Prof. Bryan Caplan

bcaplan@gmu.edu

http://www.bcaplan.com

Econ 854

 

Week 5: Voter Motivation, II: Ideological 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 U.S. Political Opinion

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 U.S. political opinion?

C.                Strong result: As with intelligence, empirical tests typically find that political opinion is roughly one dimensional.

D.                What is the dimension?  Empirically, U.S. political opinion "fits" well on the liberal-conservative or left-right spectrum.

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.                 Poole and Rosenthal's long-term study of the U.S. Congress finds that a one-dimensional l-c model works very well. 

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 ADA scores give better predictions of politicians' behavior than measures of constituent interests.  Marginal predictive value of alternative scores is limited.

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.               The Ideology*Education Interaction

A.                 Ideology and education interact in an interesting way.  Despite their slight correlation, ideology*education has more predictive power than ideology alone.

B.                 Simple explanation: The higher your education level, the more likely you are to know what your ideology says about a given topic.  For someone with a grade-school education, "liberal" is just a word; for a Ph.D., it is an integrated worldview.

C.                This works for party identification: The tstat on ideology*education is higher than the tstat on ideology alone, rising from 44 and 61 to 48 and 67. [Tables 3a&3b vs. Tables 6a&6b]

D.                It also works on individual issues.  For immigration, the tstat rises from 3.9 to 4.3 [Table 4a versus 7a]; for excessive profits, from 4.6 to 4.9 [Table 4b versus 7b].

E.                 Returning to the two-dimensional diagram, education "stretches" the liberal-conservative spectrum.

 

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

 


 Interac

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