GCOE Discussion Paper Series
Global COE Program

Human Behavior and Socioeconomic Dynamics
Discussion Paper No. 300

Caste comparisons: evidence from India

Xavier Fontaine and Katsunori Yamada

March 2013

GCOE Secretariat
Graduate School of Economics

OSAKA UNIVERSITY

1-7 Machikaneyama, Toyonaka, Osaka, 560-0043, Japan

Caste comparisons: evidence from India ∗Xavier Fontaine†

Katsunori Yamada‡

Abstract
The caste issue dominates a large part of India’s social and political life. Casteshapes Indians’ identities, and strong tensions exist between castes. This paper
evaluates how caste-based comparisons may be exacerbated in such a conﬂictual
context. Using subjective well-being data from an original panel survey, together
with a national representative survey on expenditure, we ﬁnd that both within-caste
comparisons and between-rival-caste comparisons reduce well-being. Between-caste
comparisons aﬀect well-being three times more than within-caste comparisons. In
absolute value, an increase in rival castes’ expenditure aﬀects well-being as muchas own expenditure. These ﬁndings highlight the strength of comparisons between
rival castes. Yet this comparison scheme turns out to be asymmetrical: only low
castes care about the economic successes of their rivals, and only high-caste Indianscompete with their fellows.

keywords: Subjective Well-being; Relative Utility; Caste classes; India; Panel Data∗

The previous version of the paper was circulated under the titles of “Economic Comparison andGroup Identity: Lessons from India”. We are grateful to Shinsuke Ikeda, Fumio Ohtake, and Yoshiro
Tsutsui for allowing us to use original data from “Survey on Preferences toward, and Satisfaction with,
Life” of Osaka University. We are also grateful to the CEPREMAP and the India Research Group forproviding us the Indian National Sample Survey data. We would like to thank Alpaslan Akay, Andrew
Clark, Ashwini Desphpande, Rakesh Gupta, Clément Imbert, Claudia Senik, Zahra Siddique and Pankaj
Verma for their helpful comments, as well as the participants of the World-Bank / IZA 2012 conference.
Financial support provided from JSPS research fund KAKEN (24683006), is appreciated. Any remaining
error is the sole responsibility of the authors.
†
Paris School of Economics and Sciences-Po. Corresponding author: xavier.fontaine@sciences-po.org‡
ISER, Osaka University and MOVE, Universitat Autonoma de Barcelona

11Introduction

India is one of the rare countries with a clear social stratiﬁcation. Identity is deeply
rooted in caste at birth. Caste also largely deﬁnes position in society and the economy.
Importantly, there is strong antagonism between castes. This antagonism often takes theform of discrimination, and sometimes violence. In addition, low castes have called for –and are still calling for – reservations in education and the labor market to oﬀset theirsituation.
This paper aims at evaluating whether caste-based comparisons are exacerbated inthis environment. While many studies have established that individuals’ well-being canbe aﬀected by the consumption/income of others (see Clark et al. 2008 for a survey), a
relatively newer, and still open, question is if people with speciﬁc characteristics tend
to compare themselves to certain types of rivals. This question is answered in part by
Clark and Senik (2010) for European countries. India provides rare material to contributeto this line of research, as this country is one of the very few to be characterized by welldeﬁned social groups and strong between-group tensions.
We use subjective well-being data to assess the strength of within and between-caste
comparisons in India. More speciﬁcally, we make joint use of two data sets. The ﬁrst
one is an original urban panel survey for India conducted by Osaka University, Japan. A
nice advantage of the survey is that it contains a happiness question, making it a rareexample among surveys conducted in developing countries. The second data set is a large,
representative Indian population survey. This second survey enables us to estimate theexpenditure of the groups to which respondents are likely to compare themselves.
Our main results are threefold. Firstly, within-caste comparisons aﬀect well-being
negatively. This result suggests that expenditure by others from the same caste triggers
envy more than it acts as a positive signal about own future consumption. This ﬁnding
diﬀers from what might have been expected in such a rapidly growing and changing
country (Senik 2004, Senik 2008). Secondly, Indians view the rival castes’ economic
situation enviously. Between-caste comparisons are unexpectedly strong and, in absolutevalue, comparisons to rival castes aﬀect well-being as much as own expenditure does. We
found that they matter for happiness three times more than within-caste comparisons.
Thirdly and most importantly, this comparison pattern appears to be asymmetrical: low
castes indeed compare themselves to higher castes, but the reverse is not true. Besides,
within-caste comparisons are mostly made by members of high castes. These results hold
both when pooling the cross-sections and when adding individual ﬁxed-eﬀects into the2regression.
The rest of the paper is organized as follows. The next section introduces our conceptual framework. After providing some background elements on the Indian caste systemand on the tensions in this society, we discuss the importance that economic comparisons
may have in this society. We then review the existing literature on comparisons in India,
as well as on between-group comparisons in other countries. The third section details ourdata sets, as one of our data sets is original. The fourth section provides empirical results
and the last section concludes the paper.

2

Research Background

2.1

A Conﬂictual Caste Society

Although 3,000 years old, the caste system continues to play a central role in India.1
This system divides Indians into four classes (varna) and thousands of small communities(jati). The ﬁrst Indian Constitution (1950) groups the diﬀerent jati into four broader
categories, depending on the level of disadvantage and social stigma they face. Thistypology simpliﬁes the study of caste inequalities, and will be used throughout this paper.
To be speciﬁc, caste determines an individuals level of pureness. First, the Scheduled
Castes (“untouchables”, or Dalits) and the Scheduled Tribes (the tribals) are considered
as “impure”. Above them are the Other Backward Castes. Even though the castes
composing this latter category are mostly considered as “pure”, they are still beneath therest of the population in the caste hierarchy, and suﬀer from the caste system. Lastly, therest of the population is categorized as the Other.
This caste system is not just a clustering. It is a social ordering. Caste profoundly
inﬂuences one’s role and position in society (occupation, marriage, with whom one caninteract, etc.). For example, for an orthodox Hindu from the highest castes, interactingwith low-caste members may even soil purity. As a matter of fact, Indians still mostly
marry within their own jati (Munshi and Rosenzweig (2006)). This rigid hierarchy is asource of conﬂict between the classes, and generates massive economic inequalities. It
translates social inequalities into economic inequalities, because low-paid “impure occupations” (cleaning, undertaking, etc.) are reserved for lower castes.
Despite large reservations for the disadvantaged castes in education and the admin1

This section draws substantially on Susan Bayly’s 2001 general survey of the recent history of India
as a caste society.

3istration since the 1950s (Bayly (2001), chapter 7), between-caste economic disparities
remain striking. In terms of per capita household expenditure, Indians from higher castes
consume on average 63% and 46% more than, respectively, Scheduled Tribes and Scheduled Castes,2 and 27% more than the Other Backward Castes.3 A large body of literature
documents the discrimination from which low-caste members suﬀer. On the labor market, for example, the persistency of this discrimination has been assessed using both
non-experimental (e.g. Banerjee and Knight (1985)) and experimental methods (testing
methods: Banerjee et al. (2009), Siddique (2011)). Low castes also face discrimination on
the housing market.4 Yet disadvantaged castes actively struggle to extend the reservation
policies they beneﬁt from, sometimes even asking for quotas in the private sector.5 Symptomatic of these tensions are the violent conﬂicts – or “caste wars” that ﬂare up in rural
India on a regular basis.6 Symptomatic also is the preponderant role of these tensions inIndia’s political life from Independence onwards.7

2.2

Previous Findings on Class Conﬂicts

A few papers show relative concerns playing an important role in India based, for instance,
on wedding expenditure (Bloch et al. (2004)) and more broadly conspicuous consumption(Khamis et al. (2012)). Carlsson et al. (2009) assess more speciﬁcally the role of castebased relative concerns in India, on the basis of a hypothetical choice experiment. They
asked respondents to choose between several hypothetical societies for their grandchildren.
Each of these societies is characterized by the grandchild’s income, the average income
for the grandchild’s caste, and the average income for society. The authors derive respondents’ preferences from the choices made by the respondents. They ﬁnd the caste’s average
income to reduce utility, generating evidence of negative within-caste comparisons. Keeping both own and caste income constant, society’s average income also aﬀects well-being
negatively. The coeﬃcient associated with society’s income appears to be higher thanthe coeﬃcient associated with own caste income, making a case that Indians compare
themselves even more to the rest of society (including rival castes) than to people from2
Authors’ computation based on the 2009-10 round of the National Sample Survey, with a sample sizeof 570 000 individuals.
3
Even in the historically highly egalitarian, anti-caste state of Kerala, Deshpande (2000) ﬁnds caste
disparities driving overall inequalities.
4
Bayly (2001), pp. 359-362.
5
Bayly (2001), chapter 7.
6
Bayly (2001), chapter 9, pp. 342-358.
7
Cf. the rise of the anti-reservation party BJP in the 80s and 90s (Bayly (2001), pp.296-300).

4

their own caste.
A few other papers also seek to ﬁnd out which groups individuals compare themselves
to, although not in India. In European countries, Clark and Senik (2010) use a survey
in which respondents were directly asked to whom they compare themselves, and ﬁndthat Europeans compare themselves predominantly to their colleagues. Another trend in
the literature includes the income/consumption level of plausible reference groups in thewell-being equation to check for between-group comparisons. Using this methodology,
Kingdon and Knight (2007) study income comparisons in South Africa, ﬁnding evidenceof between-race comparison. Jiang et al. (2011) and Akay et al. (2012) focus on the relation between rural-to-urban migrants and urban “natives” in China. All three papers
conclude similarly that, whereas within-group comparisons aﬀect well-being negatively,
between-group comparisons make people feel they are better oﬀ. This phenomenon canbe understood from the point of view that the other group’s economic situation acts as apositive signal about the future level of income in one’s own group.

2.3

Speciﬁcation and Prediction

In keeping with the literature, we consider the standard relative utility function:(1)

Ui = βy ln(yi ) + βc ln(ycastei ) + βr ln(yrivali ) + βXit ,where yi stands for i’s own expenditure, ycastei represents i’s caste expenditure level, yrivaliis the expenditure level in the rival castes, and Xit stands for a set of socio-demographic
characteristics. This logarithmic-type speciﬁcation is widely used to model relative utility.8 Section 4 details the speciﬁcation of each variable.
We saw in section 2.1 that caste class conﬂicts are between the “pure” and the “impure.” Hence, caste rivalry is deﬁned as Other Castes (i.e. higher castes) versus the restof the Scheduled Castes, Scheduled Tribes and Other Backward Castes. It is true that,to some extent, some Indians from the Other Backward Castes have seen the Scheduled
Tribes as a threat. Tensions are also known to exist between Other Backward Castes and
Scheduled Castes.9 However, due to the limited size of our sample, this paper sticks to
the main focus of “pure” vs. “impure.”
In the speciﬁcation, our prime interest is with the sign of βc and βr . The sign of
βc is hard to predict. The envy and conspicuous consumption theories (Veblen (1899),
89

See Clark et al. (2008) for a development of the model and a literature review.
See Bayly (2001), chapter 9 for this point.

5

Duesenberry (1949)) posit that an increase in others’ expenditure makes one feel deprived,
thus decreasing well-being. In this case, βc < 0.
Conversely, well-being may actually increase with own caste expenditure.10 This may
happen when people take others’ consumption to predict their own future level of expenditure. In that case, the higher the level of expenditure in the caste, the higher the
expected level of own expenditure. Because expectations improve, well-being increases aswell (Hirschman and Rothschild (1973); see Card et al. (2012) for a formalization). When
this informational eﬀect overwhelms envy, βc > 0. These two eﬀects may also cancel eachother out (βc = 0).
Within-caste insurance mechanisms might also partly oﬀset the negative feelings triggered by envy, not forgetting altruism (or fellowship feelings) toward people from thesame caste.11
The impact of rival castes’ expenditure is also hard to predict. The literature often
assumes that comparison occurs among similar people. In India, however, caste rivalries
appear so important (section 2.1) that we could expect βr < 0. Some signal eﬀect may,
however, also exist. Because occupation is partly determined by caste, castes are complementary rather than substitutes. Improvements in a given caste’s economic conditions
may thus act as a signal for its rivals as well. In this case, we could also have βr = 0 or
βr > 0. However, there are obviously no between-caste informal insurance mechanisms,
nor should any “between-rival-caste” fellowship feelings be expected.
In a next stage, we study separately the relative utility functions of low castes andhigher castes. In a 1949 article, Duesenberry posited that comparisons are mostly upwardlooking. Individuals at the bottom of the social ladder do indeed compare themselves tothose above them, but individuals at the top of the ladder do not focus on those below
them. In India, the social hierarchy is strict and well-known. Following Duesenberry’s
logic, only low castes should compare themselves to higher castes.
The intensity of within-caste comparisons may also vary with the caste. This could
happen where the intensity of, and balance between, envy and signal / fellowship / insurance mechanisms diﬀers from low castes to higher castes.

10

We draw here from the rich set of explanations developed in Kingdon and Knight (2007).
Envy has often been found to dominate the informational eﬀect in developed countries (Clark et al.
(2008), Card et al. (2012)). In developing countries, the evidence is mixed (Clark and Senik (2011)).
11

6

3

Databases

Our analysis makes joint use of two databases. The ﬁrst is the “Survey on Preferences
toward, and Satisfaction with, Life” (SPSL). This survey comprises a three-year panel
collected in six of the ten largest Indian cities (Delhi, Mumbai, Bangalore, Chennai,
Kolkata, and Hyderabad) in January 2009, 2010 and 2011, covering 1,857, 1,280, and
1,037 respondents, respectively. Fewer observations than these are usable for regressions
due to missing survey response information and inconsistent answers across waves. The
data was collected in keeping with the following design. Each city was divided into
four areas. Fifteen residential districts were randomly selected in each area. Indigenousinterviewers were sent to interview ﬁve subjects face to face in each residential district.
Interviewers were free to choose on which door to knock, but they were required to comply
with two rules: (i) they could not interview a subject living next to other subjects, (ii) the
distributions of gender and age category in the collected data set had to be as designed.
For the surveys in 2010 and 2011, interviewers went back to the subjects’ homes if they
had not moved.
In addition to demographic questions covering age, gender, years of education, monthly
and annual household expenditure levels, caste information and so forth,12 the questionnaire contained the following happiness question:
Overall, how happy would you say you are currently? Using a scale from 0 –10 where 10 is very happy and 0 is very unhappy. How would you rate you
current level of happiness?
The caste information in the SPSL calls for caution for two reasons. First, the ﬁrst
wave did not collect any information on caste. We thus have to extrapolate this information from the subsequent two waves of data. This should be ﬁne since caste membershipis exogenously assigned at birth. Second, a sizable part of the SPSL sample changed caste
between the second wave (January 2010) and the third wave (January 2011). Speciﬁcally,
38% of observations concern those respondents who changed their caste in the course of
the survey. We call these respondents “movers”. These changes are surprising, as they do
not occur for other variables such as education or gender. We hypothesize these changes
to be due to the announcement (May 2010) of the ﬁrst Caste Census since 1931. Low caste
members indeed suﬀer from a strong stigma. Yet, at the same time, they beneﬁt from
12

The deﬁnition of castes in the survey took up the deﬁnition in the National Sample Survey forcomparability.

7

extensive aﬃrmative action programs (mostly quotas in administration and education).
For that reason, people may be willing to manipulate their caste identity (from low caste
to a higher caste or vice versa), especially when the government is known to be collecting
this information. Some respondents may have confused the SPSL with this Caste Census,
consequently deciding to adjust their caste identity.13
For that reason, in the following analyses we deﬁne respondents’ castes as they were
given in the wave prior to the announcement of the Caste Census (i.e. the second wave).
For the regression analyses, as a robustness check, we tried regression where we dropped
all the respondents who changed caste in the course of the survey. We obtained the same
results as with the whole sample, which suggests that movers do not raise sample selection
problems (see section 4.2).
When using the abovementioned happiness equation, we need proxy variables for others’ expenditure levels. This calls for the second data set, which has a greater number ofobservations: given the small sample size of the SPSL, it is not a good idea to computereference expenditure levels within the data set for the range of cells deﬁned by individual
characteristics. Thus we need the second database. We make use of the “Employment
and Unemployment Survey” by the National Sample Survey Oﬃce, which is a part larger,socially representative survey program called the National Sample Survey (NSS). The last
two waves of this survey were collected from July 2007 to June 2008, and from July 2009
to June 2010 respectively (with respective sample sizes of about 750,000 and 460,000).
See Imbert and Papp (2013) for more survey details.
When interviewing a household, the NSS measures average monthly household expenditure for the previous year. We thus match the January 2008 – June 2008, July 2009
– December 2009, and January 2010 – June 2010 monthly household expenditure information from the NSS with the 2009, 2010, and 2011 waves of the SPSL respectively. In
the six cities we study, the sample sizes of these NSS subsets are 9,712, 6,731 and 6,561
respectively. All our computations use the weights provided in the NSS. The average
number of NSS observations used to compute the median household expenditure levelin each reference group for each year can be found under each regression table. Whenperforming the main regression described in the previous sub-section, this number is 32
for the within-caste comparison variable and 26 for the between-caste variable.
Table 1 compares the SPSL and the NSS samples with respect to the main population

13

A small body of literature exists on the manipulation of caste identity to obtain caste advantages.
See Cassan (2012).

8

characteristics we are interested in.14 The median level of household expenditure is fairly
similar in both samples. The caste distribution in the SPSL appears to be partly imbalanced, with the share of Scheduled Tribes giving cause for concern: they represent 8.33%
of the SPSL, and 1.26% of the NSS. The distribution of education is not equal across thetwo samples, but there is no clear shift in general education between them. The sample
from the SPSL also appears to be signiﬁcantly older.
Perfect representativeness of the sample answering the happiness question is not necessary to recover the parameters from the utility function. The sample used should, however,resemble the general population enough to avoid getting a result fallaciously driven byan over-represented sub-population. Hence the concern about the Scheduled Tribes, who
are clearly over-represented in our sample. For that reason, we test the robustness of ourresult to the exclusion of these respondents, and we are happy to report that the results
obtained in this paper are robust to the exclusion.

4

Empirical Results

In this section, we estimate the happiness equation (1), but ﬁrst consider a variation in
the deﬁnition of the reference expenditure variables. In keeping with the literature, we
include only one reference expenditure variable in the happiness equation, by considering
the reference group to be “similar others” (Clark and Oswald 1996) irrespective of the
respondents’ castes. We consider the cell deﬁned by age bracket, level of education andlocation, which is the same as Ferrer-i-Carbonell’s (2005) deﬁnition of the reference group.
The reference variable we include into the regression, log(ygeneralit ), is thus deﬁned as thelogarithm of the median real household expenditure level for those who share i’s levelof education, age, and location during year t. This ﬁrst stage analysis is called “generalcomparison” because we do not cluster the reference group by caste.
We then add caste information, bringing log(ycasteit ) and log(yrivalit ) into the regression
instead of log(ygeneralit ). Here, again, we focus on how individuals compare themselves topeople similar to them, both in their own caste and in rival castes. The variable ln(ycasteit )(log(yrivalit )) is thus deﬁned as the logarithm of the median real household expenditure
14

We restrict the NSS sample to individuals matching the SPSL restrictions: only people over 20, living
in one of the six cities studied in the SPSL. For the sake of simplicity, we compare the SPSL with thelast wave of the NSS. In the SPSL, the sampling procedure is designed to be random at city level. The
number of individuals drawn from each city is, however, the same by design. To ensure comparability
with the NSS, we thus weight each observation according to the share of its city among the six cities inwhich respondents live.

9

level for those in i’s caste (rival castes) who share i’s level of education, age, and location
during year t. In constructing these reference expenditure variables, we consider medianhousehold expenditure at year t rather than the average. The median is used because it
is less sensitive to outliers. See Clark et al. (2009) on this point.
In a last stage, we run the same regression separately on low and higher castes tocheck whether low and higher castes diﬀer in the way they compare themselves to one
another.
The variables determining the reference groups are deﬁned as follow. Education isdeﬁned by seven categories, from “illiterate” to “graduate and above” (see table 5 fordetails). Three age groups are considered, each containing 1/3 of the adult populationin the cities we study. The happiness survey’s respondents live in six cities: Bangalore,
Chennai, Delhi, Hyderabad, Kolkata and Mumbai. Respondents can be Scheduled Castes,
Scheduled Tribes, Other Backward Castes or Other (i.e. higher castes). These variables
are also added as covariates in the regression, together with labor force status, number ofchildren, family status, gender and wave.
Our main analyses use pooled cross-sections OLS. In this case, the standard errors are
clustered at the respondent’s peer group level. We then add individual-speciﬁc ﬁxed-eﬀects
to exploit the panel structure of the data set, and check the consistency of the results. The
ﬁxed-eﬀect and pooled-OLS regressions actually generate very similar results, in terms ofboth sign and signiﬁcance. However, due to the small sample size of our SPSL dataset, we
prefer the results for pooled OLS since adding ﬁxed-eﬀects into the regressions attenuates
the variance in the data, and thus increases the variance in the estimated coeﬃcients,
which in turn artiﬁcially inﬂates some coeﬃcients. See Luttmer (2005) for potential
problems in ﬁxed eﬀects regressions of happiness equations.

4.1

Findings

Table 2 displays our main results. As mentioned above, we ﬁrst consider the general
setting in which the reference group is deﬁned irrespective of the respondents’ castes,by taking Ferrer-i-Carbonell’s (2005) deﬁnition of the reference group. This regression
gives us a general idea of the impact of comparisons in India. The average number ofobservations used to calculate reference expenditure is 63.
The ﬁrst column of Table 2 suggests that others’ expenditure reduces happinesssharply, with a statistical signiﬁcance of 1%. This result is consistent with ﬁndings in
China (Knight and Gunatilaka (2011)), for instance, but contrasts with observations in10former communist countries during the transition period (Senik 2004, 2008).

15

Re-

markably, comparisons appear to aﬀect well-being to the same extent as own household
expenditure does.
The second column factors in the comparison eﬀects whereby respondents may compare themselves diﬀerently to people from their own caste than to people from rival
castes. We consequently split the reference group into two, replacing log(ygeneralit ) with
our above-deﬁned within-caste and between-castes comparison variables log(ycasteit ) andlog(yrivalit ).
As described in section 2.3, comparisons with similar others within the caste could be
expected to have a null or maybe positive impact. Given that India is a rapidly changing
country, we could expect Indians to take others’ expenditure to predict their own future
consumption. The “informational eﬀect” could thus be strong enough to oﬀset envy. Similarly, caste deﬁnes identity so strongly that we could expect close caste fellowship feelings,
which could again override within-caste jealousy. We, however, found that comparisonswith similar others within the caste appear to aﬀect well-being negatively (β̂c < 0). Envy
hence appears to be stronger than fellowship feelings and informational eﬀects. The factthat the coeﬃcient is signiﬁcant at 10-% tends to mitigate this conclusion, but we willsee below that within-caste comparisons do actually matter for half the caste hierarchy.
The rival castes’ economic successes also appear to enter negatively into the well-being
function. Between-caste comparisons indeed have a large, negative, 1%-signiﬁcant impact
on well-being (β̂r < 0).
Between-castes comparisons appears to reduce well-being remarkably more than withincaste comparisons (the diﬀerence between the two coeﬃcients is signiﬁcant at a 5% levelin a two-sided test). This result evokes Carlsson et al.’s ﬁnding: the authors ﬁnd own
caste’s income to aﬀect well-being less than the average income level in the rest of thesociety (including rival castes).
Between-caste comparisons even appear to aﬀect well-being as much as own expenditure, in absolute value. This sheds light on very strong relative concerns about rival
castes, obviously exacerbated by the tensions structuring the Indian caste system. Thisﬁnding contrasts with the case of South Africa, where comparisons between black andwhite seem to increase well-being, and with the case of China, where the same is foundfor comparisons between migrants and non-migrants. In both these cases, the signal eﬀect
appears to be stronger than envy. In India, castes are also very complementary, which
15

See Clark and Senik (2011) for a general review of the importance of relative feelings of disadvantage
in developing countries.

11

makes it plausible for rival castes’ expenditure to act as a strong signal. However, tensions
between high and low castes are so strong that envy widely overwhelms this signal eﬀect.
The third and fourth columns of Table 2 present the analysis for low castes and thenfor higher castes. We ﬁnd that the relative utility function diﬀers drastically depending on
one’s position in the caste hierarchy. First, whereas an increase in own caste’s expenditure
decreases well-being for higher caste members (1% signiﬁcance), low-caste members arefound to be unaﬀected by such a change. Within-group economic competition is thus
likely to matter more at the top of the caste hierarchy. At the same time, higher castes
do not seem to pay so much attention to the economic situation of low castes (small
insigniﬁcant impact). Strikingly, however, low-caste Indians are strongly, and negatively
aﬀected by the economic successes and failures of higher-caste Indians. This result tends tobear out Duesenberry’s view that individuals are mostly upward-looking when comparing
themselves to others.

4.2

Robustness Checks

The validity of the previous results is tested along two main lines. Given that some
respondents changed their castes in the course of the survey (see section 3), we have tocheck that they are not driving the results. We also check whether the results hold when
adding individual ﬁxed-eﬀects to the regression.
Non-movers are respondents for which the reported caste has been collected more thanonce (i.e. who did not drop out during the course of the survey) and who did not change
their caste. Table 3 displays the results obtained for this sub-sample. Despite the eﬀectof the smaller sample size on both magnitudes and signiﬁcance, these ﬁgures appear tobe pretty similar to what we have found previously.
The results are also found to be robust to the inclusion of individual-speciﬁc intercepts.
Because adding ﬁxed-eﬀects amounts to centering the variables at the individual level,and due to the sample’s small size, the estimates are quite inﬂated. Table 4, however,shows both signs and signiﬁcance to be consistent with the previous results. This stability
strongly supports our previous conclusions.
Additionally, we test the results’ robustness to the exclusion of the Scheduled Tribes,
both in pooled-cross sections and with ﬁxed-eﬀects. As evoked in section 3, this category
is oversampled. Running the analysis again without Scheduled Tribes does not change
the results.

12

5Concluding Comments

Individuals are often implicitly assumed to compare themselves to their peers. In the
Indian context, however, this view is subject to caution. Strong tensions are known toexist between clearly dissociated social groups, the castes. Consequently, both betweencaste comparisons and within-caste comparisons are likely to be of importance.
Using a unique Indian panel data set, we show that the caste system inﬂuences theway Indians compare themselves to one another, and that the comparison scheme isasymmetrical. Only low castes care about the economic successes of their rivals, and only
high-caste Indians compete with their fellows. The fact that comparisons between rival
castes are made by low-caste Indians bears out Duesenberry’s view that individuals at
the bottom of the hierarchy compare themselves to those at the top, but that the reverse
does not hold. Our results on between-caste comparisons contrast with previous researchin South Africa and China. In these countries, between-group comparisons are found toincrease happiness. In the case of India, existing tensions between rival castes are very
likely to explain this diﬀerence: even though low and higher castes are complementary,these tensions are strong enough to lead envy to widely overwhelm the signal eﬀect.
Unlike Linssen et al. (2011), who worked on data on rural India, our data was collectedin urban areas in India. While we expected signaling eﬀects to pertain in rapidly growing
urban areas, it is interesting that we obtained otherwise. We call for further happinessresearch in India, as the accumulated evidence in the country is still scarce and ouranalysis should be conducted again with diﬀerent data sets.

Appendix
This section provides the results obtained from a simple happiness regression without
including a reference expenditure variable. Table 5 shows the coeﬃcients we obtain bothin pooled cross-section OLS and ﬁxed-eﬀect OLS. We focus here on describing the ﬁrst
regression’s coeﬃcients.
As expected, log real household expenditure has a positive and very signiﬁcant impact. Education improves well-being monotonically. Other things being equal (household
expenditure included), any labor status appears to be better than working, except beinga “housewife or househusband” (97.6% of people belonging to this category are actually
women). The diﬃcult working conditions in developing countries certainly drive much ofthis result. “Not working” increases well-being as compared to the situation where the13respondent has a job. This result can be understood by noticing ﬁrst that this category
consists of 70% of women. It is quite likely that most of these respondents are women at
the head of the household, with another woman doing the housekeeping tasks (daughter,daughter-in-law, etc). Consequently, they do not consider themselves as being housewives,and declare themselves to be unoccupied instead.
Having between one and three children does not aﬀect happiness, as compared tohaving no children. Having more than three children, however, signiﬁcantly decreases
happiness. This pattern is pretty similar to the pattern observed in developed countries.
The rest of the family status variables surprisingly do not seem to have any signiﬁcant
impact. Age and gender also appear to have no signiﬁcant impact. On the contrary, boththe place of residence and the year have strong eﬀects on well-being.
Not surprisingly, belonging to one of the Other Backward Castes instead of belonging
to a higher caste (omitted category) decreases happiness. However, being neither froma Scheduled Caste nor a Scheduled Tribe has a signiﬁcant negative impact. This result
appears quite puzzling. Interestingly, it is quite similar to what Linssen et al. (2011)
obtain. They ﬁnd that belonging to a Scheduled Caste/Tribe or to an Other Backward
Caste has no signiﬁcant impact on well-being, as compared to belonging to a higher caste.
Still, caste inﬂuences expenditure and education, which in turn aﬀect happiness. However,once we control for those variables aﬀected by caste membership, caste does not appear
to aﬀect well-being as much as might have been expected.

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17

Table 1: Descriptive statistics, NSS & SPSL
NSS

SPSL

Other Backward Castes
Scheduled Castes
Scheduled Tribes
Neo-Buddhists
General Population

30.06
13.66
1.26
–
55.02

26.39
15.09
8.33
1.42
48.77

Illiterate
Literate, formal schooling < 4 years
Primary
Middle/upper primary
Secondary/higher secondary
College, but not graduate
Graduate and +Median age
Median household expenditures (real, 2005)

12.80
4.71
9.73
14.73
31.44
1.74
24.86
35
5803

12.84
1.34
6.80
24.07
31.24
5.91
17.81
44
6083

Caste (%)

Education (%)

18

Table 2: OLS regression on happiness, pooled cross-sections, main resultslog(Household expenditure)log(General ref. expenditure)log(Within-caste ref. expenditure)log(Between-caste ref. expenditure)Num. Obs.
R-SquaredGeneralcomparisons
0.517**(0.077)-0.644**(0.216)2908
0.1728Within & Between
comparisons0.549**(0.083)-0.150+(0.080)-0.608**(0.161)2590
0.1796

Low castes
only
0.554**(0.118)-0.030(0.086)-0.761**(0.191)1288
0.2213Higher castes
only
0.516**(0.111)-0.877**(0.228)-0.168(0.285)1302
0.2105+ p<0.10, * p<0.05, ** p<0.01
Cluster-robust standard errors in parenthesis – sample clustered by (age category × education × city ×
caste). Other controls: education, labor force status, number of children, family status, age category,gender, city, caste, wave. Average number of observations used to compute the reference expenditure
variables in each cell for each year: ﬁrst column, 63; second column, 32 and 26; third column, 15 and 32;
fourth column, 50 and 20.

Table 3: OLS regression on happiness, pooled cross-sections, non-movers onlylog(Household expenditure)log(General ref. expenditure)log(Within-caste ref. expenditure)log(Between-caste ref. expenditure)Num. Obs.
R-SquaredGeneralcomparisons
0.647**(0.102)-0.684*(0.292)1240
0.2304

Within & Between
comparisons0.682**(0.107)-0.258(0.220)-0.816**(0.222)1151
0.2535Low castes
only
0.654**(0.185)-0.210(0.221)-1.191**(0.264)398
0.3682Higher castes
only
0.591**(0.133)-0.639*(0.318)-0.420(0.283)753
0.2603+ p<0.10, * p<0.05, ** p<0.01
Cluster-robust standard errors in parenthesis – sample clustered by (age category × education × city ×
caste). Other controls: education, labor force status, number of children, family status, age category,gender, city, caste, wave. Average number of observations used to compute the reference expenditure
variables in each cell for each year: ﬁrst column, 58; second column, 41 and 15; third column, 22 and 18;
fourth column, 52 and 14.

19

Table 4: OLS regression on happiness, with individual ﬁxed-eﬀects

log(Household expenditure)log(General ref. expenditure)log(Within-caste ref. expenditure)log(Between-caste ref. expenditure)Num. Obs.
R-SquaredGeneralcomparisons0.383**(0.102)-0.987**(0.191)3340
0.0567

Within & Between
comparisons0.412**(0.110)-0.329*(0.133)-0.918**(0.149)2590
0.0697Low castes
only
0.357*(0.156)-0.224(0.149)-1.268**(0.200)1288
0.2087Higher castes
only
0.468**(0.152)-1.296**(0.261)-0.336(0.238)1302
0.0660+ p<0.10, * p<0.05, ** p<0.01
Cluster-robust standard errors in parenthesis – sample clustered by (age category × education × city ×
caste). Other controls: education, labor force status, number of children, family status, age category,gender, city, caste, wave. Average number of observations used to compute the reference expenditure
variables in each cell for each year: ﬁrst column, 66; second column, 32 and 26; third column, 15 and 32;
fourth column, 50 and 20.

20

Table 5: Pooled and Fixed-eﬀect regressions on happiness, no comparison variablelog(Household expenditure)
Education (omitted: illiterate)
literate but schooling < 4 years
primary
middle/upper primary
secondary/Higher secondary
college, not graduate
gradutate +Labor force status (omitted: employed)
not working (excl. housewife/husband)housewife/husband
retiredstudent# of children category (omitted: no children)
1-3>3Single without parents
Single with parents
Married with parents
Other
Age category (omitted: 20-27)
28-41
42+
Gender (omitted: male)
femaleCity (omitted: Delhi)
MumbaiBengaluru
Chennai
KolkataHyderabad
Wave (omitted: 2009)
2010
2011
Other Backward Castes
Scheduled Castes
Scheduled Tribes
Neo-Buddhists
intercept
Num. Obs.
R-squared+ p<0.10, * p<0.05, ** p<0.01Standard errors in the right columns.

21

Pooled OLS
0.510** 0.067

Fixed-eﬀect OLS
0.343** 0.102

0.337
0.060
0.308**
0.416**
0.449**
0.662**

0.275
0.152
0.115
0.115
0.165
0.131

-0.604-1.137-0.605-0.6200.329-0.570

1.172
2.013
1.088
1.192
1.799
1.424

0.533**
0.069
0.383**
0.300

0.160
0.104
0.127
0.183

0.634**
0.245
0.538**
0.570*

0.233
0.165
0.206
0.269

-0.059-0.294+-0.3790.106-0.079-0.122

0.117
0.150
0.285
0.178
0.081
0.092

0.051
0.014
0.036
-0.032-0.306*-0.157

0.208
0.322
0.443
0.256
0.136
0.136

-0.076-0.153

0.118
0.119

0.017

0.097

0.506**-0.739**0.165-0.799**
0.108

0.113
0.144
0.116
0.110
0.110

0.481
0.419
-

0.413
0.503
-

0.362** 0.075
0.700** 0.077-0.270** 0.089-0.0300.098-0.134
0.133
0.101
0.278
2.342** 0.604
2926
0.16720.266** 0.085
0.601** 0.092
3.846** 1.344
3361
0.0420