GCOE Discussion Paper Series
Global COE Program

Human Behavior and Socioeconomic Dynamics
Discussion Paper No.183

Noblesse Oblige? Preferences for
Redistribution among Urban Residents in India

Takahiro Ito, Kohei Kubota, and Fumio Ohtake

March 2011

GCOE Secretariat
Graduate School of Economics

OSAKA UNIVERSITY

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

Noblesse Oblige? Preferences for
Redistribution among Urban Residents in India†
Takahiro Ito, Kohei Kubota, and Fumio Ohtake‡
September 2010
Abstract
Using an original dataset, we investigated the determinants of individual preferences forincome redistribution in India. Different from previous studies, our results show that
economic-related conditions do not explain people’s tastes for redistribution so much.
While future prospects for economic status have a statistically significant impact on
preferences for redistributive policies, the current household income level does not
explain individual preferences for redistribution. On the contrary, the impact of thecurrent income level is lessened to a large extent by controlling caste membership. Thissuggests that the social hierarchy which is based on caste is a crucial determinant of
economic status in India. Furthermore, the results show that the relative economic
position compared with people in the neighborhood is an important factor; relatively
wealthy individuals are more likely to favor greater redistribution. This seemingly“noblesse oblige” effect can be explained mostly by religious belief.
Keywords: Governmental redistribution; Social preferences; Relative economic
position; Noblesse oblige; India
JEL classification code: D31; D63; H23; I38†

We are grateful to Takahiro Sato and Koji Yamazaki for their helpful comments and suggestions on
this paper. We also gratefully acknowledge financial support from the Osaka University Global COE
program, “Human Behavior and Socioeconomic Dynamics” and a grant obtained by the Nihon
University Population Research Institute from the "Academic Frontier" Project for Private
Universities matching fund subsidy 2006-2010 from the Ministry of Education, Culture, Sports,
Science and Technology, Japan.
‡
Takahiro Ito: Assistant Professor, Institute of Social and Economic Research, Osaka University,
Mihogaoka 6-1, Ibaraki, Osaka 567-0047 Japan. Kohei Kubota: Post doctoral fellow, Population
Research Institute, Nihon University, 12-5 Gobancho, Chiyoda, Tokyo 102-8251 Japan. Fumio
Ohtake: Professor, Institute of Social and Economic Research, Osaka University, Mihogaoka 6-1,
Ibaraki, Osaka 567-0047 Japan.
11. Introduction
Poverty and inequality are highly emotive issues: while some may abhor the very
existence of them based on their beliefs, others may think that they are linked to someextent to crime. In this paper, we examine the determinants of preferences for income
redistribution using data from urban Indian.
The many studies that have examined the determinants of preferences forincome redistribution to date have focused on developed not developing countries.
However, poverty and inequality are usually more severe in developing countries, where
adequate policies to eliminate these problems are generally lacking. The redistributionof income might also be related with economic growth: if redistribution can leadincreases in investment for the economy as a whole, alleviating inequality would
accelerate such growth(see, for instance, Galor and Zeira, 1993). Several studies havefound evidence of a negative correlation between inequality and economic growth(Benabou, 1996; and Barro, 2000). 1 At an early stage of economic development, wherein general accumulation of physical and human capital is low and its marginal
productivity is high, alleviating inequality might improve productivity of the country as

On the other hand, Banerjee and Duflo (2003) found inequality has a nonlinear effecton growth rates: changes in inequality (in any direction) are associated with lower
future growth rates.

12

a whole. 2 Thus, individual preference for redistribution in developing countries is an
issue of great concern both to policy makers and academic researchers.
The specific setting of India as the object of our study might be of particular
relevance in this day and age. First, despite experiencing two decades of significant
economic growth, India continues to have the worst poverty problems in the world. Oneof the latest estimates indicates that around 35% of the Indian population (some 360
million people), which accounts for approximately one-third of the world’s poor, still
live on less than one purchasing power parity dollar a day (UNDP, 2007). Second, India
has a strong hierarchical social structure, the caste system, which maintains substantial
economic disparity between the lowest and upper classes (Srinivasan and Kumar, 1999;
Thorat, 2002) and cripples economic mobility. The caste hierarchy also extends to thepolitical power structure (Banerjee et. al., 2005; Banerjee and Somanathan, 2007). As
government redistribution polices could be an important instrument in eliminating
poverty and inequality in India, it is important to develop policy implications regarding
redistributive policies. This paper is the first study to investigate the determinants ofpreferences for income redistribution using Indian data.

Some authors have argued that the negative correlation between inequality and later
economic growth is due to redistributive policies, since such policies impede investment
as a whole (see, for example, Alesina and Rodrick, 1994). However, Perroti (1996) foundno linkage between redistributive policies and economic growth.

23The remainder of this paper is organized as follows. In Section 2, we review
the literature on preferences for income redistribution. In Section 3, we explain theoriginal dataset used in the analysis obtained from surveys conducted in six
metropolitan cities in 2008 and 2009, covering people between the ages of 19 and 60. In
Section 4, we present the estimation results. Different from previous studies, our results
show that economic conditions do not explain individual preferences for redistribution
so much. While future prospects for economic status have a statistically significant
impact on preferences for redistributive policies, the level of current household income
does not explain individual preferences for redistribution. On the contrary, the impact of
current income level is lessened to a large extent by controlling caste membership. Thissuggests that the social hierarchy based on caste is a crucial determinant of economic
status in India. Furthermore, the results show that the relative economic position
compared with people in the neighborhood is an important factor: relatively wealthy
individuals are more likely to favor greater redistribution. This seemingly “noblesse
oblige” effect can be explained mostly by religious belief. Section 5 concludes thepaper.

2. The Determinants of Preferences for Income Redistribution
4

Past, current, and future economic status
Preferences for income redistribution have been examined theoretically and empiricallyby several authors to date. A simple theoretical model with self-interested economic
agents predicts that relatively poor individuals favor government income redistribution
while rich individuals oppose it, 3 and this has been supported empirically in many
countries. Unemployment is also an important determinant of preferences forredistribution; several studies have found that being jobless has a statistically significant
positive impact on preference for redistribution, even after controlling for income level.
In addition to current economic status, past economic conditions as well as
future prospects also matter in determining preferences for government redistribution.
For instance, Benabou and Ok (2001) suggested that an individual’s prospects
associated with his or her prospective social and economic status could be an important
determinant of preference for redistribution, and some studies have provided evidencein support of this “Prospect of Upward Mobility” (POUM) hypothesis. 4
Contrary to an individual’s future prospects, past experiences can also affectthe formation of preferences for redistribution. Ohtake and Tomioka (2004), using
Japanese data, found that experience of job loss within the previous 5 years has a3See, for instance, the well-known model by Meltzer and Richard (1981).
See, for example, Alesina and La Ferrara (2005) for an American study, Ohtake and Tomioka(2004) for a Japanese study, and Ravallion and Lokshin (2000) for a Russian study.
45

significant positive effect on approval of redistribution. In addition, Alesina andGiuliano (2009) who tested whether experiences during youth have a persistent effecton the formation of preferences for redistribution found that people who experienced avolatile macroeconomic situation between age 18 and 25 are likely to favor
redistribution. In this regard, however, it might be very difficult to distinguish between
the effect purely from past experiences and from future prospects, since future prospects
are supposed to be built upon past experiences.

Religion, world view and social class
A growing number of studies are focusing on the influence of religion on economic
behavior or individual attitudes (Iannaccone, 1998; and Kubota et al., 2011). Regarding
the relation between religion and preferences for redistribution, several studies havefound that religion matters in determining tastes for distributive policies. Alesina andGiuliano (2009) suggested people raised religiously (measured by religious
denomination at age 16) appear to hold a more favorable attitude to redistribution thanatheists, suggesting that religious people are more altruistic.
Racial and ethnic groups are also relevant in the formation of attitudes toredistribution. Especially when living standards vary widely among groups, favorability
6for redistribution may also vary according to groups. In fact, Alesina and La Ferrara,(2005) and Alesina and Giuliano (2009) found significant racial differences in
preferences for redistributive policies in the U.S.

Other demographic characteristics
Alongside economic conditions, other demographic characteristics such as age, sex,
education level, and marital status also play a part. While education was commonly
found to have a negative impact on preferences for redistribution, the impacts of age
and sex are somewhat mixed. Older people tend to favor redistribution less in Japan(Ohtake and Tomioka, 2004), whereas the situation is opposite in the U.S (Alesina and
La Ferrara, 2005; and Alesina and Giuliano, 2009). Similarly, in many countries,
women more than men tend to favor redistributive policies, but this is not the case inJapan. Regarding marital status, being married was found to increase favorability forredistribution regardless of country.

3. Data
Data and the sampling method
The data employed in this paper are from Indian surveys conducted in 2009 and 20107

by the Osaka University Global COE program “Human Behavior and Socioeconomic
Dynamics”. The 2009 wave covers 1,857 individuals in Delhi, Mumbai, Bangalore,
Chennai, Kolkata, and Hyderabad. Each city was divided into four areas, and 15
residential sections were randomly selected in each area. In a selected section, fivepeople were interviewed by the quota sampling based on age, sex, and socio-economic
classification (See Table 1). In 2010, these 1,867 respondents were re-surveyed and1,283 responses (response rate, 69.1%) were obtained.

[Table 1]

To check the representativeness of our sample, Table 2 shows the distributionof caste membership for our survey data (panel A) and the 61st National Sample Survey(NSS) data (panel B). Note that backward castes, scheduled castes and scheduled tribes
are the lowest classes in the social hierarchy. Our original data indicate that households
belonging to these castes are more likely to be poor, a tendency that can also be seen in
the NSS data. The only striking difference between our original data and the nationally
representative survey data is the proportion of scheduled tribes included: 9.0% in our
sample and 2.6% in the NSS sample. This indicates that our sample is representative of8the study region, to some extent.

[Table 2]

Variable for preference for government redistribution
Our measure of preference for redistributive policies is based on respondents’ opinionsof the following statement: “It is the government’s responsibility to take care of those
who cannot take care of themselves financially.” Respondents were instructed to chose anumber from 5 (“completely agree”) to 1 (“completely disagree”), with the chosen
number used as their preference for redistribution.
Figure 1 shows the distribution of the answers in 2009 (white bars) and in 2010
(dark gray bars). As it can be seen, approximately 80% of respondents gave a favorable
response to government redistribution in both years (rightmost and second rightmostbars), but the distribution is slightly different: compared with the 2009 survey, the 2010
survey appears to contain fewer respondents who answered “completely disagree” or“disagree.” This might in itself not be due to the attrition problem, since the answers of
the respondents in 2009 who were re-surveyed in 2010 (light gray bars) show almost thesame distribution as those of all respondents in 2009. An investigation of the changes in9distribution would also be interesting, but is beyond the scope of this paper.
In the empirical analysis, the answers (x = 1 to 5) are replaced by values
ranging from 100 to 0 (= 5×{x −1}) and the 2-year average of the value is employed as
the dependent variable to eliminate possible measurement errors in respondents’answers. 5[Figure 1]

4. Results and Discussion
The base specification
We begin with the estimation of basic models including individual and household
characteristics as explanatory variables. Individual and household basic characteristics
are age, sex, marital status, education level, household size, and per-capita householdincome. In addition to these variables, we include caste membership and religion inorder to examine the influence of social- and cultural-related factors. The summary
statistics for these variables are given in Table 3.

5

See the first row of Table 3 for the summary statistics.
10

[Table 3]

Table 4 shows the ordinary least squares estimation results of our basespecifications. 6 In column 1, we report the result of the specification including caste
membership dummies as well as the basic individual and household characteristics. In
addition, we estimate two further specifications adding religion dummies (column 2)and employing the log of household income instead of income quartile dummies.

[Table 4]

The coefficients on age group dummies show that older people are more likely
to support redistributive policies. This might be because the social security system isinadequate and work opportunities become limited with age. Education level has asignificant impact on people’s preferences for redistribution, with high-educated people
showing a less favorable attitude to government redistribution. The percentage of people
opposing redistributive policies among the college and more educated is approximately
4% less than that among those with no formal education.
In what follows, all estimation results are obtained through ordinary least squares.
Note, however, that the findings in this paper are mostly unchanged even when we
employ the ordered Probit model.

611

Turning to the per-capita household income, the results indicate no significant
effect on preferences for redistributive policy. This is inconsistent with the results ofprevious studies: a standard theoretical model predicts that relatively wealthy
individuals favor lower taxes and less redistribution, and this has been supported
empirically. Even using an alternative income measure (log of per-capita householdincome rather than income quartile dummies) does not change the result: the coefficientestimate is negative, but statistically insignificant (column 3). The influence of income
will be further examined later.
Looking at the impact of caste membership, the scheduled caste dummy has asignificant positive effect. This is consistent with our expectations, since people
belonging to a lower class have limited access to economic opportunities (Ito, 2009). As
for religion, all religion dummies have a positive impact, but only the Hindu dummy
has a statistically significant effect. The dummy for those who described themselves asa deeply religious person also has a significant positive effect. Thus, religion could alter
people’s world view.

Changes in household economic status
In addition to the current economic conditions examined thus far, changes from the past12

or to the future are also important factors in determining people’s preferences forincome redistribution. In this subsection, we examine the influence of changes inhousehold economic status, specifically unemployment and household income growth,on such preferences.

[Table 5]

For unemployment-related variables, we employ a dummy variable for people
for whom someone in the family experienced job loss in the past 5 years and a dummy
for people who think that one of their family members is highly likely to lose his or her
job within the next 2 years. Panel A of Table 5 shows the estimation results using thesevariables, and indicates that preferences for redistribution are positively correlated withthe past experience and future prospect of unemployment for a family member. Since
people’s attitudes toward unemployment might be related to their risk preferences, we
also conduct an estimation controlling for the coefficient of relative risk aversion(columns 2 and 4, Panel A), but the result is unchanged. 7 Likewise, we investigate theinfluence of income growth from the previous year and expected income growth to the7

For the calculation of the coefficient of relative risk aversion, see Appendix I.
13

next year (Panel B, Table 5). As is expected, people with a high rate of income growth(in terms of both the past year and future prospect) are less likely to support
redistributive policies, with the impact of future prospect being statistically significant
at the 5% level.
It should be noted that the results shown in columns 3 and 4 of both Panel A
and B can be interpreted as evidence supporting the POUM hypothesis, that is, anindividual’s preference for redistribution depends not only on current economic status
but also future prospects. In this regard, Ravallion and Lokshin (2000), using Russiandata, found that those who are currently rich tend to support redistribution if they expect
their living standards to fall. If this holds true for our case, no significant impact ofhousehold income found in Table 4 can be explainable on the basis of this hypothesis.
After controlling for future prospects regarding job loss or income growth, thecoefficients on income quartile dummies remain insignificant statistically andeconomically—on the contrary, the negative impacts are mitigated in some cases. These
results are not reported here, but are available on request from the authors.

Past macroeconomic shocks
In the previous subsection, we investigated the influence of unemployment and income
14growth as an individual or as a household-specific economic shock. This subsection
focuses on the impact of macroeconomic shocks. According to research in social
psychology, past experiences, especially during youth, have a profound effect on an
individual’s way of thinking and perceiving the world. The importance of this period(often identified as around age 18) could be attributable to the fact that this period
corresponds to one of “socialization.” Alternatively, economics may attach importanceto the period because it corresponds that when many start their careers after graduation.
Figure 2 depicts the growth rates of the net state domestic product (NSDP) in
six states between 1961 and 2006. Based on this NSDP growth, we build a dummy for
those who experienced a recession (bottom 10%, short dashed line) and a dummy for
those who experienced a boom (top 10%, long dashed line) at the time of adolescence.
In addition to the growth rates, we also employ a measure of whether the economy had
been stable during the time of adolescence. Similarly to the case of growth rate, we
generate a dummy for a stable economy and a dummy for an unstable economy using
variance of NSDP growth (Figure 3).

[Figure 2]

15

[Figure 3]

Table 6 shows the means of the above-mentioned variables and their coefficientestimates. Although all estimates are statistically insignificant, the results in Panel A
show that the recession dummy has a positive coefficient and the boom dummy has anegative coefficient, indicating that people who experienced a negative (positive)
macroeconomic shock at age 15 to 20 tend to be more (less) in favor of redistributive
policies. Similar results are obtained in the case of dummies for a stable or unstableeconomy (Panel B, Table 6). Unfortunately, our sample covers only six states (cities)
and has a little variation in these dummies; consequently, the influence ofmacroeconomic shocks might be captured by region dummies to a large extent. Further
examination using data covering wider areas would be an interesting exercise, but this isleft for future research.

[Table 6]

Re-examining the influence of income
In Table 4, it was seen that household income does not affect an individual’s attitude to16

government redistribution, which is inconsistent with the standard theoretical prediction.
One explanation for this would be the POUM hypothesis: people are in favor of (oraverse to) redistribution based not only on their current income level but also on future
income prospects. However, as discussed earlier, future prospects do not explain thelack of significant impact of household income. In this subsection, therefore, we
examine three more possibilities.
First, it is possible that our income measure is not appropriate. To confirm thispossibility, we conduct several estimations using alternative income measures. In
column 2 of Table 7, quartile dummies based on total household income are employed
instead of dummies based on per-capita household income. The result shows that thecoefficient estimates for the third and fourth quartile (above median) dummies arenegative yet statistically insignificant. Column 3 shows the result using per-capita realestate and per-capita financial assets in place of household income. The coefficients forthe third and fourth quartile dummies of per-capita real estate are negative, but thesenegative effects are dominated by the positive effects of per-capita financial assets.
Although not reported here, employing these quartile dummies of real estate andfinancial assets separately does not change the results mentioned above. Thus, the lackof significant impact of household income appears not to be due to a problem of17

measurement of household income.

[Table 7]

The second possibility is that social status determines economic status to alarge extent in India and economic status does not therefore seem to have any impact
after controlling for social status. Columns 1 and 2 of Table 8 show the estimation
results excluding education and caste membership dummies. As is expected, allcoefficients for the income quartile dummies become negative and their magnitude
increases after excluding the education and caste dummies, although they arestatistically insignificant.

[Table 8]

Finally, we investigate the influence of relative economic status. In a rigid,
stratified society like India, people may be concerned about their relative income levelwithin the class to which they belong rather than the absolute income level. If this is thecase, people’s economic position should be measured based on their position relative to18their peers. Our original dataset contains information on the income level of people
around the respondents, and whether the respondent’s standard of living is higher or notthan his or her neighbor. 8 Columns 3 to 6 of Table 8 show the estimation results
including these relative economic conditions. High standard of living is a dummy
variable for those who reported their living standards to be subjectively “considerably
higher” or “higher” than that of their neighbors, and high household income is a dummy
for those whose household total income is higher than that of their neighbors’ income.
As shown in columns 3 to 5 of Table 8, the coefficients for household income quartile
dummies are more likely to be negative by including these dummies of relative
economic status; excluding education and caste dummies makes the coefficients
statistically significant (column 6). Furthermore and contrary to our expectations, it isvery interesting to note that the relative income position has a positive impact on
preferences for redistribution, indicating that relatively rich individuals are more likely
to favor redistribution policies. 9 Can this result be explained by a sense of “noblesse
oblige” or other reasons? We will examine this in the following subsection.
Summarizing this subsection, we conclude that the absence of impact of(absolute) household income level on preferences for redistributive policies, as is foundRespondents were also asked with whom they would compare their household income.
This result is robust even when using alternative income measures, e.g., total household income in
Table 7.

8
919

in Table 4, might be attributable to the fact that social status can approximate economic
status to a large extent in India, and that relatively rich individuals support the notion ofincome redistribution.

Investigation of the influence of relative economic status
The question that remains to be answered is why relatively wealthy individuals lookupon redistribution more favorably than lower classes. This seemingly “noblesse
oblige” effect may, however, have nothing to do with things such as social
responsibilities or prosocial activities; for example, huge inequality may cause highcrime rates, and hence, relatively rich individuals support policy efforts trying to
mitigate inequality simply because they want to reduce the risk of being a victim ofcrime (crime deterrent hypothesis). In short, the seemingly “noblesse oblige” effectcould be explained on the basis of selfish motives.
Of course, there is the possibility that people’s belief or preferences other thanselfish motives can explain the results in Table 8. For instance, the rich may think that
their current economic status is attributed to the community to which they belong, andthat it is natural to support the poor in their community (group loyalty hypothesis). 10

10

For the group loyalty hypothesis, see Luttmer (2001).
20

Further explanations of the attitudes of relatively rich people to disadvantaged groups
could include world views based on religion or those not aligned with religion at all andindividual preferences such as being inequality averse or altruistic. Religious beliefs andaltruism reflect what we think of as “noblesse oblige” more. To investigate these
possibilities, we run further estimations (Table 9).

[Table 9]

Row 2 of Table 9 shows the result controlling for trust for other people. The‘trust for others’ dummy takes unity if people answer “strongly agree” or “agree” to thestatement, “Generally speaking, people are mostly trustworthy.” If relatively wealthypeople support redistributive policies mainly to deter crime (the crime deterrent
hypothesis is true), the interaction term between trust for other people and high standardof living would have a negative coefficient, because it is expected that fear of being a
victim of crime is related to distrust of other people. However, the coefficient for theinteraction term is positive and statistically significant, suggesting that the crime
deterrent hypothesis may not be true.
In column 3, a dummy for egalitarianism and its interaction term with high21

standard of living are controlled for. The egalitarianism dummy is generated from thesame question as that posed by Bartling et al. (2009). See Appendix II for the generationof the dummy variable. The result in column 3 indicates that the relatively rich favor
redistributive policies based on their egalitarian belief to some extent.
Column 4 shows the estimation result controlling for people’s attitudes tomaking donations to others. In our survey, we asked what percentage of householdincome would respondents be willing to donate to a destitute family whose income levelis one-fifth of the respondents’ family, where respondents chose from among thefollowing: 1 = up to 1%, 2 = up to 2%, 3 = up to 5%, 4 = up to 10%, 5 = more than 10%,and 6 = no help at all (0%). If attitude to charitable action represents altruism and if therelatively rich approve of income redistribution simply because they are altruistic, thecoefficient for the interaction term between donation and high standard of living is
expected to be positive. The estimation result in column 5 supports this hypothesis.
However, there is a possibility that the percentage of income a person would donate
does not necessarily correspond to the degree of altruism of the person. For instance,
people may give a donation to gain a reputation for doing so. This being the case, wecannot distinguish between the two effects, and accordingly, the result should beinterpreted with care.
22

We also conduct further estimations focusing on caste and other social
preferences. However, the results show that the interaction terms between such factors
and relative economic status have no significant effects statistically or economically. In
regard to the influence of the interaction term between caste dummies and relative
economic position, the result that it has no significant impact may suggest rejection of
the group loyalty hypothesis. These results are not reported in Table 9 for the sake ofsimplicity, but are available on request from the authors.
Finally, we investigate the effects of religious belief, including interaction
terms between religion dummies and relative economic status (column 5, Table 9). The
result shows that the interaction terms of Hindu and other religion (includes traditional
Eastern religions such as Buddhism, Jainism, and Sikhism) have a statisticallysignificant positive impact on favoring income redistribution. Thus, support forredistributive policies by the relatively rich can be explained by religious belief to alarge extent. This result is almost unchanged even when all the above-mentioned factors
are included (column 6). Looking at the interaction terms, the result shows that donation
and religion dummies remain significant, but trust for others and egalitarianism do not.
Trust in other people or egalitarianism might be partly based on religious belief.

23

5. Conclusion
In this paper, we investigated the determinants of preferences for income redistribution
using data from urban Indian. The main lesson obtained from the analysis is that thepreferences of Indian people for redistribution seem to be based on social and cultural
factors rather than on economic-related factors. This is partly because social factors
determine economic status to some extent in Indian society. A typical example is therigid social hierarchy in place, the caste system. Because economic status or the
standard of living differs substantially between castes, the proportion of people in favorof government redistribution also varies widely from caste to caste.
In addition, the absolute income level has no significant impact on favoring
redistribution, whereas relative income position has a significant positive impact on
support for redistributive policies. This finding suggests that relatively wealthy
individuals hold a more favorable attitude than poor individuals to government
redistribution. This is the most distinctive findings of our study compared to previous
ones, and our examination suggests that this “noblesse oblige” effect can be explained
mostly by religious belief.
In the context of the Indian society, this result might be of some considerable
importance. India has the largest population living below the poverty line, and rigid
24social stratification based on the caste hierarchy still exists. Consequently, governmental
redistributive policies could be a significant instrument to reduce poverty and inequality.
While the political friction between castes has become an important political issue,
support for redistributive policies from wealthy people (regardless of caste) is a positive
factor for pursuing further redistributive policies.

25Appendix A: Estimation of the Coefficient of Relative Risk Aversion
The coefficient of relative risk aversion (RRA) used in the analysis was calculated
based on the respondents’ answers to a series of questions regarding income risks,
following the procedure proposed by Kimball et al. (2008).
First, respondents were asked to choose a more preferable job from thefollowing two choices: (a) a job that has a 50% chance of the monthly income doubling,
but also has a 50% chance of the income decreasing by 30%, and (b) a job where themonthly income is guaranteed to increase by 3%. If the respondent chose (a), then he or
she was further asked to choose one from the following: (a-1) a job that has a 50%
chance of the income doubling, but also a 50% chance of the income decreasing by 50%
or (a-2) a job where your monthly income is guaranteed to increase by 3%. Those whochose (b) were then also asked to choose one from the following: (b-1) a job that has a
50% chance of the monthly income doubling, but also a 50% chance of the monthly
income decreasing by 10% or (b-2) a job where the income is guaranteed to increase by
3%. Thus, each respondent must choose one situation from (a-1), (a-2), (b-1), and (b-2).
In addition to this question, we asked a similar question in which the values werechanged values (see below).

26

[Questions regarding risk preferences]
Q1 In which of the following two ways would your prefer to receive your monthly income?
Assume that your job assignment is the same for each.

If you are a dependent (e.g.

student,

housewife, etc.) and not working, please answer based on your monthly income being youractual living expenses. SELECT ONLY ONE ANSWER THAT IS APPLICABLE.
(a) Your monthly income has a 50% chance(b) Your monthly income is guaranteed toof doubling, but also has a 50% chance ofincrease by 3%

decreasing by 30%

Q1-1

Q1-2

Of the following two jobs, which would you

Of the following two jobs, which would you

prefer? SELECT ONLY ONE ANSWER

prefer? SELECT ONLY ONE ANSWER

THAT IS APPLICABLE.

THAT IS APPLICABLE.

A job that has a 50% chance of themonthly income doubling, but also
a 50% chance of the monthly

A job that has a 50% chance of the(a-1)

monthly income doubling, but also
a 50% chance of the monthly

income being cut in halfincome decreasing by 10%

A job with which your monthly

A job with which your monthly

income is guaranteed to increase

(a-2)

by 3%

income is guaranteed to increase

(b-1)

(b-2)

by 3%

Q2 Which of the following two ways would you prefer to receive your monthly income? Assume
that your job assignment is the same for each.

If you are a dependent (e.g.

student,

housewife, etc.) and not working please answer based on your monthly income being your actual
living expenses.

SELECT ONLY ONE ANSWER THAT IS APPLICABLE.

(a) Your monthly income has a 50% chance(b) Your monthly income is guaranteed toof increasing by 60%, but also has a 50%

increase by 3%

chance of decreasing by 10%
Q2-1

Q2-2

Of the following two jobs, which would you

Of the following two jobs, which would you

prefer? SELECT ONLY ONE ANSWER

prefer? SELECT ONLY ONE ANSWER

THAT IS APPLICABLE.

THAT IS APPLICABLE.

A job that has a 50% chance of themonthly income increasing by
30%, but also has a 50% chance ofA job that has a 50% chance of the(a-1)

monthly income increasing by
200%, but also has a 50% chancedecreasing by 10%of decreasing by 10%

A job with which your monthly

A job with which your monthly

income is guaranteed to increase

(a-2)

by 3%

income is guaranteed to increase
by 3%

27

(b-1)

(b-2)

Assuming that people’s utilities are expressed by the constant coefficient ofrelative risk aversion (RRA) utility function, u(y) = y (1 − γ) / (1 − γ), where γ is thecoefficient of RRA, and based on the expected utility hypothesis, we have the following
four relationships in the case of Q1:21−γ0.51−γ 1.031−γ
for those who choose (a-1),
0.5
+ 0.5
>
1− γ
1− γ
1− γ
21−γ
0.51−γ 1.031−γ21−γ0.71−γfor those who choose (a-2),
0.5
+ 0.5
<
< 0.5
+ 0.5
1− γ
1− γ
1− γ
1− γ
1− γ
21−γ
0.71−γ 1.031−γ21−γ0.91−γfor those who choose (b-1),
+ 0.5
<
< 0.5
+ 0.5
0.5
1− γ
1− γ
1− γ
1− γ
1− γ
0.91−γ 1.031−γ
21−γ
for those who choose (b-2).
0.5
+ 0.5
<
1− γ
1− γ
1− γ

Solving these inequalities for γ (and also in the case of Q2), we obtain the range ofvalues that γ can take for each category (Table A-1).

[Table A-1]

Finally, we calculate the point estimates of γ based on the range of values
obtained above. Assuming that gamma has a log-normal distribution, log γ ~ N(μ, σ), we
obtain28 b − (µ + σ 2 )  a − (µ + σ 2 ) 




Φ
−
Φ


σ
σ
σ 2  .
E[ x | a < log x < b] = expµ +2 b−µ 
a−µΦ − Φ

 σ 
 σ 

The mean (μ) and variance (σ) of log γ were estimated by the maxim likelihood method(to maximize the likelihood of a person being into the category he or she really chose).
Table A-2 shows the point estimates of γ obtained using the equation above. Note, in
passing, that we employed the mean of γ between Q1 and Q2 in the estimation reported
in Table 5, but the choice of γ in Q1, γ in Q2, or the mean between the two does notchange the result.

[Table A-2]

Appendix B: Generating a variable for egalitarianism
Here, we explain the egalitarianism dummy used in Table 9. Similar to the question
posed by Bartling et al. (2009), our survey includes the hypothetical question listed
below to elicit the degree of inequality aversion. The question has four simple binary
choice games, and in each game respondents must choose a more preferable payoff.
29

While choice A offers an equal payoff (1000 rupees for each of the respondent and astranger) in all four cases, choice B offers unequal distributions: In Cases 1 and 2, therespondent’s payoff is higher than that of the stranger (the pro-sociality game and thecostly pro-sociality game, respectively), and in Cases 3 and 4, the respondent’s payoff islower than that of the stranger (the envy game and the costly envy game, respectively).

[Questions regarding egalitarianism]
Please assume that you and a total stranger can receive a certain amount of money. There
are two options for the amount of money that each of you can receive, and only you candecide which option to choose—the stranger would not know about the amount of money. In
this situation, which would you choose, A or B? Please select one answer for each case.

Case 1Case 2

Case 3

Case 4

A. 1000 Rs. for each of you and the

B. You receive 1000 Rs. and thestranger.

stranger receives 600 Rs.

A. 1000 Rs. for each of you and the

B. You receive 1600 Rs. and thestranger.

stranger receives 400 Rs.

A. 1000 Rs. for each of you and the

B. You receive 1000 Rs. and thestranger.

stranger receives 1800 Rs.

A. 1000 Rs. for each of you and the

B. You receive 1100 Rs. and thestranger.

stranger receives 1900 Rs.

Among the 1,280 respondents, 441 chose the option of equal distribution in the
pro-sociality and costly pro-sociality games, and 1,078 chose equal distribution in theenvy and the costly envy games. Respondents who chose equal distribution in all fourgames accounted for 371, and we define them as egalitarian.
30

References
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Alesina, A., Giuliano, P., 2009. Preferences for redistribution. NBER Working Papers
14825, National Bureau of Economic Research, Cambridge.
Alesina, A., Rodrik, D., 1994. Distributive politics and economic growth. Q. J. Econ.
109(2), 465-490.
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Growth. 8(3), 267-299.
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in rural India. J. Eur. Econ. Ass. 3(2-3), 639-647.
Banerjee, A., Somanathan, R., 2007. The political economy of public goods: someevidence from India. J. Dev. Econ. 292-300. 82(2), 287-314.
Barro, R.J., 2000. Inequality and growth in a panel of countries. J. Econ. Growth. 5(1),5-32.
Bartling, B., Fehr, E., Maréchal, M.A., Schunk, D., 2009. Egalitarianism andcompetitiveness. Am. Econ. Rev. 99(2), 93–98.
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(Eds.), NBER Macroeconomic Annual 1996, MIT Press, Cambridge, 11-74.
Benabou, R., Ok, E.A., 2001. Social mobility and the demand for redistribution: thepoum hypothesis. Q. J. Econ. 116(2), 447-487.
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60(1), 35-52.
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from rural north India. J. Dev. Econ. 88(2), 292-300.
Kimball, M.S., Sahm, C.R., Shapiro, M.D., 2008. Imputing risk tolerance from survey
31responses. J. Am. Stat. Assoc. 103(483), 1028-2038.
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Luttmer, E., 2001. Group loyalty and the taste for redistribution. J. Polit. Econ. 109(3),500-528.
Meltzer, A.H., Richard, S.F., 1981. A rational theory of the size of government. J. Polit.
Econ. 89(5), 914-927.
Ohtake, F., Tomioka, J., 2004. Who supports redistribution? Jpn. Econ. Rev. 55(4),333-354.
Perotti, R., 1996. Growth, income distribution, and democracy: What the data say. J.
Econ. Growth. 1(2), 149-187.
Ravallion, M., Lokshin, M., 2000. Who wants to redistribute? The tunnel effect in1990s Russia, J. Public. Econ. 76 (1), 87-104.
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backwardness. Econ. Polit. Wkly. 34 (42 & 43), 3052-3057.
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Wkly. 37(6), 572-578.
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32

Tables
Table 1: Distribution of observations by age category and city
Age category (years)

A: 2009

Total

20-29

30-39

40-49

50-59

60-69

Delhi

3.3%

3.6%

3.4%

3.2%

3.4%

16.9%

Mumbai

3.2%

3.6%

3.4%

3.3%

3.2%

16.8%

Bangalore

3.5%

3.4%

3.3%

3.2%

3.2%

16.7%

Chennai

3.3%

3.5%

3.3%

3.2%

3.3%

16.6%

Kolkata

3.2%

3.4%

3.4%

3.2%

3.2%

16.6%

Hyderabad

3.2%

3.3%

3.3%

3.3%

3.2%

16.4%

Total

19.8%

20.8%

20.2%

19.5%

19.6%

100.0%(N = 1,857)

Age category (years)

B: 2010

Total

20-29

30-39

40-49

50-59

60-69

Delhi

2.3%

3.0%

3.4%

3.0%

3.7%

15.4%

Mumbai

1.7%

4.2%

4.2%

2.7%

3.1%

15.9%

Bangalore

1.6%

2.7%

3.1%

1.5%

2.4%

11.3%

Chennai

2.7%

4.4%

4.1%

3.8%

4.1%

19.0%

Kolkata

3.4%

4.6%

4.1%

3.9%

3.6%

19.6%

Hyderabad

2.7%

3.9%

4.1%

3.6%

4.5%

18.8%

Total

14.4%

22.8%

23.0%

18.4%

21.4%

100.0%(N = 1,280)

33

Table 2: Comparison of the caste composition by consumption level between our data
and National Sample Survey data
A. Our data (2010)

MPCE (Rs.)
Backward castes
Scheduled castes
Scheduled tribes
Others# of observations
B. NSS data
(2004, 61st round)
MPCE (Rs.)
Backward castes
Scheduled castes
Scheduled tribes
Others# of observations

Monthly per-capita expenditure (MPCE)
Quartile
Poorest
2nd
3rd
Richest
818
1,372
1,968
3,641
18.1%
30.8%
36.8%
31.4%
22.5%
18.7%
15.2%
12.4%
17.5%
8.3%
6.3%
3.8%
41.9%
42.2%
41.6%
52.4%
315
315
315
315
Monthly per-capita expenditure (MPCE)
Quartile
Poorest
2nd
3rd
Richest
412
636
976
2,353
29.3%
32.1%
30.7%
19.5%
31.6%
27.1%
16.0%
6.4%
5.3%
1.2%
3.3%
0.5%
33.8%
39.6%
49.9%
73.5%
9,088
9,088
9,083
9,085

34

Overall
1,950
29.3%
17.2%
9.0%
44.5%
1,260

Overall
1,112
27.8%
20.0%
2.6%
49.6%
36,344

Table 3: Summary statistics of empirical variables
Variable
Preferences for redistribution *Age group dummies
30 to 39
40 to 49
50 to 59
60 or overFemale dummy
Marital status dummies
With spouse
Divorced/bereaved
Education dummies
Primary school
Middle school
Secondary school
College or moreHousehold size *Log of per-capita household income *Per-capita household income (Rs.) *Caste dummies
Backward castes
Scheduled castes
Scheduled tribes
Religion dummies
Hindu
ChristianMuslimOther
Deeply religious# of obs.
1280

Mean
76.62

1280
1280
1280
1280
1280

0.23
0.23
0.18
0.21
0.52

1280
1280

0.78
0.11

1280
1280
1280
1280
1280
1280
1280

0.07
0.26
0.37
0.17
4.67
10.37
42,363.79

1280
1280
1280

0.29
0.17
0.09

1280
1280
1280
1280
1280

0.86
0.04
0.05
0.03
0.87

Std. Dev.
17.07

Min
25

Max
100

1.56
0.67
52,398.76

1.50
8.49
4,888.89

16.00
13.70
890,000.00

Note: Single asterisk (*) doubles that the variable is calculated based on the 2-year
average. Other variables are time-invariant or represent the figures in 2010.

35

Table 4: Determinants of preferences for redistribution: Base specifications
(1)

(2)

Age group dummies
30 to 392.780 (1.706)
2.365 (1.707)
3.044 (1.740)*
2.711 (1.743)
40 to 49
3.902 (1.847)**
3.365 (1.857)*
50 to 593.653 (1.880)*
3.289 (1.877)*
60 or over
0.222 (0.995)
0.232 (0.999)
Female dummy
Marital status dummies-0.810 (1.774)-0.925 (1.769)
With spouse-1.053 (2.461)-1.199 (2.458)
Divorced/bereaved
Education dummies-3.250 (2.158)-3.281 (2.132)
Primary school-2.232 (1.528)-2.685 (1.487)*
Middle school-2.695 (1.524)*-3.011 (1.487)**
Secondary school-4.204 (1.924)**-4.336 (1.896)**
College or more
0.323 (0.305)
0.328 (0.307)
Household size
Per-capita household income quartile dummies-0.613 (1.408)-0.545 (1.402)
2nd quartile
0.148 (1.570)
0.130 (1.571)
3rd quartile-0.472 (1.650)-0.532 (1.660)
4th quartile
Log of per-capita household income
Caste dummies (reference category is Upper/Middle castes)
2.113 (1.474)
1.318 (1.506)
Backward castes
4.725 (1.450)***4.188 (1.470)***
Scheduled castes-1.292 (2.036)-1.870 (2.043)
Scheduled tribes
Religion dummies (reference category is atheist)
6.059 (2.925)**
Hindu
0.037 (3.997)
Christian5.073 (3.448)
Muslim2.836 (3.608)
Other
2.681 (1.517)*
Deeply religious
Region dummies (reference category is Delhi)-0.189 (1.548)
0.552 (1.585)
Mumbai-2.192 (1.964)-1.618 (1.928)
Bangalore-4.626 (2.016)**-3.849 (2.035)*
Chennai-0.199 (1.610)-0.133 (1.630)
Kolkata-4.653 (1.662)***-3.610 (1.687)**
Hyderabad
76.450 (3.133)***69.110 (4.340)***Intercept# of observations
1280
1280
R-squared
0.034
0.044

(3)
2.382
2.726
3.413
3.353
0.232

(1.702)(1.740)(1.851)*(1.869)*(0.997)-0.874 (1.769)-1.172 (2.458)-3.178-2.557-2.705-3.8420.258(2.133)(1.483)*(1.484)*(1.912)**(0.308)-0.715 (0.915)
1.249 (1.502)
4.145 (1.462)***-1.871 (2.032)
6.074
0.044
5.078
2.887
2.709

(2.928)**(4.000)(3.446)(3.600)(1.513)*0.552-1.680-3.838-0.353-3.58676.330(1.583)(1.923)(2.025)*(1.649)(1.669)**(10.63)***
1280
0.044

Note: Numbers in parentheses are standard errors. Single asterisk (*), double asterisks (**), and triple
asterisks (***) denote that the coefficient is statistically significant at the 10%, 5%, and 1% levels,
respectively.
36

Table 5: Influence of change in household economic conditions
A. Unemployment

including:
Coef. of relative risk aversion# of observations
R-squared
B. Income growth

including:
Coef. of relative risk aversion# of observations
R-squared

Type of unemployment variable
A family member lost a job in
Likely that a family member
the past 5 years (dummy,
will lose a job in 2 years
mean = 0.15)(dummy, mean = 0.05)
(1)
(2)
(3)
(4)
3.088
3.088
3.791
3.800
(1.414)**(1.414)**(2.285)*(2.288)*
No
Yes
No
Yes
1280
1280
1280
1280
0.048
0.048
0.046
0.046
Type of income growth variable
Income growth from 2008 to
Expected income growth from2009 (%, mean = 3.25, std.
2009 to 2010 (%, mean =dev. = 3.47)
3.74, std. dev. = 3.57)
(1)
(2)
(3)
(4)
−0.241−0.244−0.308−0.313(0.151)(0.152)(0.154)**(0.156)**
No
Yes
No
Yes
1280
1280
1280
1280
0.046
0.046
0.047
0.047

Note: All estimations are implemented with other controls (as in column 2, Table 4).
Numbers in parentheses are standard errors. Single asterisk (*), double asterisks (**), andtriple asterisks (***) denote that the coefficient is statistically significant at the 10%, 5%,
and 1% levels, respectively.

37

Table 6: Influence of past macro-economic shocks
A. Growth level

At the age 15 to 18
Coef.

Mean
Recession dummy(Std. Error)

0.401

1.410

At the age 15 to 20
Coef.

Mean

(Std. Error)

0.531

1.942

(1.437)
Boom dummy−0.2250.373(1.451)−0.5300.501(1.221)

B. Stability# of obs.

1146# of obs.

1093

R-squared

0.046

R-squared

0.051

5-year moving variance7-year moving varianceat age 20

at age 20

Mean
Unstable dummy(1.264)

0.102

Coef.
(Std. Error)
0.128

Mean
0.096

(2.040)Stable dummy

0.109−0.957

Coef.
(Std. Error)
1.420(2.249)0.110(1.980)−0.931(2.304)# of obs.

1110# of obs.

1074

R-squared

0.048

R-squared

0.047

Note: All four estimations are implemented with other controls (as in column 2, Table
4).

38

Table 7: Re-examining the influence of household income:Using alternative income measures(1)(Column 2,Table 4)
Per-capita household income
2nd quartile
3rd quartile
4th quartile
Total household income
2nd quartile
3rd quartile
4th quartile
Per-capita real estate
2nd quartile
3rd quartile
4th quartile
Per-capita financial assets
2nd quartile
3rd quartile
4th quartile# of observations
R-squared

(2)

(3)

-0.545 (1.402)
0.130 (1.571)-0.532 (1.660)
0.198 (1.385)-0.645 (1.482)-0.177 (1.608)
2.468 (1.494)*-2.024 (1.538)-1.923 (1.730)
2.184 (1.438)
2.253 (1.604)
2.894 (1.811)
1280
0.044

1280
0.044

1280
0.055

Note: All estimations are implemented with other controls (as in column 2, Table 4).
Numbers in parentheses are standard errors. Single asterisk (*), double asterisks (**), andtriple asterisks (***) denote that the coefficient is statistically significant at the 10%, 5%,
and 1% levels, respectively.

39

Table 8: Re-examining the influence of household income:Excluding social status / including relative economic status variables
(1)

(2)

(3)

(4)

(5)

(6)

-0.978-1.471-0.612-2.171-2.216-3.072(1.376)(1.404)(1.459)(1.460)(1.430)**-1.312-0.084-1.923-1.919-3.297(1.508)(1.573)(1.657)(1.661)(1.592)**-2.443-0.550-2.678-2.630-4.599(1.548)(1.516)(1.662)(1.767)(1.770)(1.642)***

Education dummies

No

No

Yes

Yes

Yes

No

Caste dummies

Yes

No

Yes

Yes

Yes

No

2.290

2.468

2.419

(1.090)**(1.127)**(1.130)**

1.696

1.531

1.398

(1.093)(1.094)(1.093)Per-capita household income
2nd quartile(1.377)

3rd quartile-0.690(1.515)

4th quartile-1.585

Relative economic status dummies
High standard of living
High household income# of observations

1280

1280

1280

1183

1183

1183

R-squared

0.040

0.031

0.047

0.052

0.055

0.042

Note: All estimations are implemented with other controls (as in column 2, Table 4).
Numbers in parentheses are standard errors. Single asterisk (*), double asterisks (**), andtriple asterisks (***) denote that the coefficient is statistically significant at the 10%, 5%,
and 1% levels, respectively. The means of high standard of living and high householdincome dummies are 0.245 (# of observations = 1280) and 0.585 (# of observations =1183), respectively.

40

Table 9: Influence of relative economic status
Mean

(1)
Col. (3) of Table 8

Relative economic status:
High standard of living
2.290 (1.090)**
Trust for others
0.648
× High standard of living
Egalitarianism
0.290
× High standard of living
Donation
1.789
× High standard of living
Religion dummies (reference = no religion):
Hindu
0.856
× High standard of living
Christian
0.035
× High standard of living
Muslim0.053
× High standard of living
Other religion
0.034
× High standard of living
Deeply religious
0.864
× High standard of living# of observations
R-squared

1280
0.047

(2)

(3)

(4)

(5)

-0.835 (1.999)
2.233 (1.196)*
4.053 (2.368)*

0.775 (1.371)-1.173 (1.711)-13.908 (5.607)**-20.213
2.486
3.223-1.7482.978-1.1002.016(6.316)***(1.195)**(2.377)(1.800)(2.277)(0.288)***(0.748)***1.002
16.929-0.067-3.246
2.307
7.093-2.93422.3972.702

0.977
15.613-0.215-2.5091.997
6.155-2.60822.0082.839(3.644)(5.225)***(4.695)(7.157)(4.179)(6.910)(4.316)(6.064)***(1.784)-1.782 (1.815)
4.388 (2.262)*-1.100 (0.284)***
1.974 (0.736)***

1280
0.056

1280
0.050

1280
0.058

(3.431)(4.894)***(4.580)(7.164)(4.000)(6.656)(4.154)(5.680)***(1.797)(6)

0.868 (3.213)

1.328 (3.196)

1280
0.063

1280
0.083

Note: All estimations are implemented with other controls (as in column 2, Table 4). Note that religion dummies are controlled for incolumns (1) to (4) too, but are not reported in order to make the table simple. The coefficient estimates are almost the same as those in
Table 4. Numbers in parentheses are standard errors. Single asterisk (*), double asterisks (**), and triple asterisks (***) denote that thecoefficient is statistically significant at the 10%, 5%, and 1% levels, respectively.
41

Table A-1: Ranges of the coefficient of relative risk aversion (γ)
A: Case of Q1
Choice# of obs.
(a-1)
174
(a-2)
68
(b-1)
295
(b-2)
743
B: Case of Q2
Choice# of obs.
(a-1)
198
(a-2)
52
(b-1)
301
(b-2)
729

Γ
Lower limit
0.00
0.88
2.06
6.00

Upper limit
0.88
2.06
6.00
∞
Γ

Lower limit
0.00
4.04
5.64
6.12

Upper limit
4.04
5.64
6.12
∞

Table A-2: Point Estimates of the coefficient of relative risk aversion (γ)γ
Choice

Q1

Q2

(a-1)

0.18

3.21

(a-2)

1.34

4.83

(b-1)

3.43

5.88

(b-2)

19.96

8.69

42

Figures

Figure 1: Preferences for redistribution

43

Figure 2: Growth rates of the net state domestic product (NSDP), 1961-2006

44

Figure 3: Five-year moving variance of NSDP Growth, 1966-2006

45