This paper analyzes how attitudes toward redistribution in the US have changed over time from 1972 to 2010 using data from the General Social Survey. The authors find that while overall support for redistribution has remained flat, the determinants of those attitudes have changed significantly. Younger people and those with lower incomes or less education now support redistribution more, while the attitudes of older people and those with higher incomes or more education have become more polarized. Non-white groups also showed higher support for redistribution, but this difference has decreased over time. These results suggest preferences around redistribution in the US have become more stratified along socioeconomic lines in recent decades.

1. Has the attitude of US citizens towards
redistribution changed over time?
By Maria Grazia Pillau and Roberto Zelli
Andrew E. Clark (Paris School of Economics and IZA)
http://www.parisschoolofeconomics.com/clark-andrew/
IARIW, Rotterdam, August 29th 2014

2. This is an interesting paper for a number of different
reasons
1) What people say predicts how they behave, and the
authors here consider preferences for redistribution
(PFR).
2) The US is sometimes considered as a poster child
for bad behaviour.
3) We almost always consider that one size fits all, so
that one regression coefficient tells the whole story
of the relationship between Y and X. This paper does
not.
4) And some of the results are going to turn your
prejudices on their head.

3. US inequality has grown sharply...

4. Was this growth in inequality a result of some kind of
structural change in the economy (the death of
manufacturing?)...
Or was it in some sense “demand-driven”, as Americans
now wanted less redistribution (and thus were
willing to accept greater inequality)?
The authors use US GSS data from 1972 to 2010 to find
out.

5. Their three questions.
⁻ Have PFR fallen?
⁻ Have the determinants of PFR changed?
⁻ Are these changes period effects or cohort effects
(e.g. those born during the Great Depression will
always be pro-redistribution). In other words, are
these between changes or within changes?

6. They create five-year birth cohorts, which appear in
repeated cross-section waves of the GSS.
Not all cohorts appear in all waves, although most do.
The 1900 cohort only appears up to the 1993 wave…
and the 1990 cohort doesn’t appear until the 2008
wave.

7. Support for redistribution from question EQWLTH:
“Some people think that the government in Washington
ought to reduce the income differences between the
rich and the poor, perhaps by raising the taxes of
wealthy families or by giving income assistance to
the poor. Others think that the government should
not concern itself with reducing this income
ditterence between the rich and the poor”
PFR are on a 1 to 7 scale (from Should to Should Not,
which they recode to binary (1-3 vs. 4-7).
N = 23,765

8. What’s happened to PFR in the US?
- Not much.
Different symbols for different cohorts

9. There is a lot of variability, but the trend line is almost
flat.
[Would have been nice to see this figure overlaid with
red and blue to denote Democratic and Republican
Presidencies]
Model PFR with the following right-hand side variables:
household equivalent income, age, gender, marital
status, children at home, race, years of education,
labour-force status, past experience of
unemployment in the last ten years, religious
denomination, religious attendance, and political
views

10. The usual way of running a regression is something like
this:
PFR = β’X + γ’Wave + ε
We have all done this, for our sins.
But this estimates only one β per right-hand side
variable.
And we might think that the effect of a given X variable
may have changed over time, as in the Oaxaca-
Blinder decomposition

11. We could address this coefficient heterogeneity by
estimating separate equations for each GSS year.
But the coefficients will move around probably due to
sample variability (the individual N’s in each year
are not that large).
The authors use multi-level models (partial pooling).
The multilevel estimates are a weighted average of
the specific regression estimates in each year and of
the overall regression coefficient estimated pooling
together all the years. They are also known as
shrinkage estimates.

12. Individual observations are nested within survey years
and cohorts.
They use a method inspired by Yang and Land to carry
out Age-Cohort-Period (ACP) analyses.
Model PFR as
The coefficients differ by time (t) and cohort (k).

13. Decompose intercepts and slopes as:

14. T time periods
K birth cohorts
P individual-level predictors whose coefficients vary
over time
R individual-level predictors whose coefficients are
unmodeled (think this means non time-varying).
Three kinds of variation: the model is complex and can
fall over.
ML estimation with random effects that are integrated
out.

15. The authors say they use:

16. My
understanding
of this method

17. Which variables are P (time-varying coefficients) and
which R?
Estimate separately by year and see which of the
estimated β’s are small and don’t change
[But isn’t this the procedure that you criticised in the
first place?]
R = marital status, gender, religion, religious practice,
labour-force status, previous unemployment.

18. PFR is higher for women, previous unemployment, and
lower for the married, the self-employed, and the
religious: I guess that these results are standard.
The main time-varying effects are...
Age
• The young have higher PFR, and this hasn’t
changed over time;
• The older have lower PFR and this has changed
over time.

19. In the late 1970s there was no difference between the old
and the middle-aged in terms of PFR; by 2010 there was
a 10%-point difference. No cohort effect.

20. Income: PFR falls with income, as is standard; absolute
effect larger over time

21. There is no cohort effect here either.
The difference in PFR between the poor (-1SD) and rich
(+2.5SD) was 17% points in the late 1970s; now
estimated to be 27% points.
So that there is increasing polarisation in terms of
redistribution as a function of income.
Is this due to the inexorable rise in income inequality?
Or something else?

22. Education: Correlated with PFR (because it predicts
income?)

23. There is no cohort effect here either.
Here’s the first thing you didn’t know.
The above time trend is positive for the high-educated,
negative for the lower-educated.
So the “standard” education gap in PFR is now reversed.
The high-educated are now more PFR than the low-educated.
This is an astonishing result. The legacy of Reagan
Democrats?

24. Political Views: Self-declared L-R scale. The gap has
opened up

25. Ethnicity: Non-white have higher PFR, but this reverses
over time

26. Here there is, for the only time, a cohort effect.
But it looks pretty noisy

27. Questions questions…
1) “Traits” are not traits, but rather seem to change,
often quite drastically, over time. Link to the work
on the systematic changes in personality? See
Boyce, C., Wood, A., and Powdthavee, N. (2013).
"Is Personality Fixed? Personality Changes as Much
as “Variable” Economic Factors and More Strongly
Predicts Changes to Life Satisfaction". Social
Indicators Research, 111, 287-305.
2) What is missing here (I think) is an over-arching
story which is consistent with all of these
(somewhat surprising) changes.

28. 3) A lot of the time effects look very similar and very
linear. Is this mechanical? Can you show me a
variable for which this does not hold?
4) As the time effects look so linear, could we have put
a time trend in on the estimated coefficient, and
saved ourselves quite a lot of bother?
5) Is the US different? Would other countries give you
different results? What do you expect?
6) Selection. Education has expanded and has become
differently selected over time. Do cohort effects
pick this up adequately?

29. 7) Cohort effects largely don’t matter: can we just
ignore them? If we had panel data, does the
insignificance of cohort effects mean that individual
fixed effects wouldn’t matter?
8) The heterogeneity explored here is ex post: we
decide which groups are going to be different (high
vs. low education, for example).
9) Why not let the data decide this, and estimate a
finite mixture model, which determines any
heterogeneity ex ante (don’t know how this deals
with ACP though).