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Joint EPUNet-2003 and BHPS-2003 Conferences
3-5 July 2003, Colchester
Social Inequality and Differential Educational Plans
Anita Koo
Sociology Department
Oxford University
Email: [email protected]
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Abstract:
Intergenerational correlations on individuals' educational attainment and economic
success later in their lives constitute one of the classic research questions in
sociology. Educational choice is made when people were young, but has long term
determining effects on one's educational attainment and occupational status. This
paper presents the regularities of differentiated patterns of educational plans across
sections of the populations. It also shows that there are inheritance features in the
pattern of students' educational aspiration (their school plans are partly influenced by
their parental educational qualifications and occupational status). My analysis
suggests that economic factors, which measured in household income and household
size, do not have significant effect on youth's school plans. However, parents'
educational attainment and occupational status, which always be treated as sources of
class-based culture and values, are shown to have some impacts on students'
educational plans. In this paper, I have explored some intervening variables that may
contribute to the intergenerational inherence of educational aspiration. They are (1)
students' perceptions of probability of success in school that measured by their
satisfaction levels of schoolwork, (2) students' educational values measured by how
they evaluate the valuation of doing well in school. Significant associations are found
between these variables to students' educational aspirations. Also, some associations
between these variables and youth's family structure and class backgrounds are shown
in my results.
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I. Introduction:
Educational choices are made when people are young, but have long-term
determining effects on one’s educational attainment and socio-economic status.
Higher education is associated with access to more desirable jobs and higher incomes.
It is the key to economic success in modern society, which has substantial impact on
occupational outcomes and mobility chances. Simple human capital theory claims
that students are motivated to advance to higher educational qualifications or stay in
school longer by the foreseeable increase in their future income after graduation
(Becker, 1996). The decision of investment in education, like other form of economic
investment, is guided by a rational cost-benefit calculus. In economics, students’
decision-making mechanism is a description of individualistic rational calculation
process, in which individuals will choose to stay in school only if the expected future
income returns exceed the expected costs of further education (Manski & Wise,
1983). However, the regularities of the differentiated pattern of choice across
sections of populations suggest that there may be other forces at work within the
decision making process. Sociological research has put a lot of effort examining how
individual background characteristics, such as gender, family structure and class
origin, have an effect on one’s educational choice and attainment (Halsey et al, 1980).
The most important implication of this empirical research is that we should not
overlook the group differences in educational decisions.
Another main concern is the relationship between educational outcome and social
inequality. The correlations of educational attainment between parents and children
constitute one of the classic research questions in social science. A considerable
amount of research has been done to describe and explain the nature of regular
patterns of inequality and to obtain more insight into the impact of family background
on individuals’ educational attainment (Shavit & Blossfeld, 1993; Becker & Tomes,
1986; Bowles & Gintis, 2000). A great deal of effort has gone into explaining and
understanding the consistently significant impact of class origin on individuals’
educational attainment (Boudon, 1974; Halsey, Heath and Ridge, 1980; Erikson &
Jonsson, 1996; Breen & Goldthorpe, 1997; Goldthorpe, 1996). The differences
between social classes in economic resources, academic performance and educational
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aspirations are commonly recognized as three major sets of influential factors that
lead to educational inequality. However, given the introduction of free or subsidized
educational programs in most developed countries, the accounts of inequality that are
merely based on economic deprivation among working class students are no longer
satisfactory. Besides, performance differences among students from different class
backgrounds are not large enough to explain existing levels of inequality in
educational attainment (Erikson & Jonsson, 1996). Some studies reveal that even
when educational performance are controlled, children from different class origins are
continue to differ in their tendency to stay longer in full-time education. (Gambetta,
1987; Manski & Wise, 1983). Other scholars suggest that more attention should be
placed on the selection effect on educational inequality by taking class differences in
educational aspirations and demand of schooling more seriously (Murphy, 1981;
Willis & Rosen, 1979). They perceive the social differentials in education as a result
of self-selection that emphasized the intentional processes.
In fact, there are increasing number of studies that explain the stability of class
inequality in educational attainment by adopting the ‘rational choice model’ in recent
decades (Erikson & Jonsson, 1996; Breen & Goldthorpe, 1997). The new approach
seeks to understand educational inequality with respect to educational decisions made
by students, that is to examine how individual choices lead to aggregate differences of
educational attainment between groups. They address the issue of social class
differences with regards to students’ valuations and expectations of future educational
choices, which vary considerably according to students’ position in the social
structure.
The aim of my research is to understand educational inequality with respect to
educational decisions made by students. My argument is that the apparently
‘structured’ behaviour patterns are accumulations of individually understandable
purposeful actions. I try to explain the stability of the class-differentiated patterns of
educational choice based on the question why some people choose to stay in school
after their compulsory school years, while others do not. The main concern of this
paper is to examine the roles of both structural and intentional variables that act in the
mechanism of individuals’ formation of aspiration and choice-making process. As
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students make their choices voluntarily according to their ability with different values,
expectations and preferences, I aim at investigating the source of educational
preferences and aspirations, expectations and perceptions on their probability of
success, in relation to their school plans.
The remainder of this paper is structured as follows. In section II, I will discuss
different theoretical approaches to relations between social inequality and differential
educational choice by means of a literature review. Sociologists and economists have
examined the existence of cultural and economic constraints from class backgrounds
shaping individuals’ educational aspirations and limiting the range of potential
choices. Section III identifies the potential determinants of students’ school plans.
They include students’ demographic characteristics and variables related to their class
background. Section IV describes the data and explores the relations between
student’s school plan and their family characteristics by presenting the results of
statistical analysis. I will also use other intentional data to illustrate the possible
mechanisms about how different school plans are patterned by students’ socio-
economical background. Section V is the conclusion.
II. Social Inequality and differential educational choice
The causes of inequality in educational choices have been the subject of much debate
among social scientists. The sociological and economical theories suggest different
economic, social and cultural constraints that shape the conditions under which
different individuals make their decisions. They also use different perspectives to
investigate how class origin effects have an influence in one’s decision-making
process. Sociological theories put much emphasis on the relations among structural
variables (in terms of parents’ socio-economic status), academic performance, and
differential educational aspirations. In economics, educational choices are made
mainly under the calculations of the differences in costs and expected returns for
individuals. But one’s ability and class differences in the availability of economic
resources also have an impact on their expectation and evaluation of this human
capital investment. In short, academic performance, economic resources and
educational aspiration are commonly recognized as the key determinants of
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differential educational choice. Boudon (1974) referred them to two mechanisms of
class effects on class differences in educational choice. The primary mechanism
states the effects of class-origin on students’ average ability. The secondary
mechanism formulates the effects of class differences in resources that influence
people’s aspirations and decisions. Through primary effect, students’ ability and their
school performance is affected by genetic and cultural inheritance. Apart from the
primary effects, class origin still has a secondary or indirect effect on students’ choice
over available options. That is, even controlling students’ cognitive ability or school
performance, the levels of ambition and aspiration associated with every educational
option are different according to students’ class origins. Also, this secondary effect
can be explained by the variations in availability of economic resources among
different classes.
In this paper, I use the rational choice framework to posit that a student’s educational
decision is a function of fulfilling goals or aspirations and making sensible calculation
with available information on the costs and probability of success. Different theories
approach the relations between social inequality and differential educational choice in
different ways. In the following, I try to capture these different approaches on this
issue explored with respect to academic ability, economic resources and educational
aspirations.
Among economists, human capital theory has been widely used to investigate the
decision to process schooling (Betts, 1994; Manski, 1993; Doministz & Manski,
1994; Modesto, 2003). Individuals are assumed to choose from further investment in
additional education or stop formal schooling and join the labour market. Children’s
education is an important human capital investment, and their expected potential
earnings determine their length of stay in school. Some economists suggest that class
differences may cause students to either react differently to similar information about
the job market or have access to different information on which to base their
enrollment decisions (Manski & Wise, 1983; Manski, 1993). The economic model of
choice is characterized by motivation or market incentives to maximize income
return. Students’ basic concern, or the guiding principles of their educational
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decisions is the costs and benefits of further schooling. They will choose to stay in
the educational system only if the expected benefits exceed the expected costs.
Other than the labour market’s information on expected income returns, both the
academic ability and the economic resources are relevant to individuals’ or families’
investment decisions on education. Ability is regarded as a complement of human
capital (Becker & Tomes, 1986; Becker, 1989). A high-ability child will earn more
from an additional unit of investment than a low-ability child, therefore, greater
amount of human capital will be invested in the high-ability child. The differences in
parental income are another determinant factor on educational choice. In free market,
individuals will choose an optimal mix between consumption and investment in
human capital resources to maximize value or utility. Poor families may borrow for
investment and leave negative financial bequests to later generations. In reality, with
borrowing constraints, families cannot borrow freely in the market. Those lower
income families will invest less in human capital and children from poorer families
will stay shorter in schools. The high-ability children in poor families suffer most. In
other words, the income effect overrides the ability effect which explains the low
participation rate in higher education among students from low income families.
Investment models with different budget sets under different market settings that
formulate the income effects of class on educational choice provide a good account of
how limited resources influence families’ or students’ educational choice (Mulligan,
1997). However, they have limitations in explaining the stability and persistence of
educational inequality in modern societies. In recent years, the direct costs of
education, in terms of fees and maintenance, have been reduced through growing
public provision of fee or subsidized education at all levels. Different credit and loan
systems also allow families with less income to borrow for children’s education.
Poorer families that previously faced financial constraints could benefit from the cost
cut and the relaxation of borrowing constraints. As long as the class differences in
educational choice and the participation rate in education beyond the compulsory
period remain, the income effect of parental choice fail to fully explain the class
differences in educational choice.
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On the contrary, cultural theorists and value theorists use a very different approach in
explaining how and why social origins bear upon the divergence of educational
outcome and schooling choice. They argue that cultural inheritance and class-based
values are the key determining factors which influence children’s educational choice.
Both of them have direct impact on academic performance and educational aspiration.
First, cultural theorists argue that class bias within the school system (Willis, 1977),
and differing cultural resources acquired from the family (Bourideu & Passeron,
1977) leads to class differences in students’ academic performance. And the
performance in school is expected to influence students’ educational aspirations as it
determines their evaluation of the probability of success when they choose to stay in
school. Second, they claim that the divergence of school choice and future
educational attainment is a result of different class-based value and culture which
affect one’s job preferences (Bouridue, 1973; Karlsen, 2001). The class differences in
job preferences influence students’ value attachment on educational outcome, which
result in different educational aspirations. This directly leads the children from
working class families to have lower aspirations for further education.
The class-divided pattern of aspiration has been understood in a different way from
the rational choice perspective by Breen and Goldthorpe (1997). The rational action
theory posits that class background is influential in students’ educational choice in
terms of their aspirations that are affected by their average ability and resources. The
mechanism of risk aversion, which is crucial to the theory, suggests that educational
choice is made to avoid downward mobility or to minimize the risk of ending up in a
lower social class than one’s parents. The model of risk aversion explains the class
effects on students’ aspiration formation referring to their intention of avoiding social
demotion. Students from advantaged families are more likely to stay longer in school
because their parents are under constant pressure to make greater investments in their
children’s education as a form of ‘defensive expenditure’, in order to maintain their
position at a level as good as the current generation. Given the high aspirations, even
if the investment is a rather high-risk one (when their children’s ability levels for
successful outcome is relatively low), the family will continue to input human capital
until a certain level to prevent their children from social demotion.
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The rational action theory of educational differentials accounts for the diversity of
choice by an inclusive decision-making mechanism. It considers not only the class
origin effect on students’ aspiration level but also the role of economic resources in
the decision making process. Breen and Goldthorpe suggest that the cost of taking a
risk to continue education has greater influence in stopping working class students
from continuing their education. The cost of paying tuition fees plus spending
additional years in school without getting a pass is higher for students from working-
class families. Therefore, even if they share the same occupational preferences as
middle-class students such as preferring a service class job and are ambitious to
obtain a higher educational qualification, the risk of failing in school may stop them
from having high aspirations to choose to stay longer in school. Through these
mechanisms of choice – aspiration formation and economic constraints – the rational
action theory clearly states that levels of educational aspirations and school decisions
are not determined by class specific culture and value, but are structured by students’
relative positions in the social hierarchy.
III. Determinants of school plan or educational aspiration:
The consequences of educational choices are big. At the individual level, they have a
long-term determining effect on one’s earning and occupational status. At the societal
level, the regularities of differentiated patterns of educational choices across sections
of the population (i.e. gender groups, family income groups, parents’ occupational
groups, parents’ educational attainments) might explain the phenomenon of different
levels of educational attainment and occupational status across groups of populations.
If the aspirations, school performance and expectations are really varied by class,
their choices will follow suit and the pattern of social mobility between generations
will remain undisturbed. In order to understand the pattern and causes of differential
educational choice across groups, it is useful to start with the factors that are known to
influence educational plans.
Gender
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Men traditionally achieved more years of education than women. However, from
1970s the gap between gender differences in educational attainment has declined
sharply (Shavit & Blossfeld, 1993; Erikson & Jonsson, 1996). In some countries,
results even show that there are more female students than boys in college after they
leave high school. However, at the same time, evidence shows that boys are
continuously more optimistic than girls about their future income (Bett, 1994).
Actually, women are still clustered in lower-ranking jobs with lower pay in the labour
market. Also, boys are shown to be more confident of their academic ability as they
are more likely to overestimate their school grades than girls (Sullivan, 2001).
Obviously, the relations between the trends in gender equity in schooling and the
gender differential perceptions of ability and income returns are still far from clear.
We are quite sure that gender has played a central role in the formulation of
educational and occupational aspirations.
Family structure
Family structure has important impact on students’ educational plans and decisions,
by its influences on one’s school performance. Evidence shows that the presence and
marital status of parents have an effect on children’s educational attainment (Pierret,
2001). Children in intact families with two biological parents are shown to have
better grades. Also, they are more likely to finish high school and attend college than
children in other types of families.
Family size
Family size also has an effect on the chances for further education among youth. It is
not difficult to understand as the family income and resources for each child’s
investment will decrease when the total number of children increases. Family income
is expected to play an important role in the decisions to go to college. By the same
token, family size, as measured by the number of siblings, is also often included as an
explanatory variable in models of college attendance. Besides, researchers also found
a negative relationship between family size and a student’s school achievement (Heer,
1985), which may be understood by the fact that parental attention for each child also
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decreases in large families. This means, interpersonal and economic resources will be
diluted in families with many children, leading to lower average school achievements
and years of education for such children (Downey, 1995; Heer, 1985). Children
within a family are assumed to compete for scare resources and parents are assumed
to allocate time and other resources to maximize their objective function. The larger
the number of siblings in the family, the lesser the resources are allocated for each
child.
Parental educational qualification
Parental education has long been noted as a powerful predictor of post-compulsory
education. Research results indicate strongly evidence of the intergenerational
transmission of educational achievement. These points to educational segregation
among families, a situation in which parents with high levels of educational
achievement encourage their children to stay longer in schools (Sewell et al., 1969 &
1970). Also, more highly educated parents are more likely to invest in their child’s
education as a consequence of their own educational experience and to avoid social
demotion. On the other hand, parents with low levels of educational achievement
have a greater tendency to encourage their children to enter the labour market.
Parental occupational status
Parents’ occupation is another important determinant of students’ educational
aspiration. It might capture part of the effects of family’s economic resources, but it
still has an independent effect on student’s job preferences and aspiration formation.
Household income
Family income is expected to play an important role in the decisions to go to college.
As mentioned above, children from lower-income families tend to leave school earlier
and have higher drop-out rates. Families with higher-income are expected to have
more resources available to support their children’s education. Also, they are more
willing to take the risk of investing longer years of school years in their children,
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while lower-income families have economic constraints even if their children have
high educational aspirations.
Self-assessment on schoolwork, general value towards education and occupational
aspirations are three variables that are included in my analysis. All of these variables
have influential impact on children’s school plans and educational choices. However,
at the same time, the effects could probably be explained away by variables of
students’ class background, as ability, values and occupational aspirations which are
greatly influenced by one’s social origin. These variables therefore can be treated as
intervening variables that mediate students’ class origin effects on their school plan.
IV. Data and Analysis
The analyses in this paper are based on the British Youth Panel of British Household
Panel Study, i.e. a regular survey of children in sample households who are in the age
band of 11 and 15. The British Youth Panel surveys have been conducted annually
since 1994 (the fourth wave of BHPS). To assess whether school plans are varied to
students’ individual and contextual characteristics, I will use data from the Youth
Panel for the years 1994 to 2001 which contains a sample of 1,476 youth who
participated in the survey at the age of 15. This group of young people at age 15 is
particularly suitable for my purposes as they are still in compulsory education and
will have to make their decisions of whether to stay or leave school when they turn
16. Of the responses received, 766 were male and 710 female.
This paper is based on an investigation of differential education choice across sections
of population in the UK. The British Youth Panel assess children’s school plans by
asking, “Do you want to leave school when you are 16, or do you plan to go on to
sixth form?” The data is explored in relation to children’s educational aspirations
which are classified into three groups: (1) ‘stay group’ for those who say that they
will go on to sixth form or college after 16; (2) ‘leave group’ for those who answer
that they will leave school after compulsory level; and (3) ‘unsure group’ for those
answer “don’t know”. Variables are considered at more than bivariate level. The
data is used to measure students’ plans for post-compulsory education, as well as the
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influence of their socio-economic backgrounds on their educational decision-making
processes. The survey collects data on: youth’s school plans; their self-assessment on
schoolwork; attitudes towards education and their occupational aspirations. With
information in the main panel, wide ranges of information about the children’s family
backgrounds and parental socio-economic status are ready for investigation.
The vector of covariates can be grouped into:
(1) Students’ demographic background: gender, family structure and number of
siblings: Family structure captures the number of parents in the household,
but not the relationship of the parents to the responding youth, taken from the
data source I am using. Dichotomous measures contrast two parent
households (either biological/ adoptive parent household or reconstituted
families, i.e. one biological or adoptive parent and one step-parent), with
one-parent families. Here, family size is measured as the number of children
in respondents’ household, including own, step- and adoptive children, by
directly asking the adults in household.
(2) Socio-economic characteristics: parent’s highest educational level, parents’
occupational status and household income: Parents’ educational levels equal
the highest qualification they obtained before the interview. Given some of
the youth are came from single parent families, the use of either only
‘father’s educational level’ or mother’s educational level’ as a measure of
parent’s educational qualification is problematic. Therefore, the highest level
of parent’s educational attainment will be used. When father’s educational
level is higher than the mother’s, father’s will be used, and vice versa. This
also applies to the measure of parents’ occupational status. Father’s or
mother’s information will be used depending on whose occupational status is
higher. As the occupational status is measured by fathers’ or mothers’ most
recent job, the data of this variable cannot capture their employment status in
the survey years. They may be unemployed when they were interviewed.
This information might be indirectly reflected by data on household income.
Household income equals total family income received a month before
interview. For this analysis, I use 1994 as the base year. Income data,
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collected in different years of the survey, are adjusted for inflation and keep
all measures in units of 1994’s pounds. I have grouped the data of monthly
household income into five income groups: less than 1000 pounds, 1001 to
2000 pounds, 2001 to 3000 pounds, 3001 to 4000 pounds, and more than
4001 pounds.
(3) The British Youth Panel also includes survey questions that ask about
students’ satisfaction level with their schoolwork, the meaning of doing well
in school, the job they plan to do in future. The three survey questions are
directly asked of youth respondents. As the third questions were asked only
from wave 4 to wave 8, the sample size decreased to 677. I have recoded
their chosen occupations into Goldthorpe’s seven-class scheme.
The first phase of the analysis examines relative influence of personal and family
characteristics on school plans. I use the demographical and socio-economical data to
explore associations between youth’s aspirations and factors such as gender, number
of siblings, family structure, household income, parental educational qualifications
and occupational status. Group differences are first explored by using chi-square tests
and oneway anova. Then, I use multivariate regression to examine the diversity
across aspirational groups in terms of all the available demographical and socio-
economic variables. The method of multinomial regression is employed, because I
intend to model the extensive set of choices. The model does not only allow me to
assess the effectiveness of a range of variables in terms of how well they perform in
predicting outcomes, rather, it seeks to identify those explanatory variables that are
effective in terms of classifying youth into the outcome categorical groups (i.e.
whether students plan to proceed to post-16 education, plan to leave school after 16 or
have not decided yet). It may be informative to investigate the extent to which factors
such as personal characteristics and family backgrounds have differential effect across
these three responses on school plans.
In the second phase of analysis, I consider the associations between the intervening
factors, including students’ satisfaction with their schoolwork, their evaluation on the
valuation of education and occupational aspirations. I will also examine how one’s
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personal characteristics and family background have an impact on these intervening
variables. At the end, I explore the effect of these variables on school plans with
reference to factors such as gender, number of siblings and parents’ socio-economic
status in the multinomial regressions models. My aim is to check if these factors have
direct and independent effects on students’ planning.
A. Do you want to leave school when you are 16, or do you plan to go on to sixth
form?
Figure 1: Students' School Plan
10.64 14.02
75.34
01020304050607080
don't know leave school at 16 go to sixth form orcollege
Leave school when you are 16?
Per
cent
age
Among the 1486 observations, 75.34% belong to the ‘stay group’ – plan to go on to
sixth form or college after age 16, 14.02% belong to the ‘leave group’ – plan to leave
school after age 16, and 10.64% belong to the ‘unsure group’ – responded that they do
not know whether they will stay or leave school after age 16.
Gender
The results indicate a significant association between gender and youth’s aspirations
with regard to post-16 education (X2=32.851; df=2; p<.001). A higher percentage of
females would like to stay in post-16 education. They are less likely to say that they
‘plan to leave school after 16’ or say they ‘don’t know’, while males are more likely
to give these two answers.
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Figure 2: Students' Gender in terms of SchoolPlan
0
20
40
60
80
100
don't know leave school at16
go to s ixth form orcollege
Leave school when you are 16?
Perc
enta
ge
m ale
female
Parental setup
The results indicate that there is some association between family structure and
educational aspirations (X2=7.754; df=2; p<.05). Among students who are living with
two parents, more students than expected plan to stay in post-16 education. In single-
parent families, however, fewer students choose this option. They are more likely to
plan to leave school after 16.
Figure 3: Family Structure in terms of School Plan
0
20
40
60
80
100
don't know leave school at16
go to sixthform or college
Leave school when you are 16?
Per
cent
age
2-parentssingle parents
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Family size
Associations between household size and school plans are also explored here since
students living with different number of siblings do not always fall into discrete
family structures. Although students from big families (with more than 3 children in
household) are less likely to belong to the ‘stay group’ than those from smaller
families, statistics does not show any strong association between the variables
(X2=10.596; df=6; p=.102).
Figure 4: Family Size (number of children) in terms of School Plan
0
20
40
60
80
100
don't know leave school at16
go to sixth formor college
Leave school when you are 16?
Perc
enta
ge 1
2
3m ore than 3
Parents’ highest educational level
Strong association between parents’ highest educational level and students’
educational plans is evident from the analyses (X2=66.398; df=10; p<.001). Students
with parents who have a degree are most likely to plan to stay in post-16 education.
For those parents who have A-Level qualifications or have teaching, nursing and
other higher qualifications, their children are also likely to plan to stay in post-16
education. While children whose parents have O-Level or commercial qualifications
are also likely to answer they will stay, children from the remaining groups, whose
parents have no qualifications at all, are more likely to answer they ‘plan to leave
school’ or ‘don’t know’.
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Figure 5: Parent's Highest Educational Attainmentin terms of School Plan
0
20
40
60
80
100
don't know leave school at16
go to sixth formor college
Leave school when you are 16?
Per
cent
age degree
higher QFA-LevelO-LevelCSE gradno QF
Parents’ occupational status
There is a significant association between parental socio-economic status and
students’ educational aspirations. Children who have parents working in class I and
class II occupations are more likely to plan to stay in post-16 education. On the other
hand, while students from class V and class VII families are more likely to answer
they ‘plan to leave school’, those from class VI families are more likely to answer
‘don’t know’. Statistics show a strong association between the variables (X2=75.413;
df=12; p<.001).
Figure 6: Parent's Highest Occupational Status interms of School Plan
0
20
40
60
80
100
don't know leave school at16
go to s ixth formor college
Leave school when you are 16?
Perc
enta
ge
Class IClass IIClass IIIClass IV
Class V
Class VIClass VII
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Household income
The results indicate a strong association between household income and students’
school plans (F=5.33; df=4; p<.001). Students from families in the higher-income
groups are more likely to plan to stay in post-16 education than those from families in
the lower-income groups. They are unlikely to answer they ‘plan to leave school’ or
‘don’t know’. On the other hand, students from families in the two lowest income
groups are more likely to plan to leave school than other groups.
Figure 7: Household income in terms of School Plan
0
20
40
60
80
100
don't know leave school at16
go to sixth formor college
Leave school when you are 16?
Per
cent
age
0-1001001-20002001-30003001-40004001 or more
I explore the diversity across the aspirational groups in terms of all the discussed
demographical and socio-economical variables by using multivariate analysis. Table
1 shows the results of two multinomial logistic regressions comparing the personal
characteristics and class background of the groups between (1) ‘stay group’ and
‘unsure group’, and (2) ‘stay group’ and ‘leave group’, in relation to the predictor
variables.
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Table 1: Multinominal Logit Model of Youth's School Plan (Constrast against youth who plan to stay in school after 16)
Unsure LeaveIndependent Variables Coefficient S.E. Coefficient S.E.
Constant -2.341 *** 0.468 -2.199 *** 0.463
Female -0.582 *** 0.180 -0.978 *** 0.171
Single parent family 0.112 0.244 -0.069 0.218
Number of Children in Household 2 -0.249 0.209 -0.113 0.186 3 -0.173 0.266 -0.327 0.252 4 or more 0.534 0.322 -0.056 0.329
Parent's Highest Educational Level Teaching, Nursing or other higher QF 0.097 0.322 0.659 0.377 GCE A Level -0.304 0.427 0.288 0.468 GCE O Level or Equiv 0.117 0.365 1.045 ** 0.403 Commercial, CSE Grade, Apprenticeship or other QF 0.464 0.409 1.270 ** 0.445 No qualification 0.277 0.393 1.388 *** 0.418
Parent's Highest Occupational Status (Goldthorpe's Class Scheme) II 0.458 0.331 -0.411 0.351 III 0.586 0.344 0.388 0.314 IV 0.837 0.428 0.684 0.379 V 0.914 * 0.463 1.073 ** 0.388 VI 1.096 * 0.445 0.302 0.446 VII 0.849 * 0.395 0.760 * 0.352
Household Income (Monthly) 1001-2000 -0.028 0.266 -0.157 0.230 2001-3000 0.325 0.305 -0.068 0.278 3001-4000 -0.159 0.411 -0.796 0.429 4001 -0.936 0.599 -0.109 0.411
Log likelihood -954.966Number of Observations 1422
Note: * p<0.05 (Wald statistic), ** p<0.01, *** p<0.001
It is evident from the multinomial logistic regressions that variables such as family
structure, sibling size and household income do not contribute significantly to
predicating students’ answers on school plans after controlling other explaining
variables. On the other hand, variables of gender and parents’ educational
qualifications and occupational status contributed significantly to the model. When
other variables are being controlled, girls are more unlikely to respond ‘don’t know’
or ‘plan to leave school’ than boys. Besides, when we compare the parameter
estimates of regression estimated among samples with different answers, parents’
21
occupational status also has great influence on students’ school plans. After
controlling variables of household income and parents’ educational qualifications,
children in class V, VI or VII families are less likely to plan to stay in school after 16
but tend to response ‘don’t know’ about their school plan. Compared with children
from class I, they have greater probability for not having concrete school plans at age
15. On the other hand, students having parents who are foremen or technicians (class
V) are more likely to plan to leave school after compulsory education. This result is
significant at 1% level and the coefficient is high. That means, compared to the base
group whose parents are professional or managerial workers, children with parents
who have class V occupations are the group who have greatest intention to leave
school after 16.
Parents’ educational qualifications are another powerful predictor of post-compulsory
school plan when we compare the ‘stay group’ and ‘leave group’ in the regression
model. The coefficients of the regressions provide strong evidence that the
probability of planning to leave school after compulsory level increases as the
educational qualifications of parents decreases. Compared to the base group whose
highest qualifications is ‘have a degree’, students who have parents have O-level
qualifications have higher probability to plan to leave school after 16. Students who
have parents with CSE Grade qualifications have even higher probability, while
whose parents with no qualifications have the greatest probability to plan to leave
school after 16.
At the same time, we should not overlook the role of different variables in parameter
estimates of two multinomial logistic regressions in comparing samples with different
answers. First, variables of parent’s occupational status play an important
explanatory role in both regressions (i.e. the comparison between the ‘stay group’ and
the ‘unsure group’, and the comparison between the ‘stay group’ and the ‘leave
group’), while the variables of parent’s educational level have an effect only when we
compare the ‘stay group’ and the ‘leave group’. These findings suggest that the
availability of concrete plans or purposively preferences is conditioned by
individuals’ class positions in terms of parents’ occupational status. It also shows that
the level of parent’s education have a greater influence on the direction of choice/
22
preferences of stay or leave, rather than having an effect on the eagerness of planning
at age 15 (i.e. have a concrete educational plan or not).
Another important finding is the insignificance of the effects contributed by
household income to the variation of educational aspirations when we control parent’s
educational levels and occupational status. As the effect of sibling size is
insignificant as well, the aspiration differences because of the family resources are not
as great as we expected. On the other hand, the class effect in terms of parents’
educational levels and occupational status take a more significant role in influencing
student’s school plans.
This preliminary finding describes the effects of structural factors, but reveals little
about the underlying processes that actually lead to diverse educational aspirations.
Results presented in table 1 show the ‘full’ effect of family background, without
making distinction between its direct effect of preferences or educational aspirations,
and the effect coming through school performance or assessment. For example,
students with parents having low educational qualifications or students from class V
families have a higher probability of planning to leave school after 16 may be due to
one or both of the ability effect and other class effect on aspiration. As mentioned
above, the class origin effect on educational choice may be associated with the
availability of cultural capital and other parental inputs that facilitate or hinder one’s
school performance. Students from specific class backgrounds may inherit more
cultural capital which helps them to be more adaptable to school environments and
enjoy their studies more than those from other class backgrounds. Besides, the effect
of class origin can have a direct impact on educational choice whereby students from
specific classes have higher educational aspirations. Parents will encourage their
children to stay on in school irrespective of their performance and probability of
success. Sociologists have referred to these direct and indirect impacts as the
‘secondary’ and ‘primary’ effects of class inequality in educational choice. In the
following sections, I will explore the associations between children’s school plans and
the variables that related to either primary or secondary effects of class origin. The
variables include student’s self-assessment, educational and occupational aspirations.
23
B. How do you feel about your schoolwork?
School experience and educational achievement are two important determinants on
students’ future participation in higher education. They facilitate progression to the
next educational level, and serve as an indicator to evaluate the probability of success
in continuing education. Experience in school and studying experience also
contribute to the ways in which education is perceived by students. Good experiences
may build students’ confidence and encourage them to continue or process in
education, while bad experiences could discourage their participation in post-
compulsory education. In this paper, students’ experience and schoolwork will be
measured in terms of their satisfaction with their schoolwork. This survey question
may provide a piece of important information about students’ educational
achievement, which is reflected in their self-perception of their ability and
performance in school.
The British Youth Panel includes relatively limited questions on youth’s school
performance and their self-perception of academic ability. Neither the data about
students’ self-assessment nor data on their school records is available. But the
question on how youth feel about their schoolwork may provide some insights into
the relationship between the sample’s experiences and their school plans. In the
interview the youth respondents were asked to classify how they feel about their
schoolwork into seven-scale measure, from ‘completely happy’ to ‘completely
unhappy’ in seven-scale. This survey question is not identical to students’
educational achievement. It provides on the other hand more information about
student’s self-perception on their ability and performance in school. As I argue that
individuals will rationally assess the possibility of success and the constraints
involved while making educational decisions, it is important to examine how they
make decisions based on their perceptions of their schoolwork. Here, I am going to
examine the relations between positive school experiences and the chance to have
purposive school plans. After having a general idea on relations between youth’s
educational choice and their degree of happiness about schoolwork, I use an ordinal
24
regression to further investigate the distribution of demographic and socio-economic
characteristics of youth who have different self-assessment about their schoolwork.
Figure 8: Student's Degre e of Happiness andSchool Plan
05
10152025303540
comple
te ly hap
py 2 3ne
ither 5 6
comple
te ly un
happy
De g re e o f Hap p in e s s a bo u t s ch o o l w o rk
Perc
enta
ge
don't know
leave schoolat 16
go to s ix thform orcollege
First of all, results from an analysis of variance indicate a significant association
between students’ school plans and the satisfaction level of their schoolwork (F=6.89;
df=6; p<.001). As expected, the ‘stay group’ is the happiest, and the ‘leave group’ is
least happy. In figure 8, we can see from the distributions that more than 35% of the
youth in ‘stay group’ feel very happy (degree 2) with their schoolwork and 30% of
them feel happy (degree 3). On the other hand, the degree of happiness among youth
in the ‘leave group’ is more evenly distributed among different levels of happiness
(degree 2 and 3) and ‘neither happy nor unhappy’ (degree 4). However, we have to
bear in mind that there is only a small percentage of youth who feel unhappy about
schoolwork among all three aspirational groups. Only 9% of the children from the
‘stay group’ feel unhappy about their schoolwork (from degree 5 to 7), and 13.4%
from the ‘unsure group’. Even among the ‘leave group’, only slightly more then one
fifth of the children feel unhappy about their schoolwork.
25
The positive association between students’ degree of happiness and purpose to stay in
school after 16 might suggest that students make choice according to their perception
on probability of success. If self-assessment about schoolwork is one of the concerns
in making educational decision, it is interesting to know the characteristics of youth
who are more likely to feel happy about schoolwork. In the following, I use an
ordinal logistic regression to further investigate the distribution of demographic and
socio-economic characteristics of youth who have different self-assessment about
their schoolwork.
In table 2, the dependent variable of the ordinal regression model is the different
degree of happiness about schoolwork and the independent variables including
gender, sibling size, family structure, household income, parents’ occupational status
and educational attainment. Comparing groups of youth with different degrees of
happiness, the family structure has a very significant effect on their level of self-
assessment on schoolwork. Children from single-parent families are more likely to
choose ‘completely unhappy’ than those in families with two parents. Another
variables of students’ family structure also have some effect on their satisfaction
levels of schoolwork. Youth in households that have 2 children are less likely to feel
completely unhappy compared to those in families with only one child.
26
Table 2: Ordinal logit Regression: Youth's degree of happiness about their school work
Independent Variables Coeff icient S.E.
1 Completely happy -1.500 0.2462 0.392 0.2393 1.775 0.2444 Neither happy/ unhappy 2.864 0.2535 3.680 0.2686 4.476 0.296
Female 0.020 0.096
Single Parent Family 0.456 *** 0.138
Number of Children in Household 2 -0.272 * 0.109 3 -0.260 0.144 4 or more -0.029 0.212
Parent's Highest Educational Level Teaching, Nursing or other higher QF 0.213 0.158 GCE A Level 0.202 0.208 GCE O Level or Equiv 0.115 0.189 Commercial, CSE Grade, Apprenticeship or other QF 0.373 0.230 No qualification 0.424 * 0.212
Parent's Highest Occupational Status (Goldthorpe's Class Scheme) II 0.099 0.156 III 0.213 0.169 IV 0.250 0.226 V 0.427 0.244 VI 0.597 * 0.255 VII 0.254 0.209
Household Income (Monthly) 1001-2000 0.492 *** 0.152 2001-3000 0.443 * 0.175 3001-4000 0.273 0.212 4001 0.271 0.241
Log Likelihood -2251.48Number of observations 1422
Note: * p<0.05 (Wald statistic), ** p<0.01, *** p<0.001
There are also some class origin effects on students’ satisfaction level in terms of
parental educational qualification, parental occupational status and household income.
The parameter estimates of the ordinal logistic regressions show that compared to
children who have parents with a degree or higher qualification, students whose
parents without any qualification are more likely to feel completely unhappy about
27
schoolwork. At the same time, compared to children of professionals and managers,
students from class VI families are more likely to choose ‘completely unhappy’ about
their satisfaction level. After controlling both the income effect and parent’s
educational attainment, the positive coefficient of class VI remains high. Besides,
students from families in income groups with monthly income £1001-£2000 and
£2001-£3000 have a higher probability to feel completely unhappy than those from
the lowest income group. In the regression, however, the effect of gender is small and
insignificant, which means there is no gender difference between students’
satisfaction level with schoolwork.
C. How much does it mean to you to do well in school?
Value placed on education is another determinant on students’ school performance
and purposes for continuation in education after compulsory level. Students’ school
plans and their school grades are expected to be closely linked to how they evaluate
the value of doing well in school. Presenting the pattern of educational values among
students could be a way for us to understand the reason of certain choices. And if the
educational values are products of structural variables, examining youth’s values
placed on their schoolwork is a way for us to examine the intervening variables
between class background of family and educational decisions. This allows a more
systemic discussion on the sub-cultural values or other social psychological variables
that significant others such as parents, who are supposed to contribute to the
formation and adjustment of the youth’s aspirations. Therefore, I am going to
examine students’ evaluation on the importance of education in order to clarify the
relations between class origins and educational plans of youths.
The question of ‘how much does it mean to you to do well in school?’ is asked in the
British Youth Panel from 1995. From wave 5 to wave 11, we have 1277 responses to
this question. The respondents were asked to choose among four answers to describe
how much it means to do well in school from ‘a great deal’, ‘quite a lot’, ‘a little but
not very much’ to ‘very little’. As only a small number of children choose the last
two options, I combined them into one.
28
Figu re 9: S tu de nt s' Atti tud e and S ch o ol Plan
0
1 0
2 0
3 0
4 0
5 0
6 0
7 0
8 0
a g r ea t dea l qu ite a lo t lit tle
Ho w m u ch m e an s t o d o w e ll a t s ch o o l?
Perc
enta
ged o n't kn o w
le a ve s ch o ola t 1 6
g o to s ix thfo r m o rco ll e g e
Not surprisingly, results from an analysis of variance indicate a strongly significant
association between students’ school plans and their educational attitudes (F=35.83;
df=2; p<.001). Youth in the ‘stay group’ place highest value on education. Nearly
70% of them think that doing well in school means a great deal to them. Around 30%
of them think that education means quite a lot to them, and only 3% say that it means
little. Among the ‘unsure group’, around 40% think that education means a great deal
to them, nearly 50% answer ‘quite a lot’, and only 10% think that it means little to
them. On the other hand, a relatively high percentage of students from the ‘leave
group’ (around 30%) answer that doing well in school means only a little or very little
to them.
To further investigate the distribution of demographic and socio-economic
characteristics of youth who have placed different values on education, I use an
ordinal logistic regression. In the model, gender, sibling size, family structure,
household income, parents’ occupational status and educational attainment are the
dependent variables. I am interested in the characteristics of youth who are more
likely to place higher value on education. This examination helps to check if
students’ demographic backgrounds or scoio-economic backgrounds do have cultural
or direct effect on educational aspirations through their educational values.
29
T able 3: Ordina l Logi t R egres sion: Youth's Value on Education
Inde pende nt Variables C oeff ic ien t S .E.
A gre at dea l 0.68 7 0.287Quite a lo t 2.81 9 0.302
Fema le -0.40 3 *** 0.116
Single Pare nt Fam ily 0.30 5 0.161
Num ber of Children in Ho use hold 2 -0.05 2 0.131 3 -0.16 9 0.176 4 or more -0.28 4 0.243
Paren t's High est Ed uca tion al L eve l Tea ch ing, Nursing or other h ighe r QF -0.19 1 0.193 GCE A Le vel 0.05 0 0.253 GCE O Level o r Equiv 0.06 6 0.228 Co mm ercial, CSE Gra de, Appren ticeship or othe r QF 0.48 0 0.267 No qualificatio n 0.63 4 * 0.250
Paren t's High est Occupa tion al S tatu s (Goldthorpe 's Class Sche me) II 0.00 1 0.196 III 0.08 6 0.205 IV 0.45 6 0.273 V 0.45 4 0.294 V I 0.69 6 * 0.300 V II 0.09 5 0.250
Househo ld Inco me (M onth ly) 10 01-200 0 0.27 0 0.178 20 01-300 0 0.43 7 * 0.206 30 01-400 0 -0.00 4 0.263 40 01 0.11 7 0.290
Log Likelihoo d -1069 .24Numb er of Observatio ns 1 277
Note: * p<0.05 (W ald stat ist ic), ** p<0.01, *** p< 0.001
The result show in table 3 indicates gender effects and some class effects on students’
educational value. Comparing groups of youth who place different values on
education, gender has a strong and significant effect. Girls are more likely to think
that doing well in school means a great or a lot to them, while boys tend to think that
it means only little to them. This may explain why girls’ participation rate in post-
compulsory level has increased a lot and remained high in these years. Parents’
educational qualification, occupational status and household income also have some
30
effects on children’s educational value. Compared to children whose parents have a
degree, children have parents with no qualifications are more likely to place low value
on education. Compared to children in class I families, children who have parents
have class VI occupations also place low value on education. Among the income
groups, children in families with monthly income from £2001 to £3000 are more
likely to place lowest value on education when compared to the lowest income group.
In the above analyses, I have evaluated (1) the effects of satisfaction level of
schoolwork and educational value on youth’s school plans, and (2) the effects of
children’s demographic backgrounds and class origins on their educational value and
self-assessment. The results show that there are significant associations between
students’ school plans and the two intervening variables (i.e. students’ educational
value and self-assessment of school performances). At the same time, some of these
associations may be explained away by students’ class origin effects. To determine
the collective impact of these characteristics, some multinomial logistic regressions
analyses have been performed. In table 4, I compare the results of two nominal
regression models of students’ school plans. The results of Model I have already been
presented in table 1. The model is designed to show the ‘full effect’ of students’
personal characteristics and family backgrounds on their educational aspiration. I add
another model (Model II), which contains variables of students’ degree of happiness
about schoolwork and their values placed on education, in table 4. The variables of
youths’ demographic and socio-economic characteristics are also considered in the
model. Together with students’ educational values and their satisfactory level of
schoolwork related indices, all these variables are explored in order to describe the
probable associations between students’ school plans and their family background.
My proposition is if the class effect is working through the two introduced intervening
variables, there should be considerable reductions in many of the socio-economic
backgrounds coefficients in Model II.
31
Table 4: Two Multinominal Logit Models of Youth's School Plan (Constrast against youth who plan to stay in school after 16)
Unsure LeaveIndependent Variables Model I Model II Model I Model II
Constant -2.341 *** -2.679 *** -2.199 *** -2.635 ***(0.468) (0.578) (0.463) (0.569)
Female -0.582 *** -0.511 ** -0.978 *** -0.865 ***(0.180) (0.197) (0.171) (0.191)
Single parent family 0.112 -0.011 -0.069 -0.302(0.244) (0.266) (0.218) (0.244)
Number of Children in Household 2 -0.249 -0.159 -0.113 -0.145
(0.209) (0.230) (.0186) (0.211) 3 -0.173 0.129 -0.327 -0.133
(0.266) (0.288) (0.252) (0.281) 4 or more 0.534 0.646 -0.056 0.030
(0.322) (0.348) (0.329) (0.358)Parent's Highest Educational Level Teaching, Nursing or other higher QF 0.097 0.198 0.659 0.706
(0.322) (0.347) (0.377) (0.408) GCE A Level -0.304 -0.167 0.288 0.353
(0.427) (0.452) (0.468) (0.499) GCE O Level or Equiv 0.117 0.307 1.045 ** 1.164 **
(0.365) (0.395) (0.403) (0.438) Commercial, CSE Grade, Apprenticeship or other QF 0.464 0.573 1.270 ** 1.253 **
(0.409) (0.442) (0.445) (0.484) No qualification 0.277 0.070 1.388 *** 1.090 *
(0.393) (0.433) (0.418) (0.456)Parent's Highest Occupational Status (Goldthorpe's Class Scheme) II 0.458 0.294 -0.411 -0.375
(0.331) (0.356) (0.351) (0.391) III 0.586 0.433 0.388 0.335
(0.344) (0.367) (0.314) (0.353) IV 0.837 0.912 * 0.684 0.595
(0.428) (0.460) (0.379) (0.434) V 0.914 * 0.828 1.073 ** 1.190 **
(0.463) (0.500) (0.388) (0.438) VI 1.096 * 0.939 * 0.302 0.104
(0.445) (0.479) (0.446) (0.503) VII 0.849 * 0.825 0.760 * 0.846 *
(0.395) (0.422) (0.352) (0.396)Household Income (Monthly) 1001-2000 -0.028 -0.179 -0.157 -0.379
(0.266) (0.288) (0.230) (0.257) 2001-3000 0.325 0.336 -0.068 -0.306
(0.305) (0.329) (0.278) (0.313) 3001-4000 -0.159 -0.144 -0.796 -0.682
(0.411) (0.451) (0.429) (0.455) 4001 -0.936 -0.880 -0.109 -0.373
(0.599) (0.612) (0.411) (0.454)Degree of Happiness about Schoolwork 2 -0.612 -0.186
(0.374) (0.348) 3 0.187 -0.009
(0.351) (0.348) Neither happy nor unhappy 0.197 0.164
(0.384) (0.372) 5 -0.089 0.107
(0.508) (0.473) 6 0.178 0.434
(0.669) (0.603) Completely unhappy 0.918 1.406 *
(0.667) (0.581)Importance of Schoolwork Quite a lot 0.760 *** 0.874 ***
(0.212) (0.207) Little 1.170 *** 2.350 ***
(0.382) (0.310)Note: Number in parentheses are standard error of the estimation, * p<0.05 (Wald statistic), ** p<0.01, *** p<0.001 Log likelood = -954.966 (Model I) and -790.071 (Model II)
32
Surprisingly, the parameter estimates in Model II do not have big differences between
those in Model I. For the effects of variables concerning students’ socio-economical
background on educational aspiration, there is not much change in their coefficients.
The findings show that variables of students’ class background do not have much
interaction with variables of students’ satisfaction level of their schooling and their
values placed on education in the multinomial regression models. This means both of
the variables do not significantly mediate students’ class origin effects and their
school plans. There are net effects of students’ family background on students’
school plans, or other mechanisms are working. We may need to explore another
mediating variables to explain the class effects on educational aspirations.
Also, we should not overlook the results that students’ educational values have very
significant and strong effect on students’ school plans. The effects remain strong
even after controlling the variables of students’ personal characteristics and family
backgrounds. However, students’ satisfaction levels of schoolwork have only limited
effects on students’ different educational plan. There is only some effect on students’
choice between stay and leave. Compared to youth who feel completely happy in
school, students’ who feel completely unhappy are more likely to plan to leave school
after 16.
D. What job would you like to do when you have left school?
Data on students’ occupational aspirations is another important information that is
expected to bear significant relevance to the outcome of this study. As educational
attainment is one of the main determinants of occupational attainment, participation in
post-compulsory education is essential if youth aim to succeed in the labour market.
In the British Youth Panel, the question of ‘What job would you like to do after leave
school?’ is asked of the respondents from wave 4 to wave 8. There are 677
observations in the dataset. I have recoded their chosen occupations into
Goldthorpe’s seven-class scheme.
33
Figure 10: School plan in terms of occupationalaspiration
0
20
40
60
80
100
don't know leave school at16
go to sixth formor college
Leave school when you are 16?
Perc
enta
ge Class I
Class IIClass III
Class IVClass V
Class VIClass VII
In figure 10, a simple distribution of school plans among different occupation
aspirational groups is presented. As expected, a high percentage of youth with higher
occupational aspirations (class I, II and III) plan to stay in school after 16. More than
90% of children who aspire to be professionals or managers plan to stay in school.
For those who like to have class II and III occupations, around 80% show purposive
preference to stay as well. These groups are least likely to plan to leave school after
16. Surprisingly, a high percentage of youth who plan to have a Class VI occupation
also plan to continue their education as well.
Risk aversion theory predicts that each individual set their aspirations and preferences
with reference to the status of their parents. It argues that every family or every
student aims to avoid downward mobility compared to their previous generation.
Therefore, understanding students’ occupational plans and aspiration is a way to set
the theoretical scene to explain differential educational choice. However, with limited
sample size, I have problems in associating youth’s occupational aspiration with their
class backgrounds, and other familial variables. Further analysis, with additional
relevant data, is needed and important. I am sure exploring relations between one’s
occupational aspirations with one’s class origin could shed light in our understanding
of students’ educational demand and choice. For example, empirical evidence shows
that gender acts as an important determinant of educational decision-making.
Although women have actually surpassed men in rates of high school completion and
34
college enrolment in the past few decades, women are still disproportionately
concentrated in sectors of higher education that yield smaller income returns.
Different patterns of employment by gender, occupational sex segregation therefore
could be reflected in the occupational aspirations.
Discussion and Conclusion:
To summarize the above results, there are inherent features in the pattern of students’
school plans (their probability of stay is partly influenced by their socio-economic
background and educational value). My findings corroborate the results of previous
studies, which find that family background factors, particularly parental education,
strongly influence the post-high school choice of youths. Although the income effects
(measured in household income and household size) have no significant effect on
youth’s educational plans, it does have some influences on students’ self-assessment
of their schoolwork and their values placed on education. Moreover, parents’
educational qualifications and occupational status, which are always treated as
sources of class-based culture and values, are shown to have effects on educational
plans. They also have some impact upon students’ satisfaction level and values on
education. Children whose parents have no educational qualifications are more likely
to feel completely unhappy about their schoolwork and think doing well in school
means a little to them. At the same time, compared to children whose parents are
professionals or managers, children who have parents have class VI jobs are more
likely to feel completely unhappy about schoolwork and place low values on
education.
The results obtained in this paper indicate that post-compulsory education is
influenced to a considerable extent by an individual’s values and aspirations.
Characteristics such as educational values, and family backgrounds play an important
role in determining the school plan after compulsory level. On the other hand, self-
assessment does not play a strong influential role in students’ school plan. Except
those feel completely unhappy about schoolwork are more likely to plan to leave,
other students with various levels of satisfaction have similar possibility to have each
of different answers. Students’ self-perception on ability does not a key concern for
35
them to plan to stay or leave school. In other words, whether having a higher
probability to success in later educational levels is not a main consideration in their
deciding making process.
The limited class effects which work within only certain socio-economic status
groups in the analyses also suggests that class-specific values or preferences could
only be effective to a limited extent, depending on which social class we focus on.
There are possibilities that different mechanisms are working among students with
different classes and social backgrounds that enhance responsiveness to their choice.
For example, it is found that parents’ educational attainment has an effect which
prevent students from being ambiguous about their plans. Having a clear purposively
forward looking plan in early year may encourage students to stay in school. On the
other hand, effects of parental occupational status on educational aspiration are not
direct. Compare to the base group whose parents are professionals or managers,
students from class V and class VII families tend to plan to leave school after 16,
while students from class IV and class VI families are more unlikely to have concrete
plan at age 15. Given the patterned educational preferences or plans, there are always
alternative intervening factors for different classes that mediate in between to make
sense of the result I have.
Besides, a more universal and individualistic concept on occupational aspiration may
be another possible explanation of the differences in levels of aspiration among social
classes. If each individual set their aspirations and preferences with reference to the
status of their parents, understanding students’ occupational plans and aspirations is a
way to set the theoretical scene for the explanation of differential educational choice.
Future research in this area should empirically test. In this paper, I have not
attempted to address the complexity of the process of formulating occupational
aspirations and their impact on diverse school plans and educational choice in
relations to students’ class backgrounds. The likelihood of school planning is no
doubt influenced by other potential pathways to adulthood on expectations of working
for pay. The task of assessing and modelling the complexity of youth’s choices
among different occupational aspirations is a fruitful direction for future research into
youth’s school plans and educational decisions.
36
One concern not directly addressed by this paper is whether students’ school plans at
15 are highly correlated to their educational decisions at 16. That is whether
aspirations are important as determinants of future outcomes, although the general
issue has been discussed at some length by others (Manski, 1990; Dominitz and
Manski, 1997, 1999). More extensive analyses of the educational paths of the youth
in the British Panel Survey may be able to adjudicate between the intentional
variables (school plans) and actual/ observable choice (their educational attainment).
We can even further check up who or which groups of youth success or fail to live up
to their educational goals of a college education years later.
Acknowledgment:
The data from the British Household Panel Survey used in this paper were made
available through the UK Data Archive. The data were originally collected by the
ESRC Research Centre on Macro Social Change at the University of Essex. Neither
the UK Archive and nor the original collectors bear any responsibility for the further
analyses or interpretations presented here.
37
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