Measuring SES/SEP Notes
1) Why SES?
a ‘down-n-dirty’ review
The strong relationship between SES and health has been
documented for centuries, dating back to ancient Greece,
Egypt, and China
A better understanding of the relationship between SES and
disease may reveal important new points for intervention and
epi screening
The socioeconomic structure in the US, and elsewhere, is
rapidly changing (eg, outsourcing, career?, women,
elderly.
Racial/ethnic disparities in health may be construed as
signs of genetic differences or behavioral choices rather
than powerful clues about how forms of racial
discrimination and structural constraints, past and
present, harm health
No consensus on a nominal definition of SES; it’s more
than income and/or educational attainment , it’s a latent
variable
A widely accepted SES instrument does not exist
Appears to be among the more difficult and
controversial subjects in all of social research
Prominent scholars have debated the theory,
operationalization, and usefulness of SES constructs
for about 125 years
Basically, we got nothing…
Gap between “SES Measurement” &
“SES and Health Studies”
250
150
100
SES Measurement
50
SES & Health Research
Year
97
19
95
19
93
19
91
19
89
19
87
19
85
19
83
19
81
19
79
19
77
19
75
19
73
19
71
19
69
19
67
19
65
19
63
19
61
19
59
19
55
19
19
43
0
da
te
Number of Articles
200
Chris Hamlin (2007)
“Social class as species”
2) Historical Review
Even more ‘down-n-dirty’ and American-focused
Americans are aware of social stratification and
have fairly firm views about their social standing
and that of others… think of Britney!
But social scientists have not made much progress
in measuring SES
Progress retarded due to lack of conceptual clarity
about social stratification… and it’s all about social
hyper-dimensional stratification
Early empirical sociological research mostly were
studies of single small communities
Status was assigned to households through an
unarticulated theory of stratification mainly based on
individuals’ reputation
Underlying this approach is the assumption that
everyone in a small community knows its status
hierarchy and can place most individuals in it
In 1947, NORC conducted a national sample survey
asking respondents to rate on a 5 point scale "the social
standing" of 90 occupations
The average social standing given to each occupation can
be regarded as the societal consensus (circa 1947)
concerning the status of each occupation
These social standing averages (also known as prestige
scores) were the first measures of the national consensus
on occupational status.
Problem was NORC prestige scores were known for only
90 out of the thousands of occupations.
OD Duncan wanted status scores for all Census
occupations.
Regressed known NORC occupational scores on
the median occupational educational and incomes
Predicted values were called Duncan’s SEI...
a continuous variable that could be calculated for
almost all occupational titles recognized in the
Census
Nam and Powers didn’t like “subjective” ratings in SEI and
thought an “objective” approach was better
Education as dues, Occupation as reward
Occupational status score (OSS) was a simple function of
educational attainment and income derived from a given
occupation.
In 1974, Rossi produced a Household Prestige Score
Factorial survey: Husband’s occupation and
education, along with wife’s occupation were
randomly varied in vignettes
Regressed ratings on characteristics of vignette
examples to infer the relative influence of the social
characteristics of households
Predictive equation gives HHP scores to
households based on the occupations, educational
levels and ethnicities of spouses
Worked pretty well… but ignored!
But despite, SEI, OCC, HHP, we still have two main problems:
(1) Lack of consensus on a nominal definition
Empirical researchers must either adapt vague
theories and develop idiosyncratic indicators or use
whatever vaguely related data elements exist to
construct ad hoc measures of SES
(2) Absence of sound measurement theory
Psychometrics has not been exploited in the
development, testing, and validation of SES measures
Routinely done in latent constructs, such as depression
Early efforts of Lundberg (1940) and Gordon (1952),
and the empirical efforts of Rossi (1951) have been
overlooked
Oakes & Rossi’s effort
Oakes, JM & Rossi, PH. 2003. The measurement of SES in
health research: current practice and steps toward a new
approach. Social Science & Medicine, 56(4), 769-784.
• Define SES as differential access (realized and potential) to
desired resources
• Use existing theory (Jim Coleman’s) which aims to
understand and explain the functioning and organization of
the social system
Two kinds of elements and two ways in which they are related: The
elements are (1) actors and (2) resources, related through (3) interests and
(4) control.
Components of the theory have been increasingly subjected to theoretical
and empirical scrutiny, with a few pleasing results
Resources may take the form of
(1) Material and monetary goods
(2) Skills and capabilities
(3) The strengths of social
relationships & resources of
others
SES = f (Material Capital, Human Capital, Social Capital)
CAPSES Scale
CAPSES Conceptual M odel
Scale Items
MC i
Indicator Variables
M Cj
M Ck
Subject s’
Self-rat ing of
t heir SES
Mat erial
Capit al
HC i
HC j
HC k
SC i
SC j
SC k
Human
Capit al
Social
Capit al
SES
Populat ion
Rating of
Subject s’ SES I
Populat ion
Rating of
Subject s’ SES II
CAPSES Ratings of Subject SES
Visual Analogue Scale (VAS)
The rating scale ranges from lowest possible socioeconomic
status on the left to highest possible socioeconomic status on the
right. Place an “X” on each bar to indicate your rating of the
subject’s status.
Subject Self-Rated SES
Self-reported scale (LADDER)
Self-reported social class (CLASS)
But…
• Our own (pilot) survey work showed
no contribution of social capital to
“SES” beyond income and education.
• Psychometrics were “inconclusive”
Forget SES, use Poverty!
Sit tight… more coming…
Forget SES, use Income!
Not very good…
Income changes year-to-year
Retired
20-somethings
Trust funds
Forget SES, use Wealth!
Good luck!
Forget SES, use Education!
Good, but…
Continuous or discrete,
Cohort effects,
“foreigners”,
Trade-workers
SES = Neighborhood?
My new approach
“Facts”
• People
are sorted in demarcated geographic areas,
called neighborhoods; some good, some not so good
• Thus, persons from same area are more alike than
persons from other areas… a clustering phenomena
• Early work showed SES was poor proxy for individual
SES, but I think this is backwards!
Oakes, JM. 2004. "The (mis)estimation of neighborhood effects: causal
inference for a practicable social epidemiology." Soc Sci Med 58:1929-52.
(with discussion)
Oakes, JM. 2006. “Invited Commentary: Advancing neighbourhood-effects
research--selection, inferential support, and structural confounding." Int J
Epidemiol 35:643-7.
Oakes, JM and PJ Johnson. 2006. "Propensity score matching methods
for social epidemiology." Pp. 370-392 in Methods in Social Epidemiology,
edited by JM Oakes and JS Kaufman. San Francisco: Jossey-Bass
Major talks – 2007 SER; 2006 JSM; 2005 ALR
3) Analysis with SES measures?
a) Mismeasure SES and you’ve got trouble!
What is impact of measurement error in confounder?
y  a  b1 E  b2 X  e
b) The trouble with Neighborhood SES
Hmmm… an insurmountable comparison problem!
Compare: Boulder v. Mobile
‘We can only evaluate sharply distinct treatments that
could happen to anyone.’
Paul Rosenbaum (2002)
‘If the differences between groups is large, the average
value applied to each group with adjustment may
represent “no man’s land”, a place where no actual
observations exist. Given this scenario, the interpretation
of the estimate becomes speculative rather than soundly
based. Heroic modeling assumptions are required.’
William Cochran (1957)
Estimated Probability of Exposure
1.0
Actually Exposed
0.5
Actually Unexposed
0.0
100
50
0
50
100
Number of Observed Subjects
Comparative inference is off-support of data and
thus requires “heroic” modeling assumptions.
Source: Oakes, JM and PJ Johnson. 2006. "Propensity score matching methods for social epidemiology.“ Pp. 370-392 in
Methods in Social Epidemiology, edited by Oakes and Kaufman. San Francisco: Jossey-Bass
See also – Johnson PJ. 2004. "The Effect of Neighborhood Poverty on American Indian Infant Death." PhD Dissertation, UMN
Hearst MO. 2007. "The Effect of Racial Residential Segregation on Infant Death in the US.“ PhD Dissertation, UMN
What is the effect of neighborhood poverty on
American Indian infant death in Minnesota?
Compare AI IMR in poor vs. not-so-poor neighborhood
Neighborhood Poverty
All-cause infant death
Endogenous-cause death
Exogenous-cause death
<5%
5-19%
20-39%
40-100%
7.5
3.8
3.8
16.2
7.8
8.4
17.4
10.1
7.3
23.3
12.0
13.3
But neighborhood effects are “independent”, which means we
must rule out (ie, adjust out) individual-level confounders.
The trouble is, there are few AI living in low poverty areas who
are like (ie, exchangeable) to those AI living in poverty areas.
More technically, AI with a high probability of living in poverty rarely
reside in low poverty neighborhoods, but some must if a meaningful
(ie, empirically based) counterfactual comparison is to be made.
Propensity score
.975.925.875.825.775.725.675.625.575.525.475.425.375.325.275.225.175.125.075.0250400
300
200
100
0
100
200
300
400
Number of Infants
40-100% Poverty
< 5% Poverty
Propensity of AI living in high-poverty Mpls neighborhoods
Conventional regression adjustment for individual characteristics
does not reveal that there are few comparison subjects;
the model equates subjects thru linear interpolation/extrapolation.
Heroic modeling assumptions are required.
Randomized Study
(No Confounding!)
Exposed
Y1
b1  2.0
Y2
Unexposed
PA, or any other measure!
Absent Randomization
yig  a  b1T  b 2 x  eig
Covariates serve to adjust groups for confounding…
a substitution problem called selection bias
Unless selection-equation, X, is perfect, bias
b1  ˆ  BIAS
Regression adjustment?
BMI
Exposed / High PA
Unexposed / Low PA
SES
SES
SES is a confounder
(mean SES is diff across exposures & related to BMI)
Exposed
y exposed
b1*
Unexposed
y unexposed
SES
SES
y exp
b1*
Y 1 adj
b1 adj
Y 2 adj
y unexp
The model yields adjusted Tx effect by using
slope of SES within Tx arms and then
calculating effects at (grand) mean SES
SES
SES
SES
How much adjustment is too much?
y exp
b1*
b1 adj
y unexp
The adjusted Tx effect is based on pure extrapolation…
Exposure groups have non-overlapping distributions of
SES; worse, adjustment is fictitious if members of group
1 could not conceivable be members of group 2; Oakes
calls this “structural confounding.”
SES
See: Cochran WG. 1957. "Analysis of Covariance: Its Nature and Uses." Biometrics:261-281.
-- . 1969. "The Use of Covariance in Observational Studies." JRSS C 18:270-75.
Note well:
Regression adjustment makes up or imputes data!
It says, if a poor man had same bank account as rich man,
his health would be thus and such.
This may not be bad.
The health of the poor man would be as
imputed/predicted…
GIVEN THE MODEL!!!!
So the question is:
IS YOUR MODEL CORRECT?
Well, is it, punk?
NB: WHI (HRT) and perhaps new diabetes trial results
Vandenbroucke JP .1987. "Should we abandon statistical modeling
altogether?” American Journal of Epidemiology 126:10-3.
Petitti DB & DA Freedman. 2005. “Invited commentary: How far can
epidemiologists get with statistical adjustment?” American Journal of
Epidemiology 162:1–4.
Hurvich CM & Chih-Ling Tsai. 1990. “The impact of model selection on
inference in linear regression” The American Statistician 44:214-217.
Leamer EE. 1978. Specification Searches: Ad Hoc Inference with
Nonexperimental Data Wiley
4) Other issues
• Life-course approaches to SES
• Intergenerational mobility
• Differential returns to SES by race/gender
• Genes – susceptibility and SES
5) Recommendations
• We don’t know what SES is and there is no agreed upon measure of
it; be careful with composites
• You have never “controlled for SES”
• You must appreciate what regression adjustment implies
• If stuck, use educational attainment (level completed)
• Be wary of income as an SES proxy by itself
• Caution with kids (less than 30 years), SAHMs, military?
“ … how can you possibly award prizes when
everyone missed the target?” said Alice.
“Well” said the Queen, “Some missed by more than
others, and we have a fine normal distribution of
misses, so we can forget about the target.”
(Kennedy 1988, p.292)