Management Education in India: Avenue for Social Stratification or Social Mobility?
Anirudh Krishna
Professor of Public Policy and Political Science
Duke University
Durham, NC 27708-0245, USA
+1 (919) 613-7337
ak30@duke.edu
and
Ankur Sarin
Assistant Professor, Public Systems Group
Indian Institute of Management (Ahmedabad)
Ahmedabad, Gujarat 380015, India
+91 79-66324953
asarin@iimahd.ernet.in
1
ABSTRACT
Social mobility is a key understudied feature in developing countries, even though
understanding – and then raising – social mobility can help counter increasing inequalities of
income and wealth. Lacking longitudinal data sets, innovative methods of investigation are
required. Investigating the determinants of entry to educational institutions that serve as
gateways to higher-paying careers provides one way of uncovering mobility trends. An MBA
degree is close to the pinnacle of educational aspiration among Indian youth; the number of
MBA-granting institutions has vastly expanded. Have people from less well-off sections of
Indian society also benefited from this expansion of opportunity, or are these positions mostly
captured by established elites? Results from a sample of 1,137 MBA students at 12 Indian
business schools belonging to three different quality tiers present a mixed picture.
Intergenerational stickiness is evident insofar as parents’ education and occupations continue
to matter. Greater wealth, higher caste, and urban origin also make a difference. But more
than a few students scoring poorly on each of these attributes have also gained admission to
high-ranking MBA programs. A range of factors – career guidance and information,
motivation and role models – that we group under the category “soft skills,” have helped
mitigate the effects of multiple socio-economic disadvantages. Enhancing social mobility
prospects in the future will be assisted by policies that nurture soft skills.
2
The explosive growth in India of business schools offering MBA or equivalent degrees
provides a locus of inquiry into questions regarding equal opportunity and social mobility.
As in other market economies, especially richer ones, pursuing an MBA has come to be a
widely-shared aspiration among those seeking upward mobility in India (Dayal 2002; Moon
2002): “Enrolling in an MBA program, particularly at an elite school, is for some the
equivalent of taking an elevator to the executive suite.”1 Starting from a tiny base in the early
1950s, business schools in India increased slowly in number over the next 30 years, with no
more than four new schools being added every year. Since the mid-1990s, however,
following economic liberalization, more than 100 new business schools have been
established annually. Over 100,000 students start MBA programs every year, attracted by the
promise of high-paying private-sector jobs, such as existed in miniscule numbers 30 or 40
years ago. Experts in the field expect that these numbers will continue expanding rapidly
over the next ten to fifteen years, rising above 300,000 annually in response to growing
demand.2
Together with this huge expansion has come a differentiation of MBA programs
among different quality tiers. India’s National Knowledge Commission, whose report we
cited above, goes on to note that while “the number of business schools has trebled in the
last ten years… many [are] of indifferent quality. The market has already started
discriminating the quality of institutions and graduates.” Business magazines in India publish
annually their pecking orders of business schools; strikingly similar across different
publications.
3
We look to this variation across quality tiers to distinguish whether and how people
from traditionally disadvantaged backgrounds have or have not been able to avail themselves
of these fast-growing opportunities. If people from poorer, minority, and discriminated-caste
backgrounds have not able to get into top-tier and elite institutions, like the world-renowned
Indian Institutes of Management at Ahmedabad, Bangalore and Calcutta, have they, at least,
succeeded in finding places at lower-ranked MBA programs, enhancing to some extent their
chances of moving upward economically and socially?
What factors have facilitated the entry of those who have gained entry? What other
factors have worked to hold back the rest? A proximate answer can be obtained, of course,
by looking at candidates’ entrance examination results, but as Heckman (2011:78),
summarizing a body of literature, has argued, test scores are themselves influenced by
privilege and its absence, and “under adverse conditions, especially, environments are more
determinative of many child outcomes.”
To what extent do underlying socio-economic factors, like household wealth, gender,
caste, religion, geographic location (especially, in the Indian context, rural v. urban
upbringing), and parents’ education and occupational status make a difference to an
individual’s prospects? And to what extent does a second set of factors, which Heckman
(2011) has collectively termed “soft skills” – such as motivation, socialization, aspirations,
personal traits, and what Bourdieu (1986) referred to as “cultural capital” – offset or
accentuate the effects of socio-economic status? We examine these questions by looking at
4
data collected in 2010 and 2011 from 1,137 MBA students at 12 Indian business schools that
belong to three distinct quality tiers, described below in the section on data and methods.
EXAMINING SOCIAL MOBILITY
The study of social mobility is still in its infancy in India and other developing countries.
Despite a recent sharp rise in inequality (Bardhan 2010; OECD 2011), relatively little is
known about the nature of factors that can help make better opportunities more widely
available, helping hold future inequalities in check.
Even in the West, where social mobility has been studied for a longer time and where
different schools of thought have emerged, “the transmission of economic success across
generations remains something of a black box” (Bowles, Gintis and Groves 2005: 3).
Investigators have compared individuals’ social origins – most often examined in relation to
their father’s social class, occupational status, income, or education – with the individual’s
own attainment expressed in similar terms. In general, a robust correlation has been found to
exist between parent’s and children’s socioeconomic status: richer fathers tend to have richer
daughters and sons, while poorer children tend to go together with poorer parents.
Variations across time and space indicate, however, that the pattern of this relationship may
be mutable. The extent of intergenerational income mobility varies significantly across
countries; within countries, mobility prospects change over time.3
Explaining these differences has proved so far to be both contentious and
inconclusive. Diverse factors have been shown to have varying degrees of influence.
5
Researchers have found, for instance, that “IQ cannot explain why children from lessprivileged social strata systematically perform more poorly than others or why children from
privileged families systematically perform better” (Esping-Andersen 2005: 149). Education
can certainly help raise social mobility prospects. However, the effects of education are
contingent and contextual. While individual advancement is rarely possible without at least
some amount of education, having more education provides no assurance of greater
economic success.4 Very similar social mobility patterns are seen to prevail across countries
with dissimilar levels of public investment in education and diverse organizations of
education systems (Erickson and Goldthorpe 2002; Torche 2010).
Researchers have examined many other sources of influence, including early
childhood nutrition and child rearing practices, race- and neighborhood-related factors,
school quality, state-supported daycare centers and pre-school programs, health conditions,
and soft skills, including aspirations and cultural capital.5 Each of these factors makes a
significant different in particular contexts. Calculations show, however, that all of these
factors together explain no more than one-quarter of the observed intergenerational
correlation in earnings (Bowles, Gintis and Groves 2005: 20).
Initial examinations of social mobility and equal opportunity in India and other
developing countries provide indication that parents’ and children’s earnings may be even
more closely correlated – mobility may be lower and opportunity structures more
impermeable – in developing countries compared to the West. 6 Identifying the factors that
matter, however, remains even more of a black box than in the West. Few large-sample
6
projects are available for India that compare sons’ and fathers’ educations or occupations
(e.g., Asadullah and Yalonetzky 2012; Jalan and Murgai 2008; Kumar, et al., 2002a, 2002b;
Majumder 2010; Motiram and Singh 2012). Because longitudinal data are not available, such
studies are limited to making cross-sectional comparisons, examining all fathers and all sons
(or daughters), regardless of cohort differences.
A disparate set of conclusions has resulted from these studies. On the one hand, Jalan
and Murgai (2008) find encouragingly that “Inter-generational mobility in education has
improved significantly and consistently across generations. Mobility has improved, on
average, for all major social groups and wealth classes.” Similarly, Azam and Bhatt (2012)
find “significant improvements in educational mobility across generations in India.” On the
other hand, Kumar, et al. (2002b: 4096) conclude that “there has been no systematic
weakening of the links between father’s and son’s class positions… The dominant picture is
one of continuity rather than change.” In the same vein, Majumder (2010: 463) uncovers
“strong intergenerational stickiness in both educational achievement and occupational
distribution,” especially among Scheduled Castes (SCs) and Scheduled Tribes (STs), both
historically marginalized groups,7 noting how “occupational mobility is even lower than
educational mobility.”
Results from some smaller-scale examinations are also available, which have mostly
considered engineering colleges or India’s booming software industry, examining the social
origins of entrants to these fast-growing sectors. These studies support the less encouraging
view reported above, finding that relatively few individuals from poorer households or rural
7
backgrounds have managed to secure positions as software professionals (Krishna and
Brihmadesam 2006); and that “the social profile of information technology workers is largely
urban, middle class, and high or middle caste” (Upadhya 2007: 1863); because birth within
the “educated, professional, urban middle class” overwhelmingly privileges new entrants
(Fuller and Narasimhan 2006: 262). The earliest known study of this genre was conducted by
Rajagopalan and Singh (1968: 565). Looking at the social background of entry-level students
at an elite engineering institute (one of the Indian Institutes of Technology, or IITs), they
found that “even though no student is intentionally precluded from securing admission,
there are certain disabling and debilitating factors inherent in the structure of society that
prevent certain sections from taking advantage of the new educational opportunities.” The
factors that their analysis identified as being disabling included being a woman (“no girl”);
Muslim religion (“only 1.3 per cent are Muslims”); belonging to a Scheduled Caste or
Scheduled Tribe (“not a single student”); and parents with low-levels of education and/or
low-skilled and low-paying occupations.
To the best of our knowledge, no similar study has looked at these questions within
the field of management education, despite it being a towering ambition among youth in
India and elsewhere. Drawing upon his personal experience, a former director of the elite
Indian Institute of Management at Ahmedabad (IIM-A) opined that “admission policies and
methods of IIMs while fair and efficient, have worked largely in favor of the better-off
sections of society” (Paul 2012: 146). However, as noted above, other examinations of social
mobility have generated more upbeat conclusions, for example, a second study by Kumar, et
8
al. (2002a: 2985) concluded that “it is clear that for many people there has been long-range
upward mobility from the lowest ranks of the society to the highest. In that sense, India has
been a land of opportunity.” The popular media in India has especially of late been playing
up this impression by highlighting accounts of and by individuals whose rise, especially in
the world of business, has been nothing short of meteoric.8
It is opportune, therefore, to put these competing visions to the test. Looking at
background factors associated with successful entry to MBA programs of different quality
tiers, we identify important and policy-relevant influences.
Our study is necessarily exploratory and descriptive in nature. We subject our data to
rigorous analyses of different kinds, combining both qualitative and quantitative methods,
but limitations in data availability combined with the rudimentary state of current knowledge
suggest that our findings are best seen as an incremental contribution. Until investments are
made toward constructing longitudinal data sets, tracking the same individuals over longer
periods of time and regularly monitoring key variables, incrementally pushing forward the
frontiers of knowledge about social mobility in the developing world is, however, the best
that can be practically accomplished.
DATA AND METHODS
A questionnaire, available upon request, was formulated, pre-tested, and revised, before
being administered to a total of 1,137 students in 12 business schools located in diverse
regions of India, and as discussed below, belonging to different quality tiers. Our sample is
9
diverse, therefore, in terms of both geography and institutional quality but not representative
in the strictly statistical sense. Students in all but one of these colleges were administered the
survey instrument online when they appeared for the AMCAT (Aspiring Minds’ Computer
Adaptive Test), a standardized examination that helps students and employers connect with
one another.9 Students in the 12th, and highest-tier, business school were separately
administered an online version of this survey.
Three separate quality tiers were distinguished, based on a variety of criteria, including
faculty qualifications, average starting salaries of the graduating class, teaching infrastructure,
employers’ perceptions, and the rating schemes of business publications and professional
agencies. Institutions within the same tier are broadly similar with respect to admission
criteria, academic profile of students, faculty qualifications, infrastructure, and other
educational resources. Tier 1 broadly represents the top 20 Indian business schools and
besides others includes the six state-managed Indian Institutes of Management that have
been in operation for more than five years. Institutes ranked between 21 and 50 are
considered within Tier 2, while institutions ranked below 50 have been clubbed together in
Tier 3. For reasons of confidentiality, we do not refer to any institution by name. The names
of individuals, extracts from whose interviews are cited below, have also been disguised to
make good on our promises of anonymity.
One of the 12 institutions in our sample is consistently placed among the top-five
business schools in India. Almost the entire faculty of this business school has a PhD from
eminent national and international institutions. Starting salaries for the class graduating in
10
2010 averaged Indian rupees ( ) 965,000 annually. A total of 280 students from this Tier 1
institution completed our survey, representing a response rate of 38 percent. Two
institutions in our sample are Tier 2. About half of all faculty members have PhDs. Average
starting salaries for the class graduating in 2011 were 550,000. A total of 247 students from
three Tier 2 institutions completed the survey, a response rate of 78 percent. Another eight
institutions belong to Tier 3. Only a handful of faculty has PhDs. Average starting salaries
are close to 300,000. This tier contributed a total of 610 complete surveys, producing a
response rate of 55 percent.10
Three types of analyses were conducted using these data. First, we looked at some
characteristics of MBA students, guided by the questions – What makes MBA students
special? In what important respects are they different from other young people in India? In
addition to socio-economic indicators, we looked at aspects of “soft skills,” including survey
questions that help assess differences in career guidance, aspirations and motivations.
Second, we utilized logistic regression analysis in order to make comparisons across different
quality tiers. Finally, we present results from an analysis of disadvantage, examining the
proposition that cumulative liabilities tend to have especially pernicious effects.
CHARACTERISTICS OF MBA STUDENTS
“Success,” Gladwell (2008: 175-6) notes, “arises out the steady accumulation of advantages:
when and where you are born, what your parents did for a living, and what the
circumstances of your upbringing were, all make a significant difference in how well you do
11
in the world.” In the Indian context, religious and caste group can make an additional
difference (Deshpande and Yadav 2006).
We commence our analysis of business school entrants by looking at their gender,
religious and caste compositions. Next, we examine differences in household wealth, going
on to look at parents’ education and occupations. Third, we look at some circumstances of
upbringing, especially rural v. urban residence and migration to towns. Fourth, we examine
the difference made by being educated in the medium of the English language, competence
in which has come to be a characteristic feature of and almost a requirement of entry to the
professional Indian middle class (Fernandes 2006). Fifth, we look at aspects related to
information, guidance, and motivation, finding that these factors – which we group together
under a category we term “soft skills,” representing less tangible (but no less important)
circumstances of upbringing – also make an important difference.
Gender, Religion and Caste
Just under one-third of all students in these 12 business schools are women, ranging from a
low of 16.2 percent in the Tier 1 institution to 36.2 percent in the Tier 3 schools, with this
share being 40.2 percent in Tier 2 schools. While low, particularly in the top-tier institution,
this percentage is higher than the historic share of women both in higher education and in
management positions in India. In the 1960s, according to Rajagopal and Singh (1968), there
were no women in elite institutions. Partly as a consequence, “women today comprise only
two per cent of the total managerial strength in the Indian corporate sector.” 11 The observed
12
increase in the proportion of women among current-day MBA is, therefore, heartening.
However, raising the share of women is a continuing priority.
Table 1 presents the religious composition of these students. While the share of
Hindus is, on average, close to the population proportion of this religious group (as shown
in the last column); the share of Muslims in management education is less than half their
population proportion.
-
Table 1 about here -
It is not only management schools where Muslims in India are under-represented.
Deshpande’s (2006: 2439) analysis of nationally-representative data showed how Muslims
constituted only 5.0 percent of engineering students and only 5.7 percent of students in nonprofessional graduate programs. A high-level committee appointed by the Indian Prime
Minister in 2005 to examine the social, economic and educational status of the Muslim
community of India found that the disparity in graduation rates between Muslims and
others, already large, has widened further after 1970.12
Table 2 provides these students’ caste composition. In addition to SCs and STs, we
also looked at the share of Other Backward Castes (OBCs), for whom affirmative action
quotas have been mandated relatively recently; these groups, falling ritually between upper
castes and SCs, also claim historical discrimination.
-
Table 2 about here -
The shares of SCs and STs are, on average, lower than the shares of these groups in
the national population, a feature that is common across-the-board in Indian higher
13
education (Deshpande 2006). Interestingly, however, the shares of these groups (and of
OBCs) in the Tier 1 institution is considerably higher than in Tiers 2 and 3. Two likely
explanations can be adduced. First, the Tier 1 institution in our sample is state-managed,
thus more likely, compared to Tier 2 and 3 schools (which are nearly all privately-managed)
to implement faithfully the government’s caste-based affirmative action programs.13
Alternatively, since the share of those selecting “do not wish to respond” as their option to
the caste question was relatively large, the possibility of a response bias cannot be ruled out:
If a stigma still attaches to lower caste, it is likely that some SCs, STs, and OBCs selected to
not respond to this particular question.14
Household Wealth, Parents’ Occupations, and Parents’ Education
In order to examine different levels of household wellbeing, we asked respondents about the
ownership by their household of origin (i.e., their parents’ household) of 16 types of assets,
including movable assets (such as TVs, motorcycles, and refrigerators), immovable assets
(homes, commercial properties, agricultural land), and financial assets (stocks, fixed deposit
accounts).15 The survey question asked simply about the presence or absence of each asset
type in the parental household at the time when the respondent was growing up, specifically
when he or she was studying in high school. Basic and relatively low-value assets, possessed
on occasion even by less well-off households, form part of this asset list, including bicycles,
radios, and pressure cookers. Higher-value and less frequently possessed assets, including
stocks and bonds, washing machines, and cars, are also included. We used a simple asset
14
index constructed by adding the total number of assets possessed by each household.16 Table
3 shows the distribution of students by number of assets possessed.
-
Table 3 about here -
In general, MBA students come from households that are better off, on average,
compared to the average Indian household. For example, more than 81 percent of
respondents grew up within households that owned a refrigerator: 75 percent in Tier 3
schools, 94 percent in Tier 2 schools and 86 percent in the Tier 1 institution. To put these
numbers in perspective, in 2001-02 (at the time when most of our respondents would have
been at or close to high school) only 13.4 percent of all households in India possessed a
refrigerator (NCAER 2005).
While higher economic status may confer an advantage in terms of gaining entry, its
ability to buy you a place within the highest-ranked institutions is limited. A considerable
number of students (18.8 percent of the total) from relatively poor households (fewer than
six assets) have also made it into MBA programs of different types. Further, the relationship
between economic status and quality of attainment is hardly monotonic: Tier 1 has a higher
proportion of students with fewer than six assets (16.8 percent) than Tier 2 (7.9 percent) and
a lower proportion than Tier 3 (23.9 percent). Tier 1 also has the lowest proportion of
respondents from the top two wealth categories examined in Table 3. The majority (52
percent) of Tier 1 respondents come from middle economic groups (7-10 assets), the
children, as we will see below, of salaried professionals.
15
Another indication of relative wealth can be gained by looking at the natures of
schools attended from K-12. Poorer households are more likely to send their children to
government-run schools. At the primary level, at least, government schools charge no fees,
and at higher levels of school education, fees in government schools are nominal,
substantially lower than those charged in private schools. Children of relatively deprived
families are thus likelier to attend government schools, although there is no one-to-one
correspondence. On average, 18.6 percent of our sample had for some part of their school
education studied at a government-run school. As before, this proportion varied nonmonotonically across tiers, being lowest in Tier 2 (10 percent) and highest in Tier 3 (23.1
percent), with Tier 1, once again, falling in the middle (18.1 percent).
Very few MBA students undertook their entire school education in a government
school, constituting 3.9 percent of the total in Tier 3 and 1.8 percent in Tier 1, with the
proportion in Tier 2 being 2.3 percent.17 A total of 72 percent of Tier 1 MBA students had
studied entirely at private schools, while 95 percent had studied in a private school for one or
more years. To put these numbers in national context, only 32 percent of all students in
India study within private schools, with the rest attending government-run institutions
(Desai, et al. 2008).
A story of relative privilege, once again, emerges, tempered, once again, by the facts
that (a) people from lower wealth groups have also gained entry, albeit in numbers much
lower than their proportion shares; and (b) higher wealth provides no assurance of higher-
16
quality management education. Something else matters in addition to wealth (or lack of it),
and we look below at other likely sources of influence.
Parents’ occupations and education levels, because of intergenerational stickiness,
have been shown repeatedly by social mobility analyses to have a critical impact upon
children’s prospects. In the Indian context, Kumar et al. (2002 a and b) have highlighted the
critical role of what they term the salariat, comprising salaried employees in government or
private-sector offices together with self-employed professionals. We found this category to
be quite robust for our analysis. As Table 4 shows, salariat fathers constitute as many as 82.2
percent of the total within Tier 1 and 63.1 percent in Tier 2, falling to 52 percent in Tier 3.
Simultaneously, salariat mothers constitute just over 29 percent in both Tiers 1 and 2, falling
to 17.2 percent in Tier 3.
-
Table 4 about here -
As mentioned above, economic status is not alone sufficient to make it to a top-tier
management institution. However, the large numbers of salariat fathers in Tier 1, coupled
with the monotonic decline of this percentage across quality tiers, provides indication of
inter-generational reproduction of occupational class. Further, and to some extent contrary
to what Bertrand, et al. (2010) found in relation to engineering students, class seems to
matter within caste categories as well: nearly all SC and ST students in our sample have
salariat fathers. Another noteworthy result relates to the high share of government employees
among Tier 1 fathers (55.7 percent), bearing out the finding, reported earlier by Fernandes
(2006), that the children of those who benefited from the expansion of public-sector
17
positions during India’s first model of state-led development have derived large benefits
from India’s second, post-1990s, model of economic liberalization.
The share of agriculturist fathers (and mothers) is very low. According to data on
occupational classifications collected in 2004-05 by India’s National Sample Survey
Organization, more than 55 percent of India’s working population is categorized as
cultivator or agricultural labor. Yet, only 1.8 percent of Tier 1 fathers are so classified, with
this share rising within Tier 3, but still only 12.3 percent. Among mothers similarly, the share
of agriculturists is dismally low. The rural-urban divide is critically important, as we will
contend in a following sub-section.
Another noteworthy feature is the high share of homemaker mothers, which rises
monotonically from 66.8 percent in Tier 1 to 78.2 percent in Tier 3. Such mothers, likely to
be less educated than others-- thus less firmly hooked into networks rich in career-relevant
information-- are less likely to serve as a provider for their children of the kinds of “soft
skills” that we will discuss below. Not surprising, given these results, the share of collegeeducated fathers and mothers is higher among Tier 1 and 2 institutions – and considerably
lower in Tier 3. Table 5 reports these numbers.
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Table 5 about here -
Parents’ education levels serve not only as a measure of socio-economic status, but
also are related to other influences on an individual’s prospects for social mobility. In
contexts such as India, where institutions providing career guidance and relevant
18
information are virtually non-existent, parents also serve as a critical source of career
guidance.
It should not be surprising, thus, to find that a majority of MBA students come from
highly-educated households. Nearly 74 percent of all fathers and more than 58 percent of all
mothers have college degrees. To get a sense of how selective this group is consider the
corresponding national proportions. According to the Indian Human Development Survey
of 2004-05, only 6.8 percent of households in India have an adult woman with a college
degree and only 13.2 percent have a male college degree-holder. Notably, as in the case of
salariat mothers and fathers – but not in the case of household wealth – the proportion of
college-educated parents falls monotonically from higher- to lower-quality-tier institutions.
The Rural-Urban Divide
The chances of getting into business school are low for rural individuals, and the more rural
one is, the worse are these prospects. Nearly 69 percent of India’s population lives in its rural
areas, but only seven percent of MBA students lived in a rural location through age 15. Only
12 percent studied in a rural school for one or more year, and only 40 students in all (3.3
percent of the total) undertook their entire K-12 educations in rural schools. Of these
students, nearly 70 percent are in a Tier 3 institution. The proportion of rural-origin
students, which is small in all schools, is largest among schools of Tier 3.
The majority of Tier 1 (54.5 percent) and Tier 2 (67.7 percent) students reported
living either in a metropolitan city or state capital (with a small proportion living abroad)
19
during the first 15 years of their lives. This big-city exposure clearly separates them from
their Tier 3 counterparts – less than 30 percent of whom lived in a metro or state capital.
In order to examine a larger range of variation, from the most remote rural locations
(which typically do not have colleges, hospitals, national highways, and other infrastructure)
through small towns (that are better served) to the biggest cities (which typically have the
best infrastructure), we constructed a variable that added together responses related to ten
separate infrastructure types.18 Consistent with their responses about rural residence and
education, more than 50 percent of Tier 1 and Tier 2 respondents (and no more than 21
percent of Tier 3 students) reported the highest score on this variable.
People who grow up in more rural locations have a progressively lower chance of
getting into management schools, especially top-tier institutions. To be sure, we are not
making a case that only students from the biggest or richest Indian cities have made it to a
top-ranked business school. A majority (53.6 percent) of Tier 1 students lived in mediumsized towns between the ages of ten and 15. But very few lived in a rural village, with this
proportion diminishing further as these students grew older. While 11.1 percent attended
rural schools at the primary level (grades 1-4), only 7.1 percent attended rural middle schools
(grades 8-10) and fewer yet, 3.8 percent, studied in rural high schools (grades 11-12).
To overcome rural disadvantages, many families have migrated from villages to cities.
As noted above, fewer than seven percent of MBA students lived in a village for the first 15
years of their lives. However, as many as 33 percent of their fathers and 29 percent of their
20
mothers were village-based for their first 15 years, only subsequently moving to towns, quite
often with the objective of seeking better educational prospects for their children.
Geographic mobility has served in a large number of cases as a means of social
mobility. We asked respondents about whether or not their families had ever moved and
whether this move was motivated primarily by the desire “to improve the academic
prospects of you and your siblings.” On average, as many as 29 percent of students reported
moving for academic reasons, with this proportion being fairly stable across quality tiers.
Together, these results indicate that getting a MBA might seem an impossible dream
to someone growing up in an Indian village. Only those who have the will and wherewithal
to migrate to cities can expect to see their children flourish. One respondent elaborated as
follows: “I am fortunate to have parents who realized the importance of education. I have
witnessed the sacrifices they made to secure my future and to support my education till I
could stand on my own two legs. They constantly supported me to excel in studies. My
home town is Vellore. In fact, we moved from our native village to Vellore solely for my
education. My mother, a village girl, aged 19 then, must be appreciated for having taken such
a bold decision, amidst all the cautionary tales from relatives.”
Learning in English
One reason why parents move to cities for the sake of their children’s education has to do
with the growing important of learning English, or better still, attending an English-medium
school. Examining national data, Azam et al. (2013) uncover a substantial wage-premium for
21
English speakers across all occupations and skills levels, with this gap being largest among
more experienced and educated workers and growing over time.
Among our sample of MBA students, the critical importance of English shows up
starkly: 88 percent of Tier 1 students studied in high schools where English was the medium
of instruction (or first language used). As many as 71 percent of Tier 1 students attended
English-medium schools from the outset, starting from the primary level. The corresponding
proportions for Tiers 2 and 3 are 81.5 percent and 59.4 percent. MBA students, especially
those who make it to the top-tier schools, are in this sense clearly not representative of the
Indian population: Only 13 percent of schools at the primary and upper-primary stage in
India have English as the medium of instruction and a further 18 percent teach English as
the first or second language (NCERT 2005). Few village schools are able to field teachers
who are competent to teach in English. Results of standardized tests conducted among 1114 year-old schoolchildren as part of the Indian Human Development Survey of 2004-05
show that while all types of learning outcomes are at considerably lower levels in rural
compared to urban schools, falling regularly with increasing distances to towns; English
language proficiency is more than seven times higher among urban compared to rural
schoolchildren (Krishna 2012).
“SOFT” SKILLS: INFORMATION, MOTIVATION, AND CAREER GUIDANCE
Another motivation for moving one’s children from a village to a city emerges from the
greater availability within cities of diverse career-relevant resources – such as role models,
22
guidance centers, coaching classes, and higher-aspiring peers – notably missing from all but a
few rural locations. We look next at this set of variables. While aspirations, role-models and
sources of information and guidance could be thought of as intervening or proximate
variables – capturing to some extent the influences of factors missing in our analysis, such as
child-rearing practices and neighborhood effects – they may also exert an influence, as some
analysts have argued, that is independent of socio-demographic characteristics. For instance,
Easterly (2001: 73), emphasizing the role played by incentives, asserts that where the
“incentives to invest in the future are not there, expanding education is worth little.”
Incentives are linked in turn to possibilities and alternatives. When the range of career
possibilities visualized is itself impoverished, people’s incentives to invest in higher education
get reduced. Inequality of opportunity is sustained in contexts where information about
diverse career options is poorly available. Appadurai (2004: 68-70) notes how individuals
living in environments rich in career-related information, including a diversity of role
models, tend to “have a more complex experience of the relationship between a wide range
of ends and means, because they have a bigger stock of available experiences… [while
others] have a more brittle horizon of aspirations… and a thinner, weaker sense of career
pathways.”
Because they are harder to gauge compared to socio-economic features, soft skills
have less often formed part of the analysis of social mobility. Particularly within India, none
of the available analyses considers aspects such as aspirations and motivation alongside
wealth, parents’ occupations, etc. We make a beginning in this regard by using such measures
23
as we could develop and to which we could obtain meaningful responses in our pilot
surveys.
Aspirations
The survey we administered included two questions related to aspirations. One survey
question asked respondents about whether they aspired to achieve more, less or the same as
others in their neighborhood growing up at the same time as the respondent. A second
question asked respondents if they had aspirations for a specific undergraduate institution by
the time they were studying in the 10th grade.19 These results are broadly similar. On both
measures, Tier 1 respondents have distinctly higher scores compared to Tiers 2 and 3. Over
93 percent of Tier 1 respondents consistently aspired to more than others in their
neighborhood, with this number dropping to 69 percent in the lower two tiers. Similarly,
while nearly 25 percent of Tier 1 respondents aspired for a specific undergraduate
institution, this numbers drops to less than 15 percent in Tiers 2 and 3.
Role Models and Stories of Success
Another set of survey questions looked at sources of motivation, including role models.
More than 71 percent of all respondents replied in the affirmative when asked if “any
particular individual’s success story (an acquaintance, friend, relative, neighbor, or wellknown public person) inspired or motivated you.” The proportion of respondents who
answered this question in the affirmative does not vary considerably across different tiers.
However, the nature of the story that served as motivation varies considerably across
different quality tiers. A majority (31 percent) of Tier 1 respondents pointed to a story of a
24
“well-known person” as the one that inspired them the most. In contrast, Tier 2 and 3
students were most often motivated by a friend’s or personal acquaintance’s story.
More importantly, the points in their lives when respondents heard this motivating
story also varied considerably across different quality tiers. A story heard earlier in life
differentiates Tier 1 students from Tiers 2 and 3. Thirty-two percent of Tier 1 – but only 14
percent and 20 percent of Tier 2 and 3 – students who were inspired by any story heard this
account before reaching Grade 8. We will probe this particular result further in the following
section, where we look at regression results.
Information and Career Guidance
Meanwhile, it is useful to examine the roles played by information provision and career
guidance. We looked at three different types of sources of career advice: personal sources
(including parents, friends, teachers and relatives); institutional sources (newspapers, internet,
television, radio, employment exchange and caste or religious organizations); and paid or
professional sources (counselors, career centers, and private coaching institutes).
In general, our data show that personal sources were primary for the vast majority of
students across tiers. This high dependence upon personal resources of different types
should come as no surprise in a low-information society such as India. There are, however,
some differences among tiers. While parents were the most important resource for career
guidance and advice for nearly 74 percent and 67 percent of Tier 2 and Tier 3 respondents,
respectively, this number drops to 53.4 percent among Tier 1 students, who were more likely
25
compared to others to rely upon other sources, including peers, as a primary source of career
advice.
Similarly, while the use of institutional resources is low overall, Tier 1 respondents
were more likely compared to Tiers 2 and 3 to tap such resources. Very few students (only
13 percent) were able to rely upon paid sources. Interestingly, Tier 2 and 3 students were
more likely than Tier 1 to utilize paid sources.
The higher use of institutional resources by Tier 1 students can be construed either as
a marker of higher motivation or as a side-effect of greater wealth. However, as noted
earlier, Tier 1 students are not, on average, from wealthier households compared to Tier 2.
Clear differences between Tiers 1, 2 and 3 were detected only insofar as parents’ occupation
and education levels, rural education, and English-medium education were concerned.
Does having more educated or salariat parents – or being educated in an Englishmedium, big-city school – automatically result in the acquisition of superior soft skills? Or
do aspirations and motivations, information and guidance also have independent origin and
separate effects? In order to gain greater traction upon these issues, we look in the next
section at the simultaneous effect of different factors examined above, including both socioeconomic and soft-skills variables.
REGRESSION ANALYSIS
A word of caution is in order. As noted earlier, we do not have a random sample of all
Indian MBA students, neither are our tier samples proportionate to the numbers studying at
26
the corresponding quality tiers across all of India, so we have to be cautious while
interpreting the results that follow (Berk and Freedman 2003). Empirically, we have to
choose between using models that explicitly take into the account the ordering that we have
imposed (across tiers) and others that ignore this ordering. We used the multinomial logit
model (MNLM) since it treats the different tiers as nominal categories that are qualitatively
different, without imposing an order based on any underlying construct that can be
measured quantitatively (Argesti 2010). Moreover, unlike ordinal models, nominal model
allow more flexibility, by not imposing a specific way in which outcomes are associated with
the factors of interest.20 Formally, the MNLM is used to model the relative probabilities of
studying in one tier compared to another as a function of diverse covariates, which are
drawn from the preceding discussion. Other than their sign, the coefficients of a MNLM are
hard to interpret. We are further constrained in giving these coefficients substantive
meaning, because of the way in which our sample was constructed. Therefore, the results are
reported in terms of “relative risk ratios” (RRR) -- a commonly reported measure when
using multinomial logistic models.21 The RRR is an estimate of how the relative probabilities
of studying in different tiers change alongside a unit change in the value of the associated
independent variable. For example, in the case of a binary variable like gender and keeping
the conditioning on other covariates implicit, the RRR is equal to
𝐹𝑒𝑚𝑎𝑙𝑒
𝑅𝑅𝑅2,1
=
𝑃𝑟(𝑇𝑖𝑒𝑟 = 2| 𝐹𝑒𝑚𝑎𝑙𝑒 = 1, 𝒙𝒊 ′) 𝑃𝑟(𝑇𝑖𝑒𝑟 = 2| 𝐹𝑒𝑚𝑎𝑙𝑒 = 0, 𝒙𝒊 ′)
⁄
𝑃𝑟(𝑇𝑖𝑒𝑟 = 1| 𝐹𝑒𝑚𝑎𝑙𝑒 = 1, 𝒙𝒊 ′) 𝑃𝑟( 𝑇𝑖𝑒𝑟 = 1| 𝐹𝑒𝑚𝑎𝑙𝑒 = 0 , 𝒙𝒊 ′)
27
where 𝒙𝒊 ′: refers to the set of covariates other than gender. In a model with just two tiers,
this would be equivalent to an odds ratio and is independent of the sampling proportions
from the different tiers. In our models, with three outcomes (Tier 2 v Tier 1; Tier 3 v Tier 1;
and Tier 3 v. Tier 2), the RRRs provide an estimate of the extent to which a one-unit change
in an independent variable multiplies the relative risk of studying in the comparison tier
compared to the base tier. A value of 1 indicates that the relative risk associated with being
in the comparison and base tiers are identical. A value greater than 1 suggests a positive
association of being in the comparison tier rather than the base tier, while a value less than 1
indicates the opposite association. Table 6 provides a description of the different
independent variables employed for this analysis, corresponding to the different influences
explored above.
-
Table 6 about here -
Our baseline model – presented in the first three data columns of Table 7 (under the
heading “socio-economic”) considers only a subset of independent variables, related to
demographic characteristics and socio-economic status. These are the more easily measured
variables, typically utilized in analyses of social mobility. The particular variables considered
here relate to gender, religion, caste,22 parents’ occupation, asset ownership, and rural origin
of parents.
-
Table 7 about here -
After estimating our baseline (or reduced-form) model, we looked at several other
specifications. For the sake of brevity and since these representations are most illustrative,
28
we report results only from two further sets of models.23 The second set of models (reported
under the heading “+parents”) added to the variables considered earlier two others that are
related to fathers’ and mothers’ education. Given our interests in exploring intergenerational
educational mobility we have presented these results separately. The third set of models
(under “+soft skills”) add a further battery of variables related, respectively, to infrastructure
availability, nature of schools attended, migration, role models, aspirations, guidance and
information. Considering this series of results, we report below how the associations for the
basic set of socio-demographic variables change as we added more variables to the
regression model.
The results show that the likelihood of a female candidate finding a place is higher in
Tiers 2 and 3 compared to Tier 1. These associations do not change considerably even after
other variables are added to the model. Similarly, belonging to the majority religion (Hindu) is
consistently associated with a higher likelihood of being in Tier 2 or Tier 3 compared to Tier
1. On the other hand, the adjusted relative risk for OBC students studying in Tier 1 is larger
than that of studying in Tier 2 but smaller than Tier 3.
Parents’ and occupations and household wealth
The variable that we use to measure father’s occupation is whether or not he belonged to the
salariat (as defined above). We also considered whether the respondent’s mother worked
outside the household. In the first and most basic set of regression models, having a mother
who is employed outside the house is negatively associated with studying in a Tier 3 school.
However, this association seems to arise largely because less educated mothers are also more
29
likely to be homemakers in our sample, and mother’s working status seems to have no
independent influence on quality of educational outcome once parental education is
controlled for in the later set of models. In contrast, father’s membership of the salariat class
is a consistent differentiator between Tier 1 students and those of lower-quality tiers,
remaining significant even after other variables are added to the model. The significance of
father’s occupation in differentiating between Tier 2 and Tier 3 students disappears once
parent’s educational levels and the nature of the town where they grew up are added to the
model.
Confirming what was noted above, economic wealth does not suffice to buy you
entry to a Tier 1 institution. Consistently across different specifications of the regression
model, the economically best-off respondents do not have any advantage (and to the
contrary are disadvantaged) in getting into a Tier 1 school compared to the middle economic
groups (the omitted category). This does not imply that household economic status does not
matter at all. Contingent on being at a MBA-granting institution, household wealth can be a
significant differentiator across different tiers. But these effects are confounded by the
association between the socio-economic status of households and the nature of student’s
home towns. Without controlling for the type of schooling, geographical location, and other
mediating variables – i.e., looking at results in the column headed “+parents” – the relative
risk of studying in a Tier 2 school for those in the wealthiest category (as opposed to the
middle category) is 3.9 times the similar “risk” of studying in a Tier 1 school. The RRR
associated with the wealthiest category for even Tier 3 schools compared to Tier 1 schools is
30
1.693 and statistically significant in the basic model. However, once school type and other
variables are introduced (under the columns headed “+soft skills”), the advantage that the
most economically well-off students enjoy (vis-à-vis those in the middle economic category)
in avoiding Tier 3 schools is no longer statistically significant.
While we are unable to explain the origins of the disadvantage that the wealthiest
experience in gaining admittance to a Tier 1 school, we are able to go further in relation to
the poorest category examined here. The absolute magnitude of the baseline RRR (1.912)
for the comparison between the poorest and middle category indicates a positive association
with studying in a Tier 3 compared to a Tier 1 institution. However, once we control for the
infrastructure available in the town where the respondent grew up (result not shown), there
is no statistically significant difference associated with belonging to the poorest group in our
sample. A similar loss of significance is observed in the case of the comparison between
Tiers 2 and 3, indicating that the disadvantage experienced by poorer students essentially
seems to stem from the fact that such students are also more likely to come from places that
have poorer infrastructure. In fact, the poorest category suffer no significant disadvantage
whatsoever once a latter battery of variables is brought into play.
The importance of parents’ education is evident from the effect that its inclusion has
upon the associations with other independent variables. Individually, the education levels of
both mothers and fathers are significant differentiators between Tier 3 students and those at
higher tiers but not between the top two tiers. This association did not disappear even when
31
other variables are controlled suggesting that that parent’s educational attainment confers an
advantage in terms of moving up to Tier 2 from Tier 3, but not any further.
Parents rural origins and geographical mobility
While mother’s rural origins has no independent association with the quality of outcome,
fathers’ rural origins have a positive association with being in a Tier 1 school compared to
other tiers, as well as being in a Tier 3 compared to a Tier 2 school, although the last of these
relationships loses significance in the “+soft skills” columns. These results once again show
how geographic mobility has been a precursor of social mobility, with rural-origin fathers
moving to cities, particularly within the first 15 years of a respondent’s life. The variable
associated with families migrating to a bigger town during the first 15 years of the
respondent’s life is positively associated with landing up in a Tier 1 or Tier 2 school. These
associations remained statistically significant in other specifications of the model.
Type of K-12 schooling
Compared to having at least some private schooling, not having studied in a private school at
all, has a negative association with making it to a Tier 1 school. However, studying in private
schools throughout seems to confer no independent advantage. Surprisingly, neither does
the length of time spent in schools where English is the first language or medium of
instruction. In contrast, a variable that sharply distinguishes Tier 3 respondents from other
tiers is not having studied in a school governed by the syllabus and rules of the Central
Board – a reasonable proxy for school quality that seems to dominate all other school-
32
related attributes. School quality, in general, and not just medium of instruction, makes a
consistent difference.
Soft Skills
Controlling for the acknowledgement of a story about upward mobility serving as a source
of inspiration and motivation, Tier 1 students are significantly different from others in both
acknowledging being inspired by a story about a “well-known” person (as opposed to that of
a personal acquaintance or relative) and of hearing this story earlier in their lives. There are
no significant differences between Tiers 2 and 3 on this dimension. As reported earlier, there
appears to be a clear gradient in terms of aspiration levels and quality of educational
outcomes. Aspiring more than others (as opposed to less or the same) and aspiring for
specific colleges by the 10th grade are both positively and significantly associated with finding
a place in Tier 1.
Interestingly, the addition of variables measuring role models and aspirations did not
by itself affect the estimated coefficients for other variables included in the model, indicating
that aspirations and role models do not simply represent pathways through which other
influences tend to operate. Instead, our models suggest that one does not need to come
from an advantaged socio-economic status to have higher aspirations, be motivated early in
life, and have more motivating role models – all of which independently assist with obtaining
better educational outcomes.
Further, as discussed earlier, respondents from different tiers differ in the number
and type of sources consulted for guidance and information. Tier 1 respondents are much
33
more likely to seek guidance and to do it from a diverse range of sources, including from
teachers. The regression analysis suggests that this association is also statistically significant
and independent from other likely correlates. Providing career information and guidance can
also be considered as independent policy interventions that have the effect of equalizing
opportunity and raising social mobility, especially among poorer individuals and marginalized
communities, whose ability to access alternative and paid-for resources (such as coaching
institutes) is more constrained.
OVERCOMING MULTIPLE SOCIO-ECONOMIC DISADVANTAGES
People from disadvantaged social origins – poorer households, less educated parents, rural
and vernacular-medium schools, and ritually low castes – tend to be excluded from MBA
programs. However, this exclusion is far from complete. Although they constitute a small
proportion of all students, their presence within business schools, including Tier 1 and Tier 2
institutions, shows evidence of demonstrated upward mobility. Moreover, initially
disadvantaged students end up joining MBA programs of different quality. The question we
turn to next is what takes students down different journeys, in particular what factors seem
to be associated with covering greater distances between origins and destinations along these
journeys of upward mobility?
Using four aspects of social origin: economic status, parents’ education, rural origin,
and ritually low-caste origins, we developed a composite score based on the number of
disadvantages experienced, counting as one each particular aspect of disadvantage.
34
Disadvantage in economic status is measured as growing up in households with fewer than
five assets; in relation to parents’ education it is measured as fathers with less than college
education; disadvantage in terms of rural origin is scored 1 if the respondent spent the first
five years of her or his life in a village; and SCs, STs, and OBCs score 1 in relation to the
fourth aspect, with everyone else scoring zero.
Hardly any student experienced all four types of disadvantage, although there are
many whose disadvantage score is 3. Table 8 shows the distribution of aggregate
disadvantage scores. For ease in presentation, we combine the numbers for Tiers 1 and 2,
contrasting these scores with the corresponding disadvantage scores for Tier 3 students.
-
Table 8 about here -
Overcoming multiple disadvantages requires some combination of working harder,
geographic mobility, higher motivation and aspirations, more information and better
guidance, and greater external support in the form of financial assistance and affirmative
action policies. On each of these dimensions, more disadvantaged students score higher than
less disadvantaged ones.
Among those who made it to Tier 1 and 2 schools, more disadvantaged schools have
higher 10th grade scores compared to less disadvantaged ones. A greater percentage has work
experience. Encouragingly, these results show that hard work can help overcome liabilities
associated with relative poverty and rural origin.
35
But hard work is rarely enough by itself. Other factors matter as well. A greater
proportion of more disadvantaged students moved along with their families from villages to
cities, leaving behind at least one source of disadvantage, and in the process, making it
possible for themselves to gain better soft skills.
More disadvantaged students have higher aspirations, especially within Tiers 1 and 2.
They are also more motivated by publicly known examples who serve as role models, having
come under the influence of these examples relatively early in their lives.
It is necessary for more disadvantaged students to look across multiple sources for
inspiration, information, and advice. Since their parents, being less educated, are less likely to
provide information-rich career guidance, disadvantaged students who end up being
successful tend to rely upon a greater variety of information and guidance resources.
Outside assistance in the form of financial aid helps with the hard work and other
efforts that disadvantaged students need to put in. Across tiers, the more disadvantaged are
more likely to have received financial assistance. Our case studies, not presented here for
lack of space, showed how it is almost impossible for those from more disadvantaged
backgrounds to make it to a MBA program without financial assistance. Twelve of 15 Tier 1
students who experienced at least three of these four disadvantages, received some form of
financial assistance, with the majority, 56 percent, receiving such assistance from the
government or public institutions.
Affirmative action policies have also helped. As we saw earlier, the share of SC and
ST students is highest in the Tier 1 institution – a counter-intuitive finding that cannot be
36
explained except by referring to affirmative action. More closely observed in the Tier 1
institution, which is state managed, affirmative action policies have resulted in SC and ST
students becoming, respectively, 10.2 percent and 4.7 percent of all Tier 1 students, far
higher than in Tiers 2 and 3.
The fact that these students have worked hard for their places, achieving higher 10 th
grade scores, on average, compared to other and less-disadvantaged students, is an
encouraging fact. But it leaves open a question about the existence of other disadvantaged
students who are also smart and hard-working but whose disadvantages were not
compensated for either by geographic mobility, or by access to guidance and role models, or
through the provision of external assistance. Are some of India’s most productive human
resources not getting their fair share of opportunities, resulting in widespread losses not only
in terms of social justice but also in relation to aggregate economic gains?
CONCLUSION
In most modern societies education has been regarded as a means for social mobility, and
the state has acted to facilitate both education and opportunity. The reality in India has not
historically matched the rhetoric. Weiner’s (1991) argument that “in India, education has
been largely an instrument for differentiation by separating children according to social
class,” a harsh indictment, has not been easy to shake off in subsequent years.
Our analysis of MBA students provides a glass half-empty (or half-full) perspective,
depending upon which parts are emphasized. Compared, for example, with the position in
37
the 1960s, examined by Rajagopal and Singh (1968), the current picture is much better. From
zero the share of women has climbed to 33 percent. Similarly, the share of SCs and STs, also
zero in the 1960s, has risen, especially within those business schools that more fully abide by
the state’s affirmative action quotas.
Parental wealth matters, but not as much as is sometimes believed, certainly there is
no one-to-one relationship between wealth and nature of institution attended. Individuals
from less well-endowed households have also gained entry to business schools, albeit still in
low numbers. It is interesting to note that middle-wealth categories are better represented,
especially within top-tier schools, and that the share of the wealthiest is higher among lowertier institutions.
“Once children’s basic material needs are met, characteristics of their parents become
more important to how they turn out than anything that additional money can buy”(Mayer
1997:12). The influence of factors such as role models and aspirations on educational
outcomes has hitherto received some attention in richer countries but little or no attention
within the developing world. As attention shifts to higher levels of educational attainment,
our results highlight the need to more intensively examine what we have termed “soft skills.”
Aspirations, role models, information and guidance matter separately from socioeconomic factors, and in fact, help overcome socio-economic disadvantages, which can hold
back so many talented individuals. Inequalities of wealth and social status are hard to
overcome, particularly over the short- to medium-term. Enhancing inequality of opportunity
38
can be assisted by devoting greater policy attention, backed by additional research, to such
soft skills.
39
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46
Table 1: Religious composition
(percentage of all respondents)
Hindu
Muslim
Christian
Buddhist
Sikh
Atheist/Agnostic
Do not wish to respond
Other
Total
Tier 1
Tier 2
Tier 3
Average
70.9
2.5
3.2
1.4
1.8
9.0
7.6
3.6
100
83.2
0.8
1.2
0
6.8
2.0
3.2
2.8
100
79.0
8.7
7.8
0.3
1.2
0.3
1.1
1.6
100
78.0
5.6
5.3
0.5
2.6
2.7
3.1
2.3
100
47
Census
2011
80.5
13.4
2.3
0.8
1.9
0.6
Table 2: Caste Composition
(percentage of all respondents)
Upper-caste Hindu
SC
ST
OBC
Other/no response
Total
Tier 1
Tier 2
Tier 3
Average
61.8
10.2
4.7
13.5
9.8
100
93.1
0
0
3.2
3.6
100
61.8
5
0.3
28.2
4.7
100
68.6
5.2
1.3
19.3
5.7
100
48
Census
2001
16.2
8.2
27.0
Table 3: Household Wealth (number of assets)
(percentage of all respondents)
Number of assets
0-4
5-6
7-8
9-10
11-12
>12
Total
Tier 1
5.4
11.4
22.5
29.3
21.8
9.6
100
49
Tier 2
3.5
4.2
10.0
13.8
28.5
40.0
100
Tier 3
12.8
11.1
15.4
18.3
23.5
18.9
100
Average
9.1
9.7
15.9
19.9
24.1
21.3
100
Table 4: Parents’ Occupations
(percentage of all respondents)
Tier 1
Tier 2
Tier 3
Average
FATHER
Self-employed professional
Government job
Military
Private sector job
Salariat*
Own business
Agriculturist
Out of work or day labor
Other
Total
6.1
55.7
2.9
17.5
82.2
12.1
1.8
0
3.9
100
6.9
38.5
1.9
15.8
63.1
34.6
1.2
0.4
0.8
100
5.5
30.9
2.2
13.4
52.0
32.8
12.3
2.2
0.7
100
5.9
38.2
2.3
14.8
61.2
28.4
7.5
1.3
1.5
100
MOTHER
Self-employed professional
Government job/military
Private sector job
Salariat*
Own business
Homemaker
Agriculturist
Other
Total
3.9
18.9
6.4
29.2
1.4
66.8
0.4
2.1
100
7.8
15.9
5.8
29.5
2.3
68.2
0
0
100
2.8
9.9
4.5
17.2
1.6
78.2
2.5
0.4
100
4.1
13.3
5.2
22.6
1.7
73.4
1.5
0.7
100
* equals the sum of the preceding categories
50
Table 5: Parents’ Education
(percentage of all respondents)
Tier 1
Tier 2
Tier 3
Average
FATHER
Ph.D
Masters
Bachelors
College degree*
Higher secondary (12 years)
High school (10 years)
Middle School
Primary
Other
Diploma
Total
6
33.5
47
86.5
7.5
2.8
0.7
0.4
0.4
1.8
100
3.1
36.8
50.8
90.7
3.1
3.9
0.4
0.8
1.2
0
100
1.9
21.6
38.5
62
17.8
11.8
4.2
2.2
1.9
0
100
3.1
27.6
43.1
73.8
12.2
8
2.6
1.5
1.4
0.4
100
MOTHER
Ph.D
Masters
Bachelors
College degree*
Higher secondary (12 years)
High school (10 years)
Middle School
Primary
Other
None
Total
6.8
28.5
37.4
72.7
12.5
8.9
3.6
1.1
0.4
1.1
100
1.6
29.1
50.4
81.1
9.3
7
1.2
1.6
0
0
100
1.2
12.9
29.5
43.6
20.7
19.2
8.7
6
1.8
0
100
2.6
20
35.8
58.4
16.3
14.2
5.9
3.9
1.1
0.2
100
* equals the sum of the preceding categories
51
Table 6: Independent Variables
Variable
female
hindu
obc
fsalar
mwork
fbach
Mean
0.319
0.743
0.179
0.606
0.262
0.727
S.D.
0.466
0.437
0.384
0.489
0.44
0.446
0.573
0.188
0.454
6.928
0.495
0.391
0.498
3.099
frur
mrur
pvtschnone
pvtschall
engfnone
Mother completed bachelor’s degree
0-6 Assets
> 10 Assets
Count of available infrastructure in home town (national highway, state
highway, district road; medical college, hospital, clinic; university, high school,
middle school)
Father grew up in rural location
Mother grew up in rural location
No private schooling
All private schools
Never studied in a school with English as first language/medium of instruction
0.33
0.284
0.133
0.658
0.208
0.471
0.451
0.339
0.475
0.406
engfall
Always studied in schools with English as first language/medium of instruction
0.616
0.487
cenboard
Graduated Tenth Grade from Central Board of Secondary Education (one
indicator of school quality)
0.503
0.5
movecityacad Moved location for academic reasons
0.163
0.369
migup
e5story
wellknownst
earlystory
aspcol
Migrated from smaller to larger town before 15 years of age
Motivated by a particular story of some individual’s upward mobility
Motivated by a story of a well-known person
Heard story before or during middle school
Aspired for a specific college in Tenth grade
0.125
0.719
0.14
0.168
0.168
0.33
0.45
0.348
0.374
0.374
aspmore
Aspired to achieve more than others in one’s neighborhood
0.79
0.407
indgud
Obtained career guidance from parents/friends/relatives
0.903
0.296
indinf
instigud
instinf
Obtained information about colleges and jobs from parents/friends/relatives
Obtained career guidance from TV, radio, news, internet
Obtained information about colleges from TV, radio, internet
0.897
0.322
0.8
0.305
0.467
0.4
paidgud
Obtained career guidance from private training institutes
0.13
0.337
paidinf
Obtained information about colleges and jobs from private institute
0.456
0.498
gudteach
c1infteach
Obtained career guidance from teacher
Obtained information about colleges and jobs from teacher
0.485
0.585
0.5
0.493
mbach
ass3cat_1
ass3cat_3
numtotinfra
Description
Female
Hindu
Other Backward Caste
Father is a salaried employee (public or private sector)
Mother works outside the house
Father completed bachelor’s degree
52
Table 7: Multinomial Linear Logistic Regression Results
socio-economic
VARIABLES
female
hindu
obc
fsalar
mwork
fbach
mbach
ass3cat_1
ass3cat_3
numtotinfra
frur
mrur
pvtschnone
pvtschall
engfnone
engfall
cenboard
movecityacad
migup
e5story
wellknownst
earlystory
aspcol
aspmore
indgud
indinf
instigud
instinf
paidgud
paidinf
gudteach
c1infteach
Constant
Observations
ll
df_m
chi2
+parents
T2 v T1
2.259***
1.887***
0.292***
0.493***
0.981
T3 v T1
2.992***
1.450**
2.295***
0.283***
0.619***
T3 v T2
1.324
0.768
7.854***
0.574***
0.631**
1.137
3.927***
1.912***
1.693***
0.360***
0.875
0.519**
(0.167)
1,040
+ soft skills
1.682
0.431***
T2 v T1
2.167***
1.867***
0.285***
0.527***
1.072
1.267
0.806
1.178
3.960***
T3 v T1
3.536***
1.518**
1.706**
0.364***
0.887
0.496***
0.313***
1.724**
1.995***
T3 v T2
1.632***
0.813
5.986***
0.692*
0.828
0.392***
0.388***
1.464
0.504***
0.608**
1.396
1.690**
1.596*
0.360***
0.872
0.615**
0.887
1.709**
1.017
2.029***
(0.517)
1,040
-914.0
18
303.1
3.909***
(1.109)
1,040
0.466*
(0.195)
1,040
5.534***
(1.720)
1,040
-872.2
22
386.7
11.87***
(4.335)
1,040
*** p<0.01, ** p<0.05, * p<0.1
53
T2 v T1
2.244***
1.958***
0.297***
0.554**
1.036
1.011
0.693
0.998
3.440***
0.889**
0.453***
0.905
2.521*
1.195
0.560
0.996
1.188
0.740
0.368***
1.394
0.581*
0.384***
0.612*
0.445**
0.216***
0.475
0.260***
1.732*
0.296***
2.533***
0.474***
0.839
33.12***
(33.33)
1,040
T3 v T1
3.711***
1.451
1.694*
0.460***
0.783
0.475**
0.349***
0.895
2.674***
0.705***
0.585**
0.966
4.053***
1.192
0.916
1.059
0.228***
0.716
0.435***
1.494
0.558**
0.561**
0.403***
0.171***
0.437*
0.344**
0.325***
2.395***
0.394***
1.428
0.960
0.644*
3,378***
(3,129)
1,040
-661.3
64
808.5
T3 v T2
1.654**
0.741
5.698***
0.830
0.756
0.470**
0.504***
0.897
0.777
0.793***
1.290
1.067
1.608
0.997
1.636
1.062
0.192***
0.967
1.182
1.072
0.961
1.463
0.657
0.384***
2.029*
0.724
1.251
1.383
1.333
0.564**
2.024***
0.767
102.0***
(84.50)
1,040
Table 8: Overcoming Disadvantages
(percentage of respondents)
Tiers 1&2
Disadvantage Score 
0
1
2
Tier 3
>=3
0
1
2
>=3
SC
0.0
10.8 27.8
18.8
0.0
4.1
6.7
15.1
ST
0.0
6.5
8.3
6.3
0.0
0.0
0.7
1.2
OBC
0.0
19.4 27.8
50.0
0.0
23.9 46.3 70.9
Percentage score in 10th grade board exam 81.9 82.9 83.3
86.3
67.9 67.8 64.8 63.6
Had previous job
43.0 49.2 51.4
62.5
21.7 25.7 22.9 23.0
Moved home town
14.9 20.1 25.0
37.5
10.8 14.7 19.4 23.3
Aspired more than neighbours
86.7 91.9 97.2 100.0
76.9 65.3 66.1 70.1
“Well-known” story
16.4 17.2 22.2
37.5
9.1
Heard story before 8th grade
14.1 22.4 30.6
56.3
15.1 13.3 15.7 19.4
Parents
70.8 54.8 36.1
25.0
73.3 67.0 61.4 54.8
Friends
9.7
14.1 27.8
18.8
8.3
7.1
10.2 21.4
Relatives
5.7
7.4
11.1
12.5
3.7
5.2
11.8
6.0
Teachers
5.2
11.9
8.3
18.8
8.7
13.2
9.4
13.1
Newspapers
1.7
2.2
5.6
18.8
0.4
0.5
0.8
0.0
29.7 38.1 44.4
75.0
16.9 22.5 20.1 38.4
8.3
14.4 17.8
Primary source of guidance
Received financial assistance
54
NOTES
1
Bolshaw,
L. “Push to Help Women find the Keys to the C-suite.” Financial Times, November 21,
2011.Retrieved from http://www.ft.com/cms/s/2/23b91ca8-0ee0-11e1-b58500144feabdc0.html#axzz1fAbUCUcd
2
Report of the Working Group on Management Education of the National Knowledge
Commission, established by the Prime Minister of India in 2005. Available at
http://www.knowledgecommission.gov.in/downloads/documents/wg_managedu.pdf.
3
See, for instance, Bowles and Gintis (2002); Corak (2004); Erickson and Goldthorpe (1992, 2002);
Hout (2006); Hout and DiPrete (2006); Jantti, et al. (2005); Morgan (2006); OECD (2010); Roemer
(2004); Solon (2002); and Smeeding (2005).
4
See, for example, Behrman, Birdsall, and Szekely (2001); ECLAC (2007); Paxson and Schady
(2005); Scott and Litchfield (1994); and Trzcinski and Randolph (1991).
5
See, for example, Bourdieu (1986); Currie (2001); Danziger and Waldvogel (2005); DiMaggio
(1982); Esping-Andersen (2004); Hannum and Buchmann (2005); and Mayer (1997).
6
See, for example, Behrman, Birdsall and Szekely (2001); Birdsall and Graham (2000); Castaneda
and Aldaz-Carroll (1999); Graham (2000); Grawe (2004); Moser (2009); Perlman (2011); and
Quisumbing (2006).
7
Scheduled Castes (SCs, former untouchables) and Scheduled Tribes (STs, roughly translatable to
India’s indigenous people) are historically deprived groups, whose representation in institutions of
higher learning has remained low despite affirmative action. No more than 1.4 percent of all SCs and
0.9 percent of all STs are estimated to have post-graduate or professional degrees, with these tiny
55
percentages falling further among women and poorer segments of these groups (Deshpande and
Yadav 2006).
8
One such story that attracted a great deal of public attention was reported with the provocative
title: “Your Birthplace, Background Don’t Determine Your Success.” Retrieved June 27, 2012, from
http://www.rediff.com/getahead/slide-show/slide-show-1-achievers-vikas-khemani-yourbirthplace-background-don-t-determine-your-success/20120626.htm
9
A fuller description of this test, as well as details about the innovative company, Aspiring Minds,
that has designed and which administers this test, are available at the web site: www.aspiringminds.in
10
This range of response rates is more than the average achieved in surveys of this kind. The average
response rate for online surveys is around 34 percent, according to Cook, et al. (2000).
11
“Why are there so few women managers in India?” Reported on October 6, 2006 at
http://www.rediff.com/money/2006/oct/06guest.htm
12
This committee, popularly known as the Sachar Committee, also advanced useful suggestions for
remedying this pathology. See http://minorityaffairs.gov.in/sachar.
13
The implementation of quotas for OBCs was being commenced at the time when these data were
collected.
14
To examine the nature and extent of the non-response bias, we compared values on other non-
missing attributes for the group missing their caste status to those disclosing their caste status. The
results of this comparison supported the view about a greater non-response on the caste variable
among the SC, ST, and OBC categories.
15
Incomes are particularly hard to recall accurately, especially in rural contexts where seasonality can
result in considerable fluctuations. Following Brandolini et al. (2010) and Carter and Barrett (2006),
56
we preferred to examine households’ usual (or structural) material conditions using asset ownership
as our measure.
16
We also used principal component analysis to create other asset-based indices, weighted in
different ways. However, the correlation of these indices with the simple count of the total number
was > 0.95 in each case, reinforcing our preference for using the simpler and more intuitive
measure.
17
These numbers do not include Kendriya Vidyalas (Central Schools or KVs), elite government
schools created primarily to serve the children of central government employees, especially those
who are relocated frequently. Including KVs does not change the reported proportions substantially:
there are only 19 students in our sample who studied in a KV throughout and 51 who studied for
one year or more.
18
We asked for information on the availability of the following infrastructure in the town or village
where the respondent grew up: national highway, state highway, district road; medical college,
hospital, clinic; university, college, high school and middle school.
19
This is in line with the use of “plans for college” question used by studies like Buchmann and
Hannum (2001) to measure aspirations among adolescents.
20
We experimented with using Stata’s cluster option to account for non-independence of
observations among students from the same institution. In all cases where there was a change in
statistical significance, this change was in the direction of smaller standard errors and therefore
finding a larger group of variables that were statistically significant. Since, there is no consensus in
the literature of the appropriateness of using correction in models estimated using Maximum
57
Likelihood (Freedman 2006), we take the more conservative approach and present results that do
not correct for clustering.
21
Multinomial logit models were estimated using the mlogit command in Stata (Version 11).
22
Since there are no SC and ST students in Tier 2, and since several minority religions are also
under-represented, we could not include these variables in the regression analyses.
23
Readers interested in viewing additional regression results can obtain them on request from the
authors.
58