1. G George (Presenting Author) B Maughan-Brown, M Evans & S Beckett
An examination of men’s wealth and age disparate partnerships in South Africa: A nationally representative cross-sectional survey
G George (Presenting Author)
B Maughan-Brown, M Evans & S Beckett
10th International AIDS Economics Network Preconference
21 July 2018

2. Out of Context Paper Published July 2018

3. Builds on Existing Work

4. Background
Adolescent girls and young women (AGYW) account for 74% of new HIV infections among adolescents in sub-Saharan Africa, with more than 1,000 acquisitions occurring daily in 2016
One of the drivers of this is age disparate relationships.
Studies have suggested that age disparate sex is fuelled by pervasive wealth inequalities.
Older and economically more well off men (so called “sugar daddies” or “Blessers”) engage in relationships with vulnerable young women.
Motivations by young poor women for entering these relationships may include: potential upward economic mobility, improved symbolic capital, or as a consequence of coercion to enter into relationships with older men (Leclerc-Madlala, 2008; Hunter, 2002; Zembe et al, 2013).

5. Myth perpetuated by media

6. Translates into campaigns

7. Background
In more poverty stricken areas (rural or informal settlements) age disparate relationships may only be weakly linked to wealth when compared to wealthier (urban) areas.
Unemployment and poverty in rural/informal settlements areas is exceptionally high and limits the potential for resource transfers from older men to younger women.
Very little evidence on the men who engage in age disparate relationships
With specific reference to their socio-economic status and;
insufficiently accounted for variation by geographic context with very little or no comparative work (i.e. rural vs. urban evidence) 

8. Study Aims Primary Secondary
To assess whether the SES of men engaging in age-disparate partnerships is different from men in age-similar partnerships.
Secondary
To compare the relationship between men’s SES and their likelihood of engaging in age disparate relationships across geographic contexts (rural, urban formal and urban informal areas).
We limited men’s age to less than 40 because most young women (< 24 yrs. old) are having sex with men younger than 40.

9. Methods - Data The National HIV Communication Survey of South Africa.
Cross-sectional survey among adults in SA (16-55 yrs.) from February to May 2012.
Random sample representative of South Africa
Multi stage stratification by province, district and geographic context.
Primary sampling unit selected on probability proportionate to size technique.
One person randomly selected to be interviewed per household.
Response rate = 83%.
Face-to-face questionnaire
3 most recent sexual partnerships, socio-demographics, attitudes and behaviours, knowledge of HIV communication campaigns, HIV-related stigma and access to HIV prevention services.

10. Methods - Measures Age disparate relationships (Dependent variables)
DV1: Age disparate sexual partnership in previous 12 months. UNAIDS definition used female partner is 5 or more years younger. Men had to be younger than 40 (n=1606).
DV2: partner is 5 to 9 years younger.
DV3: Intergenerational partners (partner is 10 or more years younger).
Socio-Economic Status Variables (Independent variables)
IV1: Household wealth = a count of seven functioning household assets: microwave oven, flush toilet, washing machine, built-in kitchen sink, water inside their home or on their property, electricity and motor vehicle ownership.
IV2: Essential Services = a count of the household’s access to 4 essential services. These include access to water, food, medical supplies and fuel for cooking in the previous 12 months.
IV3: Employment status of the individual = currently employed or unemployed.
Geographic context and SES Interactions
Urban by HH wealth
Urban by access to essential services
Urban by access to employment
Rural is reference category for interaction variables.

11. Methods - Analysis Analysis Multiple logistic regression.
Separate models for 3 measures of SES.
3 more models for interaction between geographic context and wealth.
Control variables (age, marital status, HIV prevention knowledge, perceived risk of contracting HIV, alcohol use, concurrent sexual relationships and media exposure).
Weighted data & adjusted standard errors (clustering at the enumeration area level).

12. Results: Sample characteristics for men (> 24 yrs.)
Unweighted N
Unweighted %
Weighted %
25-34 yrs.
755
56.8%
35-55 yrs.
575
43.2%
Unmarried
408
31.1%
30.6%
Married
904
68.9%
69.4%
unemployed
508
39.0%
38.9%
employed
768
58.9%
student 27
2.1%
2.3%
Incomplete schooling
643
48.4%
48.6%
Completed schooling
686
51.6%
51.4%
rural
441
36.2%
47.1%
Urban formal
417
34.2%
29.3%
Urban informal
361
29.6%
23.6%
Never engaged in age-disparate relationship
606
45.6%
44.6%
Engaged in intragenerational age-disparate
505
38.0%
38.5%
Engaged in intergenerational age-disparate
219
16.5%
16.8%
Largely married, high unemployment, just more than half completed their schooling, just more than half engage in ADR

13. Results: Age disparate relationships (5 or more years older) and SES
(1)
HH wealth
UOR
(95% CI)
(2)
HH Wealth
AOR
(3)
Access to essential
(4)
Access to
essential
(5)
Employment
(6)
HH wealth (0-7)
0.94*
( )
0.94
( )
Access to essential score
(0-4)
0.91
( )
( )
Unemployed
ref.
Employed
1.01
( )
1.04
( )
Student
1.02
( )
1.43
( )
Controls included No
Yes n
1330
1280
Pseudo R2
0.01
0.10
<0.01
UOR: Unadjusted Odds ratio; AOR = Adjusted odds ratio. No relationship between SES (once control variables included) and ADR. Same for access to goods and essential services.

14. Results: Age disparate relationships (5 or more years older) and SES with interaction effects
(7)
HH wealth
+urban
AOR
(95% CI)
(8)
Access to essential
(9)
Employment
HH wealth (0-7)
0.97
( )
n/a
Access to goods score (0-4)
0.96
( )
Unemployed
ref
Employed
1.01
( )
Student
1.59
( )
Urban settlement
1.21
( )
1.11
( )
0.90
( )
Rural settlement
Urban*HH wealth
0.95
( )
Urban*Access to essential
( )
Urban*employed
1.07
( )
Urban*Student
0.81
( )
Controls included
Yes n
1184
1280
Pseudo R2
0.11
0.10
AOR = Adjusted odds ratio. No relationship between SES and ADR. Results hold after including the interaction variables between geographical context and SES.

15. Results: sensitivity analysis
No change in relationship between SES and ADR when we restrict the ADR to men 5-9 years older than their partner compared to men in similar age relationships.
Results indicate that men in inter-generational (10+ years) partnerships came from poorer households than individuals in age-similar partnerships (Household wealth AOR: 0.89, 95% CI: ; p = 0.03).
In all, the sensitivity analysis reveals that overall SES is not related (except for one measure) to age-disparate relationships regardless of the definition applied to age-disparate relationships.

16. Conclusions
Findings indicate that comparatively wealthier men in both urban and rural areas are no more likely to engage in age-disparate partnering than poorer men. Little variation in the relationship between SES and age disparate sex according to geographical context
Whilst age-disparate relationships are characterised by transactional sex, the relationships are not the sole domain of wealthier men.
HIV prevention messaging highlighting the risk posed by the economically advantaged ‘sugar daddy’ may be not be accurately representing the risk posed by older men across the economic spectrum

17. Conclusions - Limitations
Could not assess the impact that difference in wealth between male and female partners has on the formation of age disparate partnerships.
Self-reporting of partners age may have led to measurement error.
Data were unavailable on the SES of men at the start of each relationship.

18. Acknowledgements Study participants who gave up their time. Funders:
SA NDoH; USAID through PEPFAR; the Global Fund.
Investigators:
HEARD, Southern Africa Labour and Development Research Unit (SALDRU), York University
Data collection and study team:
JHHESA; loveLife; Soul City; HAD; The Johns Hopkins Bloomberg School of Public Health Center for Communication Programmes; Freshly Grounds Insights.

19. Thank you
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