Prof.
Bryan Caplan
bcaplan@gmu.edu
http://www.bcaplan.com
Advanced
Public Choice
Lecture 2: Voter Motivation, II:
Ideological and Other Voting
I.
Factor Analysis
A.
One statistical technique social scientists outside of economics use a
great deal is factor analysis.
B.
The main idea of factor analysis: reducing a lot of variables to a
smaller number of "summary" variables, aka "factors" or
"dimensions."
C.
The classic example: intelligence testing. A test has 100 items. Is it possible to extract a smaller
number of summary variables?
D.
Yes. In fact, factor
analysis on variables related to cognitive ability normally finds ONE
over-riding factor (called g for "general intelligence"). Cognitive ability is essentially
"one-dimensional."
E.
Performance on individual test items can be seen as a function of g
plus noise. The greater the
predictive power of g, the higher we say the item's g-loading is.
1.
Ex: Analogies have a higher g-loading than pure memory tasks.
F.
Factor analysis in no way guarantees the existence of a single over-riding
factor. For example, on personality
tests, factor analysis normally extracts FIVE unrelated factors.
G.
Factors do not label themselves.
Ordinary language terms are convenient, though occasionally misleading.
1.
Ex: OCEAN
H.
On purely random data, no factors would emerge.
II.
The Dimensionality of
A.
There are many different ways to analyze political beliefs.
1.
Libertarian-statist spectrum
2.
Christian-secular spectrum
B.
What can factor analysis tell us about the dimensionality of
C.
Strong result: As with intelligence, empirical tests typically find
that political opinion is roughly one dimensional.
D.
What is the dimension?
Empirically,
B.
On a deep level, this spectrum may not make a great deal of sense. Libertarians, for example, often argue
that there are really two dimensions - personal freedom and economic freedom:
1.
Libertarians - pro-personal, pro-economic
2.
Populists - anti-personal, anti-economic
3.
Liberals - pro-personal, anti-economic
4.
Conservatives - anti-personal, pro-economic
E.
But empirically, most people line up on the diagonal, and the other two
boxes are sparsely inhabited.
F.
G.
A second dimension (related to race) occasionally pops up, but is no
longer important. P&R's story:
During the 50's, otherwise liberal Southern Democrats often opposed civil
rights measures, and otherwise conservative Republicans often favored them. Once the Southern Democrats left the
party, and debate shifted from "equality of opportunity" to
"equality of result," position on further civil rights measures began
to correlate well with the rest of the liberal-conservative dimension.
H.
Similarly, Levitt and earlier researchers have found that
one-dimensional ideological measures of l-c like
I.
Less work has been done on the dimensionality of individual citizens'
opinions, but once again, a strong liberal-conservative dimension pops out of
the data.
J.
Remarkably, voting in the U.N. is also one-dimensional, in spite of the
extreme heterogeneity of the participants.
The dimension is something like "attitudes towards the
U.S./Israel."
III.
Ideological Voting
A.
As mentioned earlier, the main problem with the simple sociotropic
voting model is that it has trouble explaining disagreement.
B.
The empirical evidence on ideology suggests a more sophisticated
interpretation of sociotropic voting.
C.
Motivation is indeed sociotropic: People support the policies they
think are in the public interest.
D.
But: There are large ideological disagreements about the public
interest. Ideology determines
beliefs about what policies "work" and what counts as
"working."
E.
Ex: Affirmative action.
Conservatives and liberals argue about whether it works (are blacks
better-off because of it?), but also disagree about what it means to
"work" (a "level playing field" versus a "fair
outcome"?).
F.
Important theoretical point: If ideology is one-dimensional, and people
largely vote ideologically, then the simple MVT's seemingly strong assumptions are
satisfied. Perhaps the issue-space
only looks multi-dimensional.
V.
Ideology and Reduction
A.
Main objection to ideological voting model: Can't ideology be reduced
to personal interests?
B.
Ex: Isn't conservatism just the "ideology of the rich," and
liberalism the "ideology of the poor"?
C.
No. The correlation between
income and professed ideology is very low.
In the GSS, for example, the correlation between real income and
POLVIEWS (a 1-7 measure of left-right ideology) is .06.
D.
So what does determine ideology? Is it education?
E.
Once again, no. Education
and ideology are close to unrelated (r=-.03) when you look at a random sample of
Americans from the GSS (as opposed to, say, a 50/50 sample of random Americans
and university faculty!).
F.
In a multiple regression framework, there is a tendency for income to
make people more conservative and education to make people more liberal. [Table 2]
G.
Both are clearly statistically significant, but the actual effect is
small. On a 6-point scale:
1.
Raising log of real income by 1 – a huge change - makes people
.096 units more conservative.
2.
Going from a high school degree to a BA makes people .084 units more
liberal.
H.
What then is ideology? As
far as anyone can show, ideology is an independent causal force. Ideology explains a great deal about
people's beliefs, but no standard social science variable does much to explain
ideology.
I.
Maybe someone will one day show that ideology reduces to something
else, but given the failure of all the obvious candidates, I doubt it. (But stay tuned for the genetics of
politics next week!)
VI.
Case Study: The Determinants of Party Identification, II
A.
Question: Returning to last
week's linear probability model of party identification, what happens in the
GSS if you also control for stated ideology? N≈41,000,
so focus on magnitudes, not t-stats.
B.
[Tables 3a&3b]
C.
Answer: Ideology matters
even more than race. Moreover, the
slight change in the other coefficients shows that ideology is far from a
"mere proxy for self-interest."
D.
Consider two examples for 2010.
1.
Ex. #1: Black female with
$1M annual income in 1986 dollars, 30 years old, college graduate.
2.
Ex. #2: White male with
$10k annual income, 30 years old, high school education, conservative ideology.
E.
Ex. #1: [Since we don't
know ideology, use Tables 1a and 1b]
Estimated probability of being a Democrat: 56.4%; estimated probability
of being a Republican: 26.6%.
F.
Ex. #2: [Using Tables 3a
and 3b] Estimated probability of
being a Democrat: 6.8%; estimated probability of being a Republican: 59.1%. (Age coefficient to one more decimal
place=.0005).
VII.
Income, Education, Ideology, and Opinion
A.
For specific opinions (as opposed to party identification), income
empirically often seems to make a large difference.
1.
Ex: High income people seem much more in favor of immigration than low
income people.
B.
But
the effect of income almost always disappears once you control for education. Ph.D.s who drive cabs think like other
Ph.D.s, not other cab drivers.
C.
How does education affect opinion? More educated people tend to be
both more tolerant and more appreciative of free markets.
D.
Even though voting is one-dimensional, opinion looks
two-dimensional.
E.
Moreover, the two dimensions more or less fit the two-dimensional
personal freedom/economic freedom diagram.
Education shifts the diagonal up and to the right.
F.
This fact suggests that politicians might really compete over two
dimensions rather than one, again raising doubts about the median voter model.
G.
In practice, however, the liberal-conservative dimension appears to be
far more electorally salient.
Education affects issue beliefs, but appears to be independent of party
identification.
H.
Why? How come liberals
ally, but not high school drop-outs?
VIII.
Case Study: Economic Beliefs
A.
Now let us go through two illustrations from the SAEE: tendency to
blame economic difficulties on:
1.
Immigration
2.
"Excessive profits"
B.
If we do not control for education, income appears to have a large effect
on these beliefs. [Table 4a, 4b]
C.
Controlling for education, though, makes the apparent effect of income
almost disappear. [Table 5a, 5b]
D.
Immigration.
1.
Opposition shrinks as education rises.
2.
Opposition grows as conservatism rises.
E.
"Excessive profits."
1.
Assigning blame falls as education rises.
2.
Assigning blame falls as conservatism rises.
IX.
Religion, Party, and Ideology
A.
Many observers of modern American politics think that the divide
between secular and religious voters matters. Does it?
B.
The General Social Survey has measures of religious belief (especially
Biblical literalism) and religious practice (especially church
attendance). N≈26,000.
Bible goes from 1-4, 1 being most literalist; ATTEND goes from 1-7, 7
being most frequent.
C.
First,
let’s add these measures to the initial linear probability models from
Week 4. (Tables 8a&8b)
D.
Results: Both measures have substantial but not overwhelming predictive
power, especially for Republicans.
E.
What about religion and ideology?
Table 9 shows that our two religious measures are by far the strongest
predictors of ideology. If the
liberal-conservative divide is “really” something else, it’s
a secular-religious divide.
F.
Note, however, that the magnitudes are still not huge. The ideology variable goes from -3 to
+3, but moving from the minimum to the maximum on the religious variables only
makes you 1.32 units more conservative.
G.
What if we race ideology against religion as a determinant of party
identification? Ideology still
crushes religion, especially for P(Dem). (Tables 10a and 10b)
X.
Personality and Ideology
A.
The Five Factor Model now reigns supreme in personality
psychology. Acronym: OCEAN. (Myers-Briggs analogs in parenthesis).
1.
Openness to Experience (Intuitive vs. Sensing)
2.
Conscientiousness (Judging vs. Perceiving)
3.
Extraversion (Extraverted vs. Introverted)
4.
Agreeableness (Feeling vs. Thinking)
5.
Neuroticism (No MB analog, but the negative of Neuroticism is
Stability)
B.
Despite economists’ incredulity, personality measures are
predictively useful. Ex:
Occupational choice.
C.
Especially given the low MC of voting one way or another, it seems
plausible that personality would have large effects on political views. Do they?
D.
Data sets with personality and political info are scarce, but some
serious results are now in. Gerber
et al summarize past findings, and present some new ones.
E.
Robust past findings: Conservatives are lower in Openness and higher in
Conscientiousness. Lower Openness
and Higher Conscientiousness predict higher conservatism for almost all
measures; higher Stability predicts higher conservatism for most but not all
measures. (Table 4)
F.
Magnitudes in Gerber et al’s data: Personality variables go from
0-1, ideology goes from 1-5. So
going from the minimum to the maximum level of Openness makes people about a
point less conservative, and going from the minimum to the maximum level of
Conscientiousness makes people about half a point more conservative.
G.
Gerber et al distinguish economic and social ideology (scaled to have
mean=0 and SD=1) and find additional patterns, even controlling for education
and church attendance. (Table 6 and Figure 1)
H.
The robust predictors of social conservatism, like overall
conservatism, are Openness (-), and Conscientiousness (+).
I.
The robust predictors of economic conservatism are: Extraversion (+),
Agreeableness (-), Conscientiousness (+), Stability (+), and Openness (-). Free-marketeers are closed,
conscientious, disagreeable, emotionally stable extraverts.
J.
Interpretation? Two they consider, and one they
don’t:
1.
Some
personalities are less self-interested than others.
2.
Some
personalities have different interests than others.
3.
Some
personalities see the world more clearly than others.
K.
Compared to other
predictors of
ideology, these are strong. But in
absolute terms, ideology remains hard to predict.
XI.
Genes and Political Behavior
A.
Political attitudes and behavior often seem to run in families. Why is this?
B.
People usually assume it’s nurture, but in most families, there
is a confounding variable: genes.
C.
How can we distinguish the effects of nature and nurture? There are two standard “behavioral
genetic” approaches:
1.
Twin studies
2.
Adoption studies
D.
Adoption studies are still fairly unexplored for political attitudes
and behavior, but quite a few twin studies exist.
E.
Main findings: Nurture matters a lot for party identification.
F.
However, genes account for most or all of the family resemblance in:
1.
Whether you vote
2.
Whether you always vote for one party
3.
Issue positions (and a wide range have been studied!)
4.
Left-right ideology
G.
There are strong parallels between the behavior genetic results for
religion and politics. Religious affiliation is strongly influenced by
parents, but by mid-adulthood, genes explain most or all familiar resemblance
in:
1.
Church attendance
2.
Religious intensity (how religious you feel, how interested you are)
3.
Doctrinal views
XII.
Does Policy Match Public Opinion?
What Are the Unpopular Policies?
A.
Now that we have a better grip on voter motivation, let us return to
the earlier question: Is the median voter model correct?
B.
It is hard to do formal empirical tests on the federal government
(without internationally comparable data, N=1). But there is a lot of informal evidence
that the median voter gets what he wants.
C.
What does the federal government do? Is this what the median voter
wants? (http://www.cbo.gov/budget/data/historical.pdf)
1.
Spending (2015)
|
Source |
Share |
|
Social
Security |
23.9% |
|
Defense |
15.8% |
|
Domestic
Discretionary |
15.8% |
|
Medicare |
17.2% |
|
Net
Interest |
6.1% |
|
Income
Security |
8.2% |
|
Medicaid |
9.5% |
|
Other
Retirement/Disability |
4.4% |
|
Other |
6.1% |
|
Offsetting
receipts |
-7.0% |
2.
Taxes (2015)
|
Source |
Share |
|
Individual
Income Taxes |
47.4% |
|
Payroll
Taxes |
32.8% |
|
Corporate
Income Taxes |
10.6% |
|
Excise
Taxes/Customs |
4.1% |
|
Other |
5.1% |
3.
A lot of regulation: Environmental, worker safety, drug safety,
anti-competitive behavior, labor...
D.
Starting with the budget: Social Security and Medicare remain extremely
popular programs; the military is also usually well-regarded. The remaining items are more
contentious.
E.
Broadly defining "welfare" as Medicaid and Income Security,
we get 17.7% of the budget. But:
1.
Few people want to actually abolish these programs
2.
Medicaid also pays for middle-class nursing home residents who have
depleted their personal savings.
F.
The national debt is unpopular, but repudiating it would be even less
popular. So "net
interest" ultimately has voter support.
G.
That leaves 22% of the budget for "domestic discretionary"
and "other" spending.
Some of this spending is "waste.” Waste is unpopular. But outside of isolated examples of $500
toilet seats, what spending do a majority of Americans agree is wasteful?
H.
Turning to spending: It is surprising that income and SS taxes are such
a large percentage of the budget.
But insofar as business "passes on" corporate and other taxes,
do a majority of Americans really want significant changes here?
I.
Regulation is more complicated.
Are there majorities in favor of weaker (or stronger) environmental regulation? Worker safety? Drugs?
J.
Challenge: What
policies exist that a majority of American voters oppose? Consider all the clichés of
politics. Do any hold water?
1.
Relatively weak
gun control?
2.
Foreign aid?
3.
NAFTA?
XIII.
Bartels’
Case that Government Is Too Small
A.
In the
GSS, the median voter wants to spend
more in most areas. The only area
where the median voter consistently favors cuts is foreign aid.
B.
Larry
Bartels generalizes this finding to all 23 of the countries he looked at:
“Citizens in every country in every year wanted additional government
spending on health, education, old age pensions, the environment, and law
enforcement.”
C.
Both the
GSS and Bartels’ data also show, however, that voters around the world
want less spending overall! “The distribution of responses to
this question is, if anything, even more skewed than for the questions in the
battery on spending for specific government programs. Averaging across
countries and years, about two-thirds of the respondents said they favored cuts
in government spending, many ‘strongly’; only 10% were
opposed.”
D.
It is
well-known that adding a warning about the connection between higher spending
and higher taxes depresses support for spending.
E.
GSS
spending preference data doesn’t have such a warning. Bartels’ data does, but it’s
weird: “Remember that if you say "much more," it might require
a tax increase to pay for it.”
Problems:
1. It suggests that moderate spending increases don't require higher taxes.
2. It fails to mention that spending cuts would reduce taxes.
F. When the GSS gives a binary choice between higher spending on “social programs like health care, social security, and unemployment benefits” or lower taxes, 60% want higher spending. But adding a status quo category would almost certainly show that the median person favors the status quo over change in either direction.
G. Overall: Since voters’ stated budgetary preferences are contradictory, it is hard to tell if they are “getting what they want.” But the contradictions are weaker for better questions, which generally show that the median voter favors the status quo.
Table
2: The Determinants of Ideology (POLVIEWS rescaled to go from -3 to +3)
Table
3a: Conditional Probability of Being a Democrat, with Ideology
Table
3b: Conditional Probability of Being a Republican, with Ideology
Table
4a: Effect of Income on Beliefs About Immigration, No Education Control
|
Dependent Variable: IMMIG |
|
||||
|
Method: Least Squares |
|
||||
|
Date: 10/23/01 Time: 13:02 |
|
||||
|
Sample(adjusted): 1 1510 IF ECON<1 |
|
||||
|
Included observations: 1362 after
adjusting endpoints |
|
||||
|
Variable |
Coefficient |
Std. Error |
t-Statistic |
Prob. |
|
|
C |
1.581155 |
0.176059 |
8.980843 |
0.0000 |
|
|
BLACK |
-0.141790 |
0.076408 |
-1.855686 |
0.0637 |
|
|
ASIAN |
-0.002224 |
0.092337 |
-0.024084 |
0.9808 |
|
|
OTHRACE |
-0.004465 |
0.090074 |
-0.049576 |
0.9605 |
|
|
AGE |
-0.009174 |
0.007457 |
-1.230223 |
0.2188 |
|
|
AGE^2 |
0.000139 |
7.59E-05 |
1.832582 |
0.0671 |
|
|
MALE |
-0.130501 |
0.042039 |
-3.104298 |
0.0019 |
|
|
IDEOLOGY*(1-OTHIDEOL) |
0.106427 |
0.023119 |
4.603419 |
0.0000 |
|
|
OTHIDEOL |
0.242322 |
0.150883 |
1.606028 |
0.1085 |
|
|
JOBWORRY |
0.049389 |
0.019877 |
2.484734 |
0.0131 |
|
|
YOURFAM5 |
-0.018488 |
0.033123 |
-0.558180 |
0.5768 |
|
|
YOURNEXT5 |
-0.037205 |
0.033983 |
-1.094799 |
0.2738 |
|
|
INCOME |
-0.041745 |
0.010383 |
-4.020541 |
0.0001 |
|
|
R-squared |
0.069468 |
Mean dependent var |
1.218796 |
|
|
|
Adjusted R-squared |
0.061191 |
S.D. dependent var |
0.779419 |
|
|
|
S.E. of regression |
0.755196 |
Akaike info criterion |
2.285819 |
|
|
|
Sum squared resid |
769.3625 |
Schwarz criterion |
2.335612 |
||
|
Log likelihood |
-1543.643 |
F-statistic |
8.392399 |
||
|
Durbin-Watson stat |
2.049180 |
Prob(F-statistic) |
0.000000 |
||
Table
4b: Effect of Income on Beliefs About “Excessive Profits,” No
Education Control
|
Dependent Variable: PROFHIGH |
||||
|
Method: Least Squares |
||||
|
Date: 10/23/01 Time: 13:02 |
||||
|
Sample(adjusted): 1 1510 IF ECON<1 |
||||
|
Included observations: 1355 after
adjusting endpoints |
||||
|
Variable |
Coefficient |
Std. Error |
t-Statistic |
Prob. |
|
C |
1.346526 |
0.164472 |
8.186947 |
0.0000 |
|
BLACK |
0.078105 |
0.071559 |
1.091486 |
0.2753 |
|
ASIAN |
-0.011367 |
0.087285 |
-0.130229 |
0.8964 |
|
OTHRACE |
0.160538 |
0.085611 |
1.875199 |
0.0610 |
|
AGE |
0.010419 |
0.006962 |
1.496472 |
0.1348 |
|
AGE^2 |
-7.23E-05 |
7.09E-05 |
-1.020087 |
0.3079 |
|
MALE |
-0.202624 |
0.039320 |
-5.153159 |
0.0000 |
|
IDEOLOGY*(1-OTHIDEOL) |
-0.090241 |
0.021657 |
-4.166787 |
0.0000 |
|
OTHIDEOL |
0.180299 |
0.140710 |
1.281355 |
0.2003 |
|
JOBWORRY |
0.037830 |
0.018623 |
2.031381 |
0.0424 |
|
YOURFAM5 |
-0.056647 |
0.030934 |
-1.831217 |
0.0673 |
|
YOURNEXT5 |
-0.104313 |
0.031768 |
-3.283568 |
0.0011 |
|
INCOME |
-0.036220 |
0.009713 |
-3.729038 |
0.0002 |
|
R-squared |
0.108802 |
Mean dependent var |
1.272325 |
|
|
Adjusted R-squared |
0.100833 |
S.D. dependent var |
0.742522 |
|
|
S.E. of regression |
0.704092 |
Akaike info criterion |
2.145732 |
|
|
Sum squared resid |
665.2902 |
Schwarz criterion |
2.195732 |
|
|
Log likelihood |
-1440.733 |
F-statistic |
13.65318 |
|
|
Durbin-Watson stat |
2.008430 |
Prob(F-statistic) |
0.000000 |
|
Table
5a: Effect of Income on Beliefs About Immigration, Education Control
|
Dependent Variable: IMMIG |
||||
|
Method: Least Squares |
||||
|
Date: 10/23/01 Time: 12:49 |
||||
|
Sample(adjusted): 1 1510 IF ECON<1 |
||||
|
Included observations: 1362 after
adjusting endpoints |
||||
|
Variable |
Coefficient |
Std. Error |
t-Statistic |
Prob. |
|
C |
1.883690 |
0.174664 |
10.78466 |
0.0000 |
|
BLACK |
-0.174951 |
0.074420 |
-2.350864 |
0.0189 |
|
ASIAN |
0.035971 |
0.089924 |
0.400013 |
0.6892 |
|
OTHRACE |
-0.032613 |
0.087676 |
-0.371975 |
0.7100 |
|
AGE |
-0.004571 |
0.007273 |
-0.628464 |
0.5298 |
|
AGE^2 |
8.37E-05 |
7.41E-05 |
1.129602 |
0.2588 |
|
MALE |
-0.115403 |
0.040928 |
-2.819625 |
0.0049 |
|
IDEOLOGY*(1-OTHIDEOL) |
0.088741 |
0.022578 |
3.930411 |
0.0001 |
|
OTHIDEOL |
0.253523 |
0.146774 |
1.727304 |
0.0843 |
|
JOBWORRY |
0.036076 |
0.019394 |
1.860182 |
0.0631 |
|
YOURFAM5 |
0.004961 |
0.032329 |
0.153453 |
0.8781 |
|
YOURNEXT5 |
-0.025312 |
0.033084 |
-0.765072 |
0.4444 |
|
INCOME |
-0.011501 |
0.010667 |
-1.078253 |
0.2811 |
|
EDUCATION |
-0.121877 |
0.013828 |
-8.814086 |
0.0000 |
|
R-squared |
0.120175 |
Mean dependent var |
1.218796 |
|
|
Adjusted R-squared |
0.111690 |
S.D. dependent var |
0.779419 |
|
|
S.E. of regression |
0.734604 |
Akaike info criterion |
2.231255 |
|
|
Sum squared resid |
727.4387 |
Schwarz criterion |
2.284878 |
|
|
Log likelihood |
-1505.485 |
F-statistic |
14.16323 |
|
|
Durbin-Watson stat |
2.020208 |
Prob(F-statistic) |
0.000000 |
|
Table
5b: Effect of Income on Beliefs About “Excessive Profits,”
Education Control
|
Dependent Variable: PROFHIGH |
||||
|
Method: Least Squares |
||||
|
Date: 10/23/01 Time: 12:49 |
||||
|
Sample(adjusted): 1 1510 IF ECON<1 |
||||
|
Included observations: 1355 after
adjusting endpoints |
||||
|
Variable |
Coefficient |
Std. Error |
t-Statistic |
Prob. |
|
C |
1.509230 |
0.166386 |
9.070651 |
0.0000 |
|
BLACK |
0.060476 |
0.071038 |
0.851317 |
0.3947 |
|
ASIAN |
0.008011 |
0.086629 |
0.092480 |
0.9263 |
|
OTHRACE |
0.144138 |
0.084945 |
1.696828 |
0.0900 |
|
AGE |
0.012815 |
0.006920 |
1.851821 |
0.0643 |
|
AGE^2 |
-0.000101 |
7.05E-05 |
-1.432204 |
0.1523 |
|
MALE |
-0.194440 |
0.039020 |
-4.983073 |
0.0000 |
|
IDEOLOGY*(1-OTHIDEOL) |
-0.099322 |
0.021551 |
-4.608611 |
0.0000 |
|
OTHIDEOL |
0.185962 |
0.139513 |
1.332934 |
0.1828 |
|
JOBWORRY |
0.030960 |
0.018516 |
1.672024 |
0.0948 |
|
YOURFAM5 |
-0.044179 |
0.030774 |
-1.435562 |
0.1514 |
|
YOURNEXT5 |
-0.097896 |
0.031524 |
-3.105476 |
0.0019 |
|
INCOME |
-0.020394 |
0.010153 |
-2.008678 |
0.0448 |
|
EDUCATION |
-0.064849 |
0.013178 |
-4.920943 |
0.0000 |
|
R-squared |
0.124610 |
Mean dependent var |
1.272325 |
|
|
Adjusted R-squared |
0.116123 |
S.D. dependent var |
0.742522 |
|
|
S.E. of regression |
0.698080 |
Akaike info criterion |
2.129311 |
|
|
Sum squared resid |
653.4895 |
Schwarz criterion |
2.183157 |
|
|
Log likelihood |
-1428.608 |
F-statistic |
14.68370 |
|
|
Durbin-Watson stat |
2.000165 |
Prob(F-statistic) |
0.000000 |
|
Table
6a: Conditional Probability of Being a Democrat, with Ideology*Educ
Table
6b: Conditional Probability of Being a Republican, with Ideology*Educ
Table
7a: Effect of Income on Beliefs About Immigration, Ideology*Educ Interaction
|
Dependent Variable: IMMIG |
||||
|
Method: Least Squares |
||||
|
Date: 10/23/01 Time: 12:54 |
||||
|
Sample(adjusted): 1 1510 IF ECON<1 |
||||
|
Included observations: 1362 after
adjusting endpoints |
||||
|
Variable |
Coefficient |
Std. Error |
t-Statistic |
Prob. |
|
C |
1.901975 |
0.174285 |
10.91305 |
0.0000 |
|
BLACK |
-0.167020 |
0.074339 |
-2.246730 |
0.0248 |
|
ASIAN |
0.038885 |
0.089935 |
0.432370 |
0.6655 |
|
OTHRACE |
-0.032774 |
0.087630 |
-0.374001 |
0.7085 |
|
AGE |
-0.004735 |
0.007263 |
-0.651871 |
0.5146 |
|
AGE^2 |
8.50E-05 |
7.40E-05 |
1.148399 |
0.2510 |
|
MALE |
-0.116930 |
0.040887 |
-2.859876 |
0.0043 |
|
IDEOLOGY*(1-OTHIDEOL)*EDUCATION |
0.020108 |
0.004718 |
4.261634 |
0.0000 |
|
OTHIDEOL*EDUCATION |
0.062666 |
0.032606 |
1.921896 |
0.0548 |
|
JOBWORRY |
0.036512 |
0.019397 |
1.882333 |
0.0600 |
|
YOURFAM5 |
0.005987 |
0.032285 |
0.185437 |
0.8529 |
|
YOURNEXT5 |
-0.025103 |
0.033047 |
-0.759605 |
0.4476 |
|
INCOME |
-0.011887 |
0.010661 |
-1.114952 |
0.2651 |
|
EDUCATION |
-0.124634 |
0.013817 |
-9.020040 |
0.0000 |
|
R-squared |
0.122481 |
Mean dependent var |
1.218796 |
|
|
Adjusted R-squared |
0.114018 |
S.D. dependent var |
0.779419 |
|
|
S.E. of regression |
0.733641 |
Akaike info criterion |
2.228631 |
|
|
Sum squared resid |
725.5321 |
Schwarz criterion |
2.282253 |
|
|
Log likelihood |
-1503.698 |
F-statistic |
14.47294 |
|
|
Durbin-Watson stat |
2.021390 |
Prob(F-statistic) |
0.000000 |
|
Table
7b: Effect of Income on Beliefs About “Excessive Profits,”
Ideology*Educ Interaction
|
Dependent Variable: PROFHIGH |
||||
|
Method: Least Squares |
||||
|
Date: 10/23/01 Time: 12:54 |
||||
|
Sample(adjusted): 1 1510 IF ECON<1 |
||||
|
Included observations: 1355 after
adjusting endpoints |
||||
|
Variable |
Coefficient |
Std. Error |
t-Statistic |
Prob. |
|
C |
1.504577 |
0.166017 |
9.062768 |
0.0000 |
|
BLACK |
0.048497 |
0.070958 |
0.683463 |
0.4944 |
|
ASIAN |
-0.004565 |
0.086640 |
-0.052685 |
0.9580 |
|
OTHRACE |
0.135789 |
0.084901 |
1.599375 |
0.1100 |
|
AGE |
0.013030 |
0.006910 |
1.885486 |
0.0596 |
|
AGE^2 |
-0.000104 |
7.04E-05 |
-1.471935 |
0.1413 |
|
MALE |
-0.192414 |
0.038981 |
-4.936041 |
0.0000 |
|
IDEOLOGY*(1-OTHIDEOL)*EDUCATION |
-0.022115 |
0.004490 |
-4.924966 |
0.0000 |
|
OTHIDEOL*EDUCATION |
0.049965 |
0.030994 |
1.612075 |
0.1072 |
|
JOBWORRY |
0.028983 |
0.018517 |
1.565193 |
0.1178 |
|
YOURFAM5 |
-0.046264 |
0.030730 |
-1.505501 |
0.1324 |
|
YOURNEXT5 |
-0.096444 |
0.031489 |
-3.062790 |
0.0022 |
|
INCOME |
-0.019645 |
0.010146 |
-1.936233 |
0.0530 |
|
EDUCATION |
-0.065029 |
0.013171 |
-4.937448 |
0.0000 |
|
R-squared |
0.126969 |
Mean dependent var |
1.272325 |
|
|
Adjusted R-squared |
0.118506 |
S.D. dependent var |
0.742522 |
|
|
S.E. of regression |
0.697138 |
Akaike info criterion |
2.126612 |
|
|
Sum squared resid |
651.7280 |
Schwarz criterion |
2.180458 |
|
|
Log likelihood |
-1426.780 |
F-statistic |
15.00220 |
|
|
Durbin-Watson stat |
2.001392 |
Prob(F-statistic) |
0.000000 |
|
Table
8a: Conditional Probability of Being a Democrat, with Literalism and Attendance
Table
8b: Conditional Probability of Being a Republican, with Literalism and
Attendance
Table 9: Determinants of Ideology, with Literalism and
Attendance
Table
10a: Conditional Probability of Being a Democrat, with Ideology and Religion
Table
10b: Conditional Probability of Being a Republican, with Ideology and Religion
