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Socioeconomic status, sexual behavior, and differential AIDS mortality: evidence from Côte d’Ivoire


Lack of knowledge about differential AIDS mortality seriously hampers the study of the economic impact of AIDS in developing countries. We derive HIV infection risk differentials by age, education, and other microeconomic characteristics using the Ivorian Demographic and Health Survey. Our model is based on econometrically estimated equations using commonly available variables, therefore it can be used whenever such a survey is available but there is no representative information about HIV infection by socioeconomic group. For instance, we found that educated people have a higher risk of HIV infection, because they are more likely to have several sexual partners. However, this effect is partly offset by a higher probability of condom use relative to less educated people. The identification of the socioeconomic characteristics of low and high risk groups seems indispensable to set up adequate AIDS prevention and therapy policies in developing countries.

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Fig. 1


  1. See, for example, Gregson, Waddell and Chandiwana (2001) or Ainsworth and Semali (1998) providing empirical evidence showing a positive relationship between socioeconomic status and HIV infection in sub-Saharan Africa. See also Hargreaves and Glynn (2002) for a literature survey on this issue. Philipson and Posner (1995) offer various theoretical arguments for a positive correlation between income and HIV infection.

  2. These estimations include all adults (15 to 49 years) with HIV infection, whether or not they have developed symptoms of AIDS.

  3. To save space we do not present the corresponding maps, but comment on them only. Interested readers can obtain them upon request from the authors.

  4. However, Brouard (1994) quotes epidemiological studies that tend to show that the risk of infection depends only weakly on the frequency of sexual intercourse with each partner. In developed countries, β would be around 0.1 for the transmission from women to men, and 0.2 for the transmission from men to women, whatever the level of sexual activity.

  5. ‘Ignoring that an infected person may seem healthy,’ ‘not considering that occasional partners may be risky,’ and ‘not quoting avoidance of prostitutes’ might reflect a ‘preferred matching’ with partners of higher incidence rates, either because of ignorance or by conscious choice. However, because of their ambiguous nature, these variables are not used.

  6. Alternatively, we could estimate Eqs. 2, 4, and 6 simultaneously, but then an identifying variable for each equation is necessary, which our survey does not provide. A third possibility would be to impose correlation coefficients between the residual terms, but lacking any information regarding their size, we prefer the independence hypothesis.

  7. Normally the interviews in the DHS are undertaken separately for men and women and each man is interviewed by a man and each woman by a woman.

  8. Similar results were found by Deheneffe, Caraël, and Noumbissi (1998).


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We benefited from discussions with Nancy Snauwaert and Damien Rwegera from the UNAIDS Inter-country Team for West and Central Africa (Abidjan), with Annabel Desgrées du Loû (IRD Abidjan), and with research seminar participants at DELTA Paris, ENSEA Abidjan and the University of Göttingen. We are also grateful for useful comments received at the 16th Annual Conference of the European Society for Population Economics held in Bilbao.

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Correspondence to Michael Grimm.



Descriptive statistics for variables a b

Table A1 ‘Did you have sexual relations during the last two months?’ ‘If yes, did you use condoms?’ ‘Do/did you know somebody with AIDS?’

  Abidjan Other cities Rural areas
Had sexual relations last two months
Men 50.4 56.6 56.6
Women 55.6 59.6 59.2
Used condom
Men 29.4 28.7 19.6
Women 9.1 8.9 4.8
Knows/knew somebody with AIDS
Men 22.2 23.8 23.7
Women 22.2 21.6 20.7
  1. Source: DHS 1994; computations by the authors

Table A2 ‘With how many partners did you have sexual intercourse during the last two months?’ (men only)

  0 Spouse only 1 2 3 4 or more
Abidjan 49.4 20.8 16.6 9.7 / /
Other cities 43.3 28 13.4 9.2 (3.6) (2.6)
Rural areas 44.6 28.5 13.2 8.6 3.0 (2.2)
All 45.1 27.0 13.8 9.0 2.9 2.3
  1. Source: DHS 1994; computations by the authors
  2. ( ) = group ≤30 obs.; / = group ≤10 obs.

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Cogneau, D., Grimm, M. Socioeconomic status, sexual behavior, and differential AIDS mortality: evidence from Côte d’Ivoire. Popul Res Policy Rev 25, 393–407 (2006).

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  • AIDS
  • Demographic and Health Survey
  • Differential mortality
  • Sexual behavior