Discussion Paper No.

603

WHO SUPPORTS REDISTRIBUTION?

Fumio Ohtake
and
Jun Tomioka

May 2004

The Institute of Social and Economic Research
Osaka University
6-1 Mihogaoka, Ibaraki, Osaka 567-0047, Japan

Who Supports Redistribution?∗

Fumio Ohtake
ISER, Osaka University, Osaka, Japanohtake@iser.osaka-u.ac.jp
Jun Tomioka
Japan Center for Economic Research, Tokyo, Japan
tomioka@jcer.or.jp

Abstract
Using an original dataset, we investigated the determinants of individual preferences over income
redistribution in Japan. Although income level is negatively correlated with support forredistribution, it does not explain much; there are other important factors, which relate to
dynamics and uncertainty such as income risk. Even after controlling for income, both risk-averse
individuals and those who expect to be unemployed in the future favor greater redistribution.
Interaction of aging and mobility proved important. Relatively poor elderly, who presumably
have few prospects of upward mobility, strongly support greater redistribution, whereas younger
people are less sympathetic to such a policy.
Author Keywords: Income redistribution; Inequality; Uncertainty, Unemployment
JEL classification codes: D31; H23; I38∗

This article is based on an Invited Lecture presented at the annual meeting of the Japanese Economic Association heldat Meiji University, 12–13 October 2003. We are grateful to Yuji Genda, Takenori Inoki, Daiji Kawaguchi, ananonymous referee of this Journal, and seminar participants at the National Institute of Population and Social Security
Research, Sophia University and the University of Tokyo for helpful comments. We appreciate financial supports fromthe Cabinet Office of the Japanese government, a Grant-in-Aid for Scientific Research from JSPS ( (B)(2)12124207
and (C)(2)14530109) and the 21st COE program.
1
Studies on inequality, such as Tachibanaki (1998), Ishikawa (1999), and Sato (2000) among others, were publishedwithin a short period and attracted wide attention from researchers and the general public.

1.

Introduction

How do people perceive and evaluate economic inequality in society? In Japan, there has been agrowing interest in income inequality among both the general public and academics.1
Representative indices of inequality, such as the Gini coefficient, have indeed exhibited a steadyrise since around 1985. Economists have proposed several possible explanations for this trend,such as the aging of the population, technological development, the rise in unemployment, andchanges in household structures (e.g., more double-income families and single-member
households). On the other hand, throughout the 1990s, the extent of redistribution has declined,
owing to the reduced progressiveness of income tax (Ohtake and Saito, 1999). Discussion of both
positive and normative aspects of income differentials has not abated and continues to this day.
At the same time, it is striking how little is known about the way the general public assesses
the existing distribution of economic resources and related policies. 2 For example, one may
conjecture that the poor favor income redistribution policies, as their net income normallybenefits from these policies.
However, this view is too simple. Figure 1, which is based on our survey, shows how people’s
preferences for greater income redistribution vary with their household income. Althoughdisapproval of redistribution clearly increases with income, there is a sizeable percentage of
people in the highest income quartile who approve of such policies. In addition, respondents inthe lower income groups do not necessarily support greater redistribution.3 The percentage of“not support” in the first income quartile is as large as 41.9% (which turns out to be identical withthe percentage of “support” in the fourth income quartile).
It would be premature to conclude from this that people are not selfish, or lack the ability tocompute personal payoffs. Benabou and Ok (2001) constructed a model in which currently poor

2

For instance, we find it difficult to explain why a keen interest in income inequality has become apparent only in thelate 1990s, which was almost ten years after the time (the late 1980s) when the index of inequality started showing an
upward trend.
3This also holds true when we use equivalent income scales instead, such as household income divided by the squareroot of the number of its members.

1individuals may oppose redistribution because they consider the potential loss from redistributive
policies should they become rich in the future. Benabou and Ok refer to this as the Prospect ofUpward Mobility (POUM) Hypothesis. Conversely, the rich may not necessarily oppose
redistribution. Using a dataset from Russia, Ravallion and Lokshin (2000) have shown that eventhose who are currently rich tend to support redistribution if they expect their welfare to fall.
Using an original dataset, we attempt to reveal the determinants of individual preferences forincome redistribution. We show that, although income is negatively correlated with support forredistribution, it does not explain very much; there are other important factors, which relate to
dynamics and uncertainty such as income risk. For instance, even after controlling for income,risk-averse individuals and those who expect to be unemployed in the future show a strong
tendency to support greater redistribution.
It was not until recently that economists began extensive use of people’s subjective opinions ineconometric analysis. Among current studies is one of people’s preferences over income
distribution and policy.
Redistributive policies normally entail a conflict of interests. Understanding the determinantsof individual preferences over redistribution may provide interesting (and yet unexplored) policy
implications. Furthermore, in a democratic state, the size of government as well as specificgovernment policies must be determined in accordance with the preferences of its members. We
expect our research to shed light on fundamental issues such as the origins of the welfare state andthe minimal state, and why some countries have more equal income distribution than others.4
We did not rely on an existing survey. Rather, we designed and conducted an original survey,
which enabled us to analyze preferences over redistribution in several new ways. For instance, wehave information on individual characteristics that are important from an economic point of view,

4

Classical economists, such as Ricard and Marx, believed that inequality could not persist in democracies. Yet this has
not turned out to be the case, and the reasons are by no means obvious. “ [I]f equalization of the distribution of wealth ispossible through the electoral process, and if it is in the interest of the large majority of people (as would appear to be
the case since median wealth is far below mean wealth in all capitalist democracies), why is it not implemented throughpolitical action by rational citizens?” (Putterman et al., 1998)2

including attitudes toward risk, time preference, subjective and objective social mobilities, manyof which have not previously been available. Further, we asked detailed questions about income
distribution from both positive and normative perspectives. We describe our dataset and variables
in detail later.
The remainder of the paper is organized as follows. In Section 2, we discuss the determinantsof preferences over redistribution. In Section 3, we present the estimable model and explain ourdataset. In Section 4, we report the estimation results. Section 5 concludes.
2.

The Determinants of Preferences Over Redistribution

We list below a set of possible determinants of preferences over redistribution that will be tested
in our empirical analysis.

(1) Current income
Under the standard assumption of self-interested economic agents in a static and deterministic
environment, the poor should favor redistribution while the rich oppose it. Meltzer and Richards(1981) consider an economy that consists of individuals with varying productivity, upon which a
linear income tax is levied, the rate of which is determined by majority voting. The government
redistributes the revenue in a lump-sum manner. The voter with the median income is decisive, inthat the tax rate he or she prefers is the one that is socially chosen. Voters with income below (resp.
above) the income of the decisive voter demand more (resp. less) redistribution than does themedian voter. The extent of redistribution (the tax rate) is increasing in the ratio of the mean
income to the median income, so a society that has a larger percentage of poor people will have amore extensive redistribution. This static median-voter model is the benchmark for our study.5 Its
prediction will be modified once we introduce dynamics and uncertainty into the picture, as
5Strictly, the model we estimate is not the median voter model per se. Our dependent variable is an indicator of whether
the respondent agrees to greater redistribution. This is probably a better way to test the essential idea of the medianvoter claim on redistribution. A direct test of the median voter model using electoral data in this context would face a
difficulty from the outset, for, in reality, electoral politics concerns not only redistributive tax but several different

3

discussed below.

(2) Expected income and social mobility
In a dynamic setting, coupled with upward social mobility and persistent redistributive policies
over a certain time period, agents’ expectations about future income will also matter: today’s poor
who are confident of becoming rich in the future will be net losers from redistribution in the longrun, so they may not agree to redistribution today. On the other hand, those who are currently welloff may be less so in future, and to the extent that they perceive this, may well demand
redistribution now. Benabou and Ok (2001) formalize this idea, which they call the Prospect ofUpward Mobility (POUM) Hypothesis. Alesina and La Ferrara (2001) constructed an index ofincome mobility using the Panel Study of Income Dynamics and tested the POUM hypothesis.
They found a negative correlation between regional mobility and individual support forredistribution. Using a dataset from Russia, Ravallion and Lokshin (2000) showed that even those
who are currently rich tend to support redistribution if they are expecting a decline in theirwelfare.

(3) Income inequality
If individual utility functions are such that people care about their relative position in societyrather than their absolute level of personal income or consumption, greater inequality can makethe poor (comparing themselves with the rich) unhappy, and the rich (comparing themselves withthe poor) happy, and people will favor income redistribution policies accordingly.6 Some people
may simply dislike inequality because it offends their sense of equity. Alesina, Di Tella and
MacCulloch (2001) show that a person’s subjective well-being can be negatively affected byissues (e.g., religion and racial policy) simultaneously. The multidimensionality of issues is a plausible reason why theprediction of the standard median voter model has failed in actuality (see Putterman et al., 1998).
6
Boskin and Sheshinski (1978) studied the optimal income tax when individuals are concerned with relative as well as
absolute income, and showed that the optimal tax rate could be much higher than those derived under the standard
assumption that only absolute income matters.

4

greater income inequality if he or she holds leftist ideas. Another possibility is that people
perceive greater inequality as a rise in income risk, and require more redistribution to prepare forthis increased uncertainty.

(4) Faith in equality of opportunities
People may not approve of inequality if they feel that ‘equal opportunity for all’ is not guaranteedin society. Alesina and La Ferrara (2001) found that equality of opportunity is a necessary
condition for Americans to see greater social mobility as a substitute to redistribution.

(5) Past income, consumption and previous experience
Individuals probably have an imperfect knowledge about the objective probability of upward or
downward social mobility. Their own experiences may then serve as crucial information when
they form perceptions about mobility. One’s personal history concerning income, employment,
and educational background (one’s parents’ as well) can have an important influence on whether
one is optimistic or pessimistic about one’s future, as well as how much faith one has in theequality of opportunity in the society. Piketty (1995) formalizes this type of idea in a Bayesianlearning model. Empirical investigation by Alesina and La Ferrara (2001), and Ravallion and
Lokshin (2000) confirmed this conjecture by finding past personal experiences important for theformation of opinions about income redistribution.

(6) Risk aversion
If an individual is uncertain about future income, risk aversion implies that he or she will be readyto give up some money to obtain a guaranteed income. The amount given up will be greater for
those who are more risk averse, for whom income redistribution may be desirable as a social
insurance against uncertainty, even though the current net benefit from such a policy is negative.
Empirical studies have used a self-employment dummy as an inverse proxy for risk aversion.

5

Alesina and La Ferrara (2001) found that the self-employed do indeed oppose redistribution. The
validity of this proxy has not been tested and is debatable. Our original survey enabled us to create
two different, direct indices of risk aversion: one is the standard certainty equivalent, derived
from a question on financial commodities. The other is obtained from the lowest percentage of
rainfall in a weather forecast that makes the respondent take an umbrella with him or her whengoing out.

(7) Age
As one approaches retirement age, the prospect of moving up the income ladder is likely todecrease. At least, one has a shorter time left to do so. One would then perceive the degree ofsubstitution between social mobility and income redistribution to be weaker, and one would cometo favor redistribution. Such a positive correlation between age and support for moreredistribution, if any, should be evident among lower-income earners, for it is they who currently
benefit most from such a policy. At one extreme are the relatively poor, retired elderly who haveno prospect of entering the labor market again (and therefore no possibility of experiencing
upward mobility). Our hypothesis implies that the effects of age on support for greater
redistribution should be positive and larger among these people than those for any other age andincome group. An analysis of how age relates to preferences for redistribution is also of interest
considering that Japan is an unusually rapidly aging society.

(8) Prospect of unemployment
Even after controlling for income and consumption, the anticipation of unemployment can
influence one’s welfare and preference over income redistribution. Indeed, recent econometricanalysis of subjective well-being showed that unemployment involves not only monetary loss but
also various psychological shocks to the unemployed, such as the loss of self-confidence and of
feelings of attachment to the workplace (Frey and Stutzer, 2002a, Ohtake and Tomioka, 2004).

6

Nonfinancial costs of expected unemployment could therefore be a significant source of demand
for social safety nets, including a greater redistribution policy.

(9) Altruism
This factor could be important in either a static or a dynamic environment. Even wealthy
individuals may agree to redistribution solely on the basis of altruism if they see redistribution asa substitute for charity. Alesina, Glaeser and Sacerdote (2001) suggest that while Europeans havegreater public provision of welfare than Americans, the US has more private provision of welfare(voluntary donations) to the poor than do European countries, and one cannot attribute this to thedifferences in taxation systems.

(10) Externalities and public goods
Redistribution need not involve a conflict of interests, or a trade-off between equity and efficiency.
It can be efficiency enhancing. Redistribution can compensate for capital-market imperfections(due to, say, high transaction costs or asymmetric information), and thus may have beneficial
effects on economic efficiency (Barr, 1992, Boadway and Keen, 2000, Krueger, 2002). The rich
may well understand public-good aspects of various policies, such as the need for stateintervention in the provision of infrastructure and education in a well-functioning society. These
policies have a redistributive consequence under a nonregressive taxation system. Greater
poverty can give rise to social unrest or increased crime, from which the rich will have a greater
fear of being injured parties. For these reasons the wealthy may, acting on their own self-interest,
rationally vote for extensive redistribution. In addition, government intervention to offset theseadverse externalities would be desirable as an efficient solution to the free-rider problem (social
prisoner’s dilemma) that would prevail when redistribution is to be done through voluntary
donations by the rich.

7

3.

Econometric Model and Data

3.1

Model and estimation strategy

We assume that the preferences of individual i over redistribution policy can be described using
the following latent variable model:

Yi* = X iβ + U iγ + M i λ + εi(1)where Yi* is unobserved and takes a higher value the more individual i favors greater
redistribution. X i is a vector of individual characteristics such as gender, age, educational
background, income, the degree of risk aversion, and the degree of altruism. U i is a dummy that
equals unity if the respondent experienced unemployment in the past five years. If the estimatedcoefficient of this variable is positive and large, one can conclude that those who have been
unemployed in the recent past will vote for redistribution by the state. M i is a binary variable thatis unity when the respondent thinks that upward social mobility has recently increased. If λ isnegative, the interpretation is that social mobility and redistribution policy are substitutes. ε i isan error term.
In practice, we do not observe Yi * . We define a binary variable Yi , which is unity if the
respondent shows support for more redistribution, and zero otherwise. 7 It also satisfies thefollowing condition:

Yi = 0 if Yi* < 0

Yi = 1 if 0 ≤ Yi*

7

Both Ravallion and Lokshin (2000) and Alesina and La Ferarra (2001) use survey questions that ask whether the
respondent supports income redistribution in general. This may be too general a question and leave ambiguity. We
believe it is useful to be somewhat more specific and ask, as we did in our survey, whether the respondent prefers more

8Assuming that ε i has a standard normal distribution and is independent of all the explanatory
variables, probit estimation yields consistent estimates of the parameters. For statistical inference,
we use cluster-robust standard errors to deal with the possible correlation of error terms withineach cluster (due to some region-specific factor), while assuming no correlation across clusters.
The appendix details variable definitions.

3.2

Data and descriptive statistics

Before presenting the results of the multivariate analysis, we describe our data and report somedescriptive statistics. We designed a survey entitled “Survey on life and society”. The survey wasadministered between 13 and 26 February, 2002. The population comprised adults aged between
20 and 65 in Japan. We sent the questionnaire to 6,000 persons selected at random. The number of
respondents was 1,928, which corresponds to a response rate of 32.1%.8 Survey questions
included those on the Japanese economy (such as the causes of rising unemployment,
determinants of individual income levels, changes in income distribution, preferences overvarious government policies), the current status of the respondent (income, wealth, expected
income, experience of unemployment, happiness, class consciousness), and other individual
characteristics (gender, age, educational background, occupation).
Our dependent variable is obtained from a question that asks whether the respondent supports
or opposes “strengthening the redistribution of income from rich to poor using tax and other
instruments of the social security system”. The respondent could choose from a 1 to 5 scale for“Support” to “Oppose”, with higher values indicating more support. From this, we created abinary variable that takes the value unity if the answer is favorable to redistribution and zerootherwise. We labeled this variable Redistr, and applied probit estimation.

or less redistribution than the current level, for in this way we know that the respondents share a common reference
point. It also tells us whether they approve of the current extent of redistribution by the government.
8
The response rate is rather high for a mail-based sampling procedure. Moreover, the authors have found relatively
small biases in the distributions of age, gender and income in our dataset, compared with the census and a
representative survey such as the Japanese General Social Survey. Nevertheless, the possibility that our respondents arenonrandom in some other characteristics remains.

9In Tables 1 to 5, we present some descriptive statistics. Table 1 shows that approval of moreredistribution is stronger in higher income groups. This is intuitively reasonable. However, it isalso true that a significant proportion (about 40%) of people in the lowest income quartile do notagree to greater redistribution, and around 40% of people in the highest income quartile do. In thissense, the current net benefit from income redistribution explains little of people’s preferences
over redistribution. In other words, a static median voter model with self-interested agents needs
to be modified in some way.
In Table 2, we report preferences for redistribution according to age groups. The aged are more
supportive of such policies, although there appears to be a nonlinear relationship. Table 3 showsthat men tend to support redistribution more than do women. Interestingly, this is in sharp contrast
to the evidence from other developed countries. Table 4 indicates that the unemployed are very
likely to support more redistribution. Table 5 contains basic descriptive statistics of all thevariables used in our multivariate analysis. Many respondents are quite conscious of the recentchanges in income distribution. For instance, the average value of a binary variable IncomeIneq
is about 0.66, indicating that 66% of respondents think that income inequality has increased in thepast five years. Similarly, the value of a dummy variable IncomeIneqFut implies that more than
75% of respondents expected income inequality to increase in the coming five years.
4.

Estimation Results

4.1

Basic model

We begin with estimation of the effects of basic individual characteristics. Table 6 shows theestimated marginal effect of each explanatory variable on the dependent variable, and theircluster-adjusted standard errors.
In the first three rows, we look at the attitude across marital status and gender. The base groupis married men. Females, especially married ones, turn out to favor redistribution less than domales. This contrasts with the findings in other developed countries.

10

Next, we examine the effects of age. If, probably after a certain point, aging makes upward
income mobility less likely (the elderly, at least, have a shorter time period to achieve a given
degree of social mobility than young people do), then under the POUM hypothesis discussed
above, older people should be more supportive of redistribution than are younger people.
Furthermore, this finding should apply to low-income groups because they enjoy a net current
income gain from redistribution.
The estimation results for the age dummies suggest that our conjecture is basically correct.
The omitted category is for those in their twenties. Elderly people tend to be more favorable togreater redistribution, and this effect is very robust. Later we confirm that this result holds, as
expected, only among lower-income groups.
Neither higher education (the omitted category is “less than college”) nor real estate has any
significant effect. On the other hand, those with larger household financial assets are less likely to
accept redistribution. Similarly, higher household income is negatively correlated with support
for redistribution (the base group is the first thirty percentile in terms of household income).
These results on age, income and wealth suggest self-interested agents and are mostly consistent
with what previous studies found in other developed countries.
Two points are worth noting in passing. First, our finding that women oppose redistribution
contrasts with that of Alesina and La Ferrara (2001) for the US, and that of Ravallion and Lokshin(2000) for Russia. Alesina and La Ferrara argue that women (along with blacks) tend to support
more redistribution, possibly because they perceive a lack of equal opportunities for all in
America. Edlund and Pande (2002) provide evidence that as a result of the recent rise in the
divorce rate, more American women are facing a decline in welfare, which makes them support
the Democratic Party. We have seen that women in Japan, married or not, actually appear todisapprove of greater redistribution.99The same results hold for women with and without jobs; they are less likely to agree to greater redistribution than aremen. Also, the estimated coefficient for female remains virtually identical whether we control for risk aversion(discussed in detail below) or not, so the significance of gender in our estimate represents something other than the11Secondly, previous studies also report the estimated impact of age on preferences. Ravallionand Lokshin (2000) show that, in Russia, the respondent’s age is positively correlated withsupport for redistribution, which is similar to our result. On the other hand, Alesina and La Ferrara(2001) found that age has a negative and significant coefficient. The difference is interesting but
we have not been able to explain it.

4.2

Risk aversion

Various government policies with a redistributive consequence have a public-insurance aspect.
People who are relatively risk-averse may therefore rationally demand more redistribution, even
if they are currently affluent enough to incur a net loss from such policies. To test this hypothesis,
we created a rather novel proxy for risk aversion using the answer to the following survey
question: “What is the lowest percentage of rainfall forecast that usually makes you bring anumbrella with you?” To be specific, we defined an index of risk aversion, RiskAvs, as 1−(therespondent’s answer)/100.
This index may vary widely across income groups, age groups, or by gender, but in a multiple
regression, we can control for these factors. The overwhelming advantage of this index is that theoriginal survey question is easy for respondents to understand and answer; it requires no special
probability calculation. This is important, for most people are probably unfamiliar withcalculating expected payoffs. On the other hand, most Japanese see weather forecasts in TV
programs or newspapers everyday. In terms of simplicity, this is arguably the best available proxy
for attitude towards risk.
Table 6 shows that this index has a positive and statistically significant coefficient (at the 1%
level). A 10% increase in risk aversion implies a 2% increase in the probability that the
respondent considers more redistribution to be desirable.
In our survey, we have a question from which another index of risk aversion (certainty

difference in attitudes toward risk.

12equivalent) could be constructed. The original survey question goes as follows. You are thinkingof choosing a pension scheme from two alternatives. One scheme ensures a constant monthly
flow of benefits, while the other provides either 300,000Yen or 100,000Yen per month with aneven chance. We then asked the respondent to report the smallest amount of sure monthly incomein the first pension plan that would make him/her indifferent between the two plans. We treat thesuggested amount as an index of risk aversion, which broadly accords with the textbook
definition of certainty equivalent. After analyzing the data, however, we concluded that thisvariable is not a very useful index. Probably, the question was unfamiliar and not well understoodby many respondents. For instance, in a linear probability model we performed instrumental
variable estimations in which we use this certainty equivalent as an IV for another index that weconstructed, RiskAvs, assuming the classical error variance setup (for this kind of multiple
indicator solution to the measurement error problem, see e.g., Wooldridge, 2002, Ch. 5.). We
found that the coefficient of RiskAvs becomes statistically and economically insignificant.
Conversely, when we instrumented the certainty equivalent measure by RiskAvs, we found theformer’s coefficient to be insignificant. Considering that RiskAvs is robustly estimated to besignificant in the usual OLS and probit estimations, the above results suggest that our certainty
equivalent measure is not a good indicator of true risk aversion. Another possible and deeper
reason why this index does not work is that the simple expected utility framework is inappropriate
in this context.
Empirical researchers (e.g., Alesina and La Ferrara, 2001) often use a self-employment
dummy as an inverse proxy for risk-aversion. Alesina and La Ferrara showed that the
self-employed tend to oppose redistribution. We also examined the effect of self-employment.
The estimated coefficient on our self-employment dummy is indeed negative but also small andnot statistically significant. However, we found subsequently that the relatively richself-employed respondents are rather negative about increasing income redistribution, and that
this is true even after controlling for income and our index of risk aversion. Therefore,13self-employment probably represents something other than attitude towards risk (assuming ourindex of risk aversion is valid). Alesina and La Ferrara (2001) suggest that the self-employed may
benefit less from redistributive policies, or that perhaps they are by nature individualistic andvalue self-help.
4.3

Unemployment

Is unemployment an important determinant of preferences over redistribution, after controllingfor income? Table 6 reports the estimated effect of a dummy variable UnemplPast, which is unity
when the respondent has experienced unemployment in the past five years. The estimatedcoefficient is positive, and is both statistically and economically significant.
For expected unemployment, we created a binary variable UnemplFuture, which equals unity
if the respondent answered affirmatively to the question: “Do you think either you or anyone inyour family is going to be unemployed within two years?” The coefficient of this variable also
turned out to be positive and significant at the 1% significant level.
Thus, for both variables, the marginal effect is estimated to be about 8%. That is, if the
respondent experienced unemployment in the recent past, or is expecting to do so in the near
future, the predicted probability that he or she supports more redistribution is about 8% higher
than otherwise. This is quantitatively important. It largely offsets the marginal effect of movingfrom the lowest income group to the highest (which is –10%). Taken at face value, this implies
that to keep unchanged some newly unemployed person’s opinion about redistribution, he/she
must be compensated for his/her household income by an amount of that order (for instance, thedifference between the average incomes of the lowest income group and the highest is roughly
equal to $100,000). UnemplPast and UnemplFuture are different variables. UnemplFuturetakes its highest value (about 50%) for the 50s age group, although the average value of
UnemplFuture is 38% for the whole sample. On the other hand, UnemplPast does not changevery much by age group. The coefficient of correlation between UnemplPast andUnemplFuture is 0.212.
14

We have seen that even after controlling for current income and wealth, unemployment exerts
a sizeable influence on preferences concerning redistribution. An implication is that themeasurement of welfare loss using only current income or consumption may be biased
downwards. Empirical results that are consistent with this point have already been obtained byresearchers on subjective measures of well-being, such as happiness or life satisfaction (Frey andStutzer, 2002b, Ohtake and Tomioka, 2004).
One can think of two explanations for unemployment’s being an important factor
independently of income. First, unemployment may be accompanied by only a small reduction incurrent income but a large reduction in lifetime income. This could occur for someone who
recently experienced unemployment if the wage profile of the former job was much steeper thanthat of the current job, with the change in the current wage level being rather small. Secondly,
unemployment can involve not only a monetary loss but also serious psychological shocks, suchas the loss of self-confidence and the loss of attachment to a workplace. Later, we examine thesetwo possibilities by controlling for changes in consumption and income.
4.4

Unemployment for young and old

The cost of losing a job may not be the same across age groups. In particular, since the elderly are
more likely to retire, they probably do not consider the effect of unemployment (be it monetary or
psychological) as seriously as do younger persons. We now examine this by interacting theunemployment variables with age dummies.
The estimation result in Table 7 is roughly consistent with our conjecture. Of the respondents
who were recently unemployed, only those in their twenties (for whom UnP_20s is unity) have astrong tendency to approve more redistribution. Its coefficient is statistically significant at the 1%
level, and the estimated marginal effect is 21.3% (the base group is people of any age with norecent experience of unemployment). Turning next to those who are expecting unemployment, wefound that people in their thirties and forties tend to favor greater redistribution of income, withthe latter age group having a larger marginal effect (19%). People in their fifties and sixties show15

no inclination to accept more redistribution, even if they were recently unemployed or expect tobe.
We examine this issue further by comparing the results across two income groups, the wealthy
and the poor. In the second column of Table 7, we estimated the same model as previously but
using only respondents with household income below the 50th percentile in the sample. Table 7
reveals that, among the recently unemployed, income redistribution policy was supported bypeople in their twenties and (to a lesser extent) by those in their fifties. On the other hand,
expected unemployment has no significant effect on any age group, although the marginal effectfor those in their forties is rather large and on the borderline of significance at the 10% level.
The third column of Table 7 reports the estimates for those above the 50th percentile ofhousehold income. Despite a quite large positive marginal effect of UnP_20s (32%), experiencesof unemployment are not statistically significant for all age groups. The estimated effects offuture unemployment for those in their thirties and forties have economically importantmagnitudes, and the effect of the latter is also statistically significant.
In sum, regardless of income, it appears that for the elderly, neither expected nor experienced
unemployment has any effect on preferences for redistributive policy. Costs of being unemployed
differ across age groups. As suggested before, the reason may be that, since elderly people are
retiring anyway, neither their lifetime income nor their psychology is affected by unemployment.
This is interesting because, as we have seen, aging itself has a strong positive correlation withapproval for redistribution.
Meanwhile, estimated effects of pure age dummies on the dependent variable show distinct
patterns across income groups. Comparing the estimation results for rich and poor in Table 7, wefind that only in the low-income sample are age dummies positively and significantly correlatedwith the dependent variable, entirely as expected: since the relatively poor elderly have few
prospects of upward mobility, it is natural that they support extensive redistribution of income.
Finally, women differ in their attitudes towards redistribution by income group. The third16

column of Table 7 reports that rich women have a rather strong aversion to more income
redistribution: among the higher income group, a married (resp. a single) woman is predicted to
be 14.7% (resp. 23.2%) less likely than a married man to agree to such a scheme, whereas thesecond column of Table 7 indicates that among the lower income group, there is little difference
between the genders.
We do not know why rich women are more negative about redistributive policies than are richmen. As shown, marital status is estimated to be irrelevant (unlike in the US, according to Edlund
and Pande, 2002). As discussed earlier, in other developed countries women are found to be insympathy for income redistribution and the welfare state in general.
4.5

Changes in income and consumption

Earlier, we suggested that unemployment involves more than a monetary loss, and may include
psychological shocks. However, it could be that the unemployment variables represent variations
in expected future income or consumption (perhaps they obtain new information from theexperience and adjust their own economic expectations), and that such variations are not well
captured by current income, which is measured in absolute terms. If so, we should not interpretthe significance of the unemployment variables (after controlling for current income) as evidence
that the unemployed suffer a nonmonetary loss.
To examine this issue, we created the variable ConsChange, which takes five distinct values(–7, –2.5, 0, 2.5, 7). It is based on our survey question about the percentage change in
consumption between the previous year and the year before. We also constructed an analogous
variable for past income variation, IncChange. The result in Table 8 suggests that a change in
consumption is negatively correlated with the dependent variable, whereas a change in income isnot.
We also included dummies for expected income changes in the coming five years. FIncStable(resp. FIncUp) equals unity if the respondent says income will be stable (resp. rising), and zerootherwise. The result in Table 8 implies that people who expect a stable or rising income are17relatively negative about redistribution compared to those who expect a fall in income.
We see that even after controlling for these variables, the coefficient of past unemployment isstill large and positive, although that of future unemployment is no longer statistically significant.
This result is consistent with the view that there exist not only pecuniary but also psychologicalcosts of unemployment.

4.6

Mobility

Even the poor may be hostile to redistribution if they believe strongly that social mobility is
sufficiently high. The rich may demand more redistribution today if they expect to fall down theincome ladder in the near future. We now examine what our data has to say on this issue.
A binary variable PoorGotRich equals unity if the respondent agreed with the view that “inthe past five years the poor have had a greater chance of getting rich.” Likewise, RichGotPoor isa dummy variable that indicates whether the respondent thinks “in the past five years the rich have
had a greater probability of getting poor”.
Table 8 shows that the coefficients of these variables are estimated to be negative and positive,
respectively. In this sense, those who feel that upward social mobility has recently increased tend
to oppose more redistribution. More downward social mobility leads people to approve greater
redistribution. That is, social mobility and income redistribution are perceived to be substitutes.
Nevertheless, these two estimates are not statistically significant.
Unlike a change in general social mobility, increased poverty turned out to be quite important.
MorePoor is a dummy that indicates whether the respondent feels the incidence of homelessness
and/or family poverty has increased in the past five years. It has a marginal effect of 13.4%, and is
statistically significant at the 1% level.
An interpretation of this finding is that people extract signals of mobility more easily andimpressively from a change in extreme poverty than from a change in general social mobility.
Another explanation, based on externalities, is that a rise in general poverty makes currently
well-off people fear social unrest and crimes, thereby inducing them to vote for alleviating18

poverty by redistribution. Although the rich will not be direct beneficiaries of such a policy, they
will benefit from it indirectly. On the other hand, greater general downward mobility, as distinct
from more poverty, is perhaps not sufficiently strong or impressive for this sort of mechanism totake effect.
Incidentally, in estimates not reported here, we found no significant correlation between thedependent variable and indices of personal mobility (as defined by the difference in (1) one’s own
educational level and that of one’s parents and (2) the subjective evaluation of one’s current living
standards and those of one’s childhood). In addition, we could not reject the null hypothesis that
subjective evaluation of current living standards has a zero coefficient, even at the 10%
significance level.
4.7

The rich and the poor

Table 8 also reports estimation results for two income groups, namely those below and thoseabove the 50th percentile of household income. That attitudes of women and the effect of aging
are different across income groups has already been discussed. The self-employment dummy issignificant only in the sample for the rich. The two unemployment variables have opposite
effects: only past unemployment is significant among the poor, whereas among the well-off it isfuture unemployment that is important.
Among the rich, the increase in consumption is negatively correlated with the dependent
variable and is quite significant, while it is not so in the lower-income group. One explanation
relates to liquidity constraints: perhaps many high-income households do not face a liquidity
constraint, and so a recent change in their consumption level could reflect an unexpected change
in future income. On the other hand, the relatively poor probably confront a severe liquidity
constraint, and thus a change in consumption would reflect to a large extent a previously
anticipated current income shock.
Concerning social mobility and redistributive policy, the estimation results for both income
groups are generally the same as those for the whole sample.
19

4.8

Economic inequalities

We have already seen in our descriptive statistics that many respondents think that economic
inequalities of one kind or another have increased in the past few years. Are these perceptions
directly related to preferences over income redistribution? The first column of Table 9 shows thatthe recognition of a rise in income inequality has a large and positive coefficient. This may be
because inequality simply offends people’s sense of equity. For instance, Alesina, Di Tella and
MacCulloch (2001) show that the subjective well-being of a leftist person is negatively affectedby greater income inequality. Alternatively, it could be that people interpret greater inequality as arise in income risk, and hence desire more redistribution to prepare for this increased uncertainty.
We found other kinds of inequalities to be insignificant.10
4.9

Altruism and reasons for unemployment

The next issue we examine is the effect of people’s perceptions about the causes of the recent risein unemployment in Japan. If unemployment is perceived to be largely involuntary, then people
may think it desirable that the government should engage in more redistribution of income. We
created four binary variables to represent four different views concerning the causes of rising
unemployment: the unemployment rate has risen because (1) “more people have difficulty
handling new technologies” (NotAdapt); (2) “there are not enough jobs” (NoJobs); (3) “people
are lazier and less competent than they were before” (LazyInabil); and (4) “welfare benefits
became too generous”(BenDepend).
The first two of these essentially characterize unemployment as “involuntary”, and so we
expect them to have positive coefficients in our model. The last two are clearly less sympathetic
to the unemployed. We thus expect the coefficient of each to have a negative sign. We found thatnone of these dummies were statistically significant, although the signs were as expected (see the10

The conclusion regarding the effect of various inequalities on perceptions remains the same when we estimate eachof these subjective variables, not simultaneously (as in the text), but separately (not shown). In that case, however, suchvariables as ConsChange and FIncStable, which becomes insignificant in Table 9 (due probably to collinearity withvarious subjective variables), turn out statistically significant again, as in Table 8.

20second column of Table 9).
Next, we examine the effect of altruism. The dummy variable HelpOthers takes unity if the
respondent agreed with the idea that “less fortunate people should be helped by society.” Not
surprisingly, these people proved quite supportive of greater redistribution. Its marginal effect islarger than that of any other variable analyzed. Note that quite a high percentage of respondents(67%) expressed altruism.
However, the term altruism may not be entirely appropriate for this variable, because it can be
an indicator of the self-interest of the rich who fear negative externalities (such as social unrest
triggered by a worsening economic situation). In the face of rising inequality or poverty, thewealthy may rationally seek greater redistribution of income to alleviate adverse externalities.
We should generally be careful not to give causal interpretations of results for subjective
explanatory variables (as opposed to the cases of such exogenous variables as age, gender, risk
aversion and possibly education or income too). A better and safer interpretation would be thatthese subjective opinions about society largely reflect individual specific fixed effects and help
predict our dependent variable. The large coefficient of, say, HelpOthers is not surprising,
because for some people, altruism may be largely synonymous with redistribution from rich topoor. Furthermore, the estimated marginal effect of such a variable would probably be reduced if
we could obtain a panel dataset and control for personal fixed effects11.. The same point would
apply to other subjective variables, to varying degrees.

4.10 Norms and realities
Table 9 also includes the estimated effects of the discrepancy between norms and realities
concerning equality of opportunities. FamilyNR equals unity if the respondent agreed that “one’s
family background should not be an important determinant of success in society, but in reality is”.
MobilNR is a dummy that indicates whether the respondent thinks “upward social mobility

21

should be high, but in reality is not”. These two variables had reasonably large estimated marginal
effects and positive signs, indicating that the gap between the norms and realities regarding “equal
opportunities for all” is important. Of the two, however, only MobilNR is statistically significant.
5.

Conclusion

Using an original dataset, we investigated the determinants of individual preferences over income
redistribution in Japan. Although income level is negatively correlated with support forredistribution, there are other important factors, which relate to dynamics and uncertainty such as
income risk. Even after controlling for income, risk-averse individuals, and those who expect tobe unemployed in the future, show a strong tendency to support more redistribution. However, itappears that the elderly do not consider the effect of unemployment as seriously as do younger
persons, probably because the elderly are going to retire anyway. As for pure effects of aging andmobility, since the relatively poor elderly have few prospects of upward mobility, they have goodreasons to, and indeed do, support extensive income redistribution. Redistribution policy targetedat aged people will be supported. Women oppose more redistribution, in contrast to the case inother developed countries.12 Unlike changes in general social mobility, increased poverty is
estimated to have a quite important influence.
Altruism is also a key factor, but perhaps the term altruism is not entirely appropriate for thecorresponding variable, for even if the rich are not the direct beneficiaries of redistribution,
supporting such a policy is consistent with their own self-interest if they fear negative
externalities such as social unrest. A rise in income inequality has a positive impact on approval ofredistribution. The discrepancy between the norms and realities regarding equality of opportunity
influences people’s preferences for redistributive policies. Although in this paper we haveanalyzed the characteristics of supporters of a stronger redistribution system, future research is11

See Bertrand and Mullainathan (2001) for this and related points.
More precisely, we have seen that it is women in the upper-half income group who oppose greater redistribution.
Interestingly, using the same dataset we found elsewhere that women tend to be more critical of greater income
inequality, compared to men (Tomioka and Ohtake, 2004). Thus, it could be that women support the equlization of
1222needed to determine the optimal degree of progressivity in income tax.

before-taz income but not that of after-tax income through forced redistribution by tax and social security.

23

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Alesina, A. and La Ferrara, E. (2001) “Preferences for Redistribution in the Land of
Opportunities”, NBER Working Paper, No. 8267.
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Edlund, L. and R. Pande (2002) “Why Have Women Become Left-Wing? The Political Gender
Gap and the Decline in Marriage”, Quarterly Journal of Economics, Vol. 117, No.3, pp.
917–961.
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Ishikawa, T. (1999) Economics of Income Distribution (Bunpai No Keizaigaku), Tokyo:
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Krueger, A. (2002) “Inequality, Too Much of a Good Thing”, mimeo, Princeton University.
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MIT Press.

25

Table 1. Household income quartiles and support for redistribution (%)
More Redistribution
Income quartiles

OpposeNeutralSupport

Total

1

11.25

30.63

58.12

100

2

13.6

29.39

57.02

100

3

20.11

31.22

48.68

100

4(Top)

22.81

35.31

41.88

100

Total

15.87

31.67

52.46

100

Table 2. Age groups and support for redistribution (%)
More Redistribution
Age group

OpposeNeutralSupport

Total

20s

20.65

35.87

43.48

100

30s

14.59

32.62

52.79

100

40s

19.44

35.42

45.14

100

50s

13.59

29.08

57.34

100

60s

10.86

24.57

64.57

100

Total

15.87

31.67

52.46

100

26

Table 3. Gender and support for redistribution (%)
More Redistribution
Gender

OpposeNeutralSupport

Total

Female

15.69

36.03

48.28

100

Male

16.02

28.04

55.94

100

Total

15.87

31.67

52.46

100

Table 4. Employment status and support for redistribution (%)
More Redistribution
OpposeNeutralSupportTotal

Not Unemployed

16.13

32.1

51.77

100

Unemployed

6.06

15.15

78.79

100

Total

15.87

31.67

52.46

100

27

Table 5. Descriptive statistics of all variables
Variable

Obs

Mean Std. Dev. Variable

Obs

Mean

Std. Dev.

Redistr

1050

0.520

0.500

IncChange

1050

–0.86

3.621

MaleNoMarried

1050

0.135

0.342

ConsChange

1050

0.67

3.805

FemMarried

1050

0.28

0.449

FIncStable

1050

0.293

0.456

FemNoMarried

1050

0.113

0.317

FIncUp

1050

0.197

0.398

age30s

1050

0.186

0.389

PoorGotRich

1046

0.102

0.302

age40s

1050

0.235

0.424

RichGotPoor

1044

0.562

0.496

age50s

1050

0.291

0.454

MorePoor

1047

0.852

0.355

age60s

1050

0.137

0.344

IncomIneq

1042

0.663

0.473

College

1050

0.318

0.466

WealthIneq

1037

0.539

0.499

GradSchool

1050

0.025

0.156

IneqSchool

1041

0.23

0.421

Estate

1050

2.39

2.998

IneqPart

1024

0.502

0.5

FAsset

1050

10.28

11.60

IneqFamily

1031

0.206

0.404

HIncClass2

1050

0.326

0.469

IncomIneqFut

1044

0.753

0.432

HIncClass3

1050

0.267

0.442

WealthIneqFut

1039

0.621

0.485

RiskAvs

1050

0.491

0.201

IneqSchoolFut

1043

0.268

0.443

SelfEmployed

1050

0.082

0.275

IneqPartFut

1041

0.485

0.5

UnemplPast

1050

0.153

0.361

IneqFamilyFut

1034

0.203

0.402

UnemplFut

1050

0.382

0.486

NotAdapt

1040

0.546

0.498

UnP_age20

1050

0.035

0.183

LazyInabil

1045

0.5

0.5

UnP_age30

1050

0.035

0.183

NoJobs

1049

0.867

0.34

UnP_age40

1050

0.026

0.159

BenDepend

1043

0.288

0.453

UnP_age50

1050

0.034

0.18

HelpOthers

1047

0.679

0.467

UnP_age60

1050

0.025

0.156

FamilyNR

1045

0.247

0.431

28

UnF_age20

1050

0.065

0.247

UnF_age30

1050

0.073

0.26

UnF_age40

1050

0.064

0.245

UnF_age50

1050

0.131

0.338

UnF_age60

1050

0.048

0.214

MobilNR

29

1046

0.489

0.5

Table 6. Estimation results for basic model
Marginal EffectStd. Err

MaleNoMarried

0.647

0.058

FemMarried–0.063*0.038

FemNoMarried–0.0220.059

age30s

0.114**

0.053

age40s

0.065

0.057

age50s0.207***

0.060

age60s

0.272***

0.057

College

0.020

0.035

GradSchool–0.1190.104

Estate–0.0030.006

FAsset–0.004**0.002

HIncClass2–0.0190.037

HIncClass3–0.105**0.047

RiskAvs0.200**

0.079

SelfEmployed–0.0790.057

UnemplPast

0.081*

0.045

UnemplFuture

0.083***

0.032

Number of obs

1122

Pseudo R2
Loglikelihood

0.0515–7360.682

Note: *** indicates significance at the 1% level, ** indicates significance at the 5% level, and * indicates significanceat the 10% level.

30

Table 7. Estimation results for models with interaction of unemployment experience and
age
All

Poor

Rich

Marginal Effect Std. Err Marginal Effect Std. Err Marginal Effect Std. Err
MaleNoMarried

0.068

0.059

0.090

0.066

–0.0590.116

FemMarried–0.066 *

0.039

0.011

0.052

–0.147***0.054

FemNoMarried–0.021

0.060

0.055

0.066

–0.232*0.112

age30s

0.111

0.066

0.108

0.080

0.075

0.154

age40s

0.046

0.073

0.139

0.091

–0.1380.132

age50s0.224*** 0.071

0.217**

0.085

0.113

0.139

age60s0.310*** 0.065

0.317*** 0.072

0.186

0.162

College

0.024

0.035

0.022

0.047

0.055

0.055

GradSchool–0.101

0.104–0.0470.146–0.1430.140

Estate–0.0030.006–0.0040.010

0.0026

0.007

FAsset–0.004**0.002–0.004*0.002–0.0027

0.002

HIncClass2–0.0140.037

HIncClass3–0.095**

0.047
0.001

0.013

–0.022**0.01

Household Inc
RiskAvs0.203*** 0.079

0.176*

0.104

0.201*

0.120

SelfEmployed–0.0780.057–0.0280.075–0.189**0.0860

UnP_age20s

0.213**

0.079

0.216**

0.079

0.323

0.181

UnP_age30s

0.044

0.091

0.100

0.104

–0.112

0.159

UnP_age40s–0.012

0.096

0.042

0.135

–0.072

0.123

UnP_age50s

0.091

0.084

0.177*

0.092

–0.09

0.150

UnP_age60s

0.063

0.121

0.126

0.127

–0.0200.294

31

UnF_age20s

0.013

0.085

–0.038

0.102

0.125

0.142

UnF_age30s

0.119*

0.061

0.086

0.067

0.216

0.139

UnF_age40s

0.195*** 0.064

0.146

0.084

0.249**

0.102

UnF_age50s

0.049

0.055

0.073

0.080

0.041

0.080

UnF_age60s–0.021

0.086–0.120

0.109

0.142

0.159

Number of obs

1122

661

461

Pseudo R2

0.05640.04590.0946

Loglikelihood–732.895–431.158–287.48951

Note: *** indicates significance at the 1% level, ** indicates significance at the 5% level, and * indicates significanceat the 10% level.

32

Table 8. Estimation results for models with perspective on mobility
Poor

All

Rich

Marginal Effect Std. Err Marginal Effect Std. Err Marginal Effect Std. Err
MaleNoMarried

0.071

0.061

0.101

0.069

–0.052

FemMarried–0.084**0.040–0.001

0.054–0.191*** 0.058

FemNoMrried–0.067

0.061

0.063

0.070

–0.242*0.113

age30s0.142**

0.055

0.152**

0.061

0.092

0.131

age40s

0.071

0.060

0.177**

0.070–0.1390.126

age50s

0.216*** 0.065

0.269*** 0.066

0.038

0.129

age60s

0.295*** 0.059

0.310*** 0.067

0.165

0.144

College

0.034

0.037

0.032

0.050

0.052

0.061

GradSchool–0.1480.111–0.0770.163–0.1730.148

Estate–0.0040.006–0.011

0.010

0.005

0.008

FAsset–0.003*0.002–0.0030.003–0.0020.002

HIncClass2–0.0130.039

HIncClass3–0.089*

0.049
0.004

0.013

–0.021**

0.010

Houseinc

0.118

RiskAvs0.141*

0.081

0.116

0.109

0.117

0.123

SelfEmployed–0.111*

0.059–0.0690.076–0.183*0.087

UnemplPast

0.096*

0.049

0.169*** 0.057–0.0390.083

UnemplFuture

0.054

0.037

0.016

0.045

0.112*

0.058

IncChange–0.004

0.005

0.000

0.006

–0.0050.009

ConsChange–0.010**0.005–0.0040.006–0.020*** 0.007

FincStable–0.076*0.039–0.0730.048–0.0600.066

FincUp–0.0340.046–0.077

0.059

0.023

0.073

33

PoorGotRich–0.0720.058–0.0720.073–0.1080.088

RichGotPoor

0.025

0.034

0.034

0.043

0.027

0.054

MorePoor

0.134*** 0.044

0.142**

0.061

0.150**

0.067

Number of obs

1043

607

436

Pseudo R2

0.072

0.059

0.1197–670.496–390.550–264.239

Loglikelihood

Note: *** indicates significance at the 1% level, ** indicates significance at the 5% level, and * indicates significanceat the 10% level.

34

Table 9. Estimation results for the models with inequality and unemployment perspectives
(1)

(2)

Marginal Effect Std. Err Marginal Effect Std. Err
IncChange–0.0070.005–0.001

0.005

ConsChange–0.0070.005–0.0080.005

FincStable–0.0640.041–0.0530.041

FincUp–0.031

0.047

0.011

0.046

IncomIneq

0.101**

0.049

WealthIneq–0.0040.046

IneqSchool–0.0220.051

IneqPart

0.006

0.043

IneqFamily

0.067

0.05

IncomIneqFut–0.042

0.056

WealthIneqFut–0.0380.047

IneqSchoolFut–0.0250.047

IneqPartFu

0.030

0.044

IneqFamilyFut

0.035

0.05

NotAdapt

0.007

0.032

LazyInabil–0.011

0.036

NoJobs

0.071

0.051

BenDepend–0.0230.040

HelpOthers

0.292*** 0.038

FamilyNR

0.059

0.040

MobilNR

0.071**

0.034

Number of obs

902

1030

35

Pseudo R2

0.06980.127

Loglikelihood–581.303–622.455

Note: In addition to those shown in the Table, the estimated models include regressors used in the model of Table 6,(namely, MaleNoMarried, FemMarried, FemNoMarried, age30s, age40s, age50s, age60s, College, GradSchool, Estate,
FAsset, HIncClass2, HIncClass3, RiskAvs, SelfEmployed, UnemplPast, UnemplFuture). *** indicates significance atthe 1% level, ** indicates significance at the 5% level, and * indicates significance at the 10% level.

36

Appendix: Variable definitions
Redistr: dummy equal to 1 if respondent thinks that the government should engage in moreredistribution of income from rich to poor. The original survey question is: “If thegovernment was going to implement the following policies, would you support or
oppose them? 1. Increasing redistribution of income from rich to poor using tax andsocial security system.” Answers were selected from a scale of 1 to 5, where 1=Support;
2 = Some support; 3 = Undecided; 4 = Rather opposed; 5 = Oppose. Our dependent
variable equals unity if the respondent chooses 1 or 2.
age20s: dummy equal to 1 if respondent is in his or her 20s. Similarly for 30s, 40s, 50s and 60s.
MaleNoMarried: dummy equal to 1 if respondent is a male and not married.
FemMarried: dummy equal to 1 if respondent is a female and married.
FemNoMarried: dummy equal to 1 if respondent is a female and not married.
DivSep: dummy equal to 1 if respondent is either divorced or separated.
College: dummy equal to 1 if respondent is a college graduate.
GradSchool: dummy equal to 1 if respondent has a degree from a graduate school.
Estate: real estate owned by the household, in millions of Yen.
FAsset: financial assets of the household, in millions of Yen.
HIncClassN: Nth household-income class, where N is either 1, 2 or 3 where 3 is the highest.
HousInc: household income, in millions of Yen.
RiskAvs: this variable is based on the answer to the following survey question: “What is thelowest percentage of rainfall forecast that usually makes you take an umbrella with you
when going out?” and is specifically constructed as 1 – (the suggested percentage)/100.
SelfEmployed: dummy equal to 1 if respondent is self-employed.
ConsChange: rate of change in the respondent’s consumption last year relative to the year before.
IncChange: rate of change in the respondent’s income last year relative to the year before.
FIncUp: dummy equal to 1 if respondent expects household income to be rising during the37

coming five years.
FIncStable: dummy equal to 1 if respondent expects household income to be stable during thecoming five years.
UnemplPast: dummy equal to 1 if respondent was unemployed in the past five years.
UnemplFuture: dummy equal to 1 if respondent is expecting to be unemployed within two years.
UnP_age30s: interaction of UnemplPast and age30s ; thus it equals unity if respondent is in
his/her 30s and became unemployed in the past five years; similarly for other age
groups.
UnF_age30s: interaction of UnemplFuture and age30s; similarly for other age groups.
Unemployed: dummy equal to 1 if respondent is currently unemployed.
Rich: dummy equal to 1 if respondent’s household income is equal to or above 50th percentile.
PoorGotRich: dummy equal to 1 if respondent thinks upward mobility has increased in the last
five years.
RichGotPoor: dummy equal to 1 if respondent thinks downward mobility has increased in thelast five years.
MorePoor: dummy equal to 1 if respondent thinks that the incidence of homelessness and family
poverty has increased in the last five years.
IncomIneq: dummy equal to 1 if respondent thinks that income inequality has increased in thelast five years.
WealthIneq: dummy equal to 1 if respondent thinks that wealth inequality has increased in thelast five years
IneqSchool: dummy equal to 1 if respondent thinks that inequality across different educational
backgrounds has increased in the last five years.
IneqPart: dummy equal to 1 if respondent thinks that income inequality between full-timeworkers and part-time workers has increased in the last five years.
IneqFamily: dummy equal to 1 if respondent thinks that inequality across different family

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backgrounds has increased in the last five years.
IncomeIneqFut: dummy equal to 1 if respondent thinks that income inequality will increase
within five years.
WealthIneqFut: dummy equal to 1 if respondent thinks that wealth inequality will increase
within five years.
IneqSchoolFut: dummy equal to 1 if respondent thinks that inequality across different
educational backgrounds will increase within five years.
IneqPartFut: dummy equal to 1 if respondent thinks that income inequality between full-timeworkers and part-time workers will increase within five years.
IneqFamilyFut: dummy equal to 1 if respondent thinks that inequality across different family
backgrounds will increase within five years.
NotAdapt: dummy equal to 1 if respondent thinks that the unemployment rate rose because somepeople could not keep up with or handle new technology.
LazyInabil: dummy equal to 1 if respondent thinks that the unemployment rate rose because
people have become lazier and less competent.
NoJobs: dummy equal to 1 if respondent thinks that the unemployment rate rose because there
have not been enough jobs.
BenDepend: dummy equal to 1 if respondent thinks that the unemployment rate rose because
welfare benefits have been too generous.
HelpOthers: dummy equal to 1 if respondent thinks that less fortunate people should be helpedby society.
FamilyNR: dummy equal to 1 if respondent thinks that economic inequality due to different
family backgrounds should not be large but it actually is.
MobilNR: dummy equal to 1 if respondent thinks that social mobility should be high but isactually low.

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Figure 1. Income groups and attitude towards strengthening income redistribution

Preferences over more redistribution
60
50
40
% 30

SupportNot Support

20
10
0

1

2

3

4(Top)

Income Quartiles

40