1. Socioeconomic Differentials in Mortality among the Oldest Old in China
Haiyan Zhu
Yu Xie
University of Michigan

2. Background Well documented inverse relationship:
Higher SES Lower mortality
However, magnitude varies by age

3. Yu Xie (1994). “Log-multipliticative Models for Discrete Time, Discrete Covariate Event-History Data”

4. SES differentials in mortality diminish with age (Convergence hypothesis)?
The oldest old are detached from economic activities and thus immune from some causal mechanisms (e.g., job hazards, work stress)
Biological determinants, rather than social determinants, play a predominant role
The oldest old are a select group with respect to unobserved health traits

5. Research Questions:
Does SES still affect mortality among the oldest old Chinese?
Do SES differentials in mortality disappear beyond an old age (e.g., 80, 90, or 100)?

6. Importance
Previous studies have not specifically studied these questions
This study focuses on these questions using a sample of oldest old in China

7. Data and Measures
The Chinese Healthy Longevity Survey:1998 and 2000 cohort data
SES: composite of education and urbanity
Illiterate, rural
Illiterate, urban
Literate, rural
Literate, urban
Covariates: age, sex, ethnicity, region, self-rated health, ADL, and “time-interval”
“time-interval”: time since the first interview

8. Analyses Three alternative target populations
80 and older, 90 and older, and 100 and older
Logit, discrete time analysis

9. Descriptive Statistics
Table 1 Distribution of SES and Mortality by SES, female
Female
Number
Percentage
Death (%)
80+
1. Illiterate, rural
3114
59.0
21.4
2. Illiterate, urban
1486
22.3
21.5
3. Literate, rural
236
8.7
11.9
4. Literate, urban
426
10.0
15.0
90+
2279
60.2
40.8
946
27.9
38.5
115
4.9
41.8
181
6.9
40.6
100+
1264
61.0
60.4
424
31.4
60.9 50
2.5
52.2 68
5.1
Note: numbers are unweighted; percentages are weighted.

10. Descriptive Statistics (Continued)
Table 2. Distribution of SES and Mortality by SES, male
Male
Number
Percentage
Death (%)
80+
1. Illiterate, rural
 958
26.7
27.1
2. Illiterate, urban
356
6.4
30.0
3. Literate, rural
1167
41.2
23.5
4. Literate, urban
1062
25.8
21.1
90+
554
29.4
51.3
197
9.2
53.5
586
36.4
45.4
419
25.0
34.8
100+
164
33.6
69.3 63
15.3
72.0
153
31.5
65.3 78
19.6
53.4
Note: numbers are unweighted; percentages are weighted.

11. Results
Table 3. Effects of SES on Mortality (controlling ethnicity and region)
Variable
80+
Mortality
90+
100+
Female
.657***
.762**
.789**
Age (time-varying)
1.092***
1.055***
1.035
Interval
1.014**
1.017***
1.030***
SES
Illiterate, urban
.997
.937
.981
Literate, rural
.773*
.912
.932
Literate, urban
.708**
.731*
.657**
* p<.05; ** p<.01; *** p<.001

12. Results (Continued)
Table 4. Effects of SES on Mortality (controlling health, ethnicity and region)
Variable
80+
Mortality
90+
100+
Female
.622***
.690***
.716***
Age (time-varying)
1.080***
1.041**
1.023
Interval
1.017**
1.022***
1.036***
SES
Illiterate, urban
1.022
.930
.939
Literate, rural
.795
.936
1.029
Literate, urban
.711**
.717*
.601**
Self-rated Health
1.649***
1.612***
1.473***
ADL
1.690***
1.824***
1.906***
 P<0.1; * p<.05; ** p<.01; *** p<.001

13. time time2 time3
1st cohort
2nd cohort
3rd cohort

14. Summary
SES differentials in mortality persist, using all three operationalizations of “old age”
Convergence was not supported
Future studies