Inequality and Happiness: Are Europeans and Americans Different?

The answer to the question posed in the title is "yes." Using a total of 128,106 answers to a survey question about "happiness," we find that there is a large, negative and significant effect of inequality on happiness in Europe but not in the US. There are two potential explanations. First, Europeans prefer more equal societies (inequality belongs in the utility function for Europeans but not for Americans). Second, social mobility is (or is perceived to be) higher in the US so being poor is not seen as affecting future income. We test these hypotheses by partitioning the sample across income and ideological lines. There is evidence of "inequality generated" unhappiness in the US only for a sub-group of rich leftists. In Europe inequality makes the poor unhappy, as well as the leftists. This favors the hypothesis that inequality affects European happiness because of their lower social mobility (since no preference for equality exists amongst the rich or the right). The results help explain the greater popular demand for government to fight inequality in Europe relative to the US.


Introduction
Most governments redistribute income, using both direct and indirect means. Even though this role of the public sector has increased vastly in the last few decades in all industrial countries, European governments are more heavily involved with redistribution than that of the United States. European fiscal systems are more progressive than in the United States and the welfare state is more generous in Europe, where the share of government in the economy is substantially larger than in the United States. For instance, in 1996 the share of total government spending (excluding interest payments) over GDP was about 30 percent in the US, versus 44 percent in Europe. The share of transfers over GDP was about 14 percent in the US and about 22 percent in Europe. 1 At the end of the nineteenth century, the share of transfers over GDP was less than 1 percent both in Europe and the US. It was about 6 percent of GDP in the US, and about 10 percent of GDP in Europe in 1960.
The growth of transfers explains almost all of the increase in the size of government and the difference in the size of government between Europe and the US.
If democratic governments redistribute so much, it must mean that a large fraction of the population favors these programs. For a start, the "poor" should be in favor of redistribution, since they gain from it on net. However, this preference is mitigated by the fact that the poor of today may become the rich of tomorrow and they do not want to be the ones who will have to support redistributive schemes. Conversely the rich should oppose redistributions, but if they fear to become poor they may see redistributive policies as an insurance against future potential misfortunes. Therefore, social mobility should influence how forward looking individuals value redistributive polices. 2 Beyond self-interest, however, inequality (which is often associated with high poverty rates) may be perceived as a social evil. That is, at least up to a point, even the net losers from redistributive schemes may favor them because they perceive poverty and inequality as social harms. In part this may also be motivated indirectly by self-interest, to the extent that inequality breeds crime and threats to property rights. But, even beyond that, the observation (or perception) of poverty may negatively affect the welfare of the rich and their sense of fairness.
In this paper, we explore whether and why inequality negatively affects individual utility even after controlling for individual income. We measure "utility" in terms of survey answers about "happiness." Some readers may feel uncomfortable using such a vague question like "are you happy?" for any useful statistical investigation. As we discuss below, however, a growing literature both in psychology and in economics successfully uses it, and the patterns observed in the answers to this question are reasonable and quite similar across countries. This gives us confidence in the significance of using such data to study inequality.
We find some intriguing results. First, Europeans seem to dislike inequality more than Americans. 3 Second, aversion to inequality is concentrated amongst different ideological and income groups across the two regions. There is no clear ideological divide in the US concerning the effect of inequality on happiness. In contrast, those who define themselves leftist show a strong distaste for inequality in Europe, while those who define themselves rightists are unaffected by it. More interestingly the breakdown of rich versus poor shows large differences between Europe and the US. In Europe, the happiness of the poor is strongly negatively affected by inequality, while the effect on the rich is weaker and statistically insignificant. In the US one finds the opposite pattern, namely that the group whose happiness seems to be most adversely affected by inequality is the rich.
A striking result is that the US poor seem totally unaffected by inequality. Any significance of the inequality coefficient in the US population is mainly driven by the rich.
We argue that these results are due to different perceptions of the degree of social mobility in the US and Europe. Americans believe that their society is mobile so the poor feel that they can move up and the rich fear falling behind. In Europe a perception of a more immobile society makes the poor dislike inequality since they feel "stuck".
Given that European citizens seem so averse to inequality, they should favor redistributive policies, i.e. the welfare state. Broadly speaking this is the message of Boeri, Borsch-Syupan and Tabellini (2000). In a survey conducted in three European countries they find that Germans, Italians and 4 Spaniards are reluctant to favor cuts in welfare programs, even though they show a lack of clear understanding of the costs associated with them namely, they tend to understate the costs. Di Tella and MacCulloch (1996) find a desire for higher unemployment benefits in 5 out of 6 European countries (the exception being Norway) and a desire for lower or equal unemployment benefits in the United States and Australia.
The present paper is at the crossroads of two lines of research. One is the study of the determinants of "happiness". The economic literature started with Easterlin (1974), who documented stagnant average happiness levels in the US in the face of large increases in income, a question recently taken up by Blanchflower and Oswald (2000) and Inglehart (1996). 4 A number of subsequent papers have focused on micro economic aspects; including the role of being unemployed on self reported well being (Clark and Oswald (1994), Winkelman and Winkelman (1991)). Di Tella, MacCulloch and Oswald (1997) show that the country-level "micro happiness" regressions display a very similar structure across 12 OECD countries. That paper also takes a macro perspective by including aggregate unemployment and a measure of the generosity of the welfare state in these happiness regressions. Other work has used happiness data to study the role of democratic institutions (Granato, Inglehart and Leblang (1996) and Frey and Stutzer (1999)), the inflation-unemployment trade-off (Di Tella, MacCulloch and Oswald (1999)), partisan versus opportunistic models (Di Tella and MacCulloch (1998)) and the role of social norms (Stutzer and Lalive (2000)). An early paper by Morawetz et al (1973) discusses how average happiness varies across two communities in Israel that have different levels of inequality. 5 The second line of research is the literature on the determinants of preferences for redistribution.
On the theoretical side some of the key papers are Romer (1977) and Meltzer and Richards (1981) on inequality and redistributions, and Piketty (1995) and Benabou and Ok (2000) on social mobility.
Recent empirical work on the demand for redistribution includes Alesina and La Ferrara (2000), Ravallion and Loshkin (2000), Corneo (2000) and Corneo and Gruner (2000). These papers find, 4 There is, to be sure, a large literature in psychology using self reported measures of well being (see Diener, Suh, Lucas and Smith (1999) and Kahneman, Diener and Schwartz (1999)). 5 5 Our paper, and we believe much of the happiness literature, can be understood as an application of experienced utility, a concept that emphasises the pleasures derived from consumption (discussed in Kahneman and Thaler (1991)). It argues, in essence, that there are circumstances where measures of experienced utility can be derived (such as happiness responses) that are reasonable substitutes to observing individual choices. Ng (1996) discusses the theoretical structure of subjective well-being responses while Kahneman, Wakker and Sarin (1997) propose a formal axiomatic defense of experienced utility (see also Tinbergen (1991) and van Praag (1991)). looking at the data from the US, Europe and in one case, Russia, that social mobility does affect the preference for redistribution. This paper is organized as follows. Section 2 describes our data set. Section 3 presents results for the US. Section 4 present results for European countries. In Section 5 we compare results for the US and Europe. The last section concludes.

Description
The analysis examines U.S. happiness data from the United States General Social Survey  For Europe, we use Gini coefficients from the Deininger and Squire (1996) data set. We use only part of their "high quality" data. These data satisfy three minimum standards of quality: they are based on household surveys, the population covered is representative of the entire country and the measure of income (or expenditure) used is comprehensive including income from self-employment, non-wage earnings as well as non-monetary income. The data set is normally considered the best available for cross-country comparison and it is widely used. However, it is not without its drawbacks, as discussed by Atkinson and Brandolini (1999). The problems concern the fact that the to have a stronger effect in the US than in Europe, an observation consistent with a larger share of public consumption and more progressive taxation in Europe than in the US.

Discussion of "happiness data"
Many readers may worry about using answers to questions like "are you happy?" for any rigorous statistical work. While a healthy dose of scepticism is always useful, there are good reasons why these "happiness" data should not be dismissed. Here, we review some of the arguments made in the previous literature in favour of using happiness data.
The first reason is based on the fact that psychologists, who study well being for a living, widely use these data. Presumably, people who insist on using bad data would be driven out of the market. A second argument is that well-being data pass what psychologists often call validation exercises.
Happiness responses are correlated with physical reactions that can be thought of as describing true, internal happiness. Pavot (1991), and Eckman, Davidson and Friesen (1990), for example, find that individuals reporting to be very happy tend to smile more. Shedler, Mayman and Manis (1993) show that happiness data are negatively correlated with heart rate and blood pressure measures of responses to stress. Sutton and Davidson (1997) show that happiness data are positively correlated with electroencephalogram measures of prefrontal brain activity (the part of the brain that is "responsible" for happiness). Other studies include Fordyce (1988) The psychology literature has also considered the possibility that subjects are influenced by what they believe to be the socially desirable response when they answer surveys. If the social norm is to be happy, subjects may bias their response upwards. Since the first studies in the area, psychologists have found evidence pointing out that this concern may be exaggerated (e.g. Rorer (1965), Bradburn (1969)). Konow and Early (1999) present experimental evidence showing that the Marlowe-Crowne measure of social desirability is uncorrelated with happiness data.
An additional argument in defence of subjective well-being data, inspired by results presented in Inglehart (1990), is that happiness data are negatively correlated with suicide rates, as Di Tella et al (1997) show. 6 The same paper (Di Tella et al. (1997)) also presents micro econometric happiness and life satisfaction regressions for 12 European countries and the US. The interesting finding is that these equations seem to share a similar structure across countries.

Empirical Strategy
We compare US states and European countries, and we relate "happiness" in different US states and European countries to inequality, other macroeconomic variables and several individual characteristics. We take advantage of cross-state (or cross-country) and time series variation in inequality and we run similar regressions in the US and Europe, in terms of the definitions of both the left and right-hand side variables, and of specification of the regressions. We run an ordered logit regression of the form where Happy ist is the answer given by individual, i, who lives in State, s, and year, t, to the happiness question "Are you Happy?". In Europe, s refers to countries. The superscript g refers to the fact that we consider the whole sample (in which case g=whole sample), but we also divide it on the basis of income (in which case g=rich, poor), or of ideological inclination (in which case g=right, left). The vector MACRO st refers to a set of variables aggregated at the State (or country) level that has previously been found to affect individual happiness. These include the inflation rate (Inflation) and the unemployment rate (Unemployment). We also present some checks using the crime rate (Murders). The vector MICRO ist refers to a set of personal characteristics of the respondents that have previously been found to affect individual happiness, including age, employment status, income, etc. We also include η s , a dummy variable for the cross sectional units (State in the US, country in Europe), µ t , a dummy for each year and ε ist , an error term (i.i.d.). We compute robust standard errors, where we correct for potential heteroscedasticity and for potential correlation of the error term across observations that are contained within a cross sectional unit in any given year (see Moulton (1986)).

Happiness and inequality in the United States
The period from the early eighties to the mid nineties is characterized by a large and noticeable increase in inequality, particularly in the US. Therefore, this is a rather interesting time period to analyze from our perspective, since there is much variability in the data. All the regressions include state and year dummies.
We begin in column 1 of Table 1 show that married individuals live longer and are healthier. The omitted category is "never married," and we find married individuals are happier than unmarried ones; divorced, separated and widowers are less happy then "never married," and, a fortiori, much less happy than married individuals.
Note that one may argue that there might be a problem of reverse causality: happy and optimistic individuals may be more likely to marry and stay married because they are better at building relationships. We are not interested in precisely establishing causality here. For us, what really matters is that the association between "happiness" and marital status is consistent with evidence drawn from other sources. There is a non-linear effect of age: the least happy decades seem to be the thirties/forties, maybe because the stress of managing professional and personal life is at a peak.
Children seem to bring about preoccupations, stress and hard work as the negative coefficients on this variable show. Finally, women seem happier than men, and, not surprisingly, members of a traditionally discriminated minority (blacks) are less happy. All these results seem quite sensible and are consistent with the literature. 7 In column 2 we add our measure of inequality. The coefficient on inequality is negative and significant at conventional levels. 8 To simplify the comparisons across columns on macroeconomic variables, we added a number below for each coefficient and standard errors: these numbers are the effect on the probability of moving from one "level" of happiness to the next, as a result of one standard deviation change in the corresponding right hand side variable. Specifically, the number 11 8 In the working paper version of this paper we found no correlation between inequality and happiness in the US using inequality data (as measured by the Gini coefficient) at the state level from the 1979 and 1989 census. Even when we incorporate the 1969 census the coefficient on inequality in a US happiness regression remains insignificant. We also experimented using state inequality measures (the 80/20 ratio) created by the Economic Policy Institute with Current Population Survey data, averaged for three years every ten years (i.e. 1978-80, 1988-90 and 1998-2000). Again the coefficient on inequality was insignificant in a US happiness regression. Finally, we used a measure of inequality (the 50/10) ratio presented in Blank (2001), calculated from annual wage data from the Outgoing Rotation Group (ORG) data of the monthly CPS, with similarly insignificant results.
Americans reported themselves as "Not too Happy" it would also correspond to a 26% increase in the number of people reporting themselves in the bottom happiness category. A one standard deviation increase in inequality (equal to 3.2 percentage points) reduces the proportion of people reporting themselves as "Very Happy" by 1.9 percentage points and increases the proportion reporting themselves as "Not too Happy" by 0.9 percentage points.
In the next column (column 3 in Table 1-US) we add an additional macroeconomic variable, the unemployment rate. The implied size of the coefficient on inequality is reduced by almost 27%, and the significance level drops to the 7 percent level. Given the relatively high correlation between inequality and unemployment, equal to 0.14, this is not too surprising. 9 Interestingly, the coefficient on unemployment is negative and significant at the 9 percent level, even after we control for personal unemployment status. In other words, unemployment brings unhappiness above and In column 4 of We classified as "right" the respondent that replied 4, 5 or 6, where 4 equals "Independent, close to Republican", 5 equals "Not very strong Republican" and 6 equals "Strong Republican". We report again the coefficients on personal characteristics, as there are some intriguing differences across groups (see also the next table). For example, comparing columns (2) and (5)  right-wingers. Perhaps right-wingers are less prone to assume that their unemployed status is due to a public policy issue rather than to personal shortcomings. Being self-employed is only correlated with higher happiness for the Right Wing sub-sample, a finding that has some relevance for the empirical literature on entrepreneurship (e.g. Blanchflower and Oswald (1998)). Other differences include the finding that the coefficient on being male and on being black are more negative in the Right Wing sub-sample. Perhaps black conservatives, in addition to being black also feel some isolation from their racially defined community, which is overwhelmingly on the left of the political spectrum. The coefficients on being married and being in school are larger for the conservatives.
The evidence also suggests that relative income or "status" is more important for right-wingers, where the coefficient on Top 2 Income Quartiles in regression (6) is 64 percent larger than that in column (3). A jump up into the top half of incomes increases the probability of reporting oneself as "Very Happy" by 3.4 percentage points for a right-winger compared to 1.8 percentage points for a left-winger. Again this result seems consistent with intuition.
In terms of our macro variables, once the state unemployment rate is included, both groups are negatively affected by inequality but the effects are not significant. The coefficient on inequality for Right Wing respondents, however, is bigger in size (more negative) than that for the sub-sample of those leaning left. The opposite is true with the effect of state unemployment, as this turns negative and significant at the 4 percent level for the Left, a result consistent with the "partisan" model of macroeconomics. 10 For the results reported in column 3 of Table 2-US, a one standard deviation increase in the unemployment rate (equal to 0.023) decreases the proportion of people reporting themselves as "Very Happy" by 1.3 percentage points and increases the proportion of people reporting themselves as "Not too Happy" by 0.7 percentage points.
In Table 3-US we break down the respondents between rich and poor. A respondent is classified as "rich" if he/she belongs to the top two income quartiles, and as "poor" otherwise. There are again interesting differences between the coefficients on personal characteristics across the two subsamples. Being self-employed has a positive and significant effect on individual happiness only in the rich sub-sample. The coefficient on Home, which is a dummy variable equal to one if the respondent is keeping the home, is negative and significant for the poor and positive and significant for the rich. Concerning inequality, we find a striking result, especially if compared with the results for Europe discussed below. The poor are unaffected by inequality, particularly after the state unemployment rate is included. On the contrary, it is the rich who show a significant and negative effect of increased unhappiness with inequality, even after controlling for the unemployment rate. For this group, a one standard deviation increase in inequality is equal to 58 percent of the effect of leaving self-employment. 11 The poor, on the other hand, are affected by a higher unemployment rate (in addition to the personal costs of falling unemployed as picked up by the personal characteristic controls). For the rich, the unemployment rate does not seem to significantly affect their happiness, a result, again, which brings support to the partisan model of macroeconomic policy. Using the results for the poor reported in column 2 in Table 3-US, a one standard deviation increase in the unemployment rate decreases the proportion of poor people reporting themselves as "Very Happy" by 1.7 percentage points. In summary, the preceding results seem to suggest that there is an overall negative, marginally significant effect of inequality in the US. The effect comes in stronger when the sample is reduced to consider only individuals that are in the top half of the income distribution.
There is no effect of inequality on the happiness of the poor or the left-wingers. In column 2 we add the inequality variable. Although it has a negative sign, the effect is only significant at the 14 percent level. The coefficient turns larger (more negative) and significant at the 2 percent level when unemployment and inflation rates are included in column 3 of Table 1-Eur.

Happiness and inequality in Europe
The coefficients on inflation and unemployment are both negative, but only the one on inflation is significant. This is in contrast with studies of Europe that do not restrict the sample according to data availability for inequality. The main results do not change, when controls for relative income or status effects are included in column 4.
Using the results in column 3 of Table 1 In Table 2 Again, the coefficients on individual characteristics provide interesting information. For example, and in direct contrast to what happens in the US, being self-employed increases happiness for the left sub-sample, but has no effect on the right. Column 1 in Table 2 Table 2-Eur, one can compute that for a European leftist an increase in inequality of one standard deviation of the Gini corresponds to a decrease in income equal to 89 percent of one standard deviation. 12 It is also equal to 2.4 times the effect of leaving self-employment. 13 For the European poor, a one standard deviation increase in Gini has the same effect as an increase in inflation of 7 percentage points. 14 Alternatively a one standard deviation increase in inequality for the poor is equal in size to 33 percent of the effect of getting a divorce. 15

Comparing Europe and the United States
The differences between the two sides of the Atlantic are striking. In Europe the poor and the left leaning respondents show a strong aversion to inequality. In the US, in contrast, the rich is the only group displaying aversion to inequality. Note that when we use other data sets on income distribution at the state level in the US, such as the census measures or the measures of inequality produced by the Economic Policy Institute from gross income data of the CPS, or the data presented in Blank (2001), the effect of inequality on individual happiness for the US is never statistically significant for the full sample or in the sub-sample of the poor. We focus on the Wu-Perloff-Golan (2002) inequality data since it seems to be particularly accurate for the purposes of our paper, but, again, remember that this the data set that gives he strongest effect of inequality on happiness in the US and it is limited to an effect on the rich. 12 This number is calculated from the coefficients in column (2) of Table2-Eur and equals 0.043*6.344/(6.7e-5*4570). 13 Using the coefficients in column (2) of Table2-Eur, this number equals 0.043*6.344/0.112. 14 Using the coefficients in column (2)  We summarize the differences on the effect of inequality in Europe and the US, in Table 4. The entries of this table represent the predicted change in the proportion of people in the top happiness category due to a one standard deviation change in inequality. They are obtained from column 3 of Table 1, columns 2 and 5 of Table 2 and columns 2 and 4 of Table 3. The last line of Table 4, labeled "Different to US?" shows the statistical significance of the test that the regression coefficients on inequality (from which the numbers in each column are derived) are different in Europe.
Column 1 shows that the effect of inequality in the entire sample is larger in absolute value and more statistically significant for Europe than for the US. Even though the test on the statistical difference between the two coefficients is only valid at the 28 per cent, the magnitude of the European effect is almost twice as large and more precisely estimated. The following 4 columns show that while in Europe it is the leftists and the poor who dislike in equality, in the US it is only the rich.
Probably the most striking result of all is the complete lack of any effect of inequality on the happiness of the American poor and the American left. The difference with the European poor and European left is strong and statistically significant. The coefficients on the rich in the US and Europe have approximately the same value, but it is insignificantly different from zero in Europe and highly significant in the US. In summary, it is the European poor who drive the strong negative effects of inequality on happiness in the European sample, and it is the rich who drive the weaker effect of inequality on happiness in the US sample.
There are two potential explanations for our finding that Europeans dislike inequality, while the effect on Americans is weaker. The first one involves taste: in Europe there may be higher preferences for equality. That is, equality enters the utility function of Europeans with higher weights than in the utility function of Americans. In fact, Thurow (1971) argues that equality is a luxury good, an observation consistent with the fact that the share of government transfers over GDP is higher in richer countries. The rich in the US then should like equality, precisely because of this luxury good argument. However this interpretation is not consistent with our results for Europe, where the rich do not seem to be bothered by inequality while the poor are. 16 A more plausible interpretation is in terms of differences in perceived social mobility, rather than relying solely on "taste for equality". If Americans perceive their country as a more mobile society

Conclusion
Countries differ greatly in the degree of income inequality that they tolerate, even at similar stages of development. European observers object to the higher (and, for much of the past few decades, growing) inequality in the US. American commentators argue that European society's "obsession" with inequality stifles creativity and creates a vicious circle of welfare addiction of the poor. Do these differences of opinion simply reflect different preferences about the merits of equality in the two sides of the Atlantic? Furthermore, is a preference for equality just a matter of "taste," or does it reflect something else in society, such as the level of social mobility?
We use the answers to a simple well-being question. We simply correlate the answers to the wellbeing question asked to thousands of individuals in Europe and America over many years with measured levels of inequality. All that this method requires is an individual's ability to introspect and evaluate his or her own happiness.
Our results show that, controlling for personal characteristics of the respondents, state/country effects and year effects, Americans seem to be less affected by inequality than Europeans. We then investigate differences across income and ideological groups. We find that the rich and the rightwingers in Europe are unaffected by inequality. Instead, we identify strong negative effects of inequality on the happiness of the European poor and leftists. In the US, the poor and the leftwingers are not affected by inequality, whereas the effect on the rich is negative and well defined.
This suggests that European aversion to inequality does not originate in different preferences in the US and Europe. Suppose that "equality" is a luxury good, the demand for which rises with income more than proportionally, or even a normal good. Then we should find that European rich dislike inequality more than the European poor, like in the US. A more reasonable interpretation is that opportunities for mobility are (or are perceived to be) higher in the US than in Europe. Note: Based on 19,895 observations. All numbers are expressed as a percentage.  Note: Based on 103,773 observations. All numbers are expressed as a percentage.    [2] Bold-face denotes significant at the 10 percent level; Starred bold-face at the 5 per cent level. [3] Cut points in Appendix 2. The cell below the standard error report the predicted change in the proportion of people in the top happiness category due to a one-standard deviation change in the corresponding explanatory variable (see text for more detail).  [2] Bold-face denotes significant at the 10 percent level; Starred bold-face at the 5 per cent level. [3] Cut points in Appendix 2. The cell below the standard error (in italics) reports the predicted change in the proportion of people in the top happiness category due to a onestandard deviation change in the corresponding explanatory variable (see text for more detail).  [2] Bold-face denotes significant at the 10 percent level; Starred bold-face at the 5 per cent level. [3] Cut points in Appendix 2. The cell below the standard error (in italics) reports the predicted change in the proportion of people in the top happiness category due to a one-standard deviation change in the corresponding explanatory variable (see text for more detail).  [2] Bold-face denotes significant at the 10 percent level; Starred bold-face at the 5 per cent level. [3] Cut points in Appendix 2. The cell below the standard error (in italics) reports the predicted change in the proportion of people in the top happiness category due to a one-standard deviation change in the corresponding explanatory variable (see text for more detail).  [2] Bold-face denotes significant at the 10 percent level; Starred bold-face at the 5 per cent level. [3] Cut points in Appendix 2. The cell below the standard error (in italics) reports the predicted change in the proportion of people in the top happiness category due to a one-standard deviation change in the corresponding explanatory variable (see text for more detail).  [2] Bold-face denotes significant at the 10 percent level; Starred bold-face at the 5 per cent level. [3] Cut points in Appendix 2. The cell below the standard error (in italics) reports the predicted change in the proportion of people in the top happiness category due to a one-standard deviation change in the corresponding explanatory variable (see text for more detail). Note: [1] Bold-face denotes significant at the 10 percent level; Starred bold-face at the 5 per cent level. [2] The numbers originate in regression (3) in Table 1, regressions (2) and (5) in Table 2, and regressions (2) and (4) in Table 3. They represent the predicted change in the proportion of people in the top happiness category due to a one-standard deviation change in inequality (see text for more detail). [3] The "Different to US?" row reports the significance level of the difference between the two inequality regression coefficients (Europe-US) obtained from a combined sample of Europeans and Americans.       [2] Bold-face denotes significant at the 10 percent level; Starred bold-face at the 5 per cent level. [3] Cut points in Appendix 2.