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Trends and transitions in labour market outcomes among adults enrolled in the Free State province’s public sector antire

Trends and transitions in labour market outcomes among adults enrolled in the Free State province’s public sector antiretroviral treatment (ART) programme. Frikkie Booysen, Department of Economics / CHSR&D, University of the Free State

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Trends and transitions in labour market outcomes among adults enrolled in the Free State province’s public sector antire

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  1. Trends and transitions in labour market outcomes among adults enrolled in the Free State province’s public sector antiretroviral treatment (ART) programme Frikkie Booysen, Department of Economics / CHSR&D, University of the Free State Annual TIPS Forum: South Africa’s Economic Miracle – has the emperor lost his clothes? , Cape Town 29-31 October 2008

  2. Acknowledgement • The financial support of CIDA, DCI, DfID, IDRC, JEAPP, USAID, AUSAID, UNDP, The World Bank’s Research Committee, and BNP Programme • The support of the Department of Social Development is greatfuly acknowledged. • Patients in the ART programme who willingy sacrificed their time and energy to participate in this research, and frankly shared their views and experiences. • The management and health care staff of the Free State Department of Health and of several local municipalities, who facilitated access to the study participants.

  3. Background • Adverse macro- and microeconomic impacts of HIV and AIDS are relatively well documented • Access to ART is expanding rapidly in South Africa, Southern Africa and beyond, although coverage remains sub-optimal … how can ART ameliorate these adverse economic impacts of HIV and AIDS?

  4. Figure 1: ART coverage in countries accounting for 75% of people receiving treatment in low- and middle-income countries (2007) Source: WHO, UNAIDS & UNICEF (2008: 21)

  5. Data: CP cohort study • Sampling frame • Eligible and certified ready to commence HAART in 2004/05 • CD4<200 and/or WHO stage 4 + clinical assessment • Randomly sampled • 80/district proportional to treatment/non-treatment numbers • Xhariep = 44 patients only, census • Follow-up interviews at approximately 6-month intervals • Replaced from original sampling frame if lost to follow-up • Written, informed consent • Nursing sister at assessment site + enumerator

  6. Table 1: Number of patient interviews, by survey round Note: Numbers represent the total number of cohort patients interviewed, including replacements randomly sampled from the original sampling frame following the loss to follow-up or death of patients interviewed in previous rounds of the study. Numbers also include 15 patients not interviewed in subsequent survey rounds (N=17).

  7. Table 2: Follow-up duration (months), by survey round Note: Only reported for consecutive interviews, i.e. excludes follow-up duration for 15 patients not interviewed in consecutive survey rounds (N=17).

  8. Table 3: Attrition and reasons for loss to follow-up, by survey round Note: Reasons only report for cases where reason for loss to follow-up is known.

  9. Labour market outcomes: (a) Too ill to work (b) Labour force participation (c) Unemployment (d) Absorption (e) Discouraged Treatment outcomes: (a) Treatment status and/or duration (b) Clinical markers CD4 count RNA level CD4 > 350 and RNA < 500 (c) Self-reported illness (d) Health-related quality of life EQ-5D EQ-VAS (e) Self-reported side-effects (f) Hospitalisation Key outcomes

  10. Key questions • How do labour market outcomes and transitions in labour market outcomes vary by treatment duration and/or treatment responses? • Are treatment dynamics significant predictors of labour market outcomes and transitions in labour market outcomes?

  11. Figure 2a: CD4 count, by treatment duration Note: Includes all clinical markers for interviewed study participants obtained from patient files. Results exclude those patients known to have interrupted their ARV treatment at some time or other during the study (n=27; N=130).

  12. Figure 2b: Change in CD4 count, by treatment duration Note: Includes all clinical markers for interviewed study participants obtained from patient files. Results exclude those patients known to have interrupted their ARV treatment at some time or other during the study (n=27; N=130). Excludes zero changes observed between consecutively dated clinical tests.

  13. Figure 3a: RNA level, by treatment duration Note: Includes all clinical markers for interviewed study participants obtained from patient files. Results exclude those patients known to have interrupted their ARV treatment at some time or other during the study (n=27; N=130). Undetectable RNA = 25 copies/mL, which represents lowest observable RNA using clinical test.

  14. Figure 3b: Change in RNA level, by treatment duration Note: Includes all clinical markers for interviewed study participants obtained from patient files. Results exclude those patients known to have interrupted their ARV treatment at some time or other during the study (n=27; N=130). Excludes zero changes observed between consecutively dated clinical tests.

  15. Table 4: Treatment outcomes, by treatment duration Note: Standard errors reported in parentheses. Clinical outcomes based on biological markers observed within +/- 90 days of the patient interview. Results for side effects only include patients on ARV treatment at the time. Results exclude those patients known to have interrupted their ARV treatment at some time or other during the study (n=27; N=130). Three asterisks denote differences that are statistically significant at the 1% level, while two asterisks denote differences that are statistically significant at the 5% level. Median values of all continuous variables also differ statistically significantly across treatment duration categories (p<0.001).

  16. Table 5: Labour market outcomes, by treatment duration Note: Exclude patients known to have interrupted their ARV treatment at some time or other during the study (n=27; N=130). Differences are statistically significant at the 1% level (chi2 = 92.37).

  17. Table 6: Transitions in labour market outcomes Note: Unemployed represent respondents who wanted to work and who actively looked for work. Discouraged workers represent respondents who wanted to work, but did not active look for work.

  18. Figure 5: Too ill to work, by ART response (%) Clinical outcome: viral load < 500 & CD4 > 350 Labour market outcome: too ill to work Note: Clinical outcomes represent ONLY those biological markers observed within +/- 90 days of the patient interview.

  19. Figure 6: Labour force participation, by ART response (%) Clinical outcome: viral load < 500 & CD4 > 350 Labour market outcome: participating in the labour force Note: Clinical outcomes represent those biological markers observed within +/- 90 days of the patient interview.

  20. Figure 8: Absorption rate, by ART response (%) Clinical outcome: viral load < 500 & CD4 > 350 Labour market outcome: absorbed in the labour force Note: Clinical outcomes represent ONLY those biological markers observed within +/- 90 days for the patient interview.

  21. Figure 10: Time trends in illness / disability among work force (%) Public sector ART clients LFS estimates, South Africa Note: Outcomes for ART patients represent results for balanced panel only, i.e. patients observed in all six survey rounds. Provincial estimates of the numbers of ill / disabled persons in the work force are not available for all LFS survey years.

  22. Figure 11: Time trends in labour force participation (%) LFS estimates, Free State province Public sector ART clients Note: Outcomes for ART patients represent results for balanced panel only, i.e. patients observed in all six survey rounds.

  23. Figure 13: Time trends in absorption rate (%) LFS estimates, Free State province Public sector ART clients Note: Outcomes for ART patients represent results for balanced panel only, i.e. patients observed in all six survey rounds.

  24. Table 7: Treatment duration as predictor of labour market outcomes Note: Results are for random effects (RE) panel probit models including ONLY treatment duration as explanatory variable. Reported as marginal effects of type eydx. Three asterisks denote differences that are statistically significant at the 1% level, while two asterisks denote differences that are statistically significant at the 5% level.

  25. Figure 15a: Predicted probability of being too ill to work, by treatment duration Note: Unadjusted results. Results exclude those patients known to have interrupted their ARV treatment at some time or other during the study (n=27; N=130).

  26. Figure 15b: Predicted probability of participating in the labour force, by treatment duration Note: Unadjusted results. Results exclude those patients known to have interrupted their ARV treatment at some time or other during the study (n=27; N=130).

  27. Figure 15a: Predicted probability of being absorped in the labour force, by treatment duration Note: Unadjusted results. Results exclude those patients known to have interrupted their ARV treatment at some time or other during the study (n=27; N=130).

  28. Table 8a: Treatment outcomes as predictors of labour market outcomes Note: Results are for random effects (RE) panel probit models including ONLY treatment dynamics as explanatory variable. Marginal effects of type eydx. Three asterisks denote differences that are statistically significant at the 1% level, while two asterisks denote differences that are statistically significant at the 5% level.

  29. Table 8b: Treatment outcomes as predictors of labour market outcomes Note: Results are for random effects (RE) panel probit models including ONLY treatment dynamics as explanatory variable. Marginal effects of type eydx. Three asterisks denote differences that are statistically significant at the 1% level, while two asterisks denote differences that are statistically significant at the 5% level.

  30. Table 8c: Treatment outcomes as predictors of labour market outcomes Note: Results are for random effects (RE) panel probit models including ONLY treatment dynamics as explanatory variable. Marginal effects of type eydx. Three asterisks denote differences that are statistically significant at the 1% level, while two asterisks denote differences that are statistically significant at the 5% level.

  31. Table 9: Predictors of being too ill to work Note: Results are for random effects (RE) panel probit models. Results are also adjusted for gender, age, race, education, marital status, dependency ratio, employment status at first HIV-positive test, treatment duration and/or outcomes, self-reported stigmatisation, district, follow-up duration, and month and year of interview. Three asterisks denote differences that are statistically significant at the 1% level, while two asterisks denote differences that are statistically significant at the 5% level.

  32. Table 10: Predictors of participating in the labour force Note: Results are for random effects (RE) panel probit models. Results are also adjusted for gender, age, race, education, marital status, dependency ratio, employment status at first HIV-positive test, treatment duration and/or outcomes, self-reported stigmatisation, district, follow-up duration, and month and year of interview. Three asterisks denote differences that are statistically significant at the 1% level, while two asterisks denote differences that are statistically significant at the 5% level.

  33. Table 11: Predictors of being absorped in the labour force Note: Results are for random effects (RE) panel probit models. Results are also adjusted for gender, age, race, education, marital status, dependency ratio, employment status at first HIV-positive test, treatment duration and/or outcomes, self-reported stigmatisation, district, follow-up duration, and month and year of interview. Three asterisks denote differences that are statistically significant at the 1% level, while two asterisks denote differences that are statistically significant at the 5% level.

  34. Table 12a: Treatment duration and/or outcomes as predictors of labour market outcomes Note: Results are for random effects (RE) panel probit models. Results are also adjusted for gender, age, race, education, dwelling, marital status, dependency ratio, employment status at first HIV-positive test, access to disability grant, breadwinner status, access to inter-household employment networks, self-reported stigmatisation, district, follow-up duration, and month and year of interview. Three asterisks denote differences that are statistically significant at the 1% level, while two asterisks denote differences that are statistically significant at the 5% level.

  35. Table 12b: Treatment duration and/or outcomes as predictors of labour market outcomes Note: Results are for random effects (RE) panel probit models. Results are also adjusted for gender, age, race, education, dwelling, marital status, dependency ratio, employment status at first HIV-positive test, access to disability grant, breadwinner status, access to inter-household employment networks, self-reported stigmatisation, district, follow-up duration, and month and year of interview. Three asterisks denote differences that are statistically significant at the 1% level, while two asterisks denote differences that are statistically significant at the 5% level.

  36. Table 12c: Treatment duration and/or outcomes as predictors of labour market outcomes Note: Results are for random effects (RE) panel probit models. Results are also adjusted for gender, age, race, education, dwelling, marital status, dependency ratio, employment status at first HIV-positive test, access to disability grant, breadwinner status, access to inter-household employment networks, self-reported stigmatisation, district, follow-up duration, and month and year of interview. Three asterisks denote differences that are statistically significant at the 1% level, while two asterisks denote differences that are statistically significant at the 5% level.

  37. Table 13: Treatment duration and outcomes as predictors of transitions in select labour market outcomes Note: Results are for random effects (RE) panel probit models. Results are also adjusted for gender, age, race, education, marital status, dependency ratio, employment status at first HIV-positive test, self-reported stigmatisation, district, follow-up duration, and month and year of interview.

  38. Table 14: Treatment duration and outcomes as predictors of transitions in being too ill to work Note: Results are for random effects (RE) or pooled panel probit models. Results are also adjusted for gender, age, race, education, marital status, dependency ratio, employment status at first HIV-positive test, self-reported stigmatisation, district, follow-up duration, and month and year of interview.

  39. Table 15: Treatment duration and outcomes as predictors of transitions in labour force participation Note: Results are for pooled probit models. Results are also adjusted for gender, age, race, education, marital status, dependency ratio, employment status at first HIV-positive test, self-reported stigmatisation, district, follow-up duration, and month and year of interview.

  40. Table 16: Treatment duration and outcomes as predictors of transitions in being absorped in the labour force Note: Results are for pooled probit models. Results are also adjusted for gender, age, race, education, marital status, dependency ratio, employment status at first HIV-positive test, self-reported stigmatisation, district, follow-up duration, and month and year of interview.

  41. Limitations • Observations of clinical (dates of facility visits) and labour market outcomes (interview dates) are not synchronised • Limited information regarding timing/duration of observed labour market outcomes • Counterfactual unclear in absence of comparative samples of HIV-negative and/or HIV-positive persons not on ART • Potential attrition and selection bias in socio-demographics and key clinical and labour market outcomes • Poor overall fit of regression models for transitions in labour market outcomes: poor specification and/or unobservables • Endogeneity of select explanatory variables

  42. Key findings • Clinical outcomes and self-reported illness and/or health-related quality of life are strongly correlated • Initial increase in labour force participation early in treatment career, accompanied by decline in participation later in treatment career • ART patients worse off than other representative samples on all outcomes, with the exception of labour force participation • Improvements in self-reported health rather than clinical markers explain labour market outcomes and/or transitions • Links to labour market and being employed at first HIV+ test significantly associated with labour market outcomes • Access to social grants remain key determinant of observed labour market outcomes • Sustainable, effective treatment key for improved labour market outcomes

  43. Conclusions • ART is not a “magic bullet” for problems of poverty, development and underdevelopment • Direct benefits for the economy relative limited among this group of ART clients (i.e. public sectors users), among which employment is low and often informal • Important therefore to estimate the indirect benefits or externalities of provision of treatment (e.g. time allocation, schooling, health care seeking behaviour) these social and economic spin-offs represents the focus of ongoing longitudinal research in this area

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