Management Education in India: Avenue for Social Stratification or Social Mobility?

Anirudh KrishnaProfessor of Public Policy and Political Science

Duke UniversityDurham, NC 27708-0245, USA

+1 (919) 613-7337ak30@duke.edu

and

Ankur SarinAssistant Professor, Public Systems Group

Indian Institute of Management (Ahmedabad)Ahmedabad, Gujarat 380015, India

+91 79-66324953asarin@iimahd.ernet.in

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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.

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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 3

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.

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 4

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 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

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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. Researchers have found, for

instance, that “IQ cannot explain why children from less-privileged 6

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).

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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 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 8

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 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 9

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 al. (2002a: 2985) concluded that “it is clear

that for many people there has been long-range upward mobility from 10

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.

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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

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 12

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

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 13

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

in the world.” In the Indian context, religious and caste group can

make an additional difference (Deshpande and Yadav 2006).14

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 15

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 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 non-

professional 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

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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 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 17

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 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 18

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.

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 19

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 non-monotonically 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 20

also gained entry, albeit in numbers much lower than their proportion

shares; and (b) higher wealth provides no assurance of higher-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 21

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

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.

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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 college-educated fathers and mothers

is higher among Tier 1 and 2 institutions – and considerably lower in

Tier 3. Table 5 reports these numbers.

- 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 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 23

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) during the first 15 years of

their lives. This big-city exposure clearly separates them from their 24

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

medium-sized 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

25

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 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 26

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

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 27

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 11-14 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, guidance centers, coaching

classes, and higher-aspiring peers – notably missing from all but a few 28

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.”

29

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 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. 30

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 well-

known 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 “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. 31

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

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 32

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 English-medium, 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 socio-economic 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 33

samples proportionate to the numbers studying at 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 34

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

RRR2,1Female=

Pr (Tier=2|Female=1 , x i ' ¿Pr (Tier=1|Female=1, x i ' ¿

¿¿/Pr (Tier=2|Female=0 , x i ' ¿Pr (Tier=1|Female=0 , x i ' ¿

¿¿

where x i ': 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

35

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, 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 36

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 likely to be

homemakers in our sample, and mother’s working status seems to 37

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 38

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 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 39

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 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 40

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-related attributes. School quality, in general, and not

just medium of instruction, makes a consistent difference.

Soft Skills41

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.

42

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 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 43

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. 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.44

- 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.

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

45

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 explained except by referring 46

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 10th 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 47

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 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 lower-tier 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 48

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 socio-economic 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 can be assisted by devoting greater policy

attention, backed by additional research, to such soft skills.

49

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59

Table 1: Religious composition(percentage of all respondents)

Tier 1

Tier 2

Tier 3

Average

Census

2011Hindu 70.9 83.2 79.0 78.0 80.5Muslim 2.5 0.8 8.7 5.6 13.4Christian 3.2 1.2 7.8 5.3 2.3Buddhist 1.4 0 0.3 0.5 0.8Sikh 1.8 6.8 1.2 2.6 1.9Atheist/Agnostic 9.0 2.0 0.3 2.7Do not wish to respond 7.6 3.2 1.1 3.1Other 3.6 2.8 1.6 2.3 0.6Total 100 100 100 100

60

Table 2: Caste Composition(percentage of all respondents)

Tier 1

Tier 2

Tier 3

Average

Census

2001Upper-caste Hindu 61.8 93.1 61.8 68.6SC 10.2 0 5 5.2 16.2ST 4.7 0 0.3 1.3 8.2OBC 13.5 3.2 28.2 19.3 27.0Other/no response 9.8 3.6 4.7 5.7Total 100 100 100 100

61

Table 3: Household Wealth (number of assets)(percentage of all respondents)

Number of assets

Tier 1

Tier 2

Tier 3

Average

0-4 5.4 3.5 12.8 9.15-6 11.4 4.2 11.1 9.77-8 22.5 10.0 15.4 15.99-10 29.3 13.8 18.3 19.911-12 21.8 28.5 23.5 24.1>12 9.6 40.0 18.9 21.3Total 100 100 100 100

62

Table 4: Parents’ Occupations(percentage of all respondents)

Tier 1

Tier 2

Tier 3

Average

FATHERSelf-employed professional 6.1 6.9 5.5 5.9Government job 55.7 38.5 30.9 38.2Military 2.9 1.9 2.2 2.3Private sector job 17.5 15.8 13.4 14.8Salariat* 82.2 63.1 52.0 61.2Own business 12.1 34.6 32.8 28.4Agriculturist 1.8 1.2 12.3 7.5Out of work or day labor 0 0.4 2.2 1.3Other 3.9 0.8 0.7 1.5Total 100 100 100 100

MOTHERSelf-employed professional 3.9 7.8 2.8 4.1Government job/military 18.9 15.9 9.9 13.3Private sector job 6.4 5.8 4.5 5.2Salariat* 29.2 29.5 17.2 22.6Own business 1.4 2.3 1.6 1.7Homemaker 66.8 68.2 78.2 73.4Agriculturist 0.4 0 2.5 1.5Other 2.1 0 0.4 0.7Total 100 100 100 100

* equals the sum of the preceding categories

63

Table 5: Parents’ Education(percentage of all respondents)

Tier 1

Tier 2

Tier 3

Average

FATHERPh.D 6 3.1 1.9 3.1Masters 33.5 36.8 21.6 27.6Bachelors 47 50.8 38.5 43.1College degree* 86.5 90.7 62 73.8Higher secondary (12 years) 7.5 3.1 17.8 12.2High school (10 years) 2.8 3.9 11.8 8Middle School 0.7 0.4 4.2 2.6Primary 0.4 0.8 2.2 1.5Other 0.4 1.2 1.9 1.4Diploma 1.8 0 0 0.4Total 100 100 100 100

MOTHERPh.D 6.8 1.6 1.2 2.6Masters 28.5 29.1 12.9 20Bachelors 37.4 50.4 29.5 35.8College degree* 72.7 81.1 43.6 58.4Higher secondary (12 years) 12.5 9.3 20.7 16.3High school (10 years) 8.9 7 19.2 14.2Middle School 3.6 1.2 8.7 5.9Primary 1.1 1.6 6 3.9Other 0.4 0 1.8 1.1None 1.1 0 0 0.2Total 100 100 100 100* equals the sum of the preceding categories

64

Table 6: Independent VariablesVariable Description Mea

nS.D.

female Female 0.319

0.466

hindu Hindu 0.743

0.437

obc Other Backward Caste 0.179

0.384

fsalar Father is a salaried employee (public or private sector) 0.606

0.489

mwork Mother works outside the house 0.262

0.44

fbach Father completed bachelor’s degree 0.727

0.446

mbach Mother completed bachelor’s degree 0.573

0.495

ass3cat_1 0-6 Assets 0.188

0.391

ass3cat_3 > 10 Assets 0.454

0.498

numtotinfra

Count of available infrastructure in home town (national highway, state highway, district road; medical college, hospital, clinic; university, high school, middle school)

6.928

3.099

frur Father grew up in rural location 0.33 0.471

mrur Mother grew up in rural location 0.284

0.451

pvtschnone

No private schooling 0.133

0.339

pvtschall All private schools 0.658

0.475

engfnone Never studied in a school with English as first language/medium of instruction

0.208

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 Migrated from smaller to larger town before 15 years of age 0.125

0.33

e5story Motivated by a particular story of some individual’s upward mobility

0.719

0.45

wellknownst

Motivated by a story of a well-known person 0.14 0.348

earlystory Heard story before or during middle school 0.16 0.3765

8 4aspcol Aspired for a specific college in Tenth grade 0.16

80.37

4aspmore Aspired to achieve more than others in one’s neighborhood 0.79 0.40

7indgud Obtained career guidance from parents/friends/relatives 0.90

30.29

6indinf Obtained information about colleges and jobs from

parents/friends/relatives0.89

70.30

5instigud Obtained career guidance from TV, radio, news, internet 0.32

20.46

7instinf Obtained information about colleges from TV, radio,

internet0.8 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 Obtained career guidance from teacher 0.485

0.5

c1infteach

Obtained information about colleges and jobs from teacher 0.585

0.493

66

Table 7: Multinomial Linear Logistic Regression Resultssocio-economic +parents + soft skills

VARIABLEST2 v T1

T3 v T1

T3 v T2 T2 v T1

T3 v T1

T3 v T2

T2 v T1

T3 v T1

T3 v T2

female2.259**

*2.992*

** 1.3242.167**

*3.536*

**1.632*

**2.244*

**3.711*

**1.654*

*

hindu1.887**

*1.450*

* 0.7681.867**

*1.518*

* 0.8131.958*

** 1.451 0.741

obc0.292**

*2.295*

**7.854*

**0.285**

*1.706*

*5.986*

**0.297*

** 1.694*5.698*

**

fsalar0.493**

*0.283*

**0.574*

**0.527**

*0.364*

** 0.692*0.554*

*0.460*

** 0.830

mwork 0.9810.619*

**0.631*

* 1.072 0.887 0.828 1.036 0.783 0.756

fbach 1.2670.496*

**0.392*

** 1.0110.475*

*0.470*

*

mbach 0.8060.313*

**0.388*

** 0.6930.349*

**0.504*

**

ass3cat_1 1.1371.912*

** 1.682 1.1781.724*

* 1.464 0.998 0.895 0.897

ass3cat_33.927**

*1.693*

**0.431*

**3.960**

*1.995*

**0.504*

**3.440*

**2.674*

** 0.777

numtotinfra0.889*

*0.705*

**0.793*

**

frur0.360**

*0.608*

*1.690*

*0.360**

*0.615*

*1.709*

*0.453*

**0.585*

* 1.290mrur 0.875 1.396 1.596* 0.872 0.887 1.017 0.905 0.966 1.067

pvtschnone 2.521*4.053*

** 1.608pvtschall 1.195 1.192 0.997engfnone 0.560 0.916 1.636engfall 0.996 1.059 1.062

cenboard 1.1880.228*

**0.192*

**movecityacad 0.740 0.716 0.967

migup0.368*

**0.435*

** 1.182e5story 1.394 1.494 1.072

wellknownst 0.581*0.558*

* 0.961

earlystory0.384*

**0.561*

* 1.463

aspcol 0.612*0.403*

** 0.657

aspmore0.445*

*0.171*

**0.384*

**

indgud0.216*

** 0.437* 2.029*

indinf 0.4750.344*

* 0.724

67

instigud0.260*

**0.325*

** 1.251

instinf 1.732*2.395*

** 1.383

paidgud0.296*

**0.394*

** 1.333

paidinf2.533*

** 1.4280.564*

*

gudteach0.474*

** 0.9602.024*

**c1infteach 0.839 0.644* 0.767

Constant 0.519**2.029*

**3.909*

** 0.466*5.534*

**11.87*

**33.12*

**3,378*

**102.0*

**(0.167) (0.517) (1.109) (0.195) (1.720) (4.335) (33.33) (3,129) (84.50)

Observations 1,040 1,040 1,040 1,040 1,040 1,040 1,040 1,040 1,040ll -914.0 -872.2 -661.3df_m 18 22 64chi2 303.1 386.7 808.5

*** p<0.01, ** p<0.05, * p<0.1

68

Table 8: Overcoming Disadvantages(percentage of respondents)

Tiers 1&2 Tier 3

Disadvantage Score 0 1 2 >=3 0 1 2 >=

3

SC 0.0 10.8

27.8

18.8 0.0 4.1 6.7 15.

1ST 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.

946.3

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 8.3 14.

417.8

Heard story before 8th grade 14.1

22.4

30.6

56.3

15.1

13.3

15.7

19.4

Primary source of guidance

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.

221.4

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

69

Tiers 1&2 Tier 3

Received financial assistance 29.7

38.1

44.4

75.0

16.9

22.5

20.1

38.4

70

NOTES

71

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-b585-

00144feabdc0.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 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-your-birthplace-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.

13The 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), 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.

17These 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 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.