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\begin{center}
{\large \textbf{Supplemental Appendix For:}}\\[0pt]
\textit{A Mixture Model for Middle-category Inflation in Ordered Survey
Responses}\\[0pt]
\end{center}

In this appendix, we first present a series of Monte Carlo experiments that
compare the performance of our OP, MiOP, and MiOPC models under various
conditions. We then describe the operationalizations of several independent
and control variables that are used in our OP, MiOP and MiOPC models of
\textit{EU support}. Following these descriptions, we evaluate the
robustness of our main OP, MiOP, and MiOPC findings via a series of
alternative model specifications for \textit{EU support}. We then report a
substantive-effects table for \textit{discuss politics}, \textit{female},
and \textit{income}, which is comparable to the Boxplots reported in Figure
2 of the main paper. Finally, we present two additional survey-data
applications. The first additional application examines individual support
for ``new social movements'' within Western Europe, and the second
application examines individual support for international trade across 23
countries.

\section{Monte Carlo Experiments}

We present the results from three main Monte Carlo exercises below that
assess the performance of the OP, MiOP and MiOPC models when the middle
category of the ordered dependent variable is \textquotedblleft
inflated\textquotedblright\ and thus generated from two distinct d.g.p's.
For the first Monte Carlo exercise, we evaluate the OP, MiOP and MiOPC model
in a finite sample where the degree of inflation in the middle category of
the ordered dependent variable is set at a relatively conservative level of
30\%.\footnote{%
This implies that 30\% of the respondents in the sample have opted for the
middle category response in the ordered survey response dependent variable.
We set the degree of middle-category inflation to the conservative level of
30\% as it helps us to explore the boundaries of the MiOP(C) model under
\textquotedblleft less-than ideal\textquotedblright\ (i.e. moderate)
conditions.} We assess the OP, MiOP, and MiOPC models for this Monte Carlo
exercise across datasets with $N$'s of 2,000, 4,000, and 8,000. For the
second Monte Carlo exercise, we hold the number of observations (i.e. $N$)
fixed at 2,000 and then explore how the OP, MiOP and MiOPC model performs
when the percent of middle category inflation in the ordered (survey
response) dependent variable is increased above 30\%. Our final Monte Carlo
exercise below uses a simulated dataset with 60\% middle category inflation (%
$N=2,000$) in order to evaluate the importance of exclusion restrictions to
the unbiased estimation of MiOP and MiOPC models; when our d.g.p.'s are
either MiOP or MiOPC.

\subsection{Monte Carlo results when sample size varies}

With respect to the first Monte Carlo analysis mentioned above, we perform
two Monte Carlo experiments for a sample where the middle category inflation
for the ordered dependent variable is set at 30\%. In the first experiment
(labeled as Experiment 1), the dependent variable follows a MiOP d.g.p which
implies no correlation between the error terms from the inflation and
outcome equations. In the second experiment (labeled Experiment 2), the
ordered dependent variable follows a MiOPC d.g.p in which the error terms
from the inflation and outcome equations are correlated. For Experiments 1
and 2, we set the number of $sims=1,000$ and compare the performance of OP,
MiOP, and MiOPC models in each experiment across datasets with $N$'s of
2,000, 4,000, and 8,000.\footnote{%
Estimation was undertaken in R with the aid of a high performance computing
(HPC) center.} In each experiment, we draw our inflation stage covariates $%
\mathbf{z}$ from $\mathbf{z}=(\mathbf{1},\mathbf{z_{1}},\mathbf{z_{2}}%
)^{\prime }$ where $\mathbf{z_{1}}$ is the natural log of $Uniform[0,100]$
and $\mathbf{z_{2}}$ is a binary variable equal to one when $%
Uniform[0,1]>0.25$ and zero otherwise. Our outcome stage covariates $\mathbf{%
x}$ are drawn from from $\mathbf{x}=(\mathbf{1},\mathbf{x_{1}})^{\prime }$
where $\mathbf{x_{1}}\equiv \mathbf{z_{1}}$. The corresponding parameter
values are set to $(\gamma _{1},\gamma _{2})^{\prime }=(0.5,0.25)^{\prime }$%
; $(\mu _{1},\mu _{2})^{\prime }=(0,2)^{\prime }$; and $(\beta _{1},\beta
_{2},\beta _{3})^{\prime }=(1,-0.25,-1)^{\prime }$. Experiment 1 then sets
our (MiOP) d.g.p. to have a correlation coefficient of $\rho _{\varepsilon
u}=0$ between the two (probit and ordered-probit) mixed d.g.p.'s, whereas
Experiment 2 (MiOPC d.g.p.) sets $\rho _{\varepsilon u}=.5$. These
specifications yield a three category ordered dependent variable $\mathbf{y}%
=(1,2,3)$ with roughly 30\% inflation in category $2$ (the middle category).

The results for Monte Carlo Experiment 1---which assesses the performance of
our OP, MiOP, and MiOPC models under a MiOP d.g.p.---appear in the top
portion of Table \ref{tab:ExperimentsOneTwo} below. Specifically, the
Experiment 1 results in Table \ref{tab:ExperimentsOneTwo} report the mean
estimates, root mean squared errors\footnote{%
Relative to each estimates' respective true values, then averaged over $%
1,000 $ simulations.} (RMSE) and 95\% empirical coverage probabilities%
\footnote{%
Calculated as the percentage of times that a true parameter falls within the
95\% confidence intervals of that parameter's estimate, out of $1,000$
simulations.} (hereafter CP) for our OP, MiOP, and MiOPC model estimates
across three datasets: $N=2000$, $N=4000$ and $N=8000$. In comparing the OP
model to the MiOP(C) models, we can first note that the MiOP(C) models
significantly outperform the OP model for most parameter estimates and
sample sizes even at the conservative level of 30\% \textquotedblleft
middle-inflation\textquotedblright . This is most evident in our estimates
of (i) the second cutpoint ($\mu _{2}$) and of (ii) the outcome state
intercept\footnote{%
Which is estimated by restricting $\mu _{1}$ to zero.} ($\beta _{1}$).
Indeed, Table \ref{tab:ExperimentsOneTwo} indicates that our OP model's 95\%
CP's for $\mu _{2}$ and $\beta _{1}$ are essentially zero in each of the
three sample sizes that we examine.

By contrast, the MiOP(C) CP's for these two model estimates range from
approximately 78\% to 90\% depending on the sample size examined. More
importantly, in moving from $N=2,000$ to $N=8,000$, we find in Table \ref%
{tab:ExperimentsOneTwo} that our MiOP(C) RMSE's fall sharply with increases
in $N $, whereas the OP RMSE's remain constant. However, the relative
superiority in MiOP(C) model performance is less clear in the case of $\beta
_{2}$, whose RMSE's slightly favor the OP model estimates in small $N$'s ($%
N=2,000$) but favor the MiOP(C) models at larger $N$'s ($N=4,000$ \& $8000$%
). Nevertheless, across each and every sample size, the $\beta _{2}$ CP's
consistently favor the MiOP(C) models. Additionally, note that even at low $%
N $'s, our substantive interpretations of $\beta _{2}$ would likely favor
the MiOP(C) models as well, given that $\mu _{2}$ and $\beta _{1}$---both
critical for deriving accurate marginal effects for $\beta _{2}$---are
overestimated by the OP model.

Lastly, we can also directly compare the performance of MiOP and MiOPC
models when the d.g.p. is MiOP. As can be seen in Table \ref%
{tab:ExperimentsOneTwo}, the RMSE's and CP's for the MiOPC model are nearly
identical to those of the MiOP model, suggesting that under MiOP d.g.p.'s,
the MiOPC model approximates a MiOP d.g.p. quite well. To summarize, under a
MiOP d.g.p. with 30\% middle category inflation, the MiOP(C) models are
decidedly superior to OP models while MiOPC models are comparable to MiOP
models in RMSE's and CP's.

\begin{center}
[Insert Table \ref{tab:ExperimentsOneTwo} about here]
\end{center}

The bottom portion of Table \ref{tab:ExperimentsOneTwo} reports the Monte
Carlo results for Experiment 2, which assumes a MiOPC d.g.p. Similar to the
Table \ref{tab:ExperimentsOneTwo} results for Experiment 1, the results for
Experiment 2 in Table \ref{tab:ExperimentsOneTwo} report the mean estimates,
RMSEs and the 95\% CP for our OP, MiOP\ and MiOPC model estimates across the
three datasets: $N=2000$, $N=4000$ and $N=8000$. As above, we first compare
the relative performance of our OP model to that of our MiOP(C) models. As
can be see in the RMSE's and the 95\% CP's reported in the lower portion of
Table \ref{tab:ExperimentsOneTwo}, the MiOP(C) models generally outperform
the OP model across all sample sizes, most notably again in our estimates of
$\beta _{1}$ and $\mu _{2}$. Indeed, across the four sample sizes examined,
our OP, MiOP, and MiOPC models respectively report averaged CP's of 0\%,
68\%, and 83\% for $\beta _{1}$ and of 0\%, 80\%, and 83\% for $\mu _{2}$.
Also consistent with our Experiment 1 results, our Experiment 2 MiOP(C)
RMSE's fall sharply as $N\rightarrow 8,000$ whereas the OP-model RMSE's do
not. Turning next to $\beta _{2}$, the lower portion of Table \ref%
{tab:ExperimentsOneTwo} indicates that---for all sample sizes---our OP
RMSE-estimates are superior to the MiOP model but inferior to the MiOPC
model when the d.g.p. is MiOP(C). Note also that as before, the $\beta _{2}$
CP's favor the MiOP(C) models over the OP model for the three $N$'s examined.

Unlike Experiment 1 however, our Experiment 2 results exhibit a notable
degree of divergence in model performance between the MiOP and MiOPC models.
Looking first at the outcome stage ($\beta $ and $\mu $) estimates in Table %
\ref{tab:ExperimentsOneTwo}, we find that our MiOPC estimates are generally
superior to MiOP estimates in terms of CP's and RMSE's, although in some
cases these differences are slight. Regarding our inflation stage estimates
however, we can conclude that our MiOP inflation stage ($\gamma $) estimates
frequently outperform our MiOPC estimates at $N$'s of $2,000$, but become
inferior to our MiOPC estimates as $N$ increases above $2,000$. Together
these results suggest that for MiOPC d.g.p.'s (i) our MiOP(C) models
significantly outperform comparable OP models and (ii) the MiOPC model
outperforms the MiOP model as well, although it only does so consistently at
$N$'s greater than $2,000$.

To gain a better sense of the trends in bias and efficiency that exist
across the OP, MiOP, and MiOPC models, we next plot each model's RMSE values
for each of the parameter-estimates and $N$'s examined within Experiments 1
and 2. These plots appear in Figures \ref{fig:expone} and \ref{fig:exptwo}
and together reinforce the above conclusions. Beginning first with the
outcome stage ($\beta $ and $\mu $) RMSE's which appear in the left-hand
columns of Figures \ref{fig:expone} and \ref{fig:exptwo}, we can note that
the OP RMSE's are generally larger than the MiOP(C) RMSE's for all $N$'s
examined. The exception, as above, is $\beta _{2}$. In this case, the OP
RMSE's for $\beta _{2}$ are superior to our MiOP $\beta _{2}$ RMSE's under a
MiOPC d.g.p. (see Figure \ref{fig:exptwo}) across all $N$'s. However, as $N$
increases under each Experiment, we observe that all OP-RMSE's remain
essentially unchanged in both Figures, whereas the MiOP(C)-RMSE's for the
outcome-stage model-parameters steadily decrease. Comparing the MiOP and
MiOPC models more directly, one can also clearly see that under the MiOP
d.g.p. (Figure \ref{fig:expone}) our two inflated-models return near
identical RMSE's for all parameters and under all $N$'s examined.

On the other hand, for the MiOPC d.g.p. (Figure \ref{fig:exptwo}), our MiOPC
estimates are consistently superior to the MiOP estimates for all outcome
stage ($\beta $ and $\mu $) parameters. The relative performances for our
MiOP and MiOPC models within the inflation stage ($\gamma $) estimates of
Experiment 2 are slightly more nuanced. To see this, first note in Figure %
\ref{fig:exptwo} that---when $N=2,000$---the inflation stage RMSE-results
are mixed; at times favoring the MiOP model over the MiOPC model and at
times favoring the MiOPC model over the MiOP. However as $N$ increases,
Figure \ref{fig:exptwo} indicates that the MiOPC RMSE's for Experiment 2
improve at a faster rate than do the MiOP RMSE's. As a result, the MiOPC
RMSE's are consistently lower than the MiOP RMSE's for all inflation stage
parameters once $N$ is greater than $2,000$. Hence, increases in $N$ improve
our MiOP(C) model estimates relative to comparable OP estimates. While the
MiOPC models are generally superior to the MiOP models under MiOPC d.g.p.'s,
the former are only decidedly so once $N$ is greater than $2,000$. Thus, the
results from Experiments 1 and 2 suggest that if the researcher suspects
there to be at least moderate levels of middle-category inflation, MiOP and
MiOPC models should be favored over OP models provided that one's data
contain at least $2,000$ observations.

\begin{center}
[Insert Figures \ref{fig:expone} and \ref{fig:exptwo} about here]
\end{center}

\subsection{Monte Carlo results when middle-category inflation increases}

To understand whether and how dependent the conclusions reported in the
preceding subsection are on the proportion of middle-category inflation
chosen, we next hold $N$ constant at $2,000$ and explore how our findings
change as we increase the percentage of inflation in the middle category of
the ordered dependent variable above 30\%. Specifically, we present two
additional MC experiments below which use identical parameter and variable
values to those specified in Experiments 1 and 2, but which alter the
proportion of inflated middle-category responses in our d.g.p.'s to
correspond to middle-category inflation levels of 30\% (i.e., replicating
the $N=2,000$ models from Experiments 1 \& 2), 60\%, and 90\% respectively.
We conduct the Monte Carlo exercise mentioned above under both a MiOP d.g.p.
(Experiment 3) and a MiOPC d.g.p. (Experiment 4). As above, we then compare
our OP, MiOP, and MiOPC models' performances under the MiOP d.g.p and MiOPC
d.g.p.

We evaluate the results of Experiments 3 and 4 graphically by again plotting
the RMSE's of our OP, MiOP, and MiOPC parameter estimates.\footnote{%
Tables of the mean estimates, coverage parameters, and RMSE's for all models
discussed here are not reported to save space, but are available on request.}
These RMSE plots are reported in Figures \ref{fig:expthree} and \ref%
{fig:expfour} below. Beginning first with Experiment 3---which assumes a
MiOP d.g.p.---we find that our MiOP and MiOPC models estimate near identical
RMSE's for all estimates and all levels of inflation, which is consistent
with our findings in Experiments 1 and 2 above. These findings thereby
suggest that, no matter the level of (MiOP) inflation, the MiOPC models
recover our true parameter values at a rate that is comparable to that of
the MiOP model. Looking at the left-hand column of Figure \ref{fig:expthree}%
, we can also note that our MiOP(C) RMSE's are generally superior to our
OP-RMSE's for all parameters and all $N$'s examined,\footnote{%
With the exception of $\beta _{2}$ when inflation is at 30\%.} and are
consistently superior to our OP model-estimates once the proportion of zero
inflation rises above 30\%. Moreover, as the proportion of MiOP inflation
increases, Figure \ref{fig:expthree} also demonstrates that the OP-model's
RMSE's worsen (i.e. increase), whereas the MiOP(C) estimates steadily
decrease within both the inflation and outcome stages. Hence, increasing the
level of MiOP inflation improves our MiOP(C) model estimates and worsens our
OP model estimates. At $N=2,000$, all MiOP(C) parameter estimates are less
biased and more efficient than OP model estimates, provided that the level
of middle-category inflation is greater than 30\%.

The results for Experiment 4 (MiOPC d.g.p.), which appear in Figure \ref%
{fig:expfour} below, are largely consistent with the results discussed for
Experiment 3 above. The left-hand column of Figure \ref{fig:expfour} reports
the outcome-stage RMSE's for Experiment 4 and suggests that for each level
of inflation examined, the MiOP(C) models outperform the OP model, with the
exception of our $\beta _{2}$ estimates when the proportion inflation is at
30\%. As the proportion of middle-category inflation increases in Experiment
4, our MiOP(C) estimates again improve while our OP parameter estimates
deteriorate. However, the outcome-stage RMSE's in Figure \ref{fig:expfour}
also indicate that---unlike Experiment 3---our MiOP and MiOPC estimates are
not equivalent, and in this case favor the MiOPC model over the MiOP model
for both $\beta _{1}$ and $\beta _{2}$. By contrast, our inflation stage
estimates in Figure \ref{fig:expfour} at times favor both models, and
increasing in the proportion inflation does not affect this conclusion. Put
another way, while an increase in N under a MiOPC d.g.p. was found to
increasingly favor the MiOPC model's inflation stage estimates over those
from the MiOP model in Experiment 2 above, the same cannot be said for
increases in the proportion of inflation (at $N=2,000$) in Experiment 4.
Rather, it appears that at this level of $N$, the MiOP model preforms
equivalent to---and at times better than---the MiOPC model in estimating our
inflation stage parameters no matter the proportion of MiOPC middle category
inflation. Hence, at $N=2,000$ and across all proportions of inflation
examined in Experiment 4; the MiOPC model outperforms the MiOP and OP models
in recovering our true outcome stage parameters but does not outperform the
MiOP model in recovering our true inflation stage parameters. To summarize,
Experiments 3 and 4 indicate that if one suspects there to be levels of
middle-category inflation greater than 30\%, then MiOP and MiOPC models
should be strongly favored over OP models provided that one's dataset
contains at least $2,000$ observations.

\begin{center}
[Insert Figures \ref{fig:expthree} and \ref{fig:expfour} about here]
\end{center}

\subsection{Monte Carlo results when exclusion restrictions are ignored}

All of the Monte Carlo results reported above maintain an exclusion
restriction. Indeed, our MiOP(C) models in Experiments 1-4 include one
additional variable---$\gamma_{2}$---in their inflation stages, but withhold
this variable from their outcome stages. This is consistent with the
d.g.p.'s described above, as in each case, $\gamma_{2}$ was simulated to
influence the inflation stage d.g.p. of our dependent variable, but not the
ordered outcome stage. Many have argued that---unlike Heckman's selection
model \citep{Heckman1979}---such an exclusion restriction is unnecessary for
the proper identification of inflated models %
\citep[176]{Winkelman1998,BurgerOortLinders2009}. However, a number of
scholars have also raised cautions in this regard, especially in the context
of inflated models allowing for correlated errors %
\citep[22]{Jackson1993,HarrisZhao2007,Xiang2010,BreenLuijkx2010}. Therefore,
we next compare our OP, MiOP and MiOPC estimates under the conditions where $%
\gamma_{2}$ is included within the d.g.p but omitted from the inflation
stages of our MiOP and MiOPC models---leading to an identical set of
covariates in each stage of our models. This allows us to directly evaluate
whether or not an exclusion restriction is needed for proper MiOP(C)
estimation. Specifically, we conduct two final Monte Carlo Experiments
(Experiments 5 and 6) wherein we follow the same d.g.p.'s as that of the
60\% inflation, $N=2000$ MiOP and MiOPC d.g.p.'s used in part within
Experiments 3 and 4 above, but then reanalyze these data while omitting the
second ``identifying'' variable ($\gamma_{2}$) from the inflation stage of
our MiOP(C) models, and compare our estimates herein to those obtained for
the 60\% inflation models from Experiments 3 \& 4. Hence, experiments 5 and
6 compare our OP, MiOP, MiOPC model estimates when an exclusion restriction
is maintained and when it is not, for both MiOP generated data (Experiment)
and MiOPC generated data (Experiment 6).

The results from Monte Carlo Experiments 5 and 6 appear in Table \ref%
{tab:ExperimentsFiveSix}. Beginning first with the mean estimates, RMSEs,
and CPs obtained from our model estimates under Experiment 5, which appear
in the top half of Table \ref{tab:ExperimentsFiveSix}, one can draw several
conclusions. First, we find that when an exclusion restriction is ignored
under a MiOP d.g.p., our MiOP estimates in some instances slightly improve
and in some instances slightly worsen, but still do an exemplary job of
recovering our true values---and remain superior to those of the OP or
MiOPC. Hence proper estimation of the MiOP model does not appear to rest on
model identification via an exclusion restriction. We find similar results
for the MiOPC model in Experiment 5, and its estimates continue to
outperform our OP estimates. However, the outcome-stage estimates for the
MiOPC become markedly more biased when the inclusion restriction is ignored.
This suggests that identification---via an exclusion restriction---may often
be required for our MiOPC model. Experiment 6 repeats the exercise presented
for Experiment 5, but in this instance for the case of a MiOPC d.g.p. The
results for Experiment 6 appear in the bottom portion of Table \ref%
{tab:ExperimentsFiveSix}, and generally provide more evidence for the need
for an exclusion restriction for proper identification and estimation of the
MiOPC, but not for the MiOP. Here we can note that, while our MiOP estimates
remain essentially unchanged no-matter whether an exclusion restriction is
maintained, our MiOPC estimates become more biased---in some cases doubling
in size---when the exclusion restriction is ignored. While our MiOPC model
estimates generally continue to outperform those of our OP, this again
implies that an exclusion restriction is needed for unbiased MiOPC
estimation.

\begin{center}
[Insert Table \ref{tab:ExperimentsFiveSix} about here]
\end{center}

The Monte Carlo findings for experiments 5 and 6 are largely consistent with
those run for the ZiOP(C) models by \citet[1084]{HarrisZhao2007}. They
together suggest that when exclusion restrictions are not maintained, one's
MiOP(C) estimates, although often similar to those obtained when an
exclusion restriction is maintained, are occasionally biased---especially in
the case of the MiOPC. These biases can in turn lead to faulty inferences
and an inability to correctly distinguish between the MiOP and MiOPC via
model fit statistics. However, we also find that---when the exclusion
restriction is ignored---our MiOPC models maintain superiority to OP models
(when the d.g.p. is either MiOP or MiOPC) and our MiOP model estimates
remain largely unchanged from those obtained when an exclusion restriction
holds. To delve further into this issue, we therefore examined and compared
the levels of non-convergence across the MiOP(C) models with and without
exclusion restrictions (i.e., those that were estimated for Experiments 5 \&
6), and found that there was no noticeable difference in convergence
patterns or rates between MiOP(C) models estimated with and without
exclusion restrictions. Nevertheless, we can conclude here that exclusion
restrictions are often helpful---and in some cases critical---to accurate
and efficient MiOP(C) model estimation, most notably for the MiOPC case.

\section{EU-Support Application}

\subsection*{Variable Operationalizations}

\begin{itemize}
\item \textit{Accession Informed} is coded as the average responses provided
to two complementary questions: (i) ``How well informed do you feel about
the enlargement, that is new countries joining the European Union?'' and
(ii) ``How well informed do you feel about (your country)'s accession
process?'' Where responses to each question ranged from ``not at all
informed'' ($=1$) to ``very well informed'' ($=4$)

\item \textit{College education} is coded as (1) if a respondent self
reports having completed their education at 21 years of age or older, and
(0) otherwise.

\item \textit{Education High} is coded as (1) if a respondent self reports
having completed their education at 22 years of age or older, and (0)
otherwise.

\item \textit{Education High-Mid} is coded as (1) if a respondent self
reports having completed their education at 20 or 21 years of age, and (0)
otherwise.

\item \textit{Education Low-Mid} is coded as (1) if a respondent self
reports having completed their education at 15-19 years of age, and (0)
otherwise.

\item \textit{Executive} is coded as (1) if a respondent self reports their
occupation as being ``executive'' and (0) otherwise

\item \textit{Farmer} is coded as (1) if a respondent self reports their
occupation as being ``farmer or fisher'' and (0) otherwise

\item \textit{Manual Worker} is coded as (1) if a respondent self reports
their occupation as being ``skilled or unskilled manual laborer'' and (0)
otherwise

\item \textit{Media} is coded as the response provided by respondents to the
question ``About how often do you watch the news on Television,'' where
responses were re-coded to range from never ($=1$) to every day ($=5$)

\item \textit{Media PolEcon} is coded as the average response provided by
respondents to the question ``In general, do you pay attention to each of
the following? (a) local politics (b) national politics (c) social issues,
such as education, health care, poverty, etc. (d) the European Union (e) the
economy,'' where responses ranged from a lot of attention ($=3$) to no
attention at all ($=1$)

\item \textit{Political trust} was coded using responses to the following
question: ``[f]or each of the following institutions, please tell me if you
tend to trust if or if you tend not to trust it: (1) Justice/the [country]'s
legal system; (2) the police; (3) the army; (4) political parties; (5) civil
service; (6) the [country]'s government; (7) the [country]'s parliament.''
We follow \citet[395]{OzlemTillman2007} and score each positive response a
(1) and each negative response a (0), and then average these scores to
create a (0-1) continuous index of \textit{political trust}

\item \textit{Professional} is coded as (1) if a respondent self reports
their occupation as being ``professional,'' and (0) otherwise

\item \textit{Rural} ranges from 1-3 and was coded as a (1) if respondents
reported living in a ``large town,'' as a (2) for respondents living in a
``small or medium sized town'', and as a (3) for respondents living in a
``rural area of village''

\item \textit{Student} is coded as a (1) if a respondent self reports their
occupation as being ``student,'' and (0) otherwise.

\item \textit{Unemployed} is coded as (1) if a respondent self reports their
occupation as being ``unemployed,'' and (0) otherwise

\item \textit{Xenophobia} is coded from respondents' answers to the
questions: \textquotedblleft Some people are disturbed by the opinions,
customs, and ways of life of people different from themselves. Do you
personally find the presence of people of another [(1) nationality; (2)
race; (3) religion] disturbing in your daily life?\textquotedblright\ where
again, following \citet[395]{OzlemTillman2007}, we score each positive
response a (1) and each negative response a (0), and then average these
scores to create a (0-1) continuous index of \textit{xenophobia}
\end{itemize}

\subsection{Robustness Models}

The OP, MiOP, and MiOPC models of \textit{EU support} in Table 1 of our main
paper represent our `full' model specifications, and include a comprehensive
set of independent variables and controls. To demonstrate that (i) these
results for \textit{EU support} are robust to alternative model
specifications and (ii) our MiOP(C) models are relatively stable; we next
present a collection of OP, MiOP and MiOPC robustness models. The first
three robustness model triplets each report specifications with more limited
subsets of controls (relative to the models reported in our main paper).
Here, we begin by reporting OP, MiOP, and MiOPC results with a fairly sparse
set of controls (Table \ref{tab:baseline-results}), and then sequentially
add-in more controls and predictors of middle-category inflation (Tables \ref%
{tab:medium baseline-results} and \ref{tab:Medium-Large baseline-results}).
Table \ref{tab:baseline-results} includes three of %
\citeauthor{OzlemTillman2007}'s main covariates of interest within the
outcome stages of the OP, MiOP, and MiOPC model (\textit{political trust,
xenophobia, and discuss politics}); along with \textit{rural}, \textit{female%
}, and an alternate measure of education (\textit{college ed.}) that is much
less fine-grained than the education dummies used in Table 1 of our paper,
and by \citet{OzlemTillman2007}. The inflation stage specifications of the
MiOP(C) models in Table \ref{tab:baseline-results} maintain exclusion
restrictions but are limited to \textit{discuss politics}, \textit{true EU
knowledge}, \textit{college ed.}, \textit{rural} and \textit{female}. Table %
\ref{tab:medium baseline-results} then adds to both stages controls for
\textit{age}, \textit{student}, \textit{unemployed}, as well as including
our measure of \textit{EU-bid knowledge} in the inflation stages of our
MiOP(C) models. Table \ref{tab:Medium-Large baseline-results} then
additionally adds \citeauthor{OzlemTillman2007}'s dichotomous occupational
variables to the outcome stages of our OP, MiOP, and MiOPC models. Across
all models reported in Tables \ref{tab:baseline-results}-\ref%
{tab:Medium-Large baseline-results}, our substantive findings for \textit{EU
support} are consistent with those discussed in in the main body of our
paper. This suggests that (i) our main paper's reported findings do not
hinge on the inclusion of any specific control(s) and (ii) the MiOP(C)
models are fairly robust in estimation.

\begin{center}
[Insert Tables \ref{tab:baseline-results}, \ref{tab:medium baseline-results}%
, and \ref{tab:Medium-Large baseline-results} about here]
\end{center}

Having demonstrated that our main findings for \textit{EU support} hold
across a number of smaller model-specifications, we next present three final
robustness tables that test how well our fully-specified models perform when
we include additional or alternate measures of our key inflation and outcome
stage covariates. Here we treat our main paper's models as the baseline, and
then examine how well our primary findings hold under alternate
specifications. The first of these three models appears in Table \ref%
{tab:IncomeKnowledge} and reports the results for our main OP, MiOP, and
MiOPC model specifications when \textit{Income} is additionally included in
the inflation stage of our MiOP and MiOPC models, and when \textit{True EU
Knowledge} is added to the outcome stage of the MiOP(C) models. As one can
see in Table \ref{tab:IncomeKnowledge}, all substantive results remain
unchanged after these additions. Our MiOPC model in Table \ref%
{tab:IncomeKnowledge} also reveals that \textit{True EU Knowledge} is an
insignificant outcome-stage predictor of \textit{EU-support}, while \textit{%
Income} is a positive and significant determinant of informed
middle-category responses. However, AICs, as well as Vuong and likelihood
ratio tests suggest that the MiOP(C) models presented here are of slightly
poorer model fit than those reported in our main paper, and therefore we
continue to favor our main models over the models reported in Table \ref%
{tab:IncomeKnowledge}.

The second of these three robustness tests (Table \ref{tab:Alt-EUBID})
includes an alternate measure of \textit{EU-bid knowledge} within the
inflation stages of our `full' MiOP and MiOPC models (\textit{accession
informed}, described above). We find here that---in line with both our
theoretical expectations and our results for \textit{EU-bid knowledge}---%
\textit{accession informed} is positive and statistically significant within
the inflation stage of our MiOP and MiOPC models, suggesting that
individuals that are better informed of EU accession are less likely to
provide a face-saving response. Note here as well that all of the main
substantive findings discussed in the main body of our paper hold when using
this alternative specification for \textit{EU-bid knowledge}. The second
robustness test presented here additionally adds an alternate measure of
\textit{media attention} (\textit{media polecon}) to our `full
specification' (Table \ref{tab:Alt-EUBID-MedieaAttn}). This alternate
measure directly assesses individuals' self-reported levels of
media-attention to political-economic issues, rather than to the news in
general. We find that \textit{media polecon} is positive and statistically
significant in Table \ref{tab:Alt-EUBID-MedieaAttn}, implying that
individuals that report consuming more political-economic news are less
likely to issue uninformed responses about their country's EU-membership
bid. Thus, our MiOP(C) findings appear to be (i) robust to alternative
model-specifications and (ii) consistent with our theoretical
expectations---i.e., uninformed individuals are likely to provide
`face-saving', middle-category responses survey questions of \textit{EU
support}.

\begin{center}
[Insert Tables \ref{tab:IncomeKnowledge}, \ref{tab:Alt-EUBID} and \ref%
{tab:Alt-EUBID-MedieaAttn} about here]
\end{center}

Table \ref{tab:marg-effects} presents a series of first differences in
predicted values for our full OP and MiOPC models of \textit{EU support},
along with their associated confidence intervals (CIs). These changes in
predicted values are equivalent to those presented within the Boxplots of
our main paper. Specifically, Table \ref{tab:marg-effects} separately
reports the predicted changes in the probability of observing each category
of \textit{EU support}, given reasonable changes in \textit{discuss politics}%
, \textit{gender}, and \textit{income}.\footnote{\textit{Discuss politics}
was changed from 1 to 3, for \textit{female} from 0 to 1, and \textit{income}
from 5 to 8. All other variables held to their means or modes, $m=1000$.}
The predicted changes for \textit{discuss politics} and \textit{female} are
consistent with our findings within in the main body of the paper---namely
that failing to account for middle category inflation leads one to (i)
dramatically overestimate the positive effect of \textit{discuss politics}
on \textit{EU support} and (ii) estimate a significant negative relationship
between \textit{female} and \textit{EU support} when the direct relationship
in fact appears to be positive. Finally, Table \ref{tab:marg-effects} also
indicates that---by not accounting for middle-category inflation---our OP
model largely overstates the positive effect of \textit{income} on \textit{%
EU support}.

\begin{center}
[Insert Table \ref{tab:marg-effects} about here]
\end{center}

\section{Support for Anti-Nuclear Movement in Western Europe}

In contrast to the ordered survey response question studied in our main
paper's application, ordered survey questions of the attitude or opinion
variety often \textit{do not} include a natural middle category response
that accommodates attitudes of `indifference' or `neutrality.' Instead, many
ordered survey responses only allow respondents to provide (weak or strong)
attitudes or opinions that can be either for or against a given issue.
Problematically, we find that scholars working with these types of response
scales often add-in all ``don't know'' responses as an artificial
middle-category to their survey response variables, ex-post %
\citep[][]{Rohrschneider1990,BaumgartnerMorrisWalth2012}. Just as the case
with ``face-saving'' responses, ``don't know'' middle-category responses do
not represent the actual midpoint of an underlying (directional) preference
dimension, but instead correspond to \textit{nondirectional} positions that
a respondent is unable to place along the ordered dimension scale. Because
they are unordered, middle-category ``don't know'' responses can add
measurement error to one's dependent variable, violate assumptions of
ordinality, and yield biased estimates of one's covariates of interest. We
examine how well our MiOP and MiOPC models account for such potential
problems below.

To do so, we draw on an existing study of individual support for new social
movements in Western Europe \citep{Rohrschneider1990}. %
\citeauthor{Rohrschneider1990} studies individual-level support for three
social movements in Germany, France, Great Britain, and the Netherlands: (i)
nature protection associations, (ii) the ecology movement, and (iii)
movements concerned with stopping the construction or use of nuclear power
plants. Survey questions assessing individuals' support for each movement
appear on the Eurobarometer (25) and include the responses of ``strongly
approve'' ($=1$), ``approve somewhat'' ($2$), ``disapprove somewhat'' ($3$),
``strongly disagree'' ($4$), and ``don't know''. We follow %
\citet[13]{Rohrschneider1990} and use these responses to create ordered
dependent variables with ``don't know'' responses added as a neutral middle
category between ``support'' and ``disagree''. \citet{Rohrschneider1990}
then combines these three response-variables---along with a separate set of
questions pertaining to citizens' behavioral intentions towards social
movements---into a single-index dependent variable for analysis. We do not
undertake this final-step, as it redistributes the indifference-category
``don't know'' responses across multiple categories of %
\citeauthor{Rohrschneider1990}'s final index variable. Rather, we analyze %
\citeauthor{Rohrschneider1990}'s three social-movement support variables
separately, and focus on \textit{Anti-Nuclear Movement Support} in our
application below.\footnote{%
For ease of interpretation, we also reverse-code this variable such that
higher values correspond to more support for the anti-nuclear movement.}

Our \textit{Anti-Nuclear Movement Support} dependent variable contains three
categories: disagree, indifference---which contains \textit{both} ``don't
know'' responses \textit{and} responses of mild support and
disagreement---and support; with the indifference category encompassing 52\%
of all responses. We estimate and compare OP, MiOP, and MiOPC models of this
dependent variable while including all of \citeauthor{Rohrschneider1990}'s
independent variables in our models' outcome stages. These outcome stage
variables include socioeconomic measures for individuals' levels of \textit{%
Social Integration},\footnote{%
Following \citep[10]{Rohrschneider1990}, this variable is coded as the sum
of three separate response variables measuring (i) marital status, (ii)
religiosity and (iii) number of children.} class,\footnote{\textit{New
Middle Class} and \textit{Old Middle Class} are binary variables indicating
whether respondents indicated their occupation as being `white-collar office
worker' or as as being `lawyer, accountant, or business owner', respectively.%
} \textit{Left-Right Orientation},\footnote{%
Respondents' left-right orientation ($1-10$, with 10 being furthest right).}
\textit{Income},\footnote{%
Respondents' income (in quartiles).} town-size,\footnote{\textit{Size of Town%
} is coded as the self-reported size of respondents' towns ($1=$Rural town, $%
2=$Medium town, $3=$Big town).} \textit{Age},\footnote{%
Respondents' actual age in years.} and \textit{Education}.\footnote{%
Education is ordinal (1-9), and corresponds to the age at which a respondent
finished education; ranging from 14 years ($=1$) to 22 years or older ($=9$)
with ``still studying'' treated as missing.} We also include in our OP,
MiOP, and MiOPC outcome stages the three indices used by %
\citeauthor{Rohrschneider1990} to measure \textit{Postmaterialism},\footnote{%
A four-item index of respondents postmaterial value priority.} environmental
\textit{Self-Interest},\footnote{%
The summed responses on respondents' indications of whether they have reason
to complain (ranging from 1-4---with 4 indicating the most reason to
complain---which is reverse coded from the actual dataset coding) about 7
specific environmental problem-areas.} and environmental \textit{Sociotropism%
}.\footnote{%
The summed responses on respondents' levels of concern (ranging from
1-4---with 4 indicating the most concern---which is reverse coded from the
actual dataset coding) across 7 different environmental issue areas.}

The middle-category of the dependent variable discussed above has been
``inflated'' with non-ordinal, ``don't know'' responses. We therefore add a
number of plausible determinants of \textit{Anti-Nuclear Movement} ignorance
to our MiOP(C) models' inflation stages in order to estimate which
middle-category responses are ``inflated'' responses, and which are true
responses of (relative) indifference. Specifically, we begin by adding %
\citeauthor{Rohrschneider1990}'s measures of environmental \textit{%
Self-Interest} and environmental \textit{Sociotropism} to our inflation
stages because the indices measure individuals' levels of environmental
awareness at the local and national levels (respectively), and hence should
be negatively correlated with ``don't know'' responses on \textit{%
Anti-Nuclear Movement Support}. We next include socioeconomic indicators for
town-size, \textit{Education}, and \textit{Age}; as each variable could be
reasonably argued to measure how informed respondents are about issues of
national political significance. Finally, we create and include three
additional variables to better identify which respondents may be more or
less likely to report that they do not know whether or not they support the
anti-nuclear movement. These variables are \textit{Env Governance-Aware},%
\footnote{%
Does respondent know if the authorities in respondent's country are
concerned about the environment ($=1$)? Otherwise, ($=0$).} \textit{%
Env-Uninformed},\footnote{%
The number (0-10) of specific environmental issue areas that the respondent
indicates that they'd like to be better informed about.} and \textit{Watch
EU Parliament on TV}.\footnote{%
Coded as a binary variable indicating whether or not respondent reported
having watched the European Parliament on Television.} Each of these three
additional variables is expected to be a positive predictor of \textit{%
informed} middle-category responses, and together with our outcome-stage
specifications, these covariates ensure that our MiOP(C) models are
overidentified.

The estimates for our OP, MiOP, and MiOPC models of \textit{Anti-Nuclear
Movement Support} appear in Table \ref{tab:Anti-Nuke}. We first compare our
three models with a range of appropriate model fit statistics. Results of a $%
t$-test of $\rho =0$ for our data application reveals that $\rho $ is
positive and significant in the MiOPC model; thereby favoring the MiOPC
model over the MiOP model. AIC values for our three models again favor the
MiOPC to the MiOP, and favor both over the OP model. Vuong tests favor the
MiOPC model to the MiOP model, and the MiOP(C) models over the OP model.
Hence, model fit statistics suggest that the MiOP(C) models are superior to
the OP, and that our MiOPC model offers an improvement in model fit over the
MiOP. We therefore limit the discussion below to a comparison of our OP and
MiOPC results for \textit{Anti-Nuclear Movement Support}.

Turing to the MiOPC inflation stage results in Table \ref{tab:Anti-Nuke}, we
can first note that several of the MiOPC inflation-stage coefficient
estimates are significant in their expected directions. Recall that a
positive sign on an inflation-stage coefficient estimate in our MiOP(C)
models indicates an increased likelihood that a middle-category response is
a true indifference response, and a decreased likelihood that a middle
category response is a ``don't know'' response. Ergo, our MiOPC inflation
stage indicates that higher levels of environmental \textit{Self-Interest}
and environmental \textit{Sociotropism} increase the likelihood of a
middle-category response being a true indifference response, rather than a
``don't know'' response, as the estimates for both variables are positive
and statistically significant. The MiOPC model also reports that the effect
of \textit{Age} is negative and significant. This suggests that age
decreases the likelihood that a respondent provides an informed
middle-category response---perhaps implying a generational gap in
levels-of-awareness about `new' environmental issues. We also find that
\textit{Environmental Governance-Aware} is positive and statistically
significant, which intuitively suggests that individuals that are more aware
of their government's roles in environmental issues are less likely to
provide a ``don't know'' response to questions of \textit{Anti-Nuclear
Movement Support}. Finally, note that while many of the other
inflation-stage variables' coefficient estimates are insignificant, their
signs generally point in their expected directions.

We next compare the outcome stage estimates for our OP and MiOPC models in
Table \ref{tab:Anti-Nuke}. First note here that several independent
variables maintain their size and significance across the OP and MiOPC
models. For example, the estimates for \textit{Social Integration}, \textit{%
New Middle Class}, \textit{Old Middle Class}, \textit{Income}, \textit{Age}
and \textit{Left-Right Orientation} are all similar and size and
significance in our OP and MiOPC models, and the findings therein are
generally consistent with those reported by \citet{Rohrschneider1990}.
However, we also find that a number of our coefficient estimates, while
maintaining direction and significance across the OP and MiOPC models,
dramatically increase in size after middle-category inflation is accounted
for. For example, the coefficient estimates (as well as the outcome-stage
marginal effects) for \textit{Self-Interest} and \textit{Sociotropism}
nearly double in size once middle-category inflation-effects are taken into
account by our MiOPC model. Finally, we also find that the outcome stage
estimate for \textit{Education}, which was negative and highly significant
in the OP model, is insignificant in the MiOPC model, implying that once
middle-category inflation is accounted for, there is no direct effect of
education on individuals' likelihood of supporting the antinuclear movement.
In sum, our MiOPC model findings suggest that ignoring middle-category
inflation in \textit{Anti-Nuclear Movement Support} can lead to biased
inferences in regards to the size and significance of the effects of several
commonly studied determinants of social movement support.

\begin{center}
[Insert Table \ref{tab:Anti-Nuke} about here]
\end{center}

To better understand the extent of the bias that can arise in studies of
\textit{Anti-Nuclear Movement Support} when middle-category inflation is
ignored, we next compare the first-differences in predicted values for
\textit{Anti-Nuclear Movement Support} within our OP and MiOPC models. For both models, we
present in Figure \ref{fig:Ed} the first-differences in predicted values of
\textit{Anti-Nuclear Movement Support} in response to a 3-to-6 change in our
ordinal \textit{Education} variable, while holding all other variables to
their means or modes. The OP subfigure in Figure \ref{fig:Ed} clearly
demonstrates that our OP model estimates that increases in \textit{Education}
will reduce individuals' levels of support of \textit{Anti-Nuclear Movement
Support}, in effect implying that anti-nuclear concerns are primarily the
domain of the uneducated. However, the MiOPC subfigure instead demonstrates
that, once one has taken into account the effect of education on
respondent-propensities to provide ``don't know'' responses, \textit{%
Education} has no effect on \textit{Anti-Nuclear Movement Support};
controlling for other socioeconomic determinants of \textit{Anti-Nuclear
Movement Support}. Hence, ignoring ``don't know'' inflation in the
middle-category of ordinal survey response variables can lead researchers to
overestimate the direct effects of key covariates---such as \textit{Education%
}---on their outcomes of interest.

\begin{center}
[Insert Figure \ref{fig:Ed} about here]
\end{center}

\section{Community/national attachment and Support for Free Trade}

To further demonstrate the robustness of our MiOP and MiOPC models when
applied to real-world data, we present a third MiOP(C) survey-data
application below. Our third application replicates a well-known study of
individual-level support for international trade entitled \textquotedblleft
Why are Some People (and Countries) More Protectionist than
Others?\textquotedblright\ \citep{MaydaRodrik2005}. In this article, %
\citet{MaydaRodrik2005} present a number of overlapping analyses of
individual support for international trade using survey data from the
International Social Survey Program \citep[ISSP;][]{ISSP1995} and the World
Values Survey \citep{Inglehart1997}. Our replication focuses on the authors'
community/national attachment models of trade opinion, which utilizes an
ISSP survey dataset covering more than 28,000 individuals and 23 countries.
As a dependent variable in these models, \citet[1415]{MaydaRodrik2005}
create a `Pro-Trade Dummy' that is coded one if an individual answers either
\textquotedblleft disagree\textquotedblright\ or \textquotedblleft disagree
strongly\textquotedblright\ to an ordered question asking whether they favor
limiting the imports of foreign products, and zero if a respondent instead
replies \textquotedblleft agree\textquotedblright , \textquotedblleft agree
strongly\textquotedblright , \textquotedblleft neither agree nor
disagree\textquotedblright , or \textquotedblleft don't
know\textquotedblright\ to this same question. The authors then estimate
their models of pro-trade support using a series of probit
specifications.  Note that \citeauthor{MaydaRodrik2005} also evaluate their
claims by employing the original 1-5 ordered survey response measure of
trade support (described below) as the dependent variable and estimating
ordered logit models for this ordered survey response dependent variable.
They point out that their results from the ordred logit models are similar
to the results (discussed below) that they obtain from their probit
specification \citep[see][1397]{MaydaRodrik2005}. Given our focus on the
MiOP(C) models, for our replication, we thus re-estimate %
\citeauthor{MaydaRodrik2005}'s models using the \textit{original} ordered
survey response measure of trade support, rather than their dichotomized
measure. To ensure that our results are comparable to \citet{MaydaRodrik2005}%
, we re-code this ordered variable to take on higher values for more
pro-trade responses, and add \textquotedblleft don't know\textquotedblright\
responses to our middle, indifference category.

Our dependent variable (\textit{Pro-Trade}) thereby ranges from 1-to-5, with
a natural middle ``indifference'' category and higher values representing
stronger support for international trade. For \textit{Pro-Trade}, we suspect
there to be middle-category inflation of both the ``face-saving'' and
``don't know'' varieties. Univariate statistics lend support to this
assertion, and indicate that the middle-category of \textit{Pro-Trade} is
the second most frequent response-category for this variable. We therefore
replicate \citeauthor{MaydaRodrik2005}' analyses using this new dependent
variable and a set of OP, MiOP, and MiOPC models. For our OP, MiOP, and
MiOPC outcome-stage variables, we include all of the independent variables
reported in \citeauthor{MaydaRodrik2005}'s full community/national
attachment model specification. These include socioeconomic controls for
\textit{Age},\footnote{%
Measured as respondents' actual reported age.} \textit{Male}, \textit{Citizen%
}, and \textit{Education},\footnote{%
Measured as the age at which a respondent completed schooling.} variables
measuring respondents levels of town, country, continent, and country
attachment,\footnote{%
Ordinal, ranging from 1-4, with high values (re)coded to denote stronger
reported levels of attachment. Note that, following \citet{MaydaRodrik2005},
country-level attachment is labeled \textit{National Pride (1)}.} and
covariates intended to capture respondent-pride in national-level factors,
democracy, political influence, the economy, and social security.\footnote{%
All pride measures are ordinal, ranging from 1-4, with higher values
(re)coded to denote more reported pride in each specific construct.}
Altogether this set of outcome-stage independent variables makes our OP,
MiOP, and MiOPC models comparable in specification to (probit) Model 4 in
Table 7 of \citet[1415]{MaydaRodrik2005}.

Given that the middle category of \textit{Pro-Trade} is likely inflated by
both ``face-saving'' responses and actual ``don't knows,'' we seek to
include inflation-stage variables in our MiOP(C) models that either (i)
capture individuals' propensities to be uninformed about the
political-economy of international trade or (ii) are related to individuals
being more (or less) likely to provide ``don't know'' or ``face-saving''
responses to survey questions in general. We therefore specify our MiOP(C)
inflation stages with \textit{Age}, \textit{Education}, \textit{Male},
\textit{Citizen}, and an ordinal variable measuring how much time a
respondent has spent living abroad.\footnote{\textit{Lived Abroad} ranges
from 1 to 4, with values of 1 corresponding to individuals that reported
``never'' having lived abroad, and values of 4 corresponding to respondents
that had lived abroad ``5 years or more''.} Based on the EU-findings
reported earlier, as well as on extant literature relating to (i)
individual-support for free trade %
\citep{HainmuellerHiscox2006,MansfieldMutz2009,MedranoBraun2011} and (ii)
gender-based response-set behaviors \citep{MondakAnderson2004}, we
hypothesize that \textit{Age}, \textit{Education}, \textit{Male}, \textit{%
Citizen}, and \textit{Lived Abroad} will all be positive predictors of an
individual's likelihood to provide a true indifference-response on \textit{%
Pro-Trade}. We also argue that, while \textit{Lived Abroad} may be
correlated with whether or not an individual is trade-informed, it is
unlikely that this variable will be directly associated with individuals'
actual levels of \textit{Pro-Trade} support in one direction or another; for
our pooled-country sample. Together with the choices of outcome stage
covariates described above, this inflation stage specification ensures that
our MiOP and MiOPC models are overidentified.

Our main OP, MiOP, and MiOPC models of \textit{Pro-Trade} appear in Table %
\ref{tab:ProTrade} below. As above, we begin by comparing these three models
with a number of relevant model fit statistics. A $t$-test of $\rho =0$ for
our MiOP and MiOPC models of trade support reports that $\rho $ is positive
and significant in the MiOPC model; which suggests that our MiOPC model
should be favored over the MiOP model. Our OP, MiOP, and MiOPC AIC-values
confirm this finding, and indicate that our MiOPC model should be favored
over both the the MiOP and OP models. Finally, we find that Vuong tests
favor the MiOPC model over our MiOP model for this particular application,
and similarly favor the MiOP(C) models over the OP model. In sum, the model
fit statistics discussed here indicate that the MiOP(C) models are superior
to the OP model, and that our MiOPC model is superior to a comparable MiOP
model. We accordingly focus on comparisons of our OP and MiOPC models below.

Beginning first with the inflation stage of our MiOPC model for trade
support, Table \ref{tab:ProTrade} indicates that a number of our
inflation-stage coefficient estimates are significant in their expected
directions. Similar to the findings in our main paper for EU-membership
support, we again find that gender is a significant predictor of
middle-category inflated responses. Specifically, we find here that males
are more likely to provide true indifference responses, whereas females are
more likely to provide ``face-saving'' responses or ``don't knows''. This
finding is consistent with our findings for EU-support, as well as with the
gender-based response set behaviors discussed in \citep{MondakAnderson2004}.
In addition, we also find that \textit{Education} is a strong positive
predictor of informed middle-category responses on \textit{Pro-Trade}, which
intuitively suggests that middle-category respondents with higher education
levels are less likely to be ``face-saving'' or ``don't know'' respondents.
Therefore, our inflation stage estimates lend some support to our assertions
of middle-category inflation within the ordered, \textit{Pro-Trade}
dependent variable. However, our inflation stage covariates measuring
\textit{Lived Abroad}, \textit{Age}, and \textit{Citizen} are never
significant in our MiOP or MiOPC models of \textit{Pro-Trade}, which
together suggests that there is more work to be done in identifying robust
predictors of individuals' trade-related knowledge-levels.

The outcome stages of our OP and MiOPC models of trade support similarly
reveal a number of interesting findings. The vast majority of outcome stage
covariates remain significant---and in their expected directions---across
our OP and MiOPC models. In support of the findings discussed in %
\citet[1415]{MaydaRodrik2005}, this suggests that outcome stage covariates
such as \textit{Male}, \textit{Education}, regional attachments, and
national pride all have a significant and robust effects on \textit{Pro-Trade%
}, no matter whether middle-category inflation is accounted for or not.%
\footnote{%
The estimates from some covariates in the OP specification are not
consistent with the sign and significance (or lack thereof) of the same
covariates reported by \citet{MaydaRodrik2005}.} However, we also find that
a number of outcome-stage estimates change in either substantive magnitude
or statistical significance once middle-category inflation is explicitly
modeled. Most notably, the estimate for \textit{Age} was positive but
insignificant within \citeauthor{MaydaRodrik2005}'s full specification and
was found to be negative but insignificant within our OP model; which in
turn is consistent with results reported in a number of similar studies %
\citep[e.g.,][]{MansfieldMutz2009}. However our MiOPC model suggests that
once middle-category inflation is accounted for, \textit{Age} has a
significant \textit{negative} direct effect on individuals' levels of
support for international trade. This finding is interesting, and is
consistent with the results for age that \citet{MaydaRodrik2005} report
within a separate set of models. Furthermore, \citet[1415]{MaydaRodrik2005}
report in their study that factors such as a respondents' pride in their
country's economy and social security system does \textit{not} have a
statistically significant on their propensity to support free trade. In
contrast, we find in the outcome stages of the MiOP and MiOPC models that
respondents' pride in their country's economy and social security system has
a strong positive and statistically significant on their propensity for
supporting free trade effect. Thus, our MiOP and MiOPC model in particular
suggests that, by not taking into account middle-category, survey-studies
may be underestimating the direct effect of the factors mentioned above (%
\textit{age}, \textit{economic pride}, \textit{pride in social security}) on
individuals' levels of support for free trade.

\begin{center}
[Insert Table \ref{tab:ProTrade} about here]
\end{center}

\newpage
\bibliographystyle{chicago}
\bibliography{BagozziMukherjee}

\newpage
\begingroup
\parindent 0pt
\parskip 2ex
\def\enotesize{\normalsize}
\theendnotes
\endgroup

\begin{table}[tbp]
\caption{Monte Carlo Results (Experiments 1 \& 2)}
\label{tab:ExperimentsOneTwo}\centering
{\footnotesize
\begin{tabular}{ll|c|ccc|ccc|ccc}
\hline
\multicolumn{12}{c}{{\small \textbf{Experiment 1 Results}}} \\ \hline
&  &  & \multicolumn{3}{c}{\small N=2,000 } & \multicolumn{3}{c}{\small %
N=4,000} & \multicolumn{3}{c}{\small N=8,000} \\
&  & True & OP & MiOP & MiOPC & OP & MiOP & MiOPC & OP & MiOP & MiOPC \\
\hline\hline
$\beta_{1}$ & $\bar{\beta_{1}}$ & 0.50 & 1.28 & 0.30 & 0.34 & 1.28 & 0.34 &
0.36 & 1.28 & 0.41 & 0.41 \\
& RMSE &  & (0.78) & (0.45) & (0.42) & (0.78) & (0.36) & (0.34) & (0.78) &
(0.24) & (0.24) \\
& CP &  & 0.00 & 0.84 & 0.86 & 0.00 & 0.86 & 0.87 & 0.00 & 0.89 & 0.90 \\%
[.1in]
$\beta_{2}$ & $\bar{\beta_{2}}$ & 0.25 & 0.13 & 0.50 & 0.50 & 0.19 & 0.28 &
0.28 & 0.19 & 0.26 & 0.26 \\
& RMSE &  & (0.06) & (0.07) & (0.07) & (0.06) & (0.05) & (0.05) & (0.06) &
(0.04) & (0.04) \\
& CP &  & 0.53 & 0.92 & 0.92 & 0.32 & 0.92 & 0.92 & 0.07 & 0.93 & 0.93 \\%
[.1in]
$\mu_{2}$ & $\bar{\mu_{2}}$ & 2 & 3.44 & 1.78 & 1.77 & 3.44 & 1.80 & 1.80 &
3.43 & 1.88 & 1.87 \\
& RMSE &  & (1.44) & (0.64) & (0.65) & (1.44) & (0.53) & (0.53) & (1.43) &
(0.36) & (0.37) \\
& CP &  & 0.00 & 0.81 & 0.78 & 0.00 & 0.83 & 0.83 & 0.00 & 0.88 & 0.87 \\%
[.1in]
$\gamma_{1}$ & $\bar{\gamma_{1}}$ & 1 &  & 1.23 & 1.24 &  & 1.04 & 1.04 &  &
1.00 & 1.00 \\
& RMSE &  &  & (0.99) & (1.00) &  & (0.69) & (0.71) &  & (0.49) & (0.50) \\
& CP &  &  & 0.85 & 0.85 &  & 0.85 & 0.86 &  & 0.90 & 0.88 \\[.1in]
$\gamma_{2}$ & $\bar{\gamma_{2}}$ & -0.25 &  & -0.27 & -0.27 &  & -0.26 &
-0.26 &  & -0.25 & -0.25 \\
& RMSE &  &  & (0.10) & (0.10) &  & (0.07) & (0.07) &  & (0.05) & (0.05) \\
& CP &  &  & 0.84 & 0.86 &  & 0.85 & 0.86 &  & 0.89 & 0.88 \\[.1in]
$\gamma_{3}$ & $\bar{\gamma_{3}}$ & -1 &  & -1.18 & -1.18 &  & -1.05 & -1.05
&  & -1.02 & -1.02 \\
& RMSE &  &  & (0.40) & (0.40) &  & (0.24) & (0.25) &  & (0.17) & (0.17) \\
& CP &  &  & 0.85 & 0.85 &  & 0.86 & 0.85 &  & 0.90 & 0.88 \\[.1in]
$\rho$ & $\bar{\rho}$ & 0 &  &  & 0.20 &  &  & -0.01 &  &  & -0.003 \\
& RMSE &  &  &  & (0.15) &  &  & (0.11) &  &  & (0.07) \\
& CP &  &  &  & 0.96 &  &  & 0.97 &  &  & 0.96 \\[.1in] \hline
\multicolumn{12}{c}{{\small \textbf{Experiment 2 Results}}} \\ \hline
&  &  & \multicolumn{3}{c}{\small N=2,000 } & \multicolumn{3}{c}{\small %
N=4,000} & \multicolumn{3}{c}{\small N=8,000} \\
&  & True & OP & MiOP & MiOPC & OP & MiOP & MiOPC & OP & MiOP & MiOPC \\
\hline\hline
$\beta_{1}$ & $\bar{\beta_{1}}$ & 0.50 & 1.84 & 0.70 & 0.35 & 1.84 & 0.70 &
0.36 & 1.83 & 0.74 & 0.41 \\
& RMSE &  & (1.34) & (0.50) & (0.42) & (1.34) & (0.42) & (0.47) & (1.33) &
(0.36) & (0.35) \\
& CP &  & 0.00 & 0.68 & 0.80 & 0.00 & 0.70 & 0.84 & 0.00 & 0.67 & 0.86 \\%
[.1in]
$\beta_{2}$ & $\bar{\beta_{2}}$ & 0.25 & 0.16 & 0.39 & 0.28 & 0.16 & 0.39 &
0.27 & 0.16 & 0.37 & 0.26 \\
& RMSE &  & (0.09) & (0.14) & (0.07) & (0.09) & (0.14) & (0.05) & (0.09) &
(0.12) & (0.03) \\
& CP &  & 0.28 & 0.76 & 0.94 & 0.06 & 0.64 & 0.95 & 0.01 & 0.33 & 0.95 \\%
[.1in]
$\mu_{2}$ & $\bar{\mu_{2}}$ & 2 & 3.57 & 1.93 & 1.86 & 3.58 & 1.90 & 1.86 &
3.57 & 1.96 & 1.92 \\
& RMSE &  & (1.57) & (0.69) & (0.65) & (1.58) & (0.59) & (0.51) & (1.57) &
(0.44) & (0.36) \\
& CP &  & 0.00 & 0.76 & 0.81 & 0.00 & 0.80 & 0.83 & 0.00 & 0.83 & 0.85 \\%
[.1in]
$\gamma_{1}$ & $\bar{\gamma_{1}}$ & 1 &  & 0.95 & 1.24 &  & 0.75 & 1.06 &  &
0.71 & 1.03 \\
& RMSE &  &  & (0.95) & (1.00) &  & (0.75) & (0.70) &  & (0.58) & (0.52) \\
& CP &  &  & 0.81 & 0.87 &  & 0.75 & 0.85 &  & 0.74 & 0.86 \\[.1in]
$\gamma_{2}$ & $\bar{\gamma_{2}}$ & -0.25 &  & -0.26 & -0.27 &  & -0.23 &
-0.26 &  & -0.23 & -0.25 \\
& RMSE &  &  & (0.13) & (0.10) &  & (0.10) & (0.09) &  & (0.07) & (0.06) \\
& CP &  &  & 0.85 & 0.87 &  & 0.82 & 0.87 &  & 0.81 & 0.88 \\[.1in]
$\gamma_{3}$ & $\bar{\gamma_{3}}$ & -1 &  & -0.84 & -1.16 &  & -0.77 & -1.04
&  & -0.75 & -1.02 \\
& RMSE &  &  & (0.31) & (0.41) &  & (0.27) & (0.25) &  & (0.26) & (0.18) \\
& CP &  &  & 0.62 & 0.86 &  & 0.54 & 0.87 &  & 0.44 & 0.88 \\[.1in]
$\rho$ & $\bar{\rho}$ & 0.50 &  &  & 0.51 &  &  & 0.52 &  &  & 0.51 \\
& RMSE &  &  &  & (0.15) &  &  & (0.12) &  &  & (0.09) \\
& CP &  &  &  & 0.91 &  &  & 0.92 &  &  & 0.91 \\[.1in] \hline\hline
\end{tabular}%
\newline
}
\end{table}

\begin{table}[tbp]
\caption{Monte Carlo Results (Experiments 5 \& 6)}
\label{tab:ExperimentsFiveSix}\centering
{\footnotesize
\begin{tabular}{ll|c|ccc|ccc}
\hline
\multicolumn{9}{c}{{\small \textbf{Experiment 5 Results (MiOP DGP)}}} \\
\hline
&  &  & \multicolumn{3}{c}{\small Exclusion Restriction} & \multicolumn{3}{c}%
{\small No Exclusion Restriction} \\
&  & True & OP & MiOP & MiOPC & OP & MiOP & MiOPC \\ \hline\hline
$\beta_{1}$ & $\bar{\beta_{1}}$ & 0.50 & 1.58 & 0.44 & 0.45 & 1.58 & 0.59 &
0.66 \\
& RMSE &  & (1.08) & (0.29) & (0.30) & (1.08) & (0.29) & (0.42) \\
& CP &  & 0.00 & 0.84 & 0.88 & 0.00 & 0.81 & 0.69 \\[.1in]
$\beta_{2}$ & $\bar{\beta_{2}}$ & 0.25 & 0.10 & 0.27 & 0.26 & 0.10 & 0.25 &
0.19 \\
& RMSE &  & (0.15) & (0.05) & (0.06) & (0.15) & (0.05) & (0.11) \\
& CP &  & 0.01 & 0.91 & 0.92 & 0.01 & 0.91 & 0.77 \\[.1in]
$\mu_{2}$ & $\bar{\mu_{2}}$ & 2 & 2.96 & 1.09 & 1.09 & 2.96 & 1.38 & 1.50 \\
& RMSE &  & (1.76) & (0.51) & (0.51) & (1.76) & (0.58) & (0.60) \\
& CP &  & 0.00 & 0.79 & 0.79 & 0.00 & 0.75 & 0.74 \\[.1in]
$\gamma_{1}$ & $\bar{\gamma_{1}}$ & 1 &  & 1.09 & 1.10 &  & 0.70 & 1.11 \\
& RMSE &  &  & (0.60) & (0.61) &  & (0.84) & (0.85) \\
& CP &  &  & 0.85 & 0.85 &  & 0.88 & 0.93 \\[.1in]
$\gamma_{2}$ & $\bar{\gamma_{2}}$ & -0.25 &  & -0.26 & -0.31 &  & -0.36 &
-0.26 \\
& RMSE &  &  & (0.08) & (0.08) &  & (0.12) & (0.15) \\
& CP &  &  & 0.87 & 0.87 &  & 0.93 & 0.94 \\[.1in]
$\gamma_{3}$ & $\bar{\gamma_{3}}$ & -1 &  & -1.06 & -1.06 &  &  &  \\
& RMSE &  &  & (0.19) & (0.19) &  &  &  \\
& CP &  &  & 0.89 & 0.89 &  &  &  \\[.1in]
$\rho$ & $\bar{\rho}$ & 0 &  &  & -0.01 &  &  & -0.03 \\
& RMSE &  &  &  & (0.15) &  &  & (0.58) \\
& CP &  &  &  & 0.97 &  &  & 0.78 \\[.1in] \hline
\multicolumn{9}{c}{{\small \textbf{Experiment 6 Results (MiOPC DGP)}}} \\
\hline
&  &  & \multicolumn{3}{c}{\small Exclusion Restriction} & \multicolumn{3}{c}%
{\small No Exclusion Restriction} \\
&  & True & OP & MiOP & MiOPC & OP & MiOP & MiOPC \\ \hline\hline
$\beta_{1}$ & $\bar{\beta_{1}}$ & 0.50 & 2.20 & 0.86 & 0.44 & 2.20 & 0.85 &
0.85 \\
& RMSE &  & (1.70) & (0.43) & (0.40) & (1.70) & (0.44) & (0.55) \\
& CP &  & 0.00 & 0.67 & 0.85 & 0.00 & 0.71 & 0.68 \\[.1in]
$\beta_{2}$ & $\bar{\beta_{2}}$ & 0.25 & 0.05 & 0.36 & 0.27 & 0.05 & 0.37 &
0.24 \\
& RMSE &  & (0.21) & (0.11) & (0.07) & (0.21) & (0.12) & (0.14) \\
& CP &  & 0.00 & 0.80 & 0.92 & 0.00 & 0.80 & 0.69 \\[.1in]
$\mu_{2}$ & $\bar{\mu_{2}}$ & 2 & 3.18 & 1.27 & 1.11 & 3.18 & 1.22 & 1.45 \\
& RMSE &  & (1.98) & (0.55) & (0.48) & (1.98) & (0.74) & (0.63) \\
& CP &  & 0.00 & 0.77 & 0.84 & 0.00 & 0.65 & 0.75 \\[.1in]
$\gamma_{1}$ & $\bar{\gamma_{1}}$ & 1 &  & 1.07 & 1.05 &  & 0.44 & 0.89 \\
& RMSE &  &  & (0.53) & (0.51) &  & (0.84) & (0.77) \\
& CP &  &  & 0.84 & 0.86 &  & 0.69 & 0.89 \\[.1in]
$\gamma_{2}$ & $\bar{\gamma_{2}}$ & -0.25 &  & -0.27 & -0.25 &  & -0.27 &
-0.34 \\
& RMSE &  &  & (0.09) & (0.08) &  & (0.12) & (0.14) \\
& CP &  &  & 0.87 & 0.88 &  & 0.77 & 0.92 \\[.1in]
$\gamma_{3}$ & $\bar{\gamma_{3}}$ & -1 &  & -0.94 & -1.03 &  &  &  \\
& RMSE &  &  & (0.15) & (0.17) &  &  &  \\
& CP &  &  & 0.81 & 0.88 &  &  &  \\[.1in]
$\rho$ & $\bar{\rho}$ & 0.50 &  &  & 0.49 &  &  & 0.15 \\
& RMSE &  &  &  & (0.18) &  &  & (0.56) \\
& CP &  &  &  & 0.90 &  &  & 0.87 \\[.1in] \hline\hline
\end{tabular}%
\newline
}
\end{table}

\begin{table}[tbp]
\caption{Baseline OP, MiOP, and MiOPC Models of EU Membership Support Among
Candidate Countries (2002)}
\label{tab:baseline-results}\centering
{\small
\begin{tabular}{lclclcl}
\hline\hline
& OP & s.e. & MiOP & s.e. & MiOPC & s.e. \\ \hline\hline
\textit{Outcome Stage:} &  &  &  &  &  &  \\
Political Trust & 0.776*** & (0.039) & 0.917*** & (0.051) & 0.866*** &
(0.048) \\
Xenophobia & -0.516*** & (0.046) & -0.593*** & (0.052) & -0.554*** & (0.050)
\\
Discuss Politics & 0.068*** & (0.019) & 0.016 & (0.023) & -0.022 & (0.026)
\\
College Ed. & 0.218 & (0.031) & 0.189*** & (0.038) & 0.152*** & (0.043) \\
Rural & -0.041** & (0.015) & -0.025 & (0.019) & -0.007 & (0.021) \\
Female & -0.103*** & (0.025) & -0.007 & (0.032) & 0.056 & (0.036) \\
Income & 0.261*** & (0.056) & 0.250*** & (0.069) & 0.207*** & (0.064) \\
\hline\hline
\textit{Inflation Stage:} &  &  &  &  &  &  \\
Constant & . &  & 0.474*** & (0.150) & 0.548*** & (0.144) \\
Discuss Politics & . &  & 0.196*** & (0.048) & 0.179*** & (0.045) \\
College Ed. & . &  & 0.137 & (0.094) & 0.130 & (0.087) \\
Rural & . &  & -0.076** & (0.039) & -0.078** & (0.036) \\
Female & . &  & -0.400*** & (0.083) & -0.363*** & (0.076) \\
True EU Knowledge & . &  & 0.161** & (0.023) & 0.147*** & (0.021) \\
\hline\hline
$\mu_{1}$ & -0.737*** & (0.077) & -0.635*** & (0.110) & -0.786*** & (0.096)
\\
$\mu_{2}$ & 0.398*** & (0.077) & 0.179 & (0.098) & -0.002 & (0.108) \\
$\rho$ & . &  & . &  & -0.672*** & (0.133) \\
No. Obs. & 9,116 &  & 9,116 &  & 9,116 &  \\ \hline\hline
\end{tabular}%
\newline
Note: *** indicates $p<.01$; ** indicates $p<.05$; * indicates $p<.10$ }
\end{table}

\begin{table}[tbp]
\caption{Expanded OP, MiOP, and MiOPC Models of EU Membership Support Among
Candidate Countries (2002)}
\label{tab:medium baseline-results}\centering
{\small
\begin{tabular}{lclclcl}
\hline\hline
& OP & s.e. & MiOP & s.e. & MiOPC & s.e. \\ \hline\hline
\textit{Outcome Stage:} &  &  &  &  &  &  \\
Political Trust & 0.788*** & (0.039) & 0.935*** & (0.052) & 0.883*** &
(0.049) \\
Xenophobia & -0.499*** & (0.046) & -0.579*** & (0.053) & -0.537*** & (0.051)
\\
Discuss Politics & 0.085*** & (0.019) & 0.036 & (0.023) & -0.007 & (0.036)
\\
College Ed. & 0.198*** & (0.031) & 0.178*** & (0.039) & 0.142*** & (0.043)
\\
Unemployed & 0.016 & (0.044) & 0.035 & (0.053) & 0.037 & (0.050) \\
Rural & -0.039* & (0.015) & -0.025 & (0.019) & -0.004 & (0.021) \\
Female & -0.09** & (0.025) & 0.004 & (0.032) & 0.069* & (0.036) \\
Age & -0.005*** & (0.001) & -0.004*** & (0.001) & -0.003*** & (0.001) \\
Student & 0.100* & (0.052) & 0.112* & (0.068) & 0.098 & (0.075) \\
Income & 0.272*** & (0.056) & 0.206*** & (0.077) & 0.079 & (0.082) \\
\hline\hline
\textit{Inflation Stage:} &  &  &  &  &  &  \\
Constant & . &  & 0.274 & (0.192) & 0.397** & (0.183) \\
Discuss Politics & . &  & 0.197*** & (0.048) & 0.173*** & (0.043) \\
College Ed. & . &  & 0.109* & (0.093) & 0.107 & (0.081) \\
Rural & . &  & -0.073* & (0.039) & -0.070** & (0.035) \\
Female & . &  & -0.398*** & (0.086) & -0.336*** & (0.072) \\
Age & . &  & -0.003 & (0.002) & -0.003* & (0.002) \\
Student & . &  & 0.038 & (0.137) & 0.063 & (0.125) \\
EU-Bid Knowledge & . &  & 0.399*** & (0.108) & 0.328*** & (0.105) \\
True EU Knowledge & . &  & 0.143*** & (0.022) & 0.124*** & (0.019) \\
\hline\hline
$\mu_{1}$ & -0.893*** & (0.086) & -0.816*** & (0.110) & -1.004*** & (0.116)
\\
$\mu_{2}$ & 0.248*** & (0.085) & -0.005 & (0.113) & -0.251 & (0.133) \\
$\rho$ & . &  & . &  & -0.737*** & (0.137) \\
No. Obs. & 9,116 &  & 9,116 &  & 9,116 &  \\ \hline\hline
\end{tabular}%
\newline
Note: *** indicates $p<.01$; ** indicates $p<.05$; * indicates $p<.10$ }
\end{table}

\begin{table}[tbp]
\caption{Expanded OP, MiOP, and MiOPC Models of EU Membership Support Among
Candidate Countries (2002) with Occupation Variables}
\label{tab:Medium-Large baseline-results}\centering
{\small
\begin{tabular}{lclclcl}
\hline\hline
& OP & s.e. & MiOP & s.e. & MiOPC & s.e. \\ \hline\hline
\textit{Outcome Stage:} &  &  &  &  &  &  \\
Political Trust & 0.771** & (0.039) & 0.903** & (0.051) & 0.857** & (0.048)
\\
Xenophobia & -0.492*** & (0.046) & -0.563*** & (0.052) & -0.521*** & (0.050)
\\
Discuss Politics & 0.074*** & (0.019) & 0.021 & (0.023) & -0.023 & (0.026)
\\
College Ed. & 0.104*** & (0.034) & 0.073* & (0.041) & 0.043 & (0.045) \\
Professional & -0.036 & (0.066) & -0.068 & (0.074) & -0.071 & (0.072) \\
Executive & 0.130 & (0.087) & 0.125 & (0.098) & 0.121 & (0.097) \\
Manual & -0.135*** & (0.041) & -0.143*** & (0.046) & -0.139*** & (0.044) \\
Farmer & -0.009 & (0.081) & -0.036 & (0.090) & -0.023 & (0.085) \\
Unemployed & 0.082* & (0.047) & 0.105* & (0.055) & 0.096* & (0.051) \\
Rural & -0.013 & (0.016) & 0.007 & (0.020) & 0.025 & (0.022) \\
Female & -0.074*** & (0.026) & 0.021 & (0.032) & 0.085** & (0.037) \\
Age & -0.002** & (0.001) & -0.002 & (0.001) & -0.001 & (0.001) \\
Student & 0.125** & (0.055) & 0.146* & (0.070) & 0.125 & (0.077) \\
Income & 0.065*** & (0.006) & 0.071*** & (0.007) & 0.066*** & (0.006) \\
\hline\hline
\textit{Inflation Stage:} &  &  &  &  &  &  \\
Constant & . &  & 0.300 & (0.203) & 0.450** & (0.187) \\
Discuss Politics & . &  & 0.212*** & (0.051) & 0.184*** & (0.045) \\
College Ed. & . &  & 0.124 & (0.102) & 0.120 & (0.088) \\
Rural & . &  & -0.082* & (0.042) & -0.078** & (0.037) \\
Female & . &  & -0.432*** & (0.095) & -0.363*** & (0.079) \\
Age & . &  & -0.003 & (0.002) & -0.003* & (0.002) \\
Student & . &  & -0.008 & (0.146) & 0.290 & (0.132) \\
EU-Bid Knowledge & . &  & 0.483*** & (0.107) & 0.385*** & (0.193) \\
True EU Knowledge & . &  & 0.142*** & (0.022) & 0.120*** & (0.019) \\
\hline\hline
$\mu_{1}$ & -0.719*** & (0.084) & -0.550*** & (0.101) & -0.653*** & (0.104)
\\
$\mu_{2}$ & 0.443*** & (0.083) & 0.306*** & (0.101) & 0.144 & (0.119) \\
$\rho$ & . &  & . &  & -0.735*** & (0.147) \\
No. Obs. & 9,116 &  & 9,116 &  & 9,116 &  \\ \hline\hline
\end{tabular}%
\newline
Note: *** indicates $p<.01$; ** indicates $p<.05$; * indicates $p<.10$ }
\end{table}
\newpage

\begin{table}[tbp]
\caption{Main OP, MiOP, and MiOPC Models of EU Membership Support Among
Candidate Countries (2002) with \textit{Income} and \textit{True EU Knowledge%
} Included in Both Stages of the MiOP(C) models}
\label{tab:IncomeKnowledge}\centering
{\small
\begin{tabular}{lclclcl}
\hline\hline
& OP & s.e. & MiOP & s.e. & MiOPC & s.e. \\ \hline\hline
\textit{Outcome Stage:} &  &  &  &  &  &  \\
Political Trust & 0.750*** & (0.040) & 0.901*** & (0.053) & 0.859*** &
(0.051) \\
Xenophobia & -0.501*** & (0.046) & -0.579*** & (0.053) & -0.538*** & (0.051)
\\
Discuss Politics & 0.054*** & (0.020) & 0.008 & (0.024) & -0.032 & (0.027)
\\
Professional & -0.059 & (0.067) & -0.093 & (0.076) & -0.091 & (0.074) \\
Executive & 0.107 & (0.087) & 0.112 & (0.101) & 0.119 & (0.100) \\
Manual & -0.116*** & (0.041) & -0.128*** & (0.048) & -0.122*** & (0.046) \\
Farmer & -0.017 & (0.081) & -0.044 & (0.092) & -0.043 & (0.088) \\
Unemployed & 0.098** & (0.047) & 0.123** & (0.057) & 0.113 & (0.054) \\
Rural & -0.008 & (0.016) & 0.010 & (0.020) & 0.026 & (0.022) \\
Female & -0.043* & (0.026) & 0.044 & (0.033) & 0.097*** & (0.037) \\
Age & -0.002** & (0.001) & -0.002 & (0.001) & -0.001 & (0.001) \\
Student & 0.039 & (0.065) & 0.115 & (0.082) & 0.162* & (0.090) \\
Income & 0.062*** & (0.005) & 0.066*** & (0.007) & 0.059*** & (0.008) \\
Educ High & 0.020 & (0.050) & 0.068 & (0.063) & 0.123* & (0.070) \\
Educ High-Mid & 0.106* & (0.056) & 0.014 & (0.070) & 0.067 & (0.077) \\
Educ Low-Mid & -0.128** & (0.038) & -0.052 & (0.048) & 0.037 & (0.053) \\
True EU Knowledge & 0.054*** & (0.007) & -0.032*** & (0.009) & 0.007 &
(0.011) \\ \hline\hline
\textit{Inflation Stage:} &  &  &  &  &  &  \\
Constant & . &  & 0.335 & (0.232) & 0.409* & (0.211) \\
Discuss Politics & . &  & 0.211*** & (0.049) & 0.180*** & (0.042) \\
Rural & . &  & -0.079** & (0.038) & -0.069** & (0.034) \\
Female & . &  & -0.397*** & (0.089) & -0.317*** & (0.069) \\
Age & . &  & -0.004** & (0.002) & -0.004** & (0.002) \\
Student & . &  & -0.337** & (0.159) & -0.281** & (0.138) \\
EU-Bid Knowledge & . &  & 0.487*** & (0.104) & 0.384*** & (0.089) \\
True EU Knowledge & . &  & 0.120*** & (0.021) & 0.122*** & (0.019) \\
Media & . &  & 0.053* & (0.028) & 0.042* & (0.022) \\
Educ High & . &  & -0.251* & (0.137) & -0.251** & (0.114) \\
Educ High-Mid & . &  & -0.515*** & (0.135) & -0.452*** & (0.118) \\
Educ Low-Mid & . &  & -0.477*** & (0.091) & -0.441*** & (0.081) \\
Income & . &  & -0.027** & (0.013) & -0.024** & (0.012) \\ \hline\hline
$\mu_{1}$ & -0.611*** & (0.093) & -0.462*** & (0.117) & -0.636*** & (0.123)
\\
$\mu_{2}$ & 0.547*** & (0.093) & 0.337*** & (0.118) & 0.082 & (0.139) \\
$\rho$ & . &  & . &  & -0.747*** & (0.129) \\
No. Obs. & 9,113 &  & 9,113 &  & 9,113 &  \\ \hline\hline
\end{tabular}%
\newline
Note: *** indicates $p<.01$; ** indicates $p<.05$; * indicates $p<.10$}
\end{table}
\newpage

\begin{table}[tbp]
\caption{Alternate Measure of EU-BID Knowledge}
\label{tab:Alt-EUBID}\centering
{\small
\begin{tabular}{lclclcl}
\hline\hline
& OP & s.e. & MiOP & s.e. & MiOPC & s.e. \\ \hline\hline
\textit{Outcome Stage:} &  &  &  &  &  &  \\
Political Trust & 0.751*** & (0.040) & 0.960*** & (0.056) & 0.858*** &
(0.050) \\
Xenophobia & -0.495*** & (0.047) & -0.612** & (0.057) & -0.523*** & (0.050)
\\
Discuss Politics & 0.073*** & (0.019) & 0.0001 & (0.026) & -0.051* & (0.027)
\\
Professional & -0.039 & (0.067) & -0.088 & (0.084) & -0.087 & (0.078) \\
Executive & 0.123 & (0.087) & 0.138 & (0.113) & 0.127 & (0.106) \\
Manual & -0.117*** & (0.042) & -0.114** & (0.053) & -0.086* & (0.047) \\
Farmer & -0.039 & (0.082) & -0.047 & (0.101) & -0.049 & (0.087) \\
Unemployed & 0.087* & (0.047) & 0.129** & (0.062) & 0.118** & (0.056) \\
Rural & -0.008 & (0.016) & 0.024 & (0.021) & 0.042* & (0.022) \\
Female & -0.072*** & (0.027) & 0.036 & (0.035) & 0.103*** & (0.037) \\
Age & -0.002** & (0.001) & -0.002 & (0.001) & -0.0003 & (0.001) \\
Student & 0.075 & (0.066) & 0.129 & (0.088) & 0.150* & (0.091) \\
Income & 0.066*** & (0.006) & 0.076*** & (0.007) & 0.068*** & (0.001) \\
Educ High & 0.079 & (0.050) & 0.117* & (0.066) & 0.124* & (0.067) \\
Educ High-Mid & 0.070 & (0.056) & 0.011 & (0.074) & 0.069 & (0.077) \\
Educ Low-Mid & -0.093** & (0.038) & -0.018 & (0.050) & 0.053 & (0.052) \\
\hline\hline
\textit{Inflation Stage:} &  &  &  &  &  &  \\
Constant & . &  & 0.048 & (0.171) & 0.045 & (0.156) \\
Discuss Politics & . &  & 0.120*** & (0.037) & 0.100*** & (0.034) \\
Rural & . &  & -0.088*** & (0.029) & 0.080*** & (0.028) \\
Female & . &  & -0.246*** & (0.052) & -0.227*** & (0.048) \\
Age & . &  & -0.005*** & (0.002) & -0.005*** & (0.001) \\
Student & . &  & -0.328*** & (0.123) & 0.297** & (0.115) \\
True EU Knowledge & . &  & 0.044*** & (0.014) & 0.035*** & (0.011) \\
Accession Informed & . &  & 0.620*** & (0.050) & 0.591*** & (0.042) \\
Media & . &  & 0.027 & (0.024) & 0.021 & (0.019) \\
Educ High & . &  & -0.356*** & (0.099) & -0.317*** & (0.090) \\
Educ High-Mid & . &  & -0.549*** & (0.107) & 0.508*** & (0.098) \\
Educ Low-Mid & . &  & -0.511*** & (0.074) & 0.467*** & (0.068) \\
\hline\hline
$\mu_{1}$ & -0.787*** & (0.091) & -0.463*** & (0.120) & -0.591*** & (0.119)
\\
$\mu_{2}$ & 0.358*** & (0.091) & 0.156 & (0.120) & -0.016 & (0.123) \\
$\rho$ & . &  & . &  & -0.892*** & (0.058) \\
No. Obs. & 8,878 &  & 8,878 &  & 8,878 &  \\ \hline\hline
\end{tabular}%
\newline
Note: *** indicates $p<.01$; ** indicates $p<.05$; * indicates $p<.10$}
\end{table}
\newpage

\begin{table}[tbp]
\caption{Alternate Measure of EU-BID Knowledge and Media Attention}
\label{tab:Alt-EUBID-MedieaAttn}\centering
{\small
\begin{tabular}{lclclcl}
\hline\hline
& OP & s.e. & MiOP & s.e. & MiOPC & s.e. \\ \hline\hline
\textit{Outcome Stage:} &  &  &  &  &  &  \\
Political Trust & 0.751*** & (0.040) & 0.965*** & (0.057) & 0.845*** &
(0.051) \\
Xenophobia & -0.493*** & (0.047) & -0.621** & (0.058) & -0.530*** & (0.051)
\\
Discuss Politics & 0.073*** & (0.020) & 0.0004 & (0.026) & -0.052* & (0.027)
\\
Professional & -0.045 & (0.067) & -0.096 & (0.086) & -0.087 & (0.080) \\
Executive & 0.122 & (0.087) & 0.155 & (0.118) & 0.137 & (0.112) \\
Manual & -0.117*** & (0.042) & -0.112** & (0.055) & -0.072 & (0.049) \\
Farmer & -0.035 & (0.082) & -0.056 & (0.103) & -0.010 & (0.092) \\
Unemployed & 0.087* & (0.047) & 0.141** & (0.064) & 0.128** & (0.057) \\
Rural & -0.008 & (0.016) & 0.023 & (0.021) & 0.041* & (0.022) \\
Female & -0.072*** & (0.027) & 0.033 & (0.036) & 0.102*** & (0.037) \\
Age & -0.002** & (0.001) & -0.002 & (0.001) & -0.0004 & (0.001) \\
Student & 0.075 & (0.066) & 0.131 & (0.090) & 0.154* & (0.092) \\
Income & 0.066*** & (0.006) & 0.077*** & (0.008) & 0.067*** & (0.007) \\
Educ High & 0.078 & (0.050) & 0.119* & (0.067) & 0.124* & (0.069) \\
Educ High-Mid & 0.070 & (0.056) & 0.009 & (0.076) & 0.066 & (0.077) \\
Educ Low-Mid & -0.092** & (0.038) & -0.016 & (0.051) & 0.055 & (0.052) \\
\hline\hline
\textit{Inflation Stage:} &  &  &  &  &  &  \\
Constant & . &  & 0.552*** & (0.157) & 0.458*** & (0.145) \\
Discuss Politics & . &  & 0.031 & (0.036) & 0.016 & (0.133) \\
Rural & . &  & -0.076*** & (0.028) & -0.068*** & (0.026) \\
Female & . &  & -0.227*** & (0.048) & -0.209*** & (0.044) \\
Age & . &  & -0.005*** & (0.001) & -0.005*** & (0.001) \\
Student & . &  & -0.285** & (0.115) & 0.254** & (0.109) \\
True EU Knowledge & . &  & 0.030*** & (0.013) & 0.022** & (0.011) \\
Accession Informed & . &  & 0.520*** & (0.046) & 0.501*** & (0.039) \\
Media PolEcon & . &  & 0.411*** & (0.048) & 0.388*** & (0.041) \\
Educ High & . &  & -0.340*** & (0.093) & -0.314*** & (0.086) \\
Educ High-Mid & . &  & -0.513*** & (0.101) & 0.483*** & (0.094) \\
Educ Low-Mid & . &  & -0.471*** & (0.071) & 0.446*** & (0.066) \\
\hline\hline
$\mu_{1}$ & -0.785*** & (0.092) & -0.449*** & (0.123) & -0.603*** & (0.120)
\\
$\mu_{2}$ & 0.359*** & (0.091) & 0.111 & (0.123) & -0.093 & (0.125) \\
$\rho$ & . &  & . &  & -0.879*** & (0.062) \\
No. Obs. & 8,871 &  & 8,871 &  & 8,871 &  \\ \hline\hline
\end{tabular}%
\newline
Note: *** indicates $p<.01$; ** indicates $p<.05$; * indicates $p<.10$}
\end{table}
\newpage

\begin{center}
\begin{table}[tbp]
\caption{Change in Predicted Values of \textit{EU Support}}
\label{tab:marg-effects}\centering
{\footnotesize
\begin{tabularx}{\linewidth}{ l l c c c}
\hline \hline
& & \qquad Probability & \qquad Probability & \qquad Probability\\
& & \qquad that \textit{EU Support}  & \qquad  that \textit{EU Support} & \qquad  that \textit{EU Support}\\
& & \qquad $=$ Bad Thing & \qquad $=$ Neither Good nor Bad & \qquad $=$ Good Thing\\
\hline \hline
&   & \qquad $-2.7\%$ & \qquad $-3.5\%$ & \qquad $6.2\%$\\
& \raisebox{1.5ex}{$\bigtriangleup$ \textit{Dsc. Politics}} & \qquad $(-3.7\% \leftrightarrow -1.6\%)$ & \qquad $(-4.5\% \leftrightarrow  -2.1\%)$ & \qquad $(3.7\% \leftrightarrow  8.7\%)$\\[.5cm]
&  & \qquad $1.2\%$ & \qquad $1.6\%$ & \qquad $-0.4\%$\\
\raisebox{1.1ex}{OP} & \raisebox{1.5ex}{$\bigtriangleup$ \textit{Female}} & \qquad $(0.4\% \leftrightarrow  1.9\%)$ & \qquad $(0.6\% \leftrightarrow  2.6\%)$ & \qquad $(-4.5\% \leftrightarrow  -1.1\%)$\\[.5cm]
&  & \qquad $-3.0\%$ & \qquad $-4.6\%$ & \qquad $7.6\%$\\
& \raisebox{1.5ex}{$\bigtriangleup$ \textit{Income}} & \qquad $(-3.4\% \leftrightarrow  -2.6\%)$ & \qquad $(-5.4\% \leftrightarrow  -3.9\%)$ & \qquad $(6.6\% \leftrightarrow  8.7\%)$\\[3pt]
\hline
&  & \qquad $0.1\%$ & \qquad $0.1\%$ & \qquad $-0.2\%$\\
&\raisebox{1.5ex}{$\bigtriangleup$ \textit{Dsc. Politics}} & \qquad $(-0.3\% \leftrightarrow  2.2\%)$ & \qquad $(-0.5\% \leftrightarrow  2.5\%)$ & \qquad $(-4.6\% \leftrightarrow  0.1\%)$\\[.5cm]
&   & \qquad $-1.4\%$ & \qquad $-1.5\%$ & \qquad $3.0\%$\\
\raisebox{1.1ex}{MiOPC} &\raisebox{1.5ex}{$\bigtriangleup$ \textit{Female}} &\qquad $(-2.3\%\leftrightarrow -0.5\%)$ &\qquad  $(-2.6\% \leftrightarrow -0.5\%)$ &\qquad  $(1.0\% \leftrightarrow 5.0\%)$\\[.5cm]
&  & \qquad $-2.6\%$ & \qquad $-3.3\%$ & \qquad $-6.0\%$\\
& \raisebox{1.5ex}{$\bigtriangleup$ \textit{Income}} & \qquad $(-3.0\% \leftrightarrow  -2.2\%)$ & \qquad $(-4.0\% \leftrightarrow  -2.8\%)$ & \qquad $(-6.9\% \leftrightarrow  -5.1\%)$\\
\hline \hline
\end{tabularx}\newline
Note: Expected Change values are calculated while holding all other
variables at their means or modes. Values in parentheses are lower and upper
bounds of $90\%$ confidence intervals. }
\end{table}
\end{center}

\begin{table}[tbp]
\caption{Support for Anti-Nuclear Movements Among Wester European Citizens}
\label{tab:Anti-Nuke}\centering
{\small
\begin{tabular}{lclclcl}
\hline\hline
& OP & s.e. & MiOP & s.e. & MiOPC & s.e. \\ \hline\hline
\textit{Outcome Stage:} &  &  &  &  &  &  \\
Social Integration & 0.043** & (0.018) & 0.064*** & (0.024) & 0.044*** &
(0.016) \\
New Middle Class & -0.038 & (0.039) & -0.042 & (0.050) & -0.026 & (0.035) \\
Old Middle Class & -0.012 & (0.057) & -0.016 & (0.072) & -0.015 & (0.051) \\
Postmaterialism & -0.010 & (0.023) & -0.018 & (0.030) & -0.017 & (0.021) \\
Self-Interest & 0.009* & (0.005) & 0.003 & (0.007) & 0.015*** & (0.006) \\
Sociotropism & 0.048*** & (0.003) & 0.046** & (0.005) & 0.071*** & (0.004)
\\
Left-Right Orientation & -0.065*** & (0.005) & -0.087*** & (0.010) &
-0.058*** & (0.006) \\
Age & -0.007*** & (0.001) & 0.008*** & (0.001) & -0.007** & (0.001) \\
Size of Town & -0.001 & (0.019) & 0.007 & (0.025) & 0.012 & (0.022) \\
Income & -0.058*** & (0.014) & -0.074*** & (0.019) & -0.051*** & (0.013) \\
Education & -0.015** & (0.006) & -0.022*** & (0.008) & -0.006 & (0.007) \\
\hline\hline
\textit{Inflation Stage:} &  &  &  &  &  &  \\
Constant & . &  & -0.994*** & (0.166) & -0.975*** & (0.254) \\
Self-Interest & . &  & 0.029** & (0.013) & 0.059*** & (0.018) \\
Sociotropism & . &  & 0.073*** & (0.009) & 0.101*** & (0.010) \\
Size of Town & . &  & -0.020 & (0.034) & 0.051 & (0.052) \\
Age & . &  & 0.003 & (0.002) & -0.005** & (0.002) \\
Education & . &  & 0.025* & (0.013) & 0.027* & (0.016) \\
Env Governance-Aware & . &  & -0.076 & (0.056) & 0.134** & (0.059) \\
Env-Uninformed & . &  & 0.004 & (0.027) & -0.019 & (0.024) \\
Watch EU Parliament on TV & . &  & 0.051 & (0.052) & 0.023 & (0.050) \\
\hline\hline
$\mu_{1}$ & -0.927*** & (0.147) & -0.856*** & (0.201) & 0.063 & (0.171) \\
$\mu_{2}$ & 0.653*** & (0.147) & -0.021 & (0.220) & 1.189*** & (0.144) \\
$\rho$ & . &  & . &  & -0.879*** & (0.062) \\
No. Obs. & 6,339 &  & 6,339 &  & 6,339 &  \\ \hline\hline
\end{tabular}%
\newline
Note: *** indicates $p<.01$; ** indicates $p<.05$; * indicates $p<.10$}
\end{table}
\newpage

\begin{table}[tbp]
\caption{Community/national attachment and Support for Trade}
\label{tab:ProTrade}\centering
{\small
\begin{tabular}{lclclcl}
\hline\hline
& OP & s.e. & MiOP & s.e. & MiOPC & s.e. \\ \hline\hline
\textit{Outcome Stage:} &  &  &  &  &  &  \\
Age & -0.001 & (0.001) & -0.001 & (0.001) & -0.001* & (0.001) \\
Male & 0.202*** & (0.016) & 0.226*** & (0.017) & 0.235*** & (0.019) \\
Citizen & 0.110** & (0.055) & 0.107* & (0.056) & 0.120** & (0.058) \\
Education & 0.043*** & (0.002) & 0.044*** & (0.003) & 0.049*** & (0.003) \\
Neighborhood Attachment & -0.031*** & (0.012) & -0.032*** & (0.012) &
-0.030** & (0.012) \\
Town Attachment & 0.022 & (0.014) & 0.023 & (0.015) & 0.019 & (0.014) \\
County Attachment & -0.085*** & (0.012) & -0.088*** & (0.013) & -0.086*** &
(0.013) \\
Continent Attachment & -0.076*** & (0.001) & 0.077*** & (0.010) & -0.075***
& (0.010) \\
National Pride (1) & -0.060*** & (0.013) & -0.062*** & (0.014) & -0.062*** &
(0.012) \\
National Pride (2) & -0.178*** & (0.009) & -0.180*** & (0.009) & -0.175*** &
(0.009) \\
National Pride (3) & -0.100*** & (0.008) & -0.102*** & (0.009) & -0.101*** &
(0.008) \\
National Pride (4) & -0.120*** & (0.007) & -0.124*** & (0.008) & -0.120*** &
(0.007) \\
Pride in Democracy & 0.052*** & (0.013) & 0.054*** & (0.013) & 0.054*** &
(0.013) \\
Pride in Political Influence & -0.041*** & (0.013) & -0.040*** & (0.014) &
0.037*** & (0.013) \\
Economic Pride & 0.082*** & (0.012) & 0.085*** & (0.012) & 0.083*** & (0.012)
\\
Pride in Social Security & 0.097*** & (0.011) & 0.097*** & (0.011) &
-0.095*** & (0.011) \\ \hline\hline
\textit{Inflation Stage:} &  &  &  &  &  &  \\
Constant & . &  & 1.349 & (0.966) & -0.072 & (0.985) \\
Age & . &  & 0.006 & (0.005) & -0.006 & (0.006) \\
Male & . &  & 0.669* & (0.369) & 0.943*** & (0.272) \\
Citizen & . &  & -0.178 & (0.899) & 0.716 & (0.790) \\
Education & . &  & 0.002 & (0.020) & 0.145*** & (0.028) \\
Lived Abroad & . &  & 0.207 & (0.150) & 0.252 & (0.264) \\ \hline\hline
$\mu_{1}$ & -1.338*** & (0.084) & -1.318*** & (0.086) & -1.229*** & (0.090)
\\
$\mu_{2}$ & -0.299*** & (0.084) & -0.251*** & (0.087) & -0.187** & (0.089)
\\
$\mu_{3}$ & 0.315*** & (0.084) & 0.301*** & (0.087) & 0.405*** & (0.088) \\
$\mu_{4}$ & 1.350 & (0.085) & 1.346*** & (0.087) & 1.443*** & (0.089) \\
$\rho$ & . &  & . &  & 0.966*** & (0.054) \\
No. Obs. & 17,185 &  & 17,185 &  & 17,185 &  \\ \hline\hline
\end{tabular}%
\newline
Note: *** indicates $p<.01$; ** indicates $p<.05$; * indicates $p<.10$}
\end{table}
\newpage

\begin{figure}[htp]
\caption{Comparisons of OP, MiOP, and MiOPC Root Mean Squared Errors for
Experiment 1}
\label{fig:expone}
\begin{center}
{\normalsize \includegraphics[width=5.3in]{RMSEExpOneSmall.eps} }
\end{center}
\end{figure}

\begin{figure}[htp]
\caption{Comparisons of OP, MiOP, and MiOPC Root Mean Squared Errors for
Experiment 2}
\label{fig:exptwo}
\begin{center}
{\normalsize \includegraphics[width=5.3in]{RMSEExpTwoSmall.eps} }
\end{center}
\end{figure}

\begin{figure}[htp]
\caption{Comparisons of OP, MiOP, and MiOPC Root Mean Squared Errors for
Experiment 3}
\label{fig:expthree}
\begin{center}
{\normalsize \includegraphics[width=5.3in]{RMSEExpThree.eps} }
\end{center}
\end{figure}

\begin{figure}[htp]
\caption{Comparisons of OP, MiOP, and MiOPC Root Mean Squared Errors for
Experiment 4}
\label{fig:expfour}
\begin{center}
{\normalsize \includegraphics[width=5.3in]{RMSEExpFour.eps} }
\end{center}
\end{figure}

\begin{figure}[htp]
\caption{Change in Western European Support for the Anti-Nuclear Movement, Given a 3-6 Unit Change in 
Education}
\label{fig:Ed}
\begin{center}
{\normalsize \includegraphics[width=5.3in]{EducNukeSupportPredict.eps} }
\end{center}
\end{figure}

\end{document}
