Evidence and Lessons on Health Impacts of Public Health Funding: Dillender (2023)

What this is. The paper’s core results, the epidemiological model of HIV dynamics, and the research designs (difference-in-differences and regression discontinuity) with their estimating equations: enough to understand what the paper found and how it identified the causal effect of federal HIV/AIDS funding. To replicate or extend the results, read the full source at the original.

TL;DR

This paper estimates the health impact of Ryan White CARE Act Title I funds, which are federal grants directed to US cities to help low-income HIV-positive people access treatment and support services. Identification exploits two quasi-experimental sources of variation. First, the original 1990 Ryan White legislation granted cities Title I status after they reported at least 2,000 cumulative AIDS cases by March 31 of a given year, while a 1996 rule change (combined with a grandfather clause) froze the set of eligible cities just as effective antiretroviral treatment emerged, generating staggered treatment timing across cities with similar baseline HIV/AIDS trajectories. Second, the sharp discontinuity in Title I funding at the 2,000-case threshold supports a regression discontinuity design to estimate effects on HIV prevalence.

Comparing 25 cities that qualified for Title I status under the original rules with the 25 cities that had the most AIDS cases but fell just below the threshold, the paper finds that Title I status reduced annual HIV/AIDS death rates by about 15-17 percent on average. Annual AIDS case rates fell by roughly 20-25 percent. A regression discontinuity at the 2,000-case threshold finds 36-40 percent fewer people living with HIV in Title I cities by 2008, indicating the funding reduced HIV spread as well as deaths. The implied cost per HIV/AIDS death avoided is $334,000, and the program’s benefit-cost ratio is approximately 30 at a $10 million value of statistical life. Total lives saved are estimated at approximately 57,000 through 2018.

Core results

Magnitudes and significance are as reported. Locators point to the source PDF (American Economic Review 113(7): 1825-1887).

#	Result	Locator	Magnitude
R1	Title I status reduced HIV/AIDS death rates (1988-2006)	Table 2, col 1, p. 1847	DiD coefficient: -0.185 log points (SE 0.069, p=0.010); 50 cities, 950 city-year obs
R2	Effect on HIV/AIDS death rates through 2018	Table 2, col 2, p. 1847	DiD coefficient: -0.163 log points (SE 0.075, p=0.036); 50 cities, 1,550 obs
R3	Title I reduced annual rates of new AIDS cases	Table 8, col 1, p. 1870	DiD coefficient: -0.227 log points (SE 0.063, p=0.001); ~25% reduction
R4	Title I reduced people living with HIV (RDD)	Table 9, Panel A, col 1, p. 1875	RDD: -0.510 log points (SE 0.152, p=0.002); -40.0% in 2008
R5	Title I reduced new HIV diagnoses (RDD)	Table 9, Panel B, col 1, p. 1875	RDD: -0.628 log points (SE 0.219, p=0.006); -46.7% in 2008
R6	Cost per HIV/AIDS death avoided; total lives saved	Table 6, p. 1860	$334,000 per death avoided; 9,421 lives in sample; ~57,000 total through 2018; BCR = 30

Overall (paper’s conclusion). Federal HIV/AIDS funding allocated to cities through Ryan White Title I had large health impacts: reducing HIV/AIDS deaths, new AIDS cases, and HIV prevalence. The funding disparities that emerged from the 1996 Ryan White reauthorization rules, which effectively froze Title I eligibility just as effective treatment arrived, are responsible for a large share of the divergent HIV/AIDS trajectories across US cities. The cost per life saved is low relative to other health programs, which the paper attributes to Ryan White targeting a vulnerable population with a deadly infectious disease for which effective treatment exists.

Theory / model

The paper has no formal economic theory model. The theoretical content consists of the identification framework and an epidemiological susceptible-infected-removed (SIR) model used to analyze HIV transmission dynamics under Title I funding.

HIV dynamics (SIR model, pp. 1871-1872). Let S, I, and R denote the susceptible, infected, and removed (deceased) populations in a city. Absent Title I, HIV-positive individuals transmit at rate trans and die from HIV/AIDS at rate death. Title I changes transmission by factor p and death rates by factor q (q < 0 since Title I reduces deaths). The annual transitions are:

$$S_{t+1} = S_t - (1+p)\times \text{trans}\times I_t\times S_t \tag{SIR-S}$$ $$I_{t+1} = I_t + (1+p)\times \text{trans}\times I_t\times S_t - (1+q)\times \text{death}\times I_t \tag{SIR-I}$$ $$R_{t+1} = (1+q)\times \text{death}\times I_t \tag{SIR-R}$$

The number of people living with HIV after t periods of Title I status is (equation 3, p. 1871):

$$I_t = I_0\times\prod_{j=1}^{t}\left[1 + (1+p)\times\text{trans}\times S\!\left(t;\,I_0,S_0,\text{trans},p,\text{death},q\right) - (1+q)\times\text{death}\right] \tag{3}$$

Taking the log difference between Title I and non-Title I cities gives the main identifying expression (equation 4, p. 1872):

$$\gamma_t = \log\!\left(I_t^{\text{Title1}}\right) - \log\!\left(I_t^{\text{NoTitle1}}\right) = \sum_{j=1}^{t}\log\!\left\{\frac{1 + (1+p)\times\text{trans}\times S(\cdot;\,p,q) - (1+q)\times\text{death}}{1 + \text{trans}\times S(\cdot;\,p{=}0,q{=}0) - \text{death}}\right\} \tag{4}$$

Because Title I reduces death rates (q < 0), and because the paper documents a reduction in people living with HIV in 2008 (Table 9, R4), equation (4) implies that p < 0 as well: Title I must have reduced HIV transmission rates, not only death rates. This rules out the scenario proposed by Lakdawalla, Sood, and Goldman (2006), in which providing treatment to HIV-positive people increases HIV spread through behavioral responses.

Identification logic. The identifying assumption is parallel trends: absent Title I, cities that qualified under the original rules would have trended similarly in HIV/AIDS outcomes to cities that fell just below the 2,000-case threshold. Event-study plots (Figure 3) confirm that treatment and control cities tracked each other in log HIV/AIDS death rates before treatment cities gained Title I status. Robustness to Callaway and Sant’Anna (2021) reweighting methods, matching on 1995 AIDS rates or population, and state-by-year fixed effects (Table 3) supports the identifying assumption.

Method

The main estimating strategy is a staggered difference-in-differences using variation arising from three features of the Ryan White CARE Act. First, the original 1990 legislation granted Title I status to any city reporting at least 2,000 cumulative AIDS cases to the CDC by March 31 of a given year. Second, Title I status, once obtained, was not lost even if a city’s AIDS burden fell below the threshold. Third, a 1996 reauthorization changed eligibility from a cumulative to a five-year rolling count, but included a grandfather clause allowing cities that had qualified by March 31, 1995 to retain Title I status regardless. Since effective antiretroviral treatment emerged in 1996, the combined rule change effectively froze Title I eligibility for the next decade, creating persistent large funding differences between cities just above and just below the 2,000-case threshold (treatment cities averaged $68.9 million in Title I funds from 1996 to 2006; control cities averaged $3.9 million).

This builds on difference-in-differences for the main estimates (equation 1), panel-regression with two-way fixed effects for the within-city-year estimator, matching for robustness checks that pair each treated city with control cities having similar baseline AIDS rates or AIDS trends (equation 2), and regression-discontinuity-design for the HIV stock and transmission analysis (equation 5).

For the regression discontinuity, the running variable is the log of cumulative AIDS cases by March 31, 1995. The 2,000-case threshold is credible because cities could not manipulate their AIDS case counts ex ante (a McCrary density test fails to reject smoothness at the cutoff; p-value 0.37), and the significance of crossing 2,000 cases by March 31, 1995 only became clear after the 1996 rule change and treatment emergence.

Empirical specifications

Main DiD specification (equation 1, p. 1839).

$$y_{jt} = \gamma_j + \delta_t + \mathbf{X}_{jt}\alpha_t + \text{Title1}_{jt}\,\beta + \varepsilon_{jt} \tag{1}$$

where j indexes cities, t indexes years; $y_{jt}$ is the log of HIV/AIDS deaths per 100,000 people (or log AIDS cases, or other health outcomes); $\gamma_j$ are city fixed effects; $\delta_t$ are fiscal-year fixed effects; $\mathbf{X}_{jt}$ is a vector of demographic controls (shares male, younger than 18, older than 64, Black, Hispanic) with coefficients $\alpha_t$ allowed to vary by year; and $\text{Title1}_{jt}$ is an indicator equal to one for city j having qualified for Title I status under the original Ryan White rules by year t. Standard errors are clustered by city. The coefficient $\beta$ is the average causal effect of Title I status on the outcome.

The baseline sample is 50 cities (25 treatment, 25 control), yielding 950 observations for the 1988-2006 window and 1,550 for 1988-2018. All city-year observations for death rates come from restricted-use Vital Statistics data. Alternative samples expanding to all AIDS Public Information Dataset cities confirm results (Table 3, cols 9-10).

Matching robustness (equation 2, p. 1856). For each treated city, control cities are selected by nearest-neighbor matching on 1995 AIDS rates per 100,000, 1995 population, or 1990-to-1991 changes in HIV/AIDS death rates or AIDS cases, creating matched groups g:

$$y_{gjt} = \gamma_j + \delta_{gt} + \mathbf{X}_{jt}\alpha_t + \text{Title1}_{jt}\,\beta + \varepsilon_{gjt} \tag{2}$$

where $\delta_{gt}$ are group-by-year fixed effects; identification comes entirely from within-matched-group variation in Title I status. Results are robust across all four matching approaches (Table 5).

HIV stock regression discontinuity (equation 5, p. 1874). For the cross-sectional analysis of 2008 HIV outcomes:

$$\log(\text{Num\_HIV}_{j,2008}) = \lambda + f(\text{AIDS\_Cases}_{j,1995}) + \text{Title1}_j\,\gamma + \eta_j \tag{5}$$

where f is a linear polynomial in the log of AIDS cases ever reported by March 31, 1995, fit separately on each side of the 2,000-case cutoff; $\text{Title1}_j$ is an indicator for cities above the threshold; and robust standard errors are used. The baseline specification uses 46 cities (all main-sample cities with nonmissing 2008 HIV data); local linear regression with the Calonico, Cattaneo, and Titiunik (2014) optimal bandwidth uses 15 cities on each side (Table 9).

Decomposition of Title I’s effect on new HIV transmissions (equation 6, p. 1875). The effect on 2008 new diagnoses decomposes as:

$$\tau = \log\!\left(\text{Num\_Trans}_{2008}^{\text{Title1}}\right) - \log\!\left(\text{Num\_Trans}_{2008}^{\text{NoTitle1}}\right) = \underbrace{\log(1+p)}_{1} + \underbrace{\hat{\gamma}}_{2} + \underbrace{\log\!\left[\frac{S(t{=}2008;\ldots;\,p,q)}{S(t{=}2008;\ldots;\,p{=}0,q{=}0)}\right]}_{3} \tag{6}$$

Term 1 is the direct effect on HIV transmissibility; term 2 is the estimated reduction in HIV-positive people (from Table 9, Panel A); term 3 is the offsetting effect of a larger susceptible population (fewer past infections means more people at risk). Estimates from Table 9 indicate that 81 to 85 percent of the reduction in new diagnoses in 2008 is accounted for by term 2 alone (fewer people living with HIV), with term 3 partially offsetting.

Datasets used

Dataset	Role in paper	Wiki page
Vital Statistics Multiple Cause of Death Files (restricted-use)	Annual HIV/AIDS death rates per 100,000 people for all US civilians, 1988-2018; primary outcome (R1, R2)	No page yet
AIDS Public Information Dataset (CDC)	Annual cumulative and annual AIDS cases by city, 1988-2002; used to determine Title I eligibility and as an outcome (R3)	No page yet
CDC HIV surveillance data (special request)	City-level HIV diagnoses and HIV prevalence in 2008 (46 cities); used for RDD analysis (R4, R5)	No page yet
SEER population data	Annual city populations and demographic denominators, 1988-2018; used to convert counts to rates	No page yet
Ryan White Title I funding (assembled)	Annual city-level Title I allocations 1991-2018, assembled from GAO reports, HRSA releases, and federal grant databases; used to construct funding treatment variable and cost-per-life estimate (R6)	No page yet

Sample: 50 US cities, annual data 1988-2018 (1,550 city-year observations for main sample). Treatment cities received on average $68.9 million in Title I funds from 1996 to 2006; control cities received on average $3.9 million over the same period (Table 1, p. 1844).

When to read the full paper

Read the original if you are: estimating causal effects of place-based health funding programs; studying the determinants of HIV/AIDS disparities across US cities; evaluating the efficiency of federal public health spending relative to Medicaid or community health centers (Bailey and Goodman-Bacon (2015) comparison, pp. 1861-1862); or assessing whether “treatment as prevention” works in a real-world public health setting. The regression discontinuity design for HIV stock (Table 9 and Figure 12, pp. 1872-1875) provides especially clean quasi-experimental evidence on spillover effects of HIV treatment on HIV transmission, directly bearing on the debate opened by Miller, Johnson, and Wherry (2021) and others on optimal public health targeting.

Attribution and rights

Source: peer-reviewed, American Economic Review 113(7), July 2023. This distillation was extracted by an LLM (claude-sonnet-4-6) on 2026-06-25 and is not human-verified or independently reproduced. Replication data are available at openICPSR doi:10.3886/E184821V1. The paper is freely accessible at pubs.aeaweb.org under AEA standard terms (no CC; redistribution restricted to extract-only).

Dillender, Marcus. “Evidence and Lessons on the Health Impacts of Public Health Funding from the Fight against HIV/AIDS.” American Economic Review 113, no. 7 (July 2023): 1825-1887. DOI: 10.1257/aer.20220089.