higher education differential in india

1

Higher Education Differential along Gender and SocioeconomicDimensions in India

Chenna Reddy Cotla1,2

1 Department of Computational Social Science, Krasnow Institute for Advanced Study, George Mason

University, Fairfax, Virginia, USA

2 Interdisciplinary Center for Economic Science, George Mason University, Fairfax, Virginia, USA

E-mail: [email protected]

Abstract

Using a recent national level survey data, I have systematically investigated the pattern of higher educa-

tion differential along gender, socioeconomic and geographical dimensions in India. I have found a salient

differential in the attainment of higher education across gender subgroups, social groups and also across

the quintiles of economic variables. I have further investigated the evolution of the higher education dif-

ferential over different time frames in the recent history of India. These analyses show that while gender

based differential is fast declining over time, the social group based differential seems to be significantly

persistent.

Introduction

Individuals with higher education are a crucial component of a country’s intellectual capital. It is well

established from various studies that in India, social and economic capital is highly differentiated along

the socio religious dimensions (Desai, 2010; Desai & Amaresh, 2011; Vanneman & Dubey, 2011; Vanne-

man, Noon, & Desai, 2006). Since social and economic capital available to a household plays a crucial

role in the educational attainment of its members, we have systematically investigated how the higher

educational attainment is differentiated along socioeconomic and gender dimensions. Using one of the

most recent nationally representative survey data, the 2004-2005 Indian Human Development Survey, we

have investigated the different gender, socioeconomic and geographic dimensions along which inequalities

in the attainment of higher education exist in India. We have further investigated the way the higher

education differential across gender and socioeconomic subgroups is patterned for individuals belonging

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to different age groups. Since these age groups correspond to different time frames of potential graduation

if an individual follows the normal course of education, the way higher education differential is patterned

along gender and social subgroup dimensions over these age groups can shed light on the evolution of

higher education differential over the corresponding time frames.

Data and Methodology

Survey Data

For the current analysis, we have made use of the cross-sectional data generated from the India Human

development Survey (IHDS), 2005 (Desai et al., 2007). IHDS is a nationally representative survey con-

ducted from November 2004 to October 2005. In total, 215, 754 individuals from 41, 554 rural and urban

households were sampled in India. This multi-topic survey included a wide range of questions about

demographic details, health, education, employment, income, assets, consumption, and gender relations.

The questions about education and socioeconomic status form the basis of the results reported in this

paper.

Outcomes and Predictors

The outcome measure of the study is a dichotomous variable indicating whether an adult is a college

graduate (1) or not (0). Since the minimum age for obtaining a college degree if an individual follows the

normal course of education in India is 21 we restrict our attention to adults with age 21 or greater. An

individual is coded in the data as a college graduate if he/she has completed 15 years of education.

The main predictors include membership in a social group that is defined along the lines of caste

and religion, economic status measured by household assets, household income, and monthly per capita

consumption of the individuals, residency location, and finally membership in a age cohort that we have

formed by dividing age into five categories.

Caste and religion were identified at the household level in the IHDS survey. These social groups

are separated into Brahmin, High Caste, Other Backward Caste, Scheduled Castes, Scheduled Tribes,

Muslims, and Religious minorities including Jains, Sikhs, and Christians. We have grouped together all

the minorities into one group in our analyses.

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Household income data was collected from queries of over 50 different income sources. These sources

include eight major household income types: family farm income, household agricultural wages, non-

agricultural wages, salaries, net business income, sum household remittance, government benefits, and

property and pensions. Similar to the income data, the household assets data was constructed from

multiple queries. The total household assets is the total number of possessions of the household of 22

equally weighted consumer goods and eight aspects of housing quality. Monthly per capita consumption

of the household was constructed from a set of 47 questions. These included 30 questions on monthly

expenditure and 17 questions on annual expenditure reported for the previous year. The final consumption

was calculated as the sum of the expenditure on monthly items and one twelfth of the expenditure on

the annual items. For our analyses, we have divided household income, household assets, and monthly

per capita consumption of the household into quintiles. For the income variable, we have also created

another group for the households that have reported a negative income in the survey along with quintiles.

Along with the socioeconomic categorical variables, we have created another categorical variable by

dividing age of individuals into five groups. Age was divided into five categories to capture the time

trends in the higher education scenario: Age Cohort 1 (21 - 30 years), Age Cohort 2 (31 - 40 years), Age

Cohort 3 (41 - 50 years), Age Cohort 4 (51 - 60 years), and Age Cohort 5 (> 60 years). For individuals

following normal course of education the first four cohorts map to four 10 year time frames in which

individuals could graduate: cohort 1 (1996 - 2005), cohort 2 (1986 - 1995), cohort 3 (1976 - 1985), cohort

4 (1966 - 1975). Cohort 5 groups all individuals who could graduate before 1966 if they have followed

the standard course of education. These categorical predictors can shed light on the time trends in the

way the roles of gender and social groups have influenced the odds of obtaining higher education.

Model Specification

We have used multivariate logistic regression to model the association between probability of obtaining

a college degree at the individual level with demographic and socioeconomic predictors. The binary

response, y, (college graduate or not) for each individual is related to a set of categorical predictors,

X, (gender, age cohort, residency location, religion, caste, household income, household assets, monthly

per capita consumption). To account for unobservable state level differences that can influence the

outcome variable we have included state level fixed effects. The logit link function that related response

to predictors is then:

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logit(πi) = log(

πi

1−πi

)= β0 + βX + ε

The probability that an individual is a college graduate is πi. The parameter β0 estimates the log

odds of obtaining a college degree for the reference group and the parameters β estimate with maximum

likelihood the differential log odds of obtaining a college degree associated with the predictor X, as

compared to the reference group. All the analyses were computed using the statistical program STATA

12 ‘logit’ procedure using sampling weights to derive inferences at the national level. Finally, we have

clustered standard errors at the primary sampling unit (PSU) level to account for the correlation among

observations in a given PSU.

Results

Descriptive Statistics

A total of 118, 224 individuals were included in the analysis after excluding individuals younger than 21

years of age and individuals for whom the data for relevant predictors is not available. In this sample,

there were a total of 9,808 graduates. The distributions of individuals with college degree for gender,

social groups, and the quintiles of household income, assets and monthly consumption per capita, age

cohorts, and residency location are listed in Table 1. The variable CG represents number of college

graduates. The descriptive figures indicate a strong differential in securing higher education along gender

and socioeconomic status (GSES), residency location dimensions. Also, the differential across age cohorts

is salient. In the following section, we will systematically explore the individual influence of each of these

predictors by mutually adjusting for other predictors.

Socioeconomic and Gender Differentials in Higher Education

The conditional odds ratio (OR) and its 95% confidence interval of each subgroup is shown in Table

2. Both individual effects of the predictors without adjusting for other socioeconomic, gender, age and

residency effects and with mutually adjusting for other socioeconomic, gender, age and residency effects

are reported. The reference group represents a Brahmin male belonging to the Age Cohort 1 (21 - 30

years) living in a metro city. He belongs to the top quintile in terms of household assets, income and per

capita consumption.

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Table 1. Descriptive Statistics

N (%) CG (%)

Total 118224 100 9808 8.30

Gender

Men 59582 50.40 7062 10.86

Women 58642 49.60 3639 5.69

Age Cohorts

21 − 30 y 37508 31.73 4319 11.51

31 − 40 y 29814 25.22 2609 8.75

41 − 50 y 21820 18.46 1521 6.97

51 − 60 y 15367 13.00 890 5.79

> 60 y 13715 11.60 469 3.42

Caste

Brahmin 7345 6.21 1679 22.86

High Caste 21352 18.06 3054 14.30

Other Backward Castes 40544 34.29 2594 6.40

Scheduled Castes 22427 18.97 835 3.72

Scheduled Tribes 8982 7.60 358 3.99

Muslim 13200 11.17 639 4.84

Sikh/Jain/Christian 4374 3.70 649 14.84

Urban - Rural Status

Metro city 11261 9.53 1913 16.99

Small city or town 32625 27.60 5046 15.47

Village 74338 62.88 2849 3.83

Household Assets

Top Quintile 26190 22.15 6684 25.52

Second Quintile 21400 18.10 1817 8.49

Third Quintile 23989 20.29 788 3.28

Fourth Quintile 25053 21.19 411 1.64

Bottom Quintile 21592 18.26 108 0.50

Income

Top Quintile 26270 22.22 6053 23.04

Second Quintile 23949 20.26 2000 8.35

Third Quintile 22478 19.01 787 3.50

Fourth Quintile 21826 18.46 453 2.08

Bottom Quintile 22355 18.91 477 2.13

Negative Income 1346 1.14 38 2.82

Monthly Consumption Per Capita

Top Quintile 27612 23.36 6090 22.06

Second Quintile 24961 21.11 2031 8.14

Third Quintile 23481 19.86 1002 4.27

Fourth Quintile 21903 18.53 458 2.09

Bottom Quintile 20267 17.14 227 1.12

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Table 2. Odds ratios of obtaining higher education across GSES, age, and residency location factors, fixedeffects on states

Unadjusted for GSES factors Adjusted for GSES factors

OR (95% CI) OR (95% CI)

Gender

Men (omitted)

Women 0.45∗∗∗ [0.43 - 0.48] 0.37∗∗∗ [0.35 - 0.40]

Age Cohorts

21 − 30 y (omitted)

31 − 40 y 0.71∗∗∗ [0.66 - 0.78] 0.68∗∗∗ [0.62 - 0.75]

41 − 50 y 0.52∗∗∗ [0.47 - 0.57] 0.38∗∗∗ [0.34 - 0.42]

51 − 60 y 0.42∗∗∗ [0.37 - 0.47] 0.27∗∗∗ [0.24 - 0.31]

> 60 y 0.25∗∗∗ [0.21 - 0.29] 0.16∗∗∗ [0.14 - 0.19]

Caste

Brahmin (omitted)

High Caste 0.56∗∗∗ [0.49 - 0.65] 0.67∗∗∗ [0.59 - 0.77]

Other Backward Castes 0.23∗∗∗ [0.20 - 0.26] 0.49∗∗∗ [0.42 - 0.56]

Scheduled Castes 0.12∗∗∗ [0.10 - 0.15] 0.37∗∗∗ [0.31 - 0.44]

Scheduled Tribes 0.09∗∗∗ [0.07 - 0.12] 0.49∗∗∗ [0.38 - 0.65]

Muslim 0.17∗∗∗ [0.14 - 0.20] 0.31∗∗∗ [0.26 - 0.37]

Sikh/Jain/Christian 0.56∗∗∗ [0.45 - 0.69] 0.63∗∗∗ [0.50 - 0.79]

Urban - Rural Status

Metro city (omitted)

Small city or town 0.91 [0.73 - 1.13] 1.09 [0.91 - 1.31]

Village 0.20∗∗∗ [0.16 - 0.25] 0.75∗∗ [0.62 - 0.91]

Household Assets

Top Quintile (omitted)

Second Quintile 0.26∗∗∗ [0.23 - 0.29] 0.43∗∗∗ [0.39 - 0.48]

Third Quintile 0.09∗∗∗ [0.08 - 0.11] 0.25∗∗∗ [0.22 - 0.30]

Fourth Quintile 0.04∗∗∗ [0.04 - 0.05] 0.18∗∗∗ [0.14 - 0.23]

Bottom Quintile 0.01∗∗∗ [0.01 - 0.01] 0.05∗∗∗ [0.03 - 0.06]

Income


Second Quintile 0.30∗∗∗ [0.27 - 0.33] 0.53∗∗∗ [0.48 - 0.59]

Third Quintile 0.11∗∗∗ [0.10 - 0.13] 0.38∗∗∗ [0.32 - 0.44]

Fourth Quintile 0.06∗∗∗ [0.05 - 0.07] 0.35∗∗∗ [0.29 - 0.42]

Bottom Quintile 0.06∗∗∗ [0.05 - 0.07] 0.45∗∗∗ [0.37 - 0.55]

Negative Income 0.10∗∗∗ [0.05 - 0.19] 0.41∗ [0.21 - 0.81]

MCPC


Second Quintile 0.33∗∗∗ [0.30 - 0.37] 0.57∗∗∗ [0.51 - 0.63]

Third Quintile 0.14∗∗∗ [0.12 - 0.16] 0.40∗∗∗ [0.34 - 0.45]

Fourth Quintile 0.08∗∗∗ [0.06 - 0.10] 0.37∗∗∗ [0.29 - 0.46]

Bottom Quintile 0.03∗∗∗ [0.02 - 0.04] 0.28∗∗∗ [0.21 - 0.37]

Note:∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001

Confidence intervals are estimated using standard errors clustered at the PSU level

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In the unadjusted case, we see a striking differential in the higher education across social groups.

Individuals in Scheduled Castes are 88% less likely to obtain a college degree compared to individuals

from Brahmins community (OR = 0.12, 95% CI = 0.10 - 0.15). More strikingly, Individuals from

Scheduled Tribes are 91% less likely to obtain a college degree compared to individuals in the Brahmins

community (OR = 0.09, 95% CI = 0.07 - 0.12). We also notice a strong differential in Muslims group

and Other Backward Castes group. When we adjust for gender, economic, residency, and age effects the

higher education differential across social groups is significantly alleviated. With adjustment for GSES,

age and geographical factors, individuals from Scheduled Castes are 51% less likely to obtain a college

degree compared to the reference group in contrast to 88% less likely to obtain a college degree compared

to Brahmins in the unadjusted case. While there is significant attenuation in the differential across the

social groups in the adjusted case, still a significant differential do exists across socio religious groups.

We found a statistically significant higher education differential along the economic status lines. For

all three economic status variables: household assets, income and per capita monthly consumption, we

find statistically significant differential in the attainment of higher education across the quintiles. The

differential is attenuated to a small extent when adjusted for other GSES variables, age, and residency

variables. The differential across the quintiles of household assets is stronger compared to that of the

differential across quintiles of income and monthly per capita consumption. The individuals from the

bottom quintile of assets are 95% (OR = 0.05, 95% CI = 0.03-0.06) less likely to obtain a college degree

compared to that of individuals in the reference group. In contrast, the individuals from bottom quintile

of income are 55% (OR = 0.45, 95% CI = 0.37-0.55) less likely to obtain a college degree and individuals

from the bottom quintile of monthly per capita consumption are 72% (OR = 0.28, 95% CI = 0.21 - 0.37)

less likely to obtain a college degree compared to that of the reference group.

While we see a stronger differential across residency locations, the differential is almost eliminated

when adjusted for other GSES and age factors. For socioeconomic variables we have seen that the

differential in the attainment of higher education attenuates across subgroups when mutually adjusted

for other GSES factors, age and residency location. However, we notice the opposite trend for the age

and gender variables. The differential in higher education is amplified for subgroups of gender and age

cohorts when mutually adjusted for other GSES factors and residency location. For example, women are

55% ( OR = 0.45, 95% CI = 0.43 - 0.48) less likely to obtain higher education compared to the men in

the unadjusted case while they are 67% (OR = 0.37, 95% CI = 0.35 - 0.40) less likely to obtain higher

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education compared to the reference group in the adjusted case.

Discussion

In the previous section, we have seen that a very significant differential exists in terms of attainment

of higher education along the lines of GSES factors and age. Here we further explore the interaction

between Gender, Social Groups and Age. We have excluded the economic factors since we cannot assume

with confidence that economic status remains unchanged over the life times of all individuals. Our

investigation is centered on exploring if differential opportunities were present for the social and gender

subgroups over the decades that correspond to the first four age cohorts and the time period before 1966

that corresponds to the fifth age cohort. For this analysis, following the methodology of hierarchically well

formulated models (Jaccard 2001), we have included in the original logit link function the three two-way

interaction terms and a three way interaction term using age, gender, social groups dummy variables.

However, we observed that all the coefficients on the three way interaction terms are simultaneously not

statistically different from zero (χ2 = 24.07, df = 24, p = 0.46). Therefore, in the actual investigation

we have dropped the three-way interaction terms and retained only two-way interaction terms among

gender, age and social groups. Table 3 presents the the conditional odds ratios (OR) and corresponding

95% confidence interval for all the interaction terms. Interestingly, interactions are significant mainly for

gender and age cohorts and emphasize a negative differential in the opportunities for women in the time

periods corresponding to the cohorts 2 - 5 compared to that cohort 1. The coefficients on the interaction

terms involving Cohort 2, Cohort 3, Cohort 4 are statistically not different (χ2 = 4.28, df = 2, p = 0.12).

Figure 1 shows the predicted probabilities in terms of number of graduates in 1000 individuals for males

and females across age cohorts. We can notice a significant change in the predicted probabilities of

obtaining higher education for women in comparison to men over age cohorts. To get even a better idea

we have plotted the ratio of predicted probabilities for males to females for the five age cohorts in Figure

2. The probability that a women in age cohort 5 obtaining higher education is almost 1/7 of the predicted

probability of a man in the same age cohort obtaining higher education. In contrast, the probability that

a woman in the age cohort 1 obtaining higher education is approximately 1/2 of the predicted probability

of a man in the same cohort obtaining higher education. More strikingly, the predicted probability

of a woman obtaining higher education in age cohort 1 is almost 3 times the corresponding predicted

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Table 3. Interaction between gender, age and social groups, adjusted for urban-rural status, assets, income,consumption with fixed effects on states

Interaction Between Gender and SES factors

OR (95% CI)

Caste × Age Cohort

High Castes × Cohort2 (31 − 40 y) 1.13 [0.88 - 1.45]



High Castes × Cohort5 (> 60 y) 1.37 [0.93 - 2.01]

Other Backward Castes × Cohort2 (31 − 40 y) 0.90 [0.69 - 1.18]

Other Backward Castes × Cohort3 (41 − 50 y) 0.63∗∗ [0.45 - 0.89]

Other Backward Castes × Cohort4 (51 − 60 y) 0.51∗∗ [0.34 - 0.77]

Other Backward Castes × Cohort5 (> 60 y) 0.63∗ [0.40 - 0.99]

Scheduled Castes × Cohort2 (31 − 40 y) 0.77 [0.55 - 1.08]

Scheduled Castes × Cohort3 (41 − 50 y) 0.53∗∗ [0.34 - 0.81]

Scheduled Castes × Cohort4 (51 − 60 y) 0.56∗ [0.35 - 0.89]

Scheduled Castes × Cohort5 (> 60 y) 0.46∗ [0.25 - 0.87]

Scheduled Tribes × Cohort2 (31 − 40 y) 0.76 [0.52 - 1.12]



Scheduled Tribes × Cohort5 (> 60 y) 0.67 [0.27 - 1.69]

Muslims × Cohort2 (31 − 40 y) 1.10 [0.78 - 1.55]

Muslims × Cohort3 (41 − 50 y) 0.90 [0.57 - 1.43]

Muslims × Cohort4 (51 − 60 y) 1.00 [0.59 - 1.69]

Muslims × Cohort5 (> 60 y) 1.86 [0.98 - 3.50]

Sikh/Jain/Christian × Cohort2 (31 − 40 y) 0.82 [0.52 - 1.29]



Sikh/Jain/Christian × Cohort5 (> 60 y) 1.24 [0.64 - 2.40]

Female × Caste

Female × High Castes 1.30∗ [1.02 - 1.66]

Female × Other Backward Castes 0.93 [0.72 - 1.20]

Female × Scheduled Castes 0.91 [0.68 - 1.21]

Female × Scheduled Tribes 0.72 [0.47 - 1.12]

Female × Muslims 0.87 [0.62 - 1.20]

Female × Sikh/Jain/Christian 2.64∗∗∗ [1.98 - 3.52]

Female × Age Cohort

Female × Cohort2 (31 − 40 y) 0.44∗∗∗ [0.37 - 0.51]

Female × Cohort3 (41 − 50 y) 0.39∗∗∗ [0.32 - 0.47]

Female × Cohort4 (51 − 60 y) 0.35∗∗∗ [0.28 - 0.44]

Female × Cohort5 (> 60 y) 0.28∗∗∗ [0.21 - 0.37]

Note:∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001

Confidence intervals are estimated using standard errors clustered at the PSU level

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21 − 30 31 − 40 41 − 50 51 − 60 > 600

10

20

30

40

50

60

70

Age Cohorts

Pre

dic

ted

Nu

mb

er

of C

olle

ge

Gra

du

ate

s in 1

00

0 I

nd

ivid

ua

ls

Males

Females

Figure 1. Predicted probabilities of obtaining higher education for males and females over differentage cohorts (error bars represent 95% confidence intervals)

probability for a woman in age cohort 2.

We have observed a very mild interaction between gender and social groups. Except for high castes and

minorities the interaction effect is absent for other groups (Table 3). One interpretation of these results

is that women were mostly not subject to selective differential opportunities across social groups. The

coefficients on high castes and minorities interaction terms with gender are positive indicating preferential

opportunities in these groups compared to the Brahmins group. Thus the existing differences in the

attainment of higher education for women in different social groups can be attributed to the differential

opportunities present for individuals in these social groups irrespective of gender. Figure 3 depicts

the gender differential in higher education across social groups. We have plotted predicted predicted

probabilities in terms of number of graduates in 1000 individuals for both males and females for different

social groups.

Finally, the odds ratios of the interaction terms between social groups and age cohorts also indicate

that interaction between social groups and age cohorts is mostly absent. Except for the interaction terms

involving Schedules Castes and Other Backward Castes the odds ratios are statistically not different from

1. Figure 4 depicts the social group differential in higher education across age cohorts. We have plotted

predicted predicted probabilities in terms of number of graduates in 1000 individuals for all social groups

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> 60 51 − 60 41 − 50 31 − 40 21 − 301

2

3

4

5

6

7

Age Cohorts

Ra

tio

of

Pre

dic

ted

Pro

ba

bili

tie

s f

or

Ma

les a

nd

Fe

ma

les

Figure 2. Ratio of predicted probabilities of obtaining higher education for males and females over agecohorts

Brahmins HC OBC SC ST Muslims Others0

10

20

30

40

50

60

70

80

90

100

Social Group

Pre

dic

ted N

um

ber

of

Colle

ge G

radu

ate

s in 1

00

0 In

div

idu

als

Males

Females

Figure 3. Predicted probabilities of obtaining higher education for males and females over differentsocial groups (error bars represent 95% confidence intervals)

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21 − 30 31 − 40 41 − 50 51 − 60 > 600

10

20

30

40

50

60

70

80

90

100

Age Cohorts

Pre

dic

ted N

um

be

r of C

olle

ge

Gra

duate

s in

10

00 in

div

idu

als

Brahmin

High Castes

Other Backward Castes

Scheduled Castes

Scehduled Tribes

Muslims

Sikhs/Jains/Christians

Figure 4. Predicted probabilities of obtaining higher education for individuals in different socialgroups over age cohorts (error bars represent 95% confidence intervals)

over age cohorts. The significant odd ratios for Other Backward Castes and Scheduled Castes indicate

the presence selectively unfavorable opportunities to obtain higher education for individuals in these

subgroups for the corresponding age cohorts compared to the reference age cohort. Overall, in contrast

to the fast declining gender differential, the social group based differential is relatively persisting in the

higher education scenario over the time frames corresponding to the age cohorts.

Conclusion

Our study suggests a very salient higher education differential along gender and socioeconomic lines. The

differential among social subgroups and gender subgroups seems to be declining over the time. Especially,

the fast shrinking differential in obtaining higher education between males and females is encouraging.

The results also indicate that females were mostly not selectively discriminated in terms of opportunities

for obtaining higher education across different social groups. At the same time, persisting higher education

differential across social groups which seems to be only moderately minimized over the considered time

frames in spite of the caste based reservations in India suggests the need for alternative policies to minimize

the differential. While reservations program do provide opportunities to the historically backward groups,

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these groups might not be able to fully utilize the benefits of such a program due to economic constraints.

Finally, apart from the differential across gender and socioeconomic factors, the percentage of individuals

obtaining higher education in India is dismal when compared to that of developed countries.

References

Desai, S (2010). The Other Half of the Demographic Dividend. Economic and Political Weekly, 45 (40),12-14.

Desai, S. & Amaresh, D. (2011). Caste in 21st Century India: Competing Narratives. Economic andPolitical Weekly, 46 (11), 40-49.

Desai, S., Dubey, A., Joshi, B. L., Sen, M., & Sheriff, A. (2007). Indian Human Development Survey. 30June 2009 ed New Delhi: University of Maryland and Notional Council of Applied Economic Research.

Jaccard, J. (2001). Interaction Effects in Logistic Regression. Sage University Press.

Vanneman, R., Noon, J., & Desai, S. (2006). Social Capital in India: Networks, Organizations, andConfidence. Presented at the Annual Meetings of the American Sociological Association. Montreal,Canada.

Vanneman, R. & Dubey, A. (2011). Horizontal and Vertical Inequalities in India. Paper prepared forInequality and the Status of the Middle Class: Lessons from the Luxembourg Income Study, 28-30 June2010, Luxembourg.

higher education differential in india

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