievement gap is seen in
countries with high corruption, low democracy, and high levels of inequality, with the
predicted effect of only 10.27 points for each standard deviation of socioeconomic
status. As in Chapter 3, Austria, Denmark, and Germany continue to fit within the
first group, where the socioeconomic achievement gap is high. Argentina, Brazil, and
China also still fit within the second, where the achievement gap is low. Based on
these predicted values, the effect of each standard deviation of socioeconomic status
10 Ten
countries do fall into the contradictory categories, but these are in the small
minority of the sample. Five fall into the category of high inequality, low democracy,
and low corruption, three fall into the category of low inequality, high democracy,
and high corruption. Two fall into the category of high inequality, high democracy,
and high corruption, and none fall into the category of low inequality, low
democracy, and low corruption. However, this does not fit my theoretical framework,
so these combinations will not serve as the focus of my analysis, although they
deserve further exploration. 94
in the latter group of countries is less than two-thirds of the size of the effect in the
former group. The results of this model suggest that the role of academics in
countries with low corruption, high democracy, and low levels of inequality must
differ from the role of academics in countries with the opposite characteristics.
Recall that in model 3.2’s set of predicted values, the respective values for these two
groups were 17.92 and 13.76. The findings are therefore consistent.
In the two other groups, results continue to mirror those from Chapter 3.
Societies with low corruption, high democracy and high inequality predict an effect
of 12.06 points for each standard deviation of socioeconomic status and societies with
high corruption, low democracy and low inequality predict an effect of 14.67 points.
Japan, Portugal, and the United States remain representative of the former category
and Croatia, Slovakia, and Kyrgyzstan continue to reflect the latter category as well.
Recall that this can be compared with the predicted values seen in Table 3.2 and
Figure 3.2, which are 15.16 and 16.52, respectively.
95
Table 4.3: Predicted Values of the Effect of SES on Achievement
High Inequality:
Gini=40
Low Inequality:
Gini=29
Low Corruption:
CPI=7.5
High Democracy:
Average=7.5
12.06
High Corruption:
CPI=3.5
Low Democracy:
Average=5
10.27
16.46
14.67
Figure 4.3: Graph of Predicted Values of the Effect of SES on Achievement
High Corruption, Low Democracy
Low Inequality
High Inequality
Low Corruption, High Democracy
0
2
4
6
8 10 12 14 16 18
Effect of SES on Achievement
These findings strengthen my argument from Chapter 3, where my predicted
values followed similar patterns, although the difference between extremes is slightly
larger in this analysis. This model thus supports my assertion that all societies are
closed, but the method through which advantage is transferred from one generation to
the next differs. Although it appears that corrupt, nondemocratic societies may be
meritocratic, this is highly unlikely, as will be discussed later in this chapter. The use
96
of a non-academic line of reproduction, as suggested by the high levels of perceived
corruption, appears to truly detract from the elite’s will to focus on academic success,
instead putting energy into perpetuating status through inheritances and family lines.
It is for this reason that the effect of socioeconomic status on achievement is minimal,
as academic success is unnecessary for gaining elite status. Additionally, the
influence of Gini index, which remains consistent with what was found in Chapter 3,
suggests that the socioeconomic achievement gap has an inverse relationship with
inequality in a country.
Together, these findings continue to support my suggestion that certain
countries use an academic route to transfer advantage, while others use more familybased methods of passing down status. This ultimately supports the conclusion that
less academic stratification by socioeconomic status does not predict a more equal
distribution of wealth and, conversely, a society with a more equal distribution of
wealth does not predict a less stratified hierarchy of academic achievement. Thus, in
spite of these differences, inequality is effectively maintained in all countries,
although different methods for its maintenance are used. The rest of this chapter will
further discuss these conclusions, as well as their implications for the transmission of
elite status in different societies.
Discussion
The results of my analyses suggest that corruption does, in fact, play a role in
determining the extent of the socioeconomic achievement gap. It also suggests that
Bourdieu’s (1989) discussion of transmission strategies can be applied to countries.
97
Additionally, the degree to which Gouldner’s (1979) New Class has come to
dominate can classify countries as well. Based on this assertion, it can be said that it
is primarily, if not only, in countries with highly formalized bureaucracies that
academic credentials will be more important in obtaining a spot in the cherished elite
than will family name or inheritance. In more corrupt and less bureaucratized
countries, education will not matter for social reproduction, thus shrinking the
socioeconomic achievement gap.
However, the issue of extensive social inequality still remains. In neither type
of society are all given equal footing and an equal chance to succeed. As we see,
supposed meritocracies allow for the academic success of the children of the elite at
higher rates than the average student. Even when mobility appears possible due to
the meritocratic and academic-based system, it remains incredibly biased, given the
production of such an extensive socioeconomic achievement gap. In corrupt states,
few aside from the elite can ever hope to gain a spot in the group, so even though the
socioeconomic achievement gap is smaller, equality and openness in the state are
certainly not present, rendering all systems effectively closed.
Exploring the Possibility of a Meritocratic, Corrupt System
In the previous discussions of my findings, I have suggested that less corrupt
and more democratic systems use academic methods of status transmission. This
would, theoretically, leave room for meritocracy, were the education system less
biased toward the children of the elite. However, as was seen in model 4.1, the effect
of socioeconomic status becomes spurious with the inclusion of its interaction with
98
corruption. One interpretation of this, of course, could be that the lack of effect of
socioeconomic status is attributable to a fully meritocratic system that has developed
alongside the high levels of perceived corruption. In this system, one’s success or
failure would not at all hinge upon one’s family background. This would entirely
contradict my position, as it would suggest that both systems are based in academic
methods of transmitting status, where one is meritocratic and the other is not.
However, this possibility is highly unlikely, considering that meritocracy and
corruption typically operate through opposing methods. It is improbable, if not
impossible, that having a high level of perceived corruption is conducive to having
methods of reproducing relying on academic achievement. If this were true, the
corruption at the government level would have to work in ways that do not at all
contradict the premise of a meritocracy, which would not come across in the measure
that I used. It is thus certainly not the case that systems with a high level of perceived
corruption foster an environment in which meritocratic methods of status attainment
reign absolutely.
So, because corruption is highly unlikely to indicate the use of a more
meritocratic system, there must be an alternate explanation. The only other option is
that socioeconomic status simply has no effect because socioeconomic status and
education are not at all related. In saying this, I assert that the system is the furthest
alternative to a meritocracy, in that education has absolutely no relation to social
status. Based on the previous discussions of the literature on methods of transmitting
advantage, my findings seem to support this latter argument. I thus suggest that
corrupt systems use methods of transmission that exist entirely outside of the
99
academic sphere. Family background is therefore likely to matter in job placement,
although it will not play a large role in academic achievement. By the same token, it
is likely that these systems have a very small effect, if an effect at all, of academic
achievement on job placement. However, further analyses would be necessary to
support this assertion.
Meritocracy: Still a Myth
From these findings, it is evident that a meritocracy exists in neither corrupt
nor non-corrupt societies. However, societies with low levels of corruption do appear
to use academic methods of transmitting advantage, demonstrated by the effect of
family background of the achievement of students in these societies. The use of these
academic methods of transmission does indicate that the system has the potential to
be meritocratic. However, the close ties between socioeconomic status and academic
achievement demonstrate that the systems are not truly meritocratic. I am thus
asserting that the myth of the possibility of meritocracy may be enough to encourage
parents and schools to focus on encouraging the academic success of the children of
the elite. As Bowles and Gintis (1976) note, if the meritocracy were genuine,
socioeconomic status would not play the role that it does in academic achievement in
more bureaucratically governed countries. Instead, the correspondence principle
takes precedence, making it quite difficult for students to break out of the cycle of
social reproduction.
Supporting this point, Bowles and Gintis (1976) use their case study of
capitalist America to demonstrate that the United States, which has a low perceived
100
corruption and a high correlation of socioeconomic status and achievement, has little
true meritocracy. Socioeconomic status itself has a high effect on performance in
schools, due to numerous reasons, such as cultural capital biases and ideological
hidden curricula taught in the school. For instance, Anette Lareau’s (2011)
ethnographic work suggests that the child-rearing practices of low-income families
are more compatible with the culture of low-income classes, making it difficult for
students of low-income to perform well when they find themselves in classes with
their higher-income peers. Such separation and dulling of the curriculum naturally
helps maintain control of the children of the lower class, making it nearly impossible
for them to succeed. For those of upper class background, who find themselves in
schools catering to the young members and future leaders of the bourgeoisie,
creativity, questioning authority, and critical thought are encouraged. In societies
with an education-based method of transmission, it is because education is the path
through which status is transmitted that these school-related factors truly matter,
leading to stratification by socioeconomic status (Bowles and Gintis 1976; Apple
2004).
Bowles and Gintis argue that education provides benefits for capitalism, in
that it works to reproduce workers at each level of social class. Rather than using
family name to reproduce this, schools are used to ensure that the social process of
training workers is properly and systematically executed. Additionally, schools
depoliticize social reproduction, making it appear as though it is in the hands of the
individual. The reproduction of social status is thus legitimized, made to be accepted
the society as a natural, necessary aspect. Schooling, therefore, serves many
101
purposes, but the “social precepts of equity and human development” are rarely
included in the role of schools (Bowles and Gintis 1976: 18).
Thus, the two systems simply have different ideologies. Both, as I have stated
repeatedly, effectively work to maintain inequality. It is only their methods that
differ in enforcing the existing closed structure’s maintenance. The reproduction of
social status in one’s children is therefore ubiquitous, but only the New Class
transmits this status through the means of education. In the old class, elite status is
maintained outside of the education system, likely through corrupt methods.
Stratified hidden curricula and ideologies within the school are therefore rendered
unimportant for the old class. Any hidden curriculum or ideology that is taught
would either have little influence on academic achievement or be taught to all
students, regardless of class. Thus, these systems of stratification and cultural
reproduction that exist within American schools may not be as prominent in countries
with family-based methods of reproduction. The difference in effect of
socioeconomic status on achievement could, therefore, be attributed to these methods
of reproduction.
The Importance of Status Groups
In spite of their differences, it appears that both paths of transmitting elite
status from one generation to the next require membership in a particular status
group. In corrupt societies, a particular social network is clearly necessary, as such
relationships allow for effective job placement within the elite. In non-corrupt
societies, credentials provide advantage more than other signifiers. This makes it
102
necessary for people to gain particular credentials through schooling, even if they
were born into the upper class. Collins (1979) argues that these credentials
demonstrate membership to a specific status group, which is incredibly important in
job placement and particularly finding a position within the elite. Demonstrating the
importance of particular credentials and membership to a particular status group, C.
Wright Mills (2007[1956]) notes that elite status determines membership of the
Power Elite. Domhoff (2007[2002]) adds that American elites benefit from social
networks and the “capitalist mentality,” which help the rich remain at the peak of the
social structure. Without the necessary educational credentials and social capital that
are bred only in particular elite institutions, it may be nearly impossible to access the
closed group from outside. In corrupt societies, social origin and birth status group is
the main determinant of access to membership. Therefore, in a society in which
advantage is maintained through education and one is not directly transmitted elite
status by birth, these credentials serve as the basis for hire and prestige.
The New Society and The Old Society
Based on my findings and other pieces of evidence in the sociological
literature, I propose that Bourdieu’s (1989) theories of social reproduction apply aptly
to countries, not only eras or sectors of business. I also suggest that Gouldner’s
(1979) theory of the New Class can be applied to countries in a similar way.
Ultimately, like Evans (1995) classifies some states as predatory and others as having
embedded autonomy, I set out two extremes in for classifications of states. I propose
a new spectrum with two poles. At one side is the “New Society,” which uses
103
primarily academic methods of status transmission and is dominated by the New
Class of academics and others with high-level credentials. At the other is the “Old
Society” which uses primarily family-based methods of status transmission and is
dominated by the old moneyed class. Although these two ideal types do not capture
all states, all fall somewhere on the spectrum between the extremes and are
characterized by different degrees of each factor.
A New Society is characterized by its highly formalized bureaucratic
structure, which, in the strictest Weberian sense, has strong academic requirements
for appointment to an elite position. It is difficult to bypass these rules, making it
difficult for corrupt methods of hire or attaining money to be fruitful. Because
education is the track through which advantage is passed, it is necessary for children
of the elite to perform at a high level if they wish to preserve their social status.
Finally, as is suggested by these theories and even more so by my results, a New
Society is marked by a strong effect of socioeconomic status on the academic
achievement of fifteen-year-old students.
An Old Society, on the other hand, is known for its reliance on family ties and
social networks for transmitting advantage from parent to child. As has been
discussed, this is often achieved through corrupt measures, such as bribes and
nepotism, which can be used to ensure that a family maintains dominance by virtue of
their money, lineage, or social network. Although it is not the focus of this
discussion, inheritances play a role in this form of status transmission as well.
Education, then, is not the path to attaining a position within the elite, making it lose
its importance in an Old Society in relation to the importance that it holds in a New
104
Society. Thus, in line with such theories and further supported by my findings, the
role of socioeconomic status in predicting academic achievement is not as large in an
Old Society, as academic achievement has little effect on one’s ultimate status
attainment.
My findings lead me to believe that the amount of inequality along the lines of
socioeconomic status that seen in academic achievement can be predicted by where a
society sits on the spectrum between New and Old Societies. As discussed above,
corruption predicts a lower effect of socioeconomic status on academic achievement,
but this does not mean that the society is more equitable. Countries instead ensure
that they maintain inequality in some way or another. The particular method used to
maintain the current structure, however, appears to come down to whether the New
Class or the old class holds more power, thus determining the importance of
education in social reproduction. Based on whether or not it is necessary to work
hard academically in order to achieve high-status occupation, the socioeconomic
achievement gap will either be large or small. In the case that it is small, my findings
suggest that there must exist a strong stratifying force elsewhere, which will
guarantee that social order is not altered. Evidently, neither form of society is
genuinely open. Both are, in most cases, closed to those who are not born into the
elite. This finding suggests that regardless of method of transmission, the elite group
remains closed to those without proper status.
Making a system truly equitable would require lessening the effect of
socioeconomic status on achievement but not having an alternate route to gaining
elite status. The two systems would necessarily be merged, such that the role of
105
socioeconomic status was lessened, yet education allowed entrance to the status group
of the elite. This is, unfortunately, unlikely to happen, unless an ideal Marxist postrevolutionary society prevails, in which the elites are absolutely no longer able to
maintain their dominance. In such a society, state generated social capital would
need to be produced and be equally and universally accessible. From here, if
education became the route to the elite, if an elite group were to come about, all
would have an equal chance at gaining the credentials necessary for entrance.
Essentially, the principles of EMI and adaptation and exclusion would need to
cease to exist for an egalitarian system to be possible. Within this system, the elite
will simply adapt their methods to guarantee the exclusion of others. However, it is
still possible, and likely in the context of EMI, that the elite still have ways to ensure
their advantage in Cuba that are not seen in Carnoy’s (2007) research. The possibility
of corruption and other paths to securing advantage, naturally, still exist. Thus, if
state-generated social capital is created and transmitted, social order is not likely to be
maintained through education, although this clearly does not prevent social order
from being maintained.
Conclusions
Ultimately, these findings support the arguments made in previous chapters,
particularly those findings regarding effectively maintained inequality and multiple
methods of exclusion when elites adapt to new situations (Lucas 2001; Alon 2009).
In the analyses in this chapter, I am able to elaborate upon my previous conclusions.
However, I have found even stronger evidence in support of Bowles and Gintis’s
106
(1976) argument in this chapter than I did in Chapter 3. Most significantly, I was able
to provide evidence suggesting that the reproduction of social status through
schooling seems to be occurring in the school systems of non-corrupt countries.
Without such a tool, it would be nearly, if not entirely, impossible for education to be
a primary method of reproduction, while the effect of socioeconomic status on
achievement is still so large.
Still, it is not entirely clear how those in corrupt systems attain their social
status. I can fairly confidently state that the effect of socioeconomic status is not
frequently seen in academics, so advantage is likely not transmitted through an
academic route. I cannot, unfortunately, make any conclusive arguments about where
status is transferred in corrupt systems, but I do propose the possibility that it is
transferred through non-academic methods, as discussed and substantiated above. In
order to gain a further understanding, I would need to add a full status attainment
model to the model that I have used in my analyses. Unfortunately, I do not have the
necessary information for these models and will thus be unable to further support this
aspect of my conclusion.
In the next chapter, I will explore another issue that I came across in my
models in Chapter 3. Specifically, I will look at a different measure of inequality.
The effects of this measure will help me uncover the meaning of the paradoxical
effect of Gini index. I will work to identify whether or not this effect is a
consequence of using Gini index as an indicator or whether my findings still hold
when an alternate measure is utilized. I also examine the links between particular
measures of inequality and different theories of inequality. From there, I will assess
107
which measure is most appropriate for my model and my overarching theory, noting
the effects of these measures on the socioeconomic achievement gap.
108
Table 4.4: Full Tables of the Effects of Corruption
Intercept
Model 4.1:
CPI
Coefficient
[Standard
Error]
429.17*
Model 4.2:
4.1+Gini
Coefficient
[Standard
Error]
439.59*
Model 4.3:
4.2+PPP
Coefficient
[Standard
Error]
424.62*
Model 4.4:
Full Model
Coefficient
[Standard
Error]
479.95*
[20.65]
[35.75]
[31.15]
[47.72]
-0.18
0.34
0.06
[0.69]
[0.72]
[0.67]
16.37*
23.29*
LEVEL 3
Gini Index
PPP*10,000
[8.66]
Participation
[7.87]
-10.04
[5.76]
Civil Liberties
-5.89
[6.42]
Democratic Political Culture
3.71
[6.37]
Democratic Election Processes
-4.36
[5.29]
Functioning of Government
14.3*
[4.91]
Corruption Perception Index
5.85*
5.38
-2.28
-9.41
[2.71]
[2.85]
[4.73]
[4.97]
LEVEL 2
One School Choice
Two or More School Choices
Dependent Private School
Independent Private School
Village
Small Town
Town
0.04
0.01
0.01
0.01
[1.49]
[1.48]
[1.19]
[1.19]
0.38
0.34
0.34
0.36
[1.32]
[1.28]
[0.99]
[0.99]
-1.47
-1.36
-1.36
-1.44
[3.21]
[3.27]
[1.50]
[1.50]
-13.37*
-12.69*
-12.66*
-12.78*
[5.52]
[5.55]
[1.82]
[1.82]
16.03*
16.3*
16.31*
16.66*
[3.47]
[3.33]
[1.45]
[1.45]
9.88*
9.89*
9.88*
9.93*
[2.49]
[2.45]
[1.14]
[1.14]
5.49*
5.43*
5.43*
5.48*
[1.98]
[1.96]
[0.98]
[0.98]
109
Table 4.4, continued
Large City
Principal controls Hiring
Principal controls Firing
Principal controls Budget
Principal controls Curriculum
Always consider Residence
Model 4.1
Model 4.2
Model 4.3
Model 4.4
-0.8
-0.73
-0.74
-0.88
[2.47]
[2.49]
[1.32]
[1.32]
1.77
1.92
1.93
1.98
[1.63]
[1.62]
[1.14]
[1.14]
0.3
0.44
0.44
0.46
[1.11]
[1.12]
[1.10]
[1.10]
2.61*
2.67*
2.68*
2.71*
[0.96]
[1.00]
[0.86]
[0.86]
-1.84
-1.73
-1.73
-1.76
[0.96]
[0.95]
[0.93]
[0.93]
-5.08*
-4.94*
-4.94*
-4.95*
[1.71]
[1.69]
[0.93]
[0.93]
Sometimes consider Residence
-4.18*`
-4.17*
-4.17*
-4.21*
[1.45]
[1.44]
[1.02]
[1.02]
Always consider Acad. Record
14.32*
14.05*
14.03*
13.86*
[2.43]
[2.35]
[1.06]
[1.06]
5.4*
5.19*
5.19*
5.15*
[1.47]
[1.45]
[1.00]
[1.01]
-4.52*
[2.27]
-4.2
[2.22]
-4.2*
[1.24]
-4.21*
[1.24]
-2.54*
-2.41*
-2.41*
-2.39*
[0.96]
[0.96]
[0.88]
[0.88]
-2.08
-1.99
-1.98
-1.96
Sometimes consider Acad.
Record
Always consider Feeder Schools
Sometimes consider Feeder
Schools
Always consider Parent
Endorsement
[1.93]
[1.93]
[1.08]
[1.08]
Sometimes consider Parent
Endorsement
-3.07*
-2.97*
-2.97*
-2.95*
Always consider Legacy
[1.14]
-1.83
[1.13]
-2.02
[1.02]
-2.02
[1.02]
-1.93
[1.48]
[1.48]
[1.16]
[1.16]
Sometimes consider Legacy
-2.31*
-2.34*
-2.34*
-2.34*
[1.06]
[1.05]
[0.91]
[0.91]
19.03*
18.45*
18.47*
18.24*
[5.66]
[5.51]
[1.71]
[1.71]
3.52
3.17
3.13*
3.34*
[3.66]
[3.69]
[1.55]
[1.55]
Percent of Teachers with Univ.
Degree
Percent of Teachers with
Certification
110
Table 4.4, continued
Student-teacher Ratio
School Size
Resource Construct
School-Level Socioeconomic
Status
Model 4.1
Model 4.2
Model 4.3
Model 4.4
-0.06*
-0.06*
-0.06*
-0.06*
[0.03]
[0.03]
[0.03]
[0.03]
0.01*
0.01*
0.01*
0.01*
[0.00]
[0.00]
[0.00]
[0.00]
-2.25*
-2.61*
-2.61*
-2.6*
[1.06]
[1.04]
[0.67]
[0.67]
48.12*
48.50*
48.48*
48.55*
[5.19]
[5.14]
[0.72]
[0.72]
-3.10*
-3.10*
-3.10*
-3.10*
LEVEL 1
Time in Math Class Out of
School
[0.50]
[0.50]
[0.07]
[0.07]
Time in Math Class In School
0.03*
[0.01]
0.03*
[0.01]
0.03*
[0.00]
0.03*
[0.00]
Attends Enrichment Math Class
-8.53*
-8.59*
-8.59*
-8.56*
Attends Remedial Math Class
Same Language Spoken at Home
as in School
Gender
Individual Socioeconomic Status
Gini Index
PPP*10,000
Participation
Civil Liberties
Democratic
Political
Culture
[2.19]
[2.19]
[0.30]
[0.30]
-25.04*
-25.07*
-25.07*
-25.07*
[1.69]
[1.68]
[0.30]
[0.30]
7.79*
7.66*
7.71*
7.77*
[2.97]
[3.01]
[0.44]
[0.44]
-15.96*
-15.95*
-15.96*
-15.96*
[1.33]
[1.32]
[0.22]
[0.22]
-0.08
15.28*
15.83*
24.26*
[2.77]
[6.10]
[0.82]
[1.58]
-0.31*
-0.33*
-0.39*
[0.11]
[0.02]
[0.02]
2.98
[1.90]
-0.02
[0.24]
0.58*
[0.18]
-0.17
[0.21]
-2.00*
[0.18]
111
Table 4.4, continued
Model 4.1
Model 4.2
Model 4.3
Democratic
Election
Processes
Model 4.4
0.31
[0.17]
Functioning of
Government
-0.47*
[0.14]
Corruption
Perception
Index
2.67*
[0.43]
2.09*
[0.39]
2.25*
[0.11]
3.23*
[0.14]
41
43
45
55
Deviance
Level 1
Variance
Level 2
Variance
Level 3
Variance
4446849.50
4446340.31
4446333.07
4446123.64
3460.3107
3457.31366
3457.23681
3455.36292
1219.3101
1201.2797
1201.6657
1203.1829
1648.9575
1647.5079
1533.1828
1055.6338
Level 1 N
393743
393743
393743
393743
Level 2 N
14612
14612
14612
14612
Level 3 N
*p<.05
49
49
49
49
Parameters
112
Table 4.5: Full Table, Removing Functioning of Government from the Model
Intercept
Model 4.4
Full Model
Coefficient
Model 4.5:
No Functioning of Gov’t
Coefficient
[Standard Error]
[Standard Error]
479.95*
444.35*
[47.72]
[50.11]
0.06
0.33
[0.67]
[0.72]
23.29*
22.76*
[7.87]
[8.54]
-10.04
-8.6
[5.76]
[6.23]
LEVEL 3
Gini Index
PPP*10,000
Participation
Civil Liberties
Democratic Political Culture
Democratic Election Processes
Functioning of Government
-5.89
-2.69
[6.42]
[6.86]
3.71
10.26
[6.37]
[6.50]
-4.36
-1.37
[5.29]
[5.63]
14.3*
[4.91]
Corruption Perception Index
-9.41
-5.8
[4.97]
[5.22]
0.01
0.02
[1.19]
[1.19]
0.36
0.37
[0.99]
[0.99]
-1.44
-1.37
[1.50]
[1.50]
-12.78*
-12.72*
LEVEL 2
One School Choice
Two or More School Choices
Dependent Private School
Independent Private School
Village
Small Town
Town
[1.82]
[1.82]
16.66*
16.62*
[1.45]
[1.45]
9.93*
9.91*
[1.14]
[1.14]
5.48*
5.49
[0.98]
[0.98]
113
Table 4.5, continued
Large City
Principal controls Hiring
Principal controls Firing
Principal controls Budget
Principal controls Curriculum
Always consider Residence
Model 4.4
Model 4.5
-0.88
-0.84
[1.32]
[1.32]
1.98
1.98
[1.14]
[1.14]
0.46
0.46
[1.10]
[1.10]
2.71*
2.73*
[0.86]
[0.86]
-1.76
-1.76
[0.93]
[0.93]
-4.95*
-4.95*
[0.93]
[0.93]
Sometimes consider Residence
-4.21*
-4.2*
[1.02]
[1.02]
Always consider Acad. Record
13.86*
13.85*
[1.06]
[1.06]
5.15*
5.14*
[1.01]
[1.01]
-4.21*
[1.24]
-4.2*
[1.24]
-2.39*
-2.4*
[0.88]
[0.88]
-1.96
-1.97
[1.08]
[1.08]
-2.95*
[1.02]
-2.97*
[1.02]
-1.93
-1.95
[1.16]
[1.16]
-2.34*
-2.34*
[ 0.91]
[0.91]
18.24*
18.32*
[1.71]
[1.71]
3.34*
[1.55]
3.24*
[1.55]
-0.06*
[0.03]
-0.06*
[0.03]
Sometimes consider Acad.
Record
Always consider Feeder Schools
Sometimes consider Feeder
Schools
Always consider Parent
Endorsement
Sometimes consider Parent
Endorsement
Always consider Legacy
Sometimes consider Legacy
Percent of Teachers with Univ.
Degree
Percent of Teachers with
Certification
Student-teacher Ratio
114
Table 4.5, continued
School Size
Resource Construct
School-Level Socioeconomic
Status
Model 4.4
Model 4.5
0.01*
0.01*
[0.00]
[0.00]
-2.6*
-2.59*
[0.67]
[0.67]
48.55*
48.53*
[0.72]
[0.72]
-3.10*
-3.1*
[0.07]
[0.07]
0.03*
[0.00]
0.03*
[0.00]
-8.56*
-8.56*
[0.30]
[0.30]
-25.07*
-25.07*
[0.30]
[0.30]
LEVEL 1
Time in Math Class Out of
School
Time in Math Class In School
Attends Enrichment Math Class
Attends Remedial Math Class
Same Language Spoken at Home
as in School
Gender
Individual Socioeconomic Status
7.77*
7.73*
[0.44]
[0.44]
-15.96*
-15.96*
[0.22]
[0.22]
24.26*
25.8*
[1.58]
[1.51]
Gini Index
-0.39*
-0.4*
PPP*10,000
[0.02]
-0.02
[0.02]
0.12
[0.24]
[0.24]
Participation
0.58*
0.49
[0.18]
[0.18]
-0.17
[0.21]
-0.32
[0.21]
-2.00*
[0.18]
-2.18*
[0.18]
0.31
[0.17]
0.24
[0.17]
Civil Liberties
Democratic
Political
Culture
Democratic
Election
Processes
115
Table 4.5, continued
Model 4.4
Functioning
of
Government
Model 4.5
-0.47*
[0.14]
Corruption
Perception
Index
3.23*
[0.14]
3.06*
[0.14]
55
53
Deviance
4446123.641
4446143.385
BIC
Level 1
Variance
Level 2
Variance
Level 3
Variance
4446832
4446826
3455.3629
3455.5030
1203.1829
1202.8404
1055.6338
1246.3267
Level 1 N
393743
393743
Level 2 N
14612
14612
Level 3 N
*p<.05
49
49
Parameters
116
Chapter 5
MEASURING INEQUALITY
Introduction
As discussed in Chapters 2 through 4, I use Gini index to assess the level of
inequality for each country in my sample. However, my findings were somewhat
paradoxical, suggesting that as a country’s wealth became more unequally distributed,
the socioeconomic achievement gap began to close. This led me to consider creating
models with alternate measures with which to compare my initial results. It is
possible that rather than accurately representing reality, this finding simply reflects a
quirk in Gini’s sensitivity.11
In an attempt to understand whether this finding is genuine or whether it is
simply contingent upon the measure used, I will explore alternate methods of
measuring social inequality in this chapter. My further study of inequality measures,
as discussed below, reveals that Gini may not be the best indicator for my purposes,
based on the theory of inequality that I propose. For this reason, I will begin with a
discussion of inequality and stratification measures in a way that relates to my model.
Then, I will quantitatively examine my preferred alternate measure of societal
inequality. Based on the theory behind my targeted measure as well as the results of
the analysis, I will determine both whether Gini index alone provides the results I
found and whether an alternate measure is more appropriate for my theory. This
analysis will ultimately help me understand how the socioeconomic achievement gap
11
I would like to thank Joyce Jacobsen, Professor of Economics at Wesleyan
University, for suggesting that I further explore this issue.
117
relates to inequality measures other than just the Gini index, which may allow me to
strengthen my argument regarding the effect of societal inequality on a country’s
socioeconomic achievement gap.
Measures of Inequality
Of indicators of inequality, Gini index is among the most popular and is thus
used in many models in the social sciences (Allison 1978; De Maio 2007). As
mentioned in Chapter 2, the Gini index “measures the extent to which the distribution
of income or consumption expenditure among individuals or households within an
economy deviates from a perfectly equal distribution” (The World Bank 2013). Each
country’s value based on this index is derived from the Lorenz curve. This curve
plots cumulative percentages of total income received in relation to the cumulative
number of recipients. The Gini index’s value is obtained by measuring the area
between the Lorenz curve and the 45-degree line that represents perfect inequality, as
predicted by the Lorenz curve. The percentage of the possible area under the line
occupied above the curve is the value given to Gini. Scores range from 0 to 100,
where 0 represents perfect equality, as the Lorenz curve matches the 45-degree line,
and 100 represents perfect inequality, as the entire space rests above the curve. In
spite of its prominence, Gini does have numerous flaws, especially when used in
certain contexts.
Paul Allison (1978) provides a fairly comprehensive analysis of multiple
strong and popular measures of inequality, including Gini index. Initially, he notes
that one’s chosen measure of inequality must match the theory with which one is
118
creating a research question. For this reason, a single measure is not appropriate for
all models. This is entirely consistent with the critiques voiced about the status
attainment model, which did not necessarily use indexes that matched the theory, if
any, about the projected outcomes (Burawoy 1977; Coser 1975). If the measure of
inequality does not match the conception of inequality proposed by the theory, the
model will not be able to provide results aligned with its theoretical basis.
Additionally, it will become a model without substance, as Coser and Burawoy warn.
Although Allison (1978) notes that the Gini index is perhaps the most
prominent measure of inequality, he does believe that there are many situations where
other measures of inequality are better. One of his critiques of Gini is that the index
is particularly sensitive to transfers at the middle of the spectrum, showing the least
amount of sensitivity to transfers made among the very rich and the very poor. This
leaves the extremes of the spectrum inadequately accounted for in this measure. An
alternate measure would thus be ideal if the extremes of the spectrum are the focus of
the study.
Many alternate measures exist. Each indicator is typically crafted around a
particular theory of inequality or made with the intent of being used with a specific
distribution of data points. De Maio (2007) describes some different and more
nuanced measures for income inequality. In his analysis, he notes strengths and
appropriate uses for each measure. Atkinson’s measure, for instance, is able to give
weight to different locations on the income spectrum. The measure has a sensitivity
parameter that allows for a model to have a stronger or weaker sensitivity to the
bottom of the income spectrum, depending on the researcher’s desire. The coefficient
119
of variation, on the other hand, assumes a normal distribution of income, rendering it
inappropriate unless this condition is true. Finally, he discusses a measure using the
proportion of total income and consumption, which shows the share of income and
consumption held by a particular group, such as the poorest or richest 10 percent.
Although this last measure does not show the distribution within the percentage
noted, it is able to shed light upon where in the spectrum wealth may be
disproportionately held. Evidently, these measures and their corresponding theories
each have particular strengths and weaknesses, especially in regard to the intended
use of the measure. It is thus impossible to use a single measure in hopes of
adequately satisfying all models. For this reason, it is important to have both a
clearly defined theory of inequality and a carefully chosen measure of inequality that
is compatible with the theory.
Identifying a more appropriate measure for my model
My initial chapters reveal that theoretically, I approach this study from a
Marxist perspective. I thus suggest that a small elite group holds much of a society’s
economic, social, and political power, so although wealth is unevenly dispersed, it is
densely concentrated at the top of the hierarchy. Gini index, as Allison (1978) notes,
is not particularly sensitive to individuals at the high and low ends of the
socioeconomic spectrum, on whom I focus in my theoretical basis for my analysis.
This makes it likely that Gini index will be unable to capture the effect of the elite,
the part of the spectrum that is of greatest interest in my study.
120
Given my theoretical focus, Gini is clearly not the proper measure for my
model. Its sensitivity to the central portion of the socioeconomic spectrum is
inappropriate, given the theories backing the study. A general index of inequality
would evidently be inadequate as well, given that it would not as directly demonstrate
the way in which a few elite individuals differ from those in the rest of society. For
instance, Gini index is designed such that two countries with a different distribution
of wealth but with the same level of inequality can be given the same number. It does
not specify the particular concentration in certain areas, only the character of the
overall distributions. A different measure would be necessary to understand this
aspect of a country’s inequality.
In order to most accurately gauge this relationship, I choose a measure that
targets certain groups’ shares of a country’s income and consumption. For this next
round of analyses, I look at the share of income and consumption held by the richest
10 percent of individuals in a country. This indicator gives explicit attention to the
upper extreme of the spectrum, which is less adequately targeted in analyses with the
Gini index. As a result, this measure will clearly display the concentration of income
and consumption among the very rich, which Gini does not. Given that the measure
of the top ten percent’s share of income and consumption provides simple
percentages and is not a mathematically derived index, is less likely to reflect any
characteristics other than precisely reported figures.
121
Variables and Models
In the models in this chapter, the new measure that I use in the place of Gini
comes from data collected in the UNDP’s Human Development Report between the
years of 1995 and 2003, varying by country based on availability of information
(Svensson, et al. 2012). The variable added to the model measures the percentage of
a country’s total income and consumption held by the richest ten percent. This
provides information about the particular distribution of income and economic power
in a country. The measure’s specificity allows me to see approximately where wealth
is concentrated, rather than simply how equitably income and consumption are
distributed among all living in a country.
Due to missing data from this new variable, I lose three countries from my
sample, giving me data from forty-six countries, 13942 schools, and 369758 students
in this set of analyses. Descriptive statistics for this smaller sample size can be found
in Table 5.3 at the end of this chapter. To more accurately compare my model to
previous models, I provide results of models 3.2 and 4.5, the models of bet fit from
the two previous chapters, with only 46 countries as well. The results of these
analyses can be seen in Table 5.5 at the end of this chapter. My analyses in this
chapter use the same models of best fit described in Chapters 3 and 4, models 3.2 and
4.5, respectively. However, I substitute the measure of the top 10 percent’s
percentage of income and consumption for Gini index. The key models described in
this chapter can be examined below in Figure 5.1.
122
Figure 5.1: Models 5.1 and 5.2
Model 5.1: Full Model without
CPI—Equivalent of Model 3.2
Level 1 Model
Y = P0 + P1*(SES) + P2*(V) + E
Level 2 Model
Model 5.2: Full Model with CPI,
without Functioning of
Government—Equivalent of
Model 4.5
Level 1 Model
Y = P0 + P1*(SES) + P2*(V) + E
Level 2 Model
P0 = B00 + B01*(W) + R0
P0 = B00 + B01*(W) + R0
P1 = B10
P1 = B10
P2 = B20
P2 = B20
Level 3 Model
Level 3 Model
B00 = G000 + G001*(Z5.1)+U00
B00 = G000 + G001*(Z5.2)+U00
B01 = G010
B01 = G010
B10 = G100+G101*(Z5.1)
B10 = G100+G101*(Z5.2)
B20 = G200
B20 = G200
Vector of each level:
For Levels 1 and 2, refer to the vectors used in Figure 3.1 in Chapter 3.
Level 3 (Model 5.1): Z5.1= PPP, political participation, civil liberties, democratic
political culture, electoral processes and pluralism, functioning of government, richest
10% share
Level 3 (Model 5.2): Z5.2= PPP, political participation, civil liberties, democratic
political culture, electoral processes and pluralism, richest 10% share, CPI
Results
With the new variable that replaces Gini index as a measure of inequality, I
have similar outcomes and add some nuances to my findings. The models in this
123
chapter provide the same story as was seen in Chapters 3 and 4, particularly in the
way that they show the same seemingly paradoxical effect regarding a country’s
general level of inequality in relation to the socioeconomic achievement gap.
Evidently, this finding is not unique to Gini index.
Not only is this finding still supported by my new measure, but the new
measure also appears to be a more appropriate model. Given that model 5.1 has a
deviance statistic of 4168075.75 and model 3.2 has a deviance statistic of
4168157.03, model 5.1 is a model of better fit.12 Recall that models 5.1 and 3.2 are
identical, aside from the different measures used to gauge inequality. The same
finding applies to model 5.2, which includes the corruption perception index and has
a deviance statistic of 4167600.20. This deviance statistic is lower than that of model
4.5, its parallel model with Gini index, which has a deviance statistic of 4167724.40.
Because model 5.2’s deviance statistic is also lower than that of model 5.1, this
renders model 5.2 the overall model of best fit. Model 5.2 will therefore be the model
that I discuss in depth later in this section. This finding is also consistent with
previous chapters, which posit model 4.5 a model of better fit than model 3.2. The
key parts of the results of this chapter’s models can be seen below, in Table 5.1. For
the full model, refer to Table 5.4 at the end of this chapter.
12
In all discussions of models from Chapters 3 and 4 in this chapter, I am referring to
the models that I re-ran with the same sample size as the models in Chapter 5. For
reasons of comparison, recall that the deviance statistic for the original model 3.2
from Chapter 3 is 4446625.85, as seen in Table 3.3, and the deviance for the original
model 4.5 from Chapter 4 is 4446143.39, as seen in Table 4.5.
124
Table 5.1: The Effects of the Richest 10%’s share of Income and Consumption
Individual Socioeconomic
Status
PPP*10,000
Participation
Civil Liberties
Democratic
Political Culture
Democratic
Election
Processes
Functioning of
Government
Corruption
Perception
Index
Share held by
Richest 10%
Parameters
Deviance
*p<.05
Model 5.1:
Full Model
without CPI
Coefficient
[S.E]
17.16*
[1.47]
4.02
[5.93]
0.83*
[0.19]
0.27
[0.21]
-0.51*
[0.17]
Model 5.2:
Full Model
with CPI
Coefficient
[S.E.]
26.94*
[1.46]
0.18
[0.34]
0.04
[0.19]
-0.33
[0.21]
-2.03*
[0.17]
-0.51*
[0.16]
0.68*
[0.13]
0.60*
[0.17]
-0.37*
[0.02]
3.05*
[0.13]
-0.54*
[0.02]
53
53
4168075.75
4167600.20
The new measure of inequality used in the place of Gini provides a model of
better fit in more than only a statistical sense. As described in my theoretical
introduction to this chapter, the use of this of the top ten percent’s share of income
and consumption also matches my theory more appropriately than does Gini index.
These factors lead me to believe that assessing inequality in terms of the
concentration of income and consumption at the top of the spectrum is the most
appropriate measure in terms of both theoretical and methodological terms.
125
In model 5.2, the results demonstrate that as the share of income and
consumption held by the richest 10 percent rises, the effect of each standard deviation
of socioeconomic status decreases by 0.54 points for each percentage of income and
consumption held by this elite group. Regarding my covariates of democracy
measures, corruption, and PPP, my findings remain largely consistent from those in
previous models. The key difference is that participation in government loses its
significance in model 5.2, in spite of its positive coefficient of 0.83 in model 5.1.
Democratic political culture’s magnitude increases from -0.51 in model 5.1 to -2.03
in model 5.2. The effect of democratic election processes changes direction, going
from -0.51 in model 5.1 to 0.60 in model 5.2. It is unclear why this occurs, but it
could be related to the inclusion of CPI in the model. CPI, when included, follows
the same pattern as in model 4.5, with an effect size of 3.05. Finally, although it is
significant in model 5.1, PPP becomes spurious in model 5.2.
Examining Predicted Values
Predicted values in table 5.2 and figure 5.2 are calculated just as they were in
Chapters 3 and 4, where I use scores at the first and third quartiles to predict values
for each of the categories seen in the table. The table of predicted values shows that
in a non-democratic, corrupt country with high inequality, where the elite ten percent
holds 32% of income and consumption, there will be an increase in achievement
score of 10.33 points for each standard deviation of socioeconomic status. At the
other pole of the spectrum, in democratic, non-corrupt countries with low inequality,
where the richest ten percent holds 23% of income and consumption, the effect of
126
each standard deviation of socioeconomic status becomes 24.53 points. Again, recall
that each standard deviation of socioeconomic status is approximately 1 and each
point in math score is equivalent to approximately one percentage of a standard
deviation. This means that with each standard deviation of socioeconomic status,
math score will increase by 10.33% of a standard deviation in the former group and
increase by 24.53% in the latter. Evidently, this difference in magnitude is quite
large, as the latter sort of society predicts an effect of socioeconomic status that is
nearly two and a half times larger than the former. These findings continue to mirror
the patterns seen in the initial round of analyses in Chapter 3, but they show a larger
difference in the size of the predicted effect of socioeconomic status on achievement
across combinations of variables.
Looking toward the other two groups, which have magnitudes between the
two extremes, my results actually show the opposite effect of the previous two
chapters. In non-democratic, corrupt countries, where inequality is low, each
standard deviation of socioeconomic status increases score by 15.19 points. In
democratic, non-corrupt countries with high inequality, the effect of each standard
deviation of socioeconomic status predicts a larger increase of 19.67 points in math
score. In the other models, countries that are non-democratic, corrupt, and have low
inequality typically had a higher predicted effect of socioeconomic status than
democratic, non-corrupt countries with high inequality.
127
Table 5.2: Predicted Values of the Effect of SES on Achievement
High Inequality:
Top 10%
share=32%
Low Inequality:
Top 10%
share=23%
Low Corruption:
CPI=7.5
High Democracy:
Average=9
19.67
High Corruption:
CPI=3.5
Low Democracy:
Average=7
10.33
24.53
15.19
Figure 5.2: Graph of Predicted Values of the Effect of SES on Achievement
High Corruption, Low Democracy
Low Inequality
High Inequality
Low Corruption, High Democracy
0
5
10
15
20
25
30
Effect of SES on Achievement
Here, it appears as though democracy and corruption, not level of inequality,
drive the effect, unlike in the previous models. I suggest that this is the driving factor
because the predicted value for countries with high inequality, low corruption, and
high democracy is higher than that of the group with low inequality, high corruption,
and low democracy, which is the opposite as was seen in the models using Gini
128
index. Whereas both categories with low inequality had the two largest effects of
socioeconomic status in the models with Gini, both categories with the more open
traits of high democracy and low corruption now have the two largest effects of
socioeconomic status. In spite of this slight change, the general trends still hold, as
do the theories derived from the previous results. This new finding, in fact, provides
even stronger support for my conclusions about the socioeconomic achievement gap
and its relation to methods of reproduction in New and Old Societies, a theory that I
proposed based on this concept of open and closed systems.
Discussion
My results from this model generally provide support for my previous
findings, suggesting that the paradoxical effect of inequality was not specific to Gini,
but it is also seen in models using other measures. In fact, it seems as though the new
indicator, a measure of the richest ten percent’s share of a country’s income and
consumption, is both more appropriate on a theoretical level and also provides a
model of better fit in a statistical sense. Even though the size of each country’s small
elite group remains unspecified by this measure and likely varies by country,
accounting for the top ten percent’s percentage of income and consumption helps
better assess each country’s distribution of income than does Gini index.
As the models with Gini suggested and these findings continue to support, the
elite’s reliance on education to provide qualifications for elite positions suggests that
the stratifying factor in New Societies resides in this education system. Old Societies,
on the other hand, have a smaller socioeconomic achievement gap, suggesting the use
129
of alternate methods of attaining elite status outside of the bureaucratic realm are used
to reproduce the elite. As noted above, the predicted values in this chapter indicate
that the openness and closure of countries drive this distinction, rather than having it
be driven by the distribution of income, as was suggested by the models using the
Gini index. This finding provides even more support for my argument about Old and
New Societies, which hinges upon the role of corruption and non-academic methods
of status transmission and their effects on the socioeconomic achievement gap.
The use of the measure of the concentration of income and consumption
among the richest ten percent certainly provides new insights. However, I would still
need to find a measure that compares the elite and the non-elite more directly in order
to most ideally assess the validity of my theory. This indicator would be strongest
were it to include both the percentage of the population in the elite as well as the
percentage of a country’s overall income and consumption they hold. The
determination of who is part of this elite varies among countries, so this would need
to be individually calculated for each country in the data set. With this information, I
would be able to gauge both the size of this elite and the degree to which they truly
dominate economic power. In spite of this shortcoming, the use of the new measure
of inequality stills reveals much that the model with Gini index was unable to show,
due to Gini’s sensitivity at the middle of the spectrum instead of the extremes.
Conclusions
This model demonstrates that the effect of Gini was not unique and that the
results still show the paradoxical effect of inequality, even with a new measure of
130
income distribution. The findings with this new measure also suggest that openness
or closure of a system, not distribution of income and consumption, drives the effects,
although distribution of income does affect outcomes as well. Although this model is
still not perfect, it provides nuances to my understanding of where income and
consumption are concentrated and the effects of this concentration on the relationship
between academic achievement and socioeconomic status. Additionally, it allows me
to more adequately assess the validity of my theory, given the close theoretical ties
between my measure of inequality and my underlying theories. The lower deviance
statistics of the models in this chapter suggest not only that my underlying theory
better assessed by this measure. They also suggest that my theory and its emphasis
on a society where a small and exclusive elite dominates economic, social, and
political power is more apt than one that focuses on general levels of inequality in a
society. It thus appears that it may be fruitful to continue exploring the role of
concentration of income and consumption among a small elite, identifying measures
that are even more able to assess where income and consumption are concentrated.
131
Table 5.3: Descriptive Statistics of the Smaller Sample
Variable
Math Achievement Score
LEVEL 3 COVARIATES
Gini Index
PPP
Political Participation
Civil Liberties
Democratic Political Culture
Democratic Election Processes
Functioning of Government
Corruption Perception Index
LEVEL 2 COVARIATES
One School Choice
Two or More School Choices
Dependent Private School
Independent Private School
Village
Small Town
Town
Large City
Principal controls Hiring
Principal controls Firing
Principal controls Budget
Principal controls Curriculum
Always consider Residence
Sometimes consider Residence
Always consider Acad. Record
Sometimes consider Acad. Record
Always consider Feeder Schools
Sometimes consider Feeder Schools
Always consider Parental
Endorsement
Sometimes consider Parental
Endorsement
Always consider Legacy
Sometimes consider Legacy
Percent of Teachers with University
Degree
Percent of Teachers with
Certification
Student-teacher Ratio
School Size
Resource Construct
School-Level Socioeconomic Status
Standard
Deviation
Mean
462.152
104.054
35.89
22350.22
5.94
8.21
6.74
8.24
6.71
5.26
28.76
8.94
13685.07
1.78
1.94
1.58
2.64
2.2
2.3
7.42
0.15
0.61
0.1
0.06
0.14
0.21
0.3
0.12
0.56
0.45
0.63
0.22
0.36
0.18
0.29
0.22
0.16
0.31
0.36
0.49
0.3
0.23
0.34
0.41
0.46
0.32
0.5
0.5
0.48
0.41
0.48
0.38
0.45
0.41
0.36
0.46
0.19
0.39
0.176
0.15
0.28
0.38
0.35
0.44
0.77
0.34
0.754
16.66
723.772
1.99
-0.44
0.37
15.444
648.2125
0.64
0.86
132
Table 5.3, continued
Standard
Deviation
Mean
LEVEL 1 COVARIATES
Time in Math Class Out of School
Time in Math Class In School
Attends Enrichment Math Class
Attends Remedial Math Class
Same Language Spoken at Home as
in School
Gender
Individual Socioeconomic Status
Level 1 N
Level 2 N
Level 3 N
1.266
227.392
0.23
0.22
1.912
108.002
0.42
0.42
0.9
0.51
-0.37
0.3
0.5
1.17
369758
13942
46
133
Table 5.4: Full Table of the Effects of the Richest 10%’s Share of Income and
Consumption
Intercept
LEVEL 3
PPP*10,000
Participation
Civil Liberties
Democratic Political Culture
Democratic Election Processes
Functioning of Government
Model 5.1:
Without CPI
Coefficient
[Standard
Error]
Model 5.2:
With CPI
Coefficient
[Standard
Error]
516.09*
[47.55]
458.79*
[49.55]
9.41
[11.06]
-9.84
[6.30]
-5.55
[6.83]
-2.1
[6.50]
-4.05
[5.58]
14.43*
[5.05]
16.34
[12.29]
-8.38
[6.71]
-1.96
[7.18]
9.2
[6.88]
-1.8
[5.92]
Corruption Perception Index
Share Held by Richest 10%
LEVEL 2
One School Choice
Two or More School Choices
Dependent Private School
Independent Private School
Village
Small Town
Town
Large City
Principal controls Hiring
-0.99
[0.83]
-3.86
[5.52]
-0.12
[0.92]
0.46
[1.21]
0.84
[1.00]
-0.77
[1.54]
-13.4*
[1.92]
16.48*
[1.48]
10.11*
[1.16]
5.9*
[1.01]
-2.11
[1.39]
2.13
[1.17]
0.34
[1.21]
0.7
[1.00]
-1.22
[1.54]
-13.25*
[1.92]
16.46*
[1.48]
10.05*
[1.16]
5.94*
[ 1.01]
-2.24
[1.39]
2.19
[1.17]
134
Table 5.4, continued
Principal controls Firing
Principal controls Budget
Principal controls Curriculum
Always consider Residence
Sometimes consider Residence
Always consider Acad. Record
Sometimes consider Acad. Record
Always consider Feeder Schools
Sometimes consider Feeder Schools
Always consider Parent Endorsement
Sometimes consider Parent Endorsement
Always consider Legacy
Sometimes consider Legacy
Percent of Teachers with Univ. Degree
Percent of Teachers with Certification
Student-teacher Ratio
School Size
Resource Construct
School-Level Socioeconomic Status
LEVEL 1
Time in Math Class Out of School
Time in Math Class In School
Model 5.1
0.73
[1.13]
2.59*
[0.87]
-1.4
[0.97]
-4.77
[0.96]
-4.44*
[1.05]
14.26*
[1.07]
4.99*
[1.03]
-3.2*
[1.28]
-2.19*
[0.90]
-2.37*
[1.10]
-2.85*
[1.05]
-1.77
[1.18]
-2.33*
[0.93]
18.98*
[1.74]
3.5*
[1.56]
-0.06*
[0.03]
0.01*
[0.00]
-2.42*
[0.69]
47.7*
[0.73]
Model 5.2
0.56
[1.13]
2.61*
[0.87]
-1.38
[0.97]
-4.7
[0.96]
-4.45*
[1.05]
14.22*
[1.07]
4.99*
[1.03]
-3.28*
[1.28]
-2.18*
[0.90]
-2.38*
[1.10]
-2.89*
[1.05]
-1.89
[1.18]
-2.39*
[0.93]
18.85*
[1.74]
3.47*
[1.56]
-0.06*
[0.03]
0.01*
[0.00]
-2.56*
[0.69]
47.97*
[0.73]
-3.22*
[0.07]
0.02*
[0.00]
-3.21*
[0.07]
0.02*
[0.00]
135
Table 5.4, continued
Attends Enrichment Math Class
Attends Remedial Math Class
Same Language Spoken at Home as in School
Gender
Individual Socioeconomic Status
PPP*10,000
Participation
Civil Liberties
Democratic
Political Culture
Democratic
Election
Processes
Functioning of
Government
Model 5.1
-8.93*
[0.30]
-24.9*
[0.31]
7.83*
[0.45]
-16.22*
[0.23]
17.16*
[1.47]
4.02
[5.93]
0.83*
[0.19]
0.27
[0.21]
Model 5.2
-8.93*
[0.30]
-24.88*
[0.30]
8.00*
[0.45]
-16.28*
[0.23]
26.94*
[1.46]
0.18
[0.34]
-0.04
[0.19]
-0.33
[0.21]
-0.51*
[0.17]
-2.03*
[0.17]
-0.51*
[0.16]
0.6*
[0.17]
0.68*
[0.13]
Corruption
Perception Index
Share held by
Richest 10%
3.05*
[0.13]
-0.37*
[0.02]
-0.54*
[0.02]
53
4168075.749
3382.7640
1203.1275
1167.8322
53
4167600.2
3378.1185
1203.4326
1332.4091
Level 1 N
369758
369758
Level 2 N
Level 3 N
*p<.05
13942
46
13942
46
Parameters
Deviance
Level 1 Variance
Level 2 Variance
Level 3 Variance
136
Table 5.5: Revisiting Models 3.2 and 4.5 with the Smaller Sample
Intercept
LEVEL 3
Gini Index
PPP*10,000
Participation
Civil Liberties
Democratic Political Culture
Democratic Election Processes
Functioning of Government
Model 3.2
(46 countries)
Coefficient
[Standard
Error]
Model 4.5
(46 countries)
Coefficient
[Standard
Error]
500.16*
[50.65]
439.73*
[52.65]
-0.4
[0.68]
13.28
[7.49]
-8.77
[6.38]
-5.93
[6.89]
-1.54
[6.59]
-4.18
[5.63]
13.49*
[5.07]
0.29
[0.75]
20.86*
[9.36]
-7.34
[6.71]
-2.16
[7.13]
10.07
[6.91]
-2.11
[5.88]
Corruption Perception Index
LEVEL 2
One School Choice
Two or More School Choices
Dependent Private School
Independent Private School
Village
Small Town
Town
Large City
Principal controls Hiring
-4.83
[5.50]
0.47
[1.21]
0.87
[1.00]
-0.95
[1.54]
-13.43*
[1.92]
16.43*
[1.48]
10.12*
[1.16]
5.92*
[1.01]
-1.98
[1.39]
2.1
[1.17]
0.35
[1.21]
0.75
[1.00]
-1.43
[1.54]
-13.26*
[1.92]
16.41*
[1.48]
10.08*
[1.16]
5.97*
[1.01]
-2.05
[1.39]
2.16
[1.17]
137
Table 5.5, continued
Principal controls Firing
Principal controls Budget
Principal controls Curriculum
Always consider Residence
Sometimes consider Residence
Always consider Acad. Record
Sometimes consider Acad. Record
Always consider Feeder Schools
Sometimes consider Feeder Schools
Always consider Parent Endorsement
Sometimes consider Parent Endorsement
Always consider Legacy
Sometimes consider Legacy
Percent of Teachers with Univ. Degree
Percent of Teachers with Certification
Student-teacher Ratio
School Size
Resource Construct
School-Level Socioeconomic Status
LEVEL 1
Time in Math Class Out of School
Time in Math Class In School
Model 3.2
(46 countries)
0.74
[1.13]
2.58*
[0.87]
-1.45*
[0.97]
-4.83*
[0.96]
-4.47*
[1.05]
14.27*
[1.07]
5.01*
[1.03]
-3.2*
[1.28]
-2.19*
[0.90]
-2.37*
[1.10]
-2.83*
[1.04]
-1.8
[1.18]
-2.31*
[0.93]
18.94*
[1.74]
3.56*
[1.56]
-0.06*
[0.03]
0.01*
[0.00]
-2.38*
[0.69]
47.66*
[0.73]
Model 4.5
(46 countries)
0.58
[1.13]
2.6*
[0.87]
-1.43*
[0.97]
-4.79*
[0.95]
-4.48*
[1.05]
14.21*
[1.07]
5.00*
[1.03]
-3.26*
[1.28]
-2.18*
[0.90]
-2.38*
[1.10]
-2.87*
[1.04]
-1.93
[1.18]
-2.37*
[0.93]
18.77*
[1.74]
3.53*
[1.56]
-0.06*
[0.03]
0.01*
[0.00]
-2.5*
[0.69]
47.91*
[0.73]
-3.21*
[0.07]
0.02*
[0.00]
-3.21*
[0.07]
0.02*
[0.00]
138
Table 5.5, continued
Attends Enrichment Math Class
Attends Remedial Math Class
Same Language Spoken at Home as in School
Gender
Individual Socioeconomic Status
Gini Index
PPP*10,000
Participation
Civil Liberties
Democratic
Political
Culture
Democratic
Election
Processes
Functioning of
Government
Model 3.2
(46 countries)
-8.89*
[0.30]
-24.92*
[0.31]
7.87*
[0.45]
-16.22*
[0.23]
15.64*
[1.54]
-0.25*
[0.02]
2.25
[0.29]
1.07*
[0.18]
0.18
[0.21]
Model 4.5
(46 countries)
-8.87*
[0.30]
-24.9*
[0.30]
8.05*
[0.45]
-16.28*
[0.23]
25.78*
[1.52]
-0.39*
[0.02]
0.24
[0.34]
0.25
[0.19]
-0.41
[0.21]
-0.47*
[0.17]
-1.98*
[0.18]
-0.65*
[0.16]
0.34*
[0.16]
0.59*
[0.13]
Corruption
Perception
Index
2.89*
[0.14]
53
4168157.033
3383.589472
1202.539374
1189.573124
53
4167724.405
3379.44466
1201.97492
1319.455522
Level 1 N
369758
369758
Level 2 N
Level 3 N
*p<.05
13942
46
13942
46
Parameters
Deviance
Level 1 Variance
Level 2 Variance
Level 3 Variance
139
Chapter 6
CONCLUSIONS
My findings in this thesis were generally unanticipated, refuting the
hypotheses about open and closed systems that I initially set out. Even after testing
my findings with a different measure of inequality, my results still stood as they were
in my initial model. That country-level factors play a role in the relationship between
socioeconomic status and academic achievement was possibly the only initial
hypothesis that I supported. Because of this, my study took a different route,
understanding exclusion and the adaptations used to develop new methods of
exclusion, rather than examining exclusion alone. Instead of distinguishing between
open and closed systems and their relationship with the socioeconomic achievement
gap, I find that open systems may not exist. This does not, however, mean that all
systems are the same. My analyses suggest that although all systems are closed, there
are two distinct forms of closed systems, each of which affects the relationship of
socioeconomic status and academic achievement uniquely. I thus suggest that a
genuinely meritocratic, open system appears to be a myth. Regardless of whether a
state is democratic, non-democratic, corrupt, or non-corrupt, inequality seems to, in
some way, be maintained.
At the most broad level, my findings reveal that patterns of social
reproduction do vary greatly by country. Beyond this, the role of education in this
process differs from country to country as well. I suggest that this international
variance may be tied to the efforts of the elite class to exclude all others and
ultimately adapt their methods of exclusion when they are no longer effective. The
140
international differences occur because the adaptations that are necessary in some
countries are not necessary in others.
In the cases that I examine, it appears as though New Societies have adapted
their methods of exclusion to include the use of the education. Old Societies, on the
other hand, may have no need to stray from their more traditional family-based
methods of transmitting advantage. However, if family-based methods of
transmitting this advantage and reproducing social status cease to suffice in Old
Societies, it is highly likely that the elite will adapt, finding a new method of
exclusion, possibly education.
The Myth of the “Great Equalizer”
Returning to the classic question of whether schools work to equalize or
stratify, as discussed in Chapter 1, my results clearly do not suggest that schools are
equalizers. However, this does not mean that schools instead act as stratifiers. My
results suggest that in New Societies alone, where academics serve as the method
through which status is transmitted, do schools serve to stratify students. Schools in
Old Societies, where family-based methods of status transmission are more common,
seem to have neither an equalizing nor a stratifying effect, implying that these
societies might not rely on schools to maintain the closed nature of their social
systems. Because there is no stratifying effect of schools and the system is also not a
meritocracy, schooling seems to have a neutral effect and be possibly irrelevant to
ultimate social status attainment.
141
As explored in Chapter 4, I reveal that when the corruption perception index
alone serves as an interaction term with socioeconomic status, the effect of
socioeconomic status disappeared entirely and there is a positive effect of
corruption.13 This would suggest that more corrupt societies have a more meritocratic
system, but this is entirely paradoxical. Because corruption operates through paths
that inherently contradict meritocracy, this cannot be true. This leads me to my
conclusion that education must not be the method through which inequality is
maintained in corrupt societies. Thus, I suggest that education systems, although
serving different purposes in different types of societies, appear to never serve as
equalizers.
These findings lead me to question whether the debate about education and its
role should continue in its current direction. Instead, it may be more fruitful to look
at a third possibility that education has little to no effect on ultimate status attainment
in some societies. This inherently contradicts the ideology that suggests that schools
provide an escape hatch for those destined for the lower class, but it also contradicts
the general sentiment that schools serve to reproduce status. Although it is likely to
be out of favor in many, if not most, ideological circles, it is still necessary for the
debate to acknowledge the possibility of methods outside of the school system
playing a larger role in promoting stratification than do schools.
13
Refer to Model 4.1 in Chapter 4. For a table of full results, see Table 4.1 in the
Appendix.
142
Expanding the Scope of my Model
As mentioned in Chapter 1, the model I use in this study examines only a
small piece of the full process of social reproduction. Because I focus on academic
achievement at age fifteen, I do have the ability to see the effect of social origin on
school performance, but I see nothing beyond secondary school. This approach
allows me to understand the question from a very different angle than has been
analyzed in previous studies (see Blau and Duncan 1967; Shavit and Blossfeld 1993),
but it still leaves much of the process of social reproduction untouched. For this
reason, I would need to expand my model if I hope to fully justify the conclusions I
reach.
Testing my theory to see whether my conclusions hold beyond secondary
school and explain social status later in life would require a broader set of variables.
In this broader model, I would want to include number of years of education attained
and a measure of occupational status, both for first and final job placement. Most
significantly, I would like a measure of the student’s adulthood socioeconomic status
that mirrors that of their parents, which I use in my models. This additional
information would let me fully test my theory, seeing whether the effects that I
predict ultimately play out.
If I did have access to these data, I would expect the same general trends to
hold. For instance, I expect success in school to map onto ultimate educational
attainment and job placement for students living in New Societies. Socioeconomic
status would predict all of these achievements. For students living in Old Societies,
the trends would differ. In these societies, I would expect to see that success in
143
school does not predict years of education attained, nor does it predict job placement.
However, job placement, unlike success in school or educational attainment, would
certainly be predicted by socioeconomic status. In both New and Old Societies,
adulthood socioeconomic status would be strongly correlated with parental
socioeconomic status. If these predictions hold, the model will fully test the ways in
which New and Old Societies differentially affect the reproduction of social status
from one generation to the next. The model could also further test whether New and
Old Societies are both forms are closed systems, neither promoting admission to the
elite from a non-elite social origin, but maintaining the social order through different
mechanisms.
In addition to having a set of variables reflecting a greater span of life, a
dataset with the full range of the world’s countries would also help provide more
conclusive findings. As mentioned in Chapter 2, my sample is limited to countries
participating in PISA, which are primarily members of the OECD. This makes it
difficult for me to assess whether my findings are simply driven by the lack of many
non-OECD countries or whether they are generalizable. With information from less
developed countries, particularly those in Sub-Saharan Africa, I may be able to
increase the depth of my understanding of the issues I discuss.
Policy Implications, or the lack thereof
My findings ultimately suggest that elite groups have a strong tendency to
maintain their elite standing across generations. For this reason, it appears as though
the elite will do what they can to organize and reorganize society in any way
144
necessary to confirm that they and their offspring remain at the top of the
socioeconomic hierarchy. I thus suggest that policy changes will do little to promote
equality in schools or in greater society. It is also important to note that even if
inequality in schools disappears, there is a high likelihood that the elite class will do
what they can to find another method through which to ensure their dominance in
society. Policy changes may therefore worsen situations further, as the elite may find
an even more rigid method of transmitting social status upon adapting to a new
method of exclusion.
However, the implications for policy changes in New Societies differ from the
implications for Old Societies. In Old Societies, if corruption and the allowance of
family-based methods of status transmission are halted or lessened, the elite may
adapt to use an academic route of status transmission. Many societies to date have
made this transition, and as the New Class has risen internationally, Old Societies
have transitioned to New Societies. The fact that this method of status transmission is
well established, it may be chosen rather than a non-academic, non-family based
method of transmitting status.
In New Societies, on the other hand, every time that policy reforms are made
to decrease stratification in education and equalize school performance, the elite will
likely find a way to maintain their advantage. This may occur outside of the school,
possibly in the form of private tutors and enrichment exercises (Sacks 2007).
Conversely, it may occur within the school, when parents, typically elite and upper
middle class parents, advocate for their children extensively, regardless of whether
this contradicts school policy (Lareau 2011; Lucas 1999). This could even, as
145
Karabel (2006) suggests, take the form of the “Iron Law of Admission,” which states
that old methods of selecting students for admission to elite institutions will be
abandoned if the desired result is not reached. Such a rationale was provided when a
subjective “character and personality” measure was added to admission requirements
at the Big Three, which effectively excluded many students of Jewish origin (Karabel
2006: 135). More recently, has been an increasing focus on standardized test score,
which inherently favors the socioeconomically advantaged, as they typically score
higher on the tests (Alon 2009). History has evidently shown that the elite tend to
adapt in order to exclude the non-elite from their ranks and there is no indication that
this pattern will change.
If the theory is accurate in suggesting that the elite are readily eager to adapt
their methods of exclusion, then policy changes are unlikely to alter the fact that all
systems are closed. The underlying factors contributing to the maintenance of social
closure are too deeply embedded in society for them to be destructed quickly or
through something as small as a change in policy. Short of a social revolution, which
would necessarily overthrow all bastions of elite power, there is little hope for
opening the system. Even in the case of a revolution, there is little reason to believe
that a new adaptation would not reinstate the exclusion of the non-elite. In fact, both
the Czech Republic and Hungary provide evidence of post-revolution adaptations that
continue to exclude the non-elite from the top of the socioeconomic hierarchy
(Mateju 1993; Szelenyi and Aschaffenburg 1993). Ultimately, this study raises many
questions as to the prospects for egalitarian projects. It does not provide any
solutions, but it unquestionably provides avenues for future study. In future research,
146
I hope to reach some conclusions as to how egalitarianism could be reached in both
education and greater society, in spite of the findings in this thesis.
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