Download 1 Does Inequality Erode Social Trust? Results

Does Inequality Erode Social Trust?
Results from Multilevel Models of U.S. States and Counties
Malcolm Fairbrother *
School of Geographical Sciences, University of Bristol, University Road, Bristol, BS8 1SS, UK
E-mail: [email protected], Tel: +44 117 9288303, Fax: +44 117 928 7878
Isaac William Martin
Department of Sociology, University of California, San Diego, 401 Social Science Building,
9500 Gilman Drive, La Jolla, CA 92093-0533, USA, E-mail: [email protected]
* corresponding author
22 June, 2012
Abstract
Previous research has argued that income inequality reduces people’s trust in other people, and
that declining social trust in the United States in recent decades has been due to rising levels of
income inequality. Using multilevel models fitted to data from the General Social Survey, this
paper substantially qualifies these arguments. We show that while people are less trusting in U.S.
states with higher income inequality, this association holds only cross-sectionally, not
longitudinally; since the 1970s, states experiencing larger increases in inequality have not
suffered systematically larger declines in trust. For counties, there is no statistically significant
relationship either cross-sectionally or longitudinally. There is therefore only limited empirical
support for the argument that inequality influences generalized social trust; and the declining
trust of recent decades certainly cannot be attributed to rising inequality.
Keywords: trust, inequality, multilevel modeling, longitudinal effects, General Social Survey
1 1. Introduction
A growing scholarly literature substantiates the belief that economic inequality has
important consequences for social life. Studies have found correlations between inequality and
social ills such as violence, poor health, political polarization and disengagement, biodiversity
loss, economic stagnation, corruption, incarceration, and residential segregation (see Easterly
2007; McCarty, Poole, and Rosenthal 2006; Mikkelson et al., 2007; Neckerman and Torche
2007; Solt 2008; Subramanian and Kawachi 2004; Wilkinson and Pickett 2009a; You and
Khagram 2005). A handful of recent studies have added generalized mistrust to this list, arguing
that inequality undermines people’s willingness to place their trust in others—particularly people
other than their family and friends (Bjørnskov 2008; Costa and Kahn 2003; Cozzolino 2011;
Freitag and Bühlmann 2009; Kawachi et al., 1997; Leigh 2006a; Uslaner 2000, 2002, 2008;
Uslaner and Brown 2005; Wilkinson and Pickett 2009b).
If inequality reduces generalized social trust, that is cause for concern, since trust—the
belief that others will not cheat us—is a precondition for social order in the broadest sense. Trust
is part of the “non-contractual” basis of contracts that makes many forms of social exchange and
interaction possible (as classically formulated by Durkheim 1893; see Beckert 2009 for a
discussion). In the absence of trust, many arms-length transactions are prevented. Evidence
suggests that nations with more trust therefore experience faster economic growth (Knack and
Keefer 1997; La Porta et al., 1997), while residents of more trusting neighborhoods are better
able to achieve collectively desirable ends, such as personal safety (Sampson et al., 2002).
Previous studies seeking to explain variations in trust have found negative correlations
with inequality both across nations (Bjørnskov 2006, 2008; Freitag and Bühlmann 2009; Knack
and Keefer 1997; Rothstein and Uslaner 2005; Uslaner 2002, 2008) and across U.S. states
(Alesina and La Ferrara 2002; Kawachi et al., 1997; Uslaner 2002; Uslaner and Brown 2005;
2 Wilkinson and Pickett 2009b).1 Given these findings, the existing literature presents inequality as
“arguably the strongest determinant of the level of social trust” (Bjørnskov 2008: 271; see also
Cozzolino 2011; Leigh 2006a; Rothstein and Uslaner 2005; Uslaner and Brown 2005). Uslaner
(2002: 186) goes so far as to assert that “the level of economic inequality is the prime mover of
generalized trust.” By this logic, the diminishment of trust in the United States over the course of
recent decades (see Robinson and Jackson 2001) is substantially a consequence of increasing
inequality (as argued by Rothstein and Uslaner 2005, p. 48).
But how strong is the evidence for such claims? This paper reassesses the evidence that
income inequality erodes our trust in others. We do so by distinguishing between, and separately
analyzing, the cross-sectional and longitudinal associations between inequality and trust. We use
multilevel models that treat self-reported trust in others as a function of individuals’
characteristics and circumstances, in interaction with time-varying and time-constant features of
their social contexts. Theoretically, we argue that inequality—a key component of the social
contexts in which people live—could affect trust in a number of different ways, at a variety of
time scales. Our models allow us to assess these diverse potential pathways simultaneously but
separately.
The key to our research design is the merger of the General Social Survey with annual
state-level measures of family income inequality for 1969 to 2004 (Galbraith and Hale 2008).
These data have the unique advantage of being repeated observations on the U.S. states over a 32
year period, providing us with ample variation in income inequality both cross-sectionally and
longitudinally, as demonstrated in Figure 1. At any given time, U.S. states vary substantially in
1
Some studies have found no association, however. Leigh (2006b) finds that there is no association
across Australian neighborhoods, Bjørnskov (2008) that the cross-national association depends on the
sample, and Berggren and Bjørnskov (2011) that the association across U.S. states disappears if
religiosity is included as a control.
3 their level of inequality. In the start of the period under study, for example, the Gini index ranged
from 0.32 (in New Hampshire) to 0.43 (in Washington DC). Over time, while all states have
become more unequal since the 1970s, they have done so to very different extents: Connecticut
shifted from being one of the more equal states in the early 1970s to one of the most unequal by
the early 2000s, while South Dakota did the reverse, increasing its Gini coefficient only slightly
in this period, from 0.38 to 0.40. As reflected in the mostly lower-left to upper-right scatter of the
observations in Figure 1, the Spearman’s rank correlation between states’ Gini coefficients in
1973 and 2004 is 0.52. We also investigate the association between county inequality and
generalized social trust, on the logic that local inequality may be more consequential for people’s
lives and experiences.
[Figure 1 here]
Turning to generalized social trust, Figure 2 shows how aggregate trust differs across
states, and how it has changed over time in each state. The measure of trust used here comes
from a question asked in the General Social Survey, which we discuss further below; the
procedure used to generate these estimates of aggregate state trust, multilevel regression with
post-stratification, is described in Appendix A. Figure 2 shows that, between 1980 and 2000,
trust declined in every state, though the magnitude of the decline varied substantially, and the
bulk of the decline occurred at different times in different states (between 1980 and 1990, or
between 1990 and 2000). As preliminary evidence of the effects of inequality, Figure 2 shows
that states on the right, with higher income inequality in 1980, evidence systematically less trust.
[Figure 2 here]
4 To preview our main findings, the analyses below confirm that residents of more unequal
states report significantly less trust, net of individual- and state-level controls. But, at the same
time, we also show that longitudinal changes in inequality have no measurable impact on trust, at
least for the time period spanned by the available data. In other words, while differences in trust
are closely tied to differences in income inequality across states, trust does not respond
systematically to changes in inequality within a state (or county) over the span of a generation. If
trust changes in response to changing levels of inequality, it does so only very slowly—on a time
scale of at least multiple generations (see Putnam 1993). We cannot know for certain if such a
slow-moving causal relationship obtains, however, because the requisite longitudinal data for
testing this hypothesis are not available. We therefore argue that claims for a causal relationship
between inequality and trust are as yet unsubstantiated.
2. How inequality might affect trust
Why might inequality lead to a lower probability of believing that strangers are
trustworthy? Existing research suggests a variety of pathways by which inequality might affect
trust. This work does not identify specific timescales or time lags on which the effects of
inequality operate, but we give the inequality-trust hypothesis the benefit of the doubt by
allowing for a range of possibilities. Following Robinson and Jackson (2001), we also identify
reasons for which trust might decline either through period effects or cohort replacement.
First, inequality might erode trust rapidly among the relatively less well-off through the
social-psychological effects of relative deprivation or disadvantage. Lower income-earners could
be more embittered and resentful about their situation where they see others enjoying more
extravagant lifestyles quite different from their own. Perceiving that others are enjoying great
5 wealth, but not themselves, they could come to believe that large numbers of people are
somehow cheating the system, and thus that people generally cannot be trusted. Similarly, in
periods of high income inequality, people who are relatively well-off might be more wary of
resentment, and therefore dishonest behavior, on the part of the poor. In both of these scenarios,
there would be period effects, as individuals’ opinions about the trustworthiness of others could
respond quite quickly to changes in income inequality, irrespective of their stage in the life
course (see e.g., experimental evidence from Cozzolino 2011). This leads to our first hypothesis:
H1: People will express less trust in periods of greater inequality.
Second, people’s perceptions of others’ trustfulness might be responses to direct personal
experiences of untrustworthy behavior by others, and the trustworthiness of people’s behavior
could be genuinely affected by levels of inequality. Robert Putnam proposed a version of this
hypothesis in Bowling Alone (2000), arguing that people who “tell pollsters that most people
can’t be trusted … are not hallucinating—they are merely reporting their experience” (2000:
138). If this mechanism explains the association between inequality and trust, then there may be
some delay before changes in inequality are reflected in what people say about others’
trustworthiness. Inequality might take time to change people’s behavior, making it less
trustworthy; and it might take time for people to accumulate sufficient experience of
untrustworthy behaviors such that they revise their views. In short, if mistrust is a generalization
from recent experiences of inequality in everyday social interactions, and inequality genuinely
does foster less trustworthy behavior, we should expect trust to covary with changes in inequality
over time with a lag of some duration. Although there have been no studies linking inequality
directly to untrustworthy behavior, experimental subjects’ self-reported trust correlates with
trustworthy behavior both across individuals (Glaeser et al., 2000) and cross-nationally (Johnson
and Mislin 2012; Knack and Keefer 1997). Given this correlation between social trust and social
6 trustworthiness, and the correlation between trust and inequality, inequality is likely also to be
correlated with—and potentially a cause of—untrustworthy behavior. Thus, our second
hypothesis:
H2: People will express less trust some time after the onset of greater inequality.
Third, perceptions of trust could also be based not on recent experiences, recent feelings
of relative deprivation, or recent concerns about threats from the poor, but on such experiences
or feelings at formative moments in the life course. In this scenario, people form opinions about
the trustworthiness of other people at an early age, and those opinions remain firm thereafter,
much as they form enduring opinions and beliefs about many other social and political issues. If
this is the case, then changes in inequality over time influence aggregate levels of trust in society,
but not by changing the trust of specific individuals; rather, such changes induce differences in
trust across cohorts. Over time, as less trusting cohorts replace more trusting cohorts, societal
trust declines, even if no single individual has changed his/her beliefs about the trustworthiness
of others. In this scenario, a given person’s trust should reflect income inequality with a time lag
that is a function of that person’s age, such that recent inequality affects younger people, while
inequality in earlier times affects older people.
H3: People will express less trust if they experienced greater inequality at a young age.
Fourth and finally, trust could respond to inequality very slowly, such that changes in
inequality over the course of years or even a generation might show no effect, but more
inequality is nonetheless correlated with less trust. Such a pattern could follow from the fact that,
as culture is transmitted from one generation to the next, people absorb the cumulative
experiences of multiple generations living in persistent contexts of greater or lesser inequality.
The experiences of multiple prior generations, based on the inequality of their times, could lead
them to convince their children to expect people to behave in one way or another—irrespective
7 of the actual experiences of current generations. Conversely, past inequality might also entrench
patterns of genuinely less trustworthy behavior, leaving current generations with an enduring
legacy of inequality that is impervious to any fluctuations in inequality that may occur during
their lifetimes. Importantly, however, evidence of lower trust in social contexts of persistently
higher inequality, in the absence of any longitudinal association, is weaker proof of a causal
relationship; a spurious relationship, or the possibility that lower trust leads to higher inequality
rather than the reverse, cannot be ruled out.
H4: People will exhibit less trust in contexts of enduringly higher levels of inequality.
In the remainder of this paper, we test for the effect of inequality on trust via each of
these pathways, which collectively allow for a number of different possible relationships.
3. Data and methods
For data on people’s trust in others, we employ 20 waves of the General Social Survey
between 1973 and 2004 which asked respondents a standardized question about trust (the 1974,
1977, 1982, and 1985 waves did not include the question): “Generally speaking, would you say
that most people can be trusted or that you can't be too careful in dealing with people?” Out of
27,899 total valid responses to this question across all waves, 38.9% answered “most people can
be trusted,” 56.6% answered “you can’t be too careful,” and 4.5% answered “it depends.” The
analyses we present in this paper treat the response as binary, grouping “it depends” with “you
can’t be too careful.”2 This question has been validated by studies correlating the mean national
response to it with data derived from experiments measuring both trusting behavior (Johnson and
Mislin 2012) and trustworthy behavior (Knack and Keefer 1997). In the latter case, researchers
2
In modeling this outcome, we use logistic regression. Substantive results are unchanged if “it depends”
answers are dropped or grouped with positive answers.
8 intentionally “lost” wallets in public places, and the proportion returned to their “owners” in
different countries proved to be significantly correlated with the proportion of people who say
that most people can be trusted.
Like a number of previous studies (e.g., Brehm & Rahn 1997; Glaeser et al 2000;
Rosenfeld, Messner, and Baumer 2001; Simpson 2006; Smith 1997; Zmerli and Newton 2008),
we also considered a three-item index combining responses to the trust question with responses
to two other similar questions: “Do you think most people would try to take advantage of you if
they got a chance, or would they try to be fair?” and “Would you say that most of the time
people try to be helpful, or that they are mostly just looking out for themselves?” For this index,
we coded “it depends” answers as 1, positive answers to each question as 2, and negative
answers as 0, whose sum therefore yields a 7-point scale ranging from 0 to 6. At the individual
level, Cronbach’s Alpha for the three-item scale is 0.67. Because this level of Cronbach’s Alpha
is arguably inadequate for a summative index, and in order to concentrate specifically on trust,
we focus in this paper on the single-item measure rather than the index (see Uslaner 2002 for a
similar defense of the single-item rather than three-item measure). Models with the three-item
index yield similar results, as do models with each of the other single-item measures.
There were valid individual-level observations in all states, including the District of
Columbia, except Nebraska and Nevada. We initially ran models, described below, using all
available states. For the analyses whose results we report here, however, we excluded 27
observations from DC, because it is a clear outlier in a crucial way: it is far more unequal than
any other state. The exclusion of DC does not change the substantive results we present, though
it does attenuate the magnitudes of some inequality-related coefficients. Many states were not
included in every wave of the GSS. Twenty-seven states were observed all twenty times, four
9 states were observed only once, and the remaining seventeen states were observed between five
and nineteen times.
The GSS records information about the state in which respondents were interviewed, and
includes a categorical variable indicating whether it was the same as their state of residence at
age 16; only some individuals can therefore be matched to characteristics of the states in which
they lived as young adults. It would be preferable for our purposes to have information on the
state of origin for all respondents, including those who moved, but this information is not
available. Except where we examine the influence of inequality at age sixteen, we include all
respondents, not just those who were interviewed in the same state that they had lived in at age
16. We did also try imposing this restriction, which discards roughly a third of respondents
(leaving 18,372), but it makes no substantive difference.
As individual level control variables we include race (the GSS categorizes respondents
simply as white, black, or other); logged family income in constant dollars, to capture the
potential effects of social class; respondent’s age in years; a dummy variable for being male;
respondent’s education in years; and a dummy variable equal to one if the respondent lives in a
suburb. In the analyses reported in this paper, we treated missing values by listwise deletion. In
supplemental analyses we imputed a small number of missing values for some individual-level
variables using the Amelia II software (Honaker et al., 2010); multiple imputations were
conducted using time, sex, age, income, and education. Item non-response was not severe:
among the 27,899 respondents who provided valid responses about their trust in others, 0.3%
were missing data for age, 9.4% for income, and 0.3% for education.
For state income inequality, we use recently developed estimates from Galbraith and
Hale (2008). They constructed their dataset, which covers the years 1969 to 2004, by modeling
measured family income inequality for Census years as a function of administrative pay data,
10 and by using their model to interpolate or extrapolate annual, state-level measures of family
income inequality to intercensal years. The result is an annual data set of state-level Gini indices
for family income. These measures overcome the difficulties that plague alternative measures of
family income inequality constructed by aggregating income data directly from the Current
Population Survey; such CPS estimates are known to be “subject to small sample sizes, a need to
interpolate values within income ranges, and top-codes that truncate large reported incomes”
(Galbraith and Hale 2008: 2). In Census years, measures of state income inequality can be
derived directly from Census data and used for cross-validating other estimates. The annual
Galbraith and Hale estimates perform very well against this benchmark—their correlation with
the Census estimates is above 0.9. Alternative CPS-based measures of inequality that we tested
did not perform as well against the Census benchmark.3
For county income inequality, we use Gini coefficients calculated from Census data by
Nielsen and co-authors (Nielsen and Alderson 1997; Moller, Alderson and Nielsen 2009). These
estimates are available for decennial Census years; we estimated county-level Gini coefficients
for intercensal years by linear interpolation.
In order to distinguish between possible causal mechanisms, we seek to identify separate
longitudinal and cross-sectional associations between inequality and trust. To do so, we calculate
the mean of all of a state’s Gini coefficients, pooling all years from 1973 to 2004; this overall
average
captures the effect of enduring differences in states’ levels of inequality. To capture
the effect of change in inequality over time, we subtract each overall average from each specific
state -year Gini coefficient X. The cross-sectional component of inequality ( , a state -level
3
For published estimates of income inequality from Census data, see
http://www.census.gov/hhes/www/income/histinc/state/state4.html.
11 variable) and the longitudinal component of inequality (
, a state -year-level variable) are
thus orthogonal to each other by construction, and their effects can be estimated separately.
As state-year level control variables we include state income per capita, controlling for
inflation; percentage of the state population that is black; and the logged state population density
in residents per square mile, as a measure of the extent to which states are urban versus rural.
These data come from the Bureau of Economic Analysis;4 Census population estimates
distributed in electronic form by the National Cancer Institute SEER program;5 and the Bureau
of the Census, respectively.6 We include each of these state-level variables because of findings in
previous studies that income, race, and (sub)urban versus rural residence are all correlated with
trust, and we wish to control for their effects when assessing the impact of state income
inequality. Also at the state level, given that many of the most unequal states in the U.S. are
located in the South, and that Southern states evidence lower levels of generalized social trust
(Simpson 2006), we include a dummy variable for the 11 Southern states, defined as those of the
former Confederacy. Table 1 provides summary statistics about all variables.
Our supplemental county-level analyses are restricted to the period from 1993 to 2004,
because the GSS only recorded respondents’ counties of residence from 1993 on. As with the
state-level analysis, we decompose county-level inequality into cross-sectional and longitudinal
components. We also report models that enter state-level and county-level measures of inequality
simultaneously.
4
For a measure of state per capita personal income in constant dollars, we use data from
http://www.bea.gov/regional/spi/default.cfm?selTable=summary, deflated by
http://www.bea.gov/national/nipaweb/TableView.asp?SelectedTable=13&Freq=Qtr&FirstYear=2007&La
stYear=2009.
5
The SEER population estimates used in our analysis are identical to the annual state-level population
estimates of the U.S. Census Bureau. See http://seer.cancer.gov/popdata/download.html#state.
6
http://www.census.gov/compendia/statab/tables/09s0013.xls. We linearly interpolated for non-census
years.
12 [Table 1 here]
The literature on social trust cautions that the determinants of trust may vary strongly by
race (Smith 2010; Uslaner 2002). Consequently, in generating the results we present below, we
were careful to investigate interactions between inequality and respondent’s race, and to run
models on single-race subsets of the data. We found differences between white and black
respondents, with whites reporting more trust (and respondents classified as “other” being in
between). Models fit only to data from white respondents did not produce results substantively
different than those from models fit to data from all respondents, so we include all respondents in
the analyses presented below.
Figure 3 provides an overview of the statistical associations between inequality and trust,
using nonparametric, locally weighted scatter plot smoothing (“Lowess” plots). Each of the plots
in Figure 3 shows the local mean value of the response variable of interest (trust, measured
dichotomously), using a non-parametric smoothing function which weights each observation
according to its distance from each point of estimation.7
[Figure 3 here]
For each row of Figure 3, the left column plots trust against the Gini coefficient for the
state and year in which each respondent was interviewed; the right column plots trust against the
Gini coefficient for respondent’s state of residence, averaged over all years; and the center
7
For each plot, only the middle 90% of the observations (ranked by inequality) have been used, to ensure
that extreme values do not provide a misleading impression of the relationships. The “rug” at the bottom
of each plot provides an indication of the distribution of the observed values.
13 column plots trust against the Gini coefficient used in the left column, minus the state-mean Gini
coefficient used in the right column. The center column therefore captures longitudinal variation,
the right column cross-sectional variation, and the left column pools the two. Almost all of the
lines plotted in Figure 3 tilt to the right, evidencing broadly negative associations between
inequality and trust. But the gradient tends to be less steep for the longitudinal (center column)
than the cross-sectional dimension (right column), suggesting that most of the association is
cross-sectional. The one longitudinal effect that appears potentially strong is that for the Gini
coefficient of a respondent’s state of residence when he/she was 16 years of age—in the bottom
row.
In order to estimate the longitudinal and cross-sectional associations between inequality
and trust while controlling for other variables, and to take into account the clustering of
observations within state-years and states, we employ a multilevel modeling approach.
Multilevel (also known as mixed) models are appropriate for analyses of complex data structures
where units are grouped, and a given unit’s expected value on the dependent variable depends on
the group(s) to which it belongs. In some instances, the failure to account statistically for such
grouping may lead to biased standard errors, and so using a multilevel approach is essential. In
other instances, single-level models are a feasible option, depending on the purpose of the
analysis. Where a model’s key independent variables are characteristics of the groups to which
individuals belong, however, multilevel models are highly advantageous. In the analysis
presented here, individual respondents are grouped in state-years (each respondent is observed in
one specific state at one specific time), and state-years are in turn grouped within states (each
state-year is a single observation of a state that is observed many times). We therefore fit a threelevel random intercepts model, where the intercept term depends on random characteristics of the
14 state-year and state to which a respondent belongs, and independent variables are characteristics
of individuals, state-years, or states.
The strategy we described above for distinguishing longitudinal from cross-sectional
effects, involving the subtraction of each state’s mean Gini index from each state-year Gini
index, extends an approach suggested by several recent studies. Others have noted the potential
analytical benefits of group mean centering when conducting multilevel analyses of crosssectionally nested data (see for example Paccagnella 2006; Raudenbush 1989; Wu and
Wooldridge 2005), and Bartels (2008) has recently argued for the benefits of the technique in
longitudinal analyses of macro-comparative time series cross-sectional data, where countries are
the unit of analysis. Moller, Alderson, and Nielsen (2009) use group mean centering in analyzing
the drivers of cross-sectional and longitudinal variations in U.S. counties’ levels of inequality.
But, to our knowledge, ours is the first study to extend this technique to three-level models that
permit measurement of both cross-sectional and longitudinal variation in individual-level survey
data.
Aside from allowing us to distinguish between longitudinal and cross-sectional
associations, this technique may prevent us from making misleading inferences. As discussed
above, we are analyzing data with time-series cross-sectional properties. The nested multilevel
approach we use here adjusts the standard errors for clustered observations, but potentially not in
the presence of autocorrelation over time. Consequently, we checked for autocorrelation in the
state-year random effects, and found very little: 0.05 for the first lag, and 0.01 for the second. We
also assessed whether state income inequality might be a non-stationary time series, using the socalled PANIC test (“Panel Analysis of Nonstationarity in Idiosyncratic and Common
components”) proposed by Bai and Ng (2004). The PANIC factor analysis yielded evidence of a
single common trend component, but augmented Dickey-Fuller tests provided no evidence of a
15 unit root in either this common component or in the state-specific series of residuals (the
“idiosyncratic components”). We conclude that the series is stationary.
The multilevel logistic regression model we fit, with all control variables included, is the
following:
logit(pijk) = ß0 + ß1timejk+ ß2gini_ state_meank + ß3gini_ longitudinaljk + ß4blackijk + ß5churchijk
+ß6incomeijk + ß7ageijk + ß8maleijk + ß9other_ raceijk + ß10educationijk + ß11state_income_ pcjk +
ß12southk + ß13black _ percentjk + ß14suburbijk + ß15densityjk + v0k + u0jk
v0k ~ N(0, σ2v0)
u0jk ~ N(0, σ2u0)
The log-odds that individual i in state-year j in state k is trusting is thus a function of
variables at three levels, and we include random state effects v0k and state-year effects u0jk. We
include a linear time variable in order to control for the fact that both inequality and trust exhibit
clear trends over time (increasing and decreasing, respectively, for almost all states). The
simultaneity of these two trends could mean they are related, but not necessarily. Controlling for
the linear time variable allows us to test whether states experiencing faster-than-average growth
in inequality also exhibit larger-than-average declines in trust—which they should, if the two
trends are indeed causally related. A linear term is appropriate, given that income inequality
followed broadly linear trends.8
All models were fitted using restricted maximum likelihood in the lme4 package in the R
language and environment for statistical computing (Bates and Maechler 2009; see Bates 2005).
8
In supplemental analyses, we estimated models that (a) permitted the slope of the linear time trend to
vary randomly by state, and that (b) included random intercepts for years. The results did not change in
either case.
16 4. Results
Table 2 reports the results of the logistic multilevel regression modeling. Model 0 is a
null model which includes only an intercept and random effects, provides an indication of the
partitioning of the variance across different levels in the model, and establishes a baseline for
Akaike’s Information Criterion—a measure of goodness of fit. The relative sizes of the random
effects variances in Model 0 show that trust is substantially more clustered within states than
within state-years.
Models 1 through 4 all include the cross-sectional component of inequality, relevant for
Hypothesis 4, about the effects of persistently higher versus lower inequality. Models 1 and 2
include the longitudinal component of inequality measured at the time that the respondent was
interviewed, which is the inequality measure of relevance to Hypothesis 1 (about a fast impact of
changing inequality on trust). Model 3 includes the longitudinal component of inequality
measured with a ten-year lag, which is relevant to Hypothesis 2. Model 4 includes the
longitudinal component of inequality measured at the time the respondent was 16 years old,
which is the inequality measure relevant to Hypothesis 3 (which addresses the possibility of
inequality in one’s formative years affecting one’s trust). Models 2 through 4 include the full
range of control variables discussed above.
[Table 2 here]
In each of Models 1 to 4, the coefficient for state mean inequality (which captures the
cross-sectional association between inequality and trust) is negative and significantly different
from 0 at the 0.01 level. This finding is consistent with Hypothesis 4: states that have persistently
17 high levels of inequality also have high levels of mistrust. In contrast, in no model is the
estimated coefficient for longitudinal variation in inequality statistically significant at the 0.05
level, even in a one-tailed test. Regardless of whether we measure inequality at the time of the
survey interview, at a time ten years previous to the interview, or at the time the respondent was
16 years old, we find no evidence that it has a longitudinal association with expressions of
mistrust. Thus, we reject Hypotheses 1 through 3, and conclude that longitudinal variation in
inequality over the period spanned by the GSS is not associated with variation in mistrust. If
there is any association, in three of four models the coefficient on the longitudinal effect of statelevel inequality is positive rather than negative—contrary to arguments linking increases in
inequality to reductions in trust.
To ensure the robustness of our main results, we also tried fitting Models 2 through 4
without the cross-sectional effect for Gini; the results were the same. We tried fitting these
models without controlling for respondent’s age, to investigate whether this coefficient might be
absorbing some of the variation properly attributable to changes in trust; the results were the
same. We tried interacting Gini (both the mean and the difference) with year of birth, on the
logic that such an interaction might be more able to capture the effects of inequality in people’s
formative years, since only younger people would be affected by recent changes inequality; we
found no difference. We tried interacting the (cross-sectional) effect of inequality with (logged)
income, to test whether the effects of inequality are limited to or greater among either poor (or
wealthy) respondents, and the interaction was not statistically significant: people are less trusting
in more unequal states irrespective of where they are located on the income spectrum. We
estimated the models separately in samples of people who said they had, and had not, lived in a
different state at age 16, to control for the possible effects of mobility on trust; we were
particularly interested to discover whether the results of Model 4, which include a time-varying
18 measure of inequality in respondent’s current state at the time the respondent was 16, would be
robust to estimation on separate samples of respondents who had and had not moved from
another state since they were 16 years old.9 The results were robust. People who live in
persistently unequal states report less trust in strangers, regardless of whether they lived in those
states since childhood, or moved there as adults; but longitudinal variations in inequality within a
state are not associated with differences in reported trust, regardless of whether those variations
are measured currently, with a ten-year lag, or at the time the respondent was 16, and regardless
of whether the sample is limited to respondents who have or who have not moved since age 16.
As discussed earlier, it is possible that social trust might change via cohort replacement
rather than period effects. As a robustness check, we allow for this possibility by investigating
the longitudinal effect of inequality at age 16, in a model with no control for age.10 In one
specification of this kind, we did in fact find a statistically significant longitudinal effect of
inequality. However, this finding was driven by the clear correlation between year of birth
(cohort) and inequality: by definition older people were 16 years old longer ago, farther back in
time inequality was lower in all states, and younger people are less trusting generally. If we
include year of birth as a control in this specification—for the same reason that we include a
linear time trend as a control in our main models—the effect of inequality changes and becomes
non-significant.11 The trend towards less trust over time among younger cohorts is no steeper in
states/counties where inequality (at age 16) has risen more over time.
9
People who report living in a different state at age 16 are on average slightly more trusting than others,
but controlling for this variable by splitting the sample into mobile and immobile respondents does not
change our results. We find a cross-sectional correlation between inequality and trust, but no longitudinal
correlation between inequality and trust, in samples of mobile respondents, and in samples of immobile
respondents.
10
We thank an anonymous reviewer for this suggestion.
11
This result holds irrespective of whether a linear time effect is included.
19 [Table 3 here]
Is the absence of any longitudinal correlation between inequality and mistrust in these
models an artifact of the high level of geographic aggregation at which inequality is measured?
Table 3 reports the results of supplementary models estimated with county-level measures of
inequality. Model c0 includes only an intercept and random effects at the county-year, county,
and state levels. The random effects variances show that levels of trust are substantially clustered
within counties and even county-years—more than in states. Model c1 introduces cross-sectional
and longitudinal components of county income inequality (measured at the time the respondent
was interviewed), along with a linear time trend and controls for respondent’s race and
religiosity. Model c2 adds controls for age, gender, education and income. In none of these
models is county-level inequality significantly correlated with mistrust, either in the cross section
or longitudinally. It is conceivable that the absence of any correlation with county-level
inequality might be an artifact of reduced statistical power, since the county-level analysis is
possible only for a subset of the sample (for the years 1993 to 2004). To test whether it is this
loss of statistical power that explains the absence of a correlation at the county level, we estimate
model c3, including both state- and county-level measures of inequality. This model decomposes
inequality into three components: Xs, the cross-sectional component of state-level income
inequality; Xcs, the cross-sectional component of county-level income inequality, measured as a
county-level deviation from Xs; and Xcl, the longitudinal component of county-level income
inequality. The results suggest that the absence of a county-level correlation between inequality
and mistrust cannot be attributed solely to the short time series, because the cross-sectional
component of state-level income inequality, Xs, is substantially correlated with mistrust, even
controlling for county-level inequality, even in a short panel data set. This model provides
20 further evidence that people who live in more enduringly unequal states are more mistrustful; but
it provides no additional evidence that inequality correlates with mistrust over time or crosssectionally within states.
5. Discussion
This paper has investigated simultaneously and independently the cross-sectional and
longitudinal associations between inequality and social trust. Distinguishing these associations is
critical for testing hypotheses about causal mechanisms linking inequality and trust. Using U.S.
data over a 32-year period, and concentrating on income inequality within either states or
counties, we have shown that states—though not counties—with higher levels of inequality tend
to have lower levels of trust. This state-level relationship holds whether or not other factors are
controlled for statistically. In contrast, we were unable to find a statistically significant
longitudinal association over time between changes in states’ (or counties’) levels of inequality
and their levels of trust. All states grew more unequal from the early 1970s to the early 2000s,
and almost all states grew less trusting; but states with greater increases in inequality did not
suffer greater losses of trust. We interpret this null finding as evidence against the thesis that
declining trust in the United States in recent decades has been a consequence of growing
inequality. Variation in people’s trust is largely independent of short-term fluctuations of
economic inequality in the places where they live.
Our findings are consistent with a causal effect of inequality on trust, but they imply that
any such effect must operate on a very slow time scale. The absence of a longitudinal correlation
between inequality and trust does leave unexplained the robust cross-sectional, state-level
association between inequality and trust. We advocate caution in interpreting this association
between inequality and trust as causal. It may indicate that inequality erodes trust very slowly,
21 but it is also consistent with the alternative hypothesis that some other, underlying historical
experience in particular states caused them to subsequently experience both inequality and
mistrust. From a policymaker’s perspective, there remain many good reasons for seeking to
mitigate inequality, but there is not (yet) a good case for thinking that policies to mitigate
inequality will maintain or build social trust. Where the aim is to build trust, policies should seek
to do so by other means.
Ours is not the first study to find no or only weak evidence for the effect of inequality on
trust (Bjørnskov 2008; Berggren and Bjørnskov 2011; Leigh 2006b). But, in analyzing the
relationship both cross-sectionally and longitudinally, we believe we have provided the most
rigorous test of the hypothesis thus far. Our models may have pushed the limits of what simple
observational data can contribute to our understanding of this issue—at least until several more
generations of survey data are available. Future research should focus on indentifying exogenous
instruments or natural experiments to better untangle the historical relationship between
inequality and social trust.
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27 Appendix A
In this paper, we report multilevel models of the effects of state- and county-level
inequality on individual-level measures of trust. For the purposes of illustration, however, we
also report graphs of aggregate trust measured at the state level. Constructing an aggregate
measure of trust is complicated by the fact that each wave of the GSS is representative at the
country level, but not the state level. Consequently, inferring a state’s overall mean trust for any
given year based only on its raw mean trust from that year’s wave of the GSS (as for example do
Alesina and La Ferrara 2002 and Uslaner 2004) could be misleading. We therefore construct
estimates using multilevel regression with post-stratification (“MRP”—see Lax and Phillips
2009; Park, Gelman, and Bafumi 2004). MRP involves modeling individual responses as
functions of both individual- and higher-level predictors, as well as state random effects, and
then estimating overall state-level values for an outcome of interest (trust, in our case) by
weighting different types of respondents according to the prevalence of various categories of
people in each state’s population. Recent articles suggest that MRP produces more reliable and
valid estimates than simple aggregation, and that it can provide reliable estimates in instances
where aggregation could not provide estimates at all (Lax and Phillips 2009; Park, Gelman and
Bafumi 2004). In the present study, MRP permits us to extrapolate estimates of trust for
Nebraska and Nevada even though the data we analyze have no valid individual-level
observations of trust for either state.
We begin by estimating a multilevel model of trust in the GSS as a function of state and
state-period random effects and population characteristics that are reported by the Census. In
particular, we stratify each state’s population by four variables: age (in four groups), sex, race
(white, black, other), and education (four categories, which differ by Census year but are not
difficult to match with GSS measures of education). For each state, we calculate the expected
28 level of trust for 96 different categories of people defined by the cross-classification of these
strata. We then calculate the state’s average level of trust using weights based on each category’s
share of the state population, plus the estimated random intercepts for each state and state-period.
Because this method relies on decennial Census data to calculate the appropriate population
weights, we are only able to calculate estimates of state-level trust for Census years.
Our model of trust uses all available waves of the GSS from 1973 to 2004. We choose
this approach, rather than fitting completely separate models for each Census year, because in
any given wave of the GSS, surprisingly large numbers of states are not sampled at all, meaning
that we would not have any information with which to estimate some states’ random effects.
Moreover, the GSS is not conducted every year, and the questions about trust have only been
asked in most, not all, waves of the study. Including all waves therefore gives us more
information about time-constant differences among states with respect to trust.
We fit a multilevel model with a linear time effect, and individuals nested within stateperiods, nested within states. Periods are clusters of survey waves. Each state contains seven
state-periods, and periods 2, 4, and 6 include the Census years 1980, 1990, and 2000,
respectively. Period 2 includes 3708 respondents from the 1978, 1980, and 1983 waves; Period
4, 3863 respondents from the 1988, 1989, 1990, and 1991 waves; and Period 6, 4606 respondents
from the 1998, 2000, and 2002 waves. The other periods contain uneven numbers of waves
before, after, and in between periods 2, 4, and 6. We use periods and random effects for stateperiods, rather than years and state-years, again because of the number of states not sampled in
any given year. Using multi-wave periods, we still lack state-period random effects for some
states in each Census year (9 for 1980 and 1990, and 11 for 2000), but we lack fewer random
effects than if we had used years.
29 One possible problem with using a single model for all years is that the associations
between the predictors and the outcome of interest may have changed over time, potentially
biasing our estimates. We investigated this possibility by examining the coefficients from models
fitted to separate waves and periods of data, and found no substantively meaningful differences.
Consequently, we believe that the benefits of using a single model for all years outweigh the risk
of biased estimates.
The model we fit is:
# p &
ln%
( = β0 + β1time + β2 male + β3other _ race + β4 black + β5 south jk +
$1 − p '
(3)
(4 )
(5)
u(2)
age _ group(i) + ueducation _ group(i) + ustate(i) + ustate − period (i)
2
u(2)
age _ group(i) ~ N(0,σ u(2) ), for i = 1,...,4
2
u(3)
education _ group(i) ~ N(0,σ u(3) ), for i = 1,...,4
)
2
u(4
state(i) ~ N(0,σ u(4 ) ), for i = 1,...,49
2
u(5)
state − period (i) ~ N(0,σ u(5) ), for i = 1,...,7
Below we present estimates of state-level trust after post-stratification, as a resource that
€ be useful to other scholars.
may
[Table A.1 here]
30 Figure 1: State Inequality Over Time
0.50
Based on Data from Galbraith and Hale (2008)
NY
DC (0.43, 0.57)
0.48
CT
0.44
NV
AZ
NC
0.42
PA RI OR
MD
CO
NM
AL
GA FL
TN
KY
0.40
AR
MI
MN
MT
UT
ME
NH
VA OK
SC
WV
MO
HI
MS
LA
TX
MANJ
IL
OH
0.38
Gini in 2004
0.46
CA
KS
VTDE
WY
WA ID
IN
AKNE
WI
SD
ND
IA
0.32
0.34
0.36
Gini in 1973
31 0.38
0.40
0.42
0.6
0.7
Figure 2: Trust Across States and Over Time
MN
ND
●
MT
NH
●
●
●
WAIA
ME
●
MI
MA
● ● ● NV
IN
0.4
●
●●
RI
NE
●
●
DE
●
●
CO
MOSD
AZ
●
●●
●
CT
●MD● PA
OH
KS
ID
●●●
●
VT
OR
●
●
AK
IL
●
HI
NJ
●
●
NM
CA
●
●
NY
●
0.3
●
WV
●
FL
VA
GATN
●
● ● TX
●
●
●
SC
AR
LA
DC
●
●
●
AL
MS
●
●
0.2
●
KY
OK ●
●
NC
●
0.1
Aggregate Trust
0.5
WI
UT
●
●
WY
1980
1990
2000
Estimated using data from the General Social Survey. States have been
ordered from left to right by inequality in 1980 (Galbraith and Hale 2008). The
procedure used to generate these estimates is described in Appendix A.
32 0.34
0.36
0.38
0.40
0.42
0.42
0.6
0.6
0.6
0.40
0.5
0.5
0.5
0.38
0.4
0.4
0.4
0.36
0.3
0.3
0.3
0.4
0.4
0.4
0.5
0.5
0.5
0.6
0.6
0.6
−0.04 −0.02
−0.03
−0.05
0.00
−0.01
−0.03
33 0.02
0.04
0.01
0.03
−0.01
0.01
Gini Lagged Ten Years
0.2
0.2
0.2
Trust (N = 21,899)
0.1
0.1
0.1
0.3
0.3
0.3
0.4
0.4
0.4
0.5
0.5
0.5
Current Gini
0.2
0.2
0.2
Trust (N = 27,899)
0.6
0.6
0.6
State−Year Gini
Gini at Age 16
0.1
0.1
0.1
0.44
0.3
0.3
0.3
0.34
0.40
0.2
0.2
0.2
Trust (N = 10,217)
0.36
0.1
0.1
0.1
Figure 3: Trust Versus Inequality
De−meaned Gini
State−Mean Gini
0.37
0.35
0.37
0.39
0.37
0.39
0.41
0.39
0.41
0.43
0.41
0.43
Table 1: Descriptive Statistics
Frequency of Outcome
0
1
2
trust
15791 (56.6%)
1249 (4.5%)
10859 (38.9%)
fair
10049 (36.9%)
1664 (6.1%)
15484 (56.9%)
helpful
12116 (44.4%)
1679 (6.2%)
13493 (49.4%)
trust (index)
Range: 0 – 6, Mean: 3.08
Summary Statistics for Individual Level Variables
Range
Mean
age (years)
18 – 89
45.3
education (years)
0 – 20
12.7
income ($)*
468 – 281,300
43,310
Male
men: 12313 (44%), women: 15586 (56%)
Suburb
suburb: 8531 (31%), other: 19368 (69%)
Black black: 3674 (13%), white or other race: 24225 (87%)
other race other race: 988 (4%), white or black: 26911 (96%)
Summary Statistics for State-Year Level Variables
Range
Mean
Inequality
(longitudinal)...
...at time of interview
-0.055 – 0.094
0.001
...10 years previous
-0.043 – 0.044
0.002 (N = 21,899)
...when R was 16
-0.061 – 0.083
-0.023 (N = 10,217)
blacks in population (%)
0.23 – 70.53
12.34
Population density*
1.0 – 10780.0
232.3
State income/capita ($K)
13.31 – 49.26
25.73
Summary Statistics for State Level Variables
Range
Mean
Inequality
0.351 – 0.433
0.399
(cross-sectional)
South
South: 8182 (29%), other: 19717 (71%)
Summary Statistics for County and County-Year Level Variables
Range
Mean
Inequality (cross0.310 – 0.559
0.388
sectional)
Inequality (longitudinal)
-0.015 – 0.036
0.002
...at time of interview
These summary statistics are presented for all 27,899 respondents with valid
responses for the trust question, in all states in all waves. Values with an
asterisk (*) are presented here in raw form for clarity, but logged when used in
models. Means of state-year- and state-level variables reflect the different
numbers of respondents from different state-years and states.
34 Table 2: Multilevel Regressions of Generalized Trust on Inequality
Model
State-Year Variance
State Variance
(Intercept)
0
0.06
0.17
-0.42
(0.06)**
Time
Inequality
(cross-sectional)
Inequality
(longitudinal)...
...at interview
1
0.03
0.05
4.53
(0.75)**
-0.02
(0.00)**
-12.26
(1.90)**
2
0.03
0.02
-3.11
(0.76)**
-0.03
(0.01)**
-6.23
(1.12)**
1.44
(1.48)
1.81
(1.56)
...10 years previous
3
0.02
0.01
-2.91
(0.67)**
-0.04
(0.01)**
-6.64
(1.66)**
0.11
(2.66)
...when R was 16
-1.32
(0.05)**
Black
Church
Income (Logged)
Age
Male
Neither Black Nor
White
Education
State Income/Capita
Southern State
Black Share of State
Population
Suburban Residence
Population Density
(Logged)
N of respondents
0.05 > * > 0.01 > **
4
0.00
0.00
-2.00
(0.83)*
-0.05
(0.01)**
-8.88
(1.81)**
27842
27842
35 -1.12
(0.05)**
0.14
(0.04)**
0.24
(0.02)**
0.02
(0.00)**
0.10
(0.03)**
-0.54
(0.08)**
0.17
(0.01)**
0.01
(0.01)
-0.16
(0.11)
0.00
(0.01)
-0.08
(0.03)*
-0.06
(0.03)**
24934
-1.12
(0.06)**
0.15
(0.04)**
0.23
(0.02)**
0.02
(0.00)**
0.08
(0.03)*
-0.56
(0.09)**
0.16
(0.01)**
0.02
(0.01)
-0.14
(0.09)
0.00
(0.00)
-0.10
(0.04)**
-0.09
(0.03)**
19366
-0.68
(3.12)
-1.00
(0.09)**
0.17
(0.06)**
0.14
(0.03)**
0.03
(0.00)**
0.12
(0.05)*
-0.48
(0.11)**
0.19
(0.01)**
0.03
(0.01)**
-0.02
(0.10)
0.00
(0.01)
-0.03
(0.05)
-0.14
(0.04)**
9275
Table 3: Multilevel Regressions of Generalized Trust on Inequality
Model
County Variance
County-Year Variance
State Variance
(Intercept)
c0
0.07
0.15
0.04
-0.63
(0.05)**
Time
County inequality
(cross-sectional)
County inequality
(difference from state mean)
County inequality
(longitudinal)
State mean inequality
Black
Church
c1
0.05
0.10
0.02
-0.63
(0.38)
0.02
(0.01)
-0.08
(0.96)
3.44
(6.88)
1.52
(7.29)
-1.22
(0.09)**
0.27
(0.06)**
-0.98
(0.10)**
0.13
(0.07)
0.02
(0.00)**
0.12
(0.05)*
-0.64
(0.13)**
0.16
(0.01)**
0.23
(0.03)**
7366,
898, 213,
37
Age
Male
Neither Black Nor White
Education
Income (Logged)
N of respondents, countyyears, countries, states
8544,
902, 213,
37
0.05 > * > 0.01 > **
36 c2
0.06
0.02
0.02
-5.52
(0.48)**
0.02
(0.02)
-1.23
(0.86)
8341,
901, 213,
37
c3
0.05
0.01
0.01
-3.17
(0.75)**
0.02
(0.02)
-0.04
(0.90)
1.39
(7.14)
-7.17
(1.70)**
-0.99
(0.10)**
0.13
(0.07)
0.02
(0.00)**
0.12
(0.05)*
-0.64
(0.13)**
0.16
(0.01)**
0.23
(0.03)**
7366,
898, 213,
37
Table A.1: Estimates of Overall State Trust
State
AL
AK
AZ
AR
CA
CO
CT
DE
DC
FL
GA
HI
ID
IL
IN
IA
KS
KY
LA
ME
MD
MA
MI
MN
MS
MO
MT
NE
NV
NH
NJ
NM
NY
NC
ND
OH
OK
OR
PA
RI
SC
SD
FIPS
1
2
4
5
6
8
9
10
11
12
13
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
44
45
46
Trust 1980
0.266
0.422
0.463
0.306
0.411
0.456
0.435
0.441
0.322
0.363
0.344
0.397
0.471
0.410
0.428
0.486
0.512
0.397
0.333
0.481
0.410
0.457
0.471
0.586
0.261
0.459
0.556
0.476
0.461
0.544
0.400
0.422
0.397
0.313
0.567
0.396
0.380
0.518
0.405
0.470
0.291
0.464
37 Trust 1990
0.234
0.397
0.414
0.284
0.363
0.419
0.389
0.411
0.304
0.338
0.345
0.368
0.442
0.413
0.403
0.455
0.515
0.383
0.293
0.452
0.416
0.400
0.454
0.562
0.243
0.398
0.515
0.445
0.423
0.516
0.370
0.395
0.379
0.309
0.567
0.383
0.357
0.466
0.422
0.437
0.279
0.432
Trust 2000
0.222
0.348
0.365
0.252
0.316
0.376
0.340
0.369
0.282
0.311
0.303
0.324
0.392
0.373
0.362
0.410
0.443
0.349
0.278
0.418
0.328
0.400
0.399
0.503
0.200
0.368
0.481
0.400
0.363
0.478
0.332
0.346
0.324
0.302
0.502
0.330
0.325
0.420
0.367
0.391
0.251
0.388
TN
TX
UT
VT
VA
WA
WV
WI
WY
47
48
49
50
51
53
54
55
56
0.347
0.316
0.492
0.515
0.342
0.487
0.323
0.505
0.524
38 0.285
0.298
0.465
0.488
0.345
0.452
0.302
0.510
0.520
0.279
0.293
0.403
0.453
0.292
0.420
0.268
0.462
0.459