1. The Effects of Male Incarceration Dynamics on AIDS Infection Rates among African-American Women and Men Rucker C. Johnson Goldman School of Public Policy University of California, Berkeley Steven Raphael Goldman School of Public Policy University of California, Berkeley

3. The figures present percentiles of the distribution of the change in male incarceration rate between 1982 and 1996 across all age-state cells used in the main analysis of the paper within racial groups. The distributions are weighted by the average of the 1982 and 1996 male population of each cell.

11. High Concentration of HIV in Prison Population ¼ of all living with HIV in U.S. passed through a correctional facility in 1997 (Hammett et al., 2002)

12. Objective Investigate potential connection b/w incarceration dynamics and AIDS – Can high levels of black male incarceration explain black-white AIDS disparity?

13. Unique Aspects of Study Nationally representative data spanning 1982-2001 Analyses by gender and mode of transmission Empirical approaches: 1) Dynamic model of AIDS-prison relationship: Sex market fixed effect approach exploits variation within sexual relationship markets to identify effects 2) IV approach using state prison overcrowding litigation, sentencing and parole regime reforms First evidence of link between incarceration and AIDS using nationally representative population data from U.S.

14. Preview of Results Demographic groups who experienced largest increases in male incarceration experienced largest increase in AIDS infection rates – Effect of increase in incarceration on subsequent AIDS rates increases with time, mimicking time lag between becoming HIV positive and onset of AIDS – Effects persist after detailed controls for relationship market effects, and (race-, age-, and state-specific) time trends – IV results using prison overcrowding litigation and intra-state changes in sentencing regimes reveal large and significant relationship High levels of incarceration of black males explain lion’s share of racial disparity in AIDS infection among both black women and men

15. Incarceration May Influence Time Path of AIDS Epidemic in Various Ways Incapacitation effect – may reduce overall AIDS incidence Concentration of high-risk inds imprisoned coupled w/behavioral responses (sexual and otherwise) to incarceration may increase transmission risk of inmates Destabilizing effect on existing heterosexual relationships – Temporal dynamics of incarceration—high turnover (median spell=2 yrs): annual turnover of inmates in U.S. is 800% in jails, 50% in prisons – Increase in concurrent partnerships & total lifetime # of sex partners General eqm effect on sexual relationship mkt, due to decline in sex ratio

16. Rate of AIDS Transmission Rate AIDS spreads thru pop depends on: – 1) initial prevalence of disease in pop – 2) rates that new sexual relationship form/dissolve – 3) riskiness of sexual activity involved – 4) degree pop has concurrent sexual relationships Transmission risk differs by sexual activity (male homosexual activity = most risky) Homosexual activity not infrequent in prison – Men who served time are 62% more likely to report “ever having sex with a man” (Francis, 2005): (men w/jail history=12.8%, men w/o jail history=7.9%) Oster (2005) shows small diffs in transmission risk can generate large diffs in HIV prevalence over time

17. Per-contact Probability of HIV Transmission by Sexual Activity Sources: Francis, 2005; Royce et al., 1997; Vittinghoff et al., 1999; Downs and de Vincenzi, 1996.

18. Cumulative New AIDS Cases Among Men Over Five Yr Periods (’87-’91 & ’92-’96) Against Contemporaneous Changes in Group-Specific Male Incarceration Rates Cumulative New AIDS Cases Among Men Over Five Yr Periods (’87-’91 & ’92-’96) Against Five-Year Lagged Changes in Group-Specific Male Incarceration Rates

19. Cumulative New AIDS Cases Among Women Over Five Yr Periods (’87-’91 & ’92-’96) Against Contemporaneous Changes in Group-Specific Male Incarceration Rates Cumulative New AIDS Cases Among Women Over Five Yr Periods (’87-’91 & ’92-’96) Against Five-Year Lagged Changes in Group- Specific Male Incarceration Rates

20. Standard errors are in parentheses. All regressions include a constant term. Statistically significant at the one percent level of confidence. Statistically significant at the five percent level of confidence. Statistically significant at the ten percent level of confidence.

25. Challenges in Estimating Causal Impacts of Incarceration Accounting for other factors that may affect time path of AIDS epidemic: – Changes in behavior – Crack cocaine epidemic

26. Approach – General Description Overwhelming majority of sexual relationships occur within demographic groups defined by age*race*state (Charles & Luoh, 2005; Laumann et al., 1994) To identify incarceration efx on AIDS, exploit variation in incarceration rates w/in these demographic sub-groups over past 2 decades, after netting out race-, age-, and state-specific time trends

27. Model Specification Lagged Male Incarc Rate Sex Market Fixed Efx Lagged Female Incarc Rate Race – State – Age Yr Efx Used a third-order polynomial distributed (Almon) lag for both male and female incarceration rates to reduce multicollinearity problems. Lag coefficients constrained to = 0 for transmission efx that correspond w/time periods that predate AIDS epidemic.

28. Distributed Lag Effect Incarceration Rate at time t Effect on AIDS at time t Effect on AIDS at time t+1 Effect on AIDS at time t+2

29. The Distributed Lag Effect Effect on AIDS at time t Incarceration Rate at time t Incarceration Rate at time t-1 Incarceration Rate at time t-2

30. Data Specifics Panel data of advanced-stage HIV, 1982-1996 Dimensions of panel defined by yr of diagnosis*state*age*race*gender – 38 states + D.C. 21,060 individual demographic groups for each gender Calculating AIDS infection rates: – CDC AIDS Public Info Data Set: Case-level info on all known AIDS cases measured by AIDS surveillance system 3 redefinitions of AIDS case over period– to control for efx of these case-reporting redefinitions, complete sets of race-, age-, and state- specific yr efx included Calculating incarceration rates: – 1980, 1990, 2000 5% PUMS--% of each demographic sub- group institutionalized – For non-census yrs, linearly interpolate

31. Dynamic Regression Models Estimate overall dynamic relationship b/w incarceration and AIDS rates among men and women Estimate % of black-white AIDS gap attributable to race differences in incarceration rates Simulate counterfactual AIDS infection rates had incarceration rates remained at 1980 levels

32. Table 1. Regression Models Examining the Role of Male Incarceration Rates & Overall Racial/Ethnic Differences in AIDS Infection Rates among Men

33. Table 2. Regression Models Examining the Role of Male Incarceration Rates & Overall Racial/Ethnic Differences in AIDS Infection Rates among Women

34. The Impact of Male Incarceration on AIDS Infection Rates, Men: ’82-’96

35. The Impact of Incarceration on AIDS Infection Rates among Women

36. Figure 13

37. Figure 14

38. Figure 15

39. Figure 16

40. Robustness Checks - Models estimated separately by race show significant male incarceration effects on male and female AIDS rates for whites, Blacks, and Hispanics. - Similar results when the model is estimated in first differences Falsification tests: – No effects found in period that predates AIDS epidemic – No effects found for female incarceration rates – Alternative age matching: No effects on incompatible relationship age matches Is the relationship between incarceration and AIDS stable over time? – Results robust to allowing incarceration efx to vary over course of epidemic – Results not sensitive to time period or minor variations in lag length. Are there unobserved determinants that vary within sexual relationship markets around removed time trends? – Results robust to inclusion of crack cocaine prevalence

41. Robustness Checks Are there unobserved determinants that vary within sexual relationship markets around removed time trends? – Results robust to inclusion of crack cocaine prevalence

42. The Effects of Incarceration Dynamics & Crack Cocaine Prevalence on AIDS Infection Rates (allowing effects to vary over the course of the epidemic)

44. Standard errors are in parentheses. a..Differences are adjusted for comparison-specific fixed effects.

54. Add’l Findings Not Reported Here

55. Summary

56. Implications: Public Health & Criminal Justice Policy What do results imply for debates of optimal level of incarceration?

57. Directions for Future Research Next steps: