Publications

Simone Zhang and Rebecca Johnson. 2023. Hierarchies in the Decentralized Welfare State: Prioritization in the Housing Choice Voucher Program. American Sociological Review 88(1), 114–153.

  • Description: Social provision in the United States is highly decentralized. Significant federal and state funding flows to local organizational actors, who are granted discretion over how to allocate resources to people in need. In welfare states where many programs are underfunded and decoupled from local need, how does decentralization shape who gets what? This article identifies forces that shape how local actors classify help seekers when they ration scarce resources, focusing on the case of prioritization in the Housing Choice Voucher Program. We use network methods to represent and analyze 1,398 local prioritization policies. Our results reveal two patterns that challenge expectations from past literature. First, we observe classificatory restraint, or many organizations choosing not to draw fine distinctions between applicants to prioritize. Second, when organizations do institute priority categories, policies often advantage applicants who are formally institutionally connected to the local community. Interviews with officials in turn reveal how prioritization schemes reflect housing authorities’ position within a matrix of intra-organizational, inter-organizational, and vertical forces that structure the meaning and cost of classifying help seekers. These findings reveal how local organizations’ use of classification to solve on-the-ground organizational problems and manage scarce resources can generate additional forms of exclusion.
    [Paper] [Online Supplement] [Replication Package]

    Rebecca A. Johnson and Simone Zhang. 2022. “What is the Bureaucratic Counterfactual? Categorical versus Algorithmic Prioritization in U.S. Social Policy.” In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22), June 21–24, 2022, Seoul, Republic of Korea.

    • Description: There is growing concern about governments’ use of algorithms to make high-stakes decisions. While an early wave of research focused on algorithms that predict risk to allocate punishment and suspicion, a newer wave of research studies algorithms that predict “need” or “benefit” to target beneficial resources, such as ranking those experiencing homelessness by their need for housing. The present paper argues that existing research on the role of algorithms in social policy could benefit from a counterfactual perspective that asks: given that a social service bureaucracy needs to make some decision about whom to help, what status quo prioritization method would algorithms replace? While a large body of research contrasts human versus algorithmic decision-making, social service bureaucracies target help not by giving street-level bureaucrats full discretion. Instead, they primarily target help through pre-algorithmic, rule-based methods. In this paper, we outline social policy’s current status quo method—categorical prioritization—where decision-makers manually (1) decide which attributes of help seekers should give those help seekers priority, (2) simplify any continuous measures of need into categories (e.g., household income falls below a threshold), and (3) manually choose the decision rules that map categories to priority levels. We draw on novel data and quantitative and qualitative social science methods to outline categorical prioritization in two case studies of United States social policy: waitlists for scarce housing vouchers and K-12 school finance formulas. We outline three main differences between categorical and algorithmic prioritization: is the basis for prioritization formalized; what role does power play in prioritization; and are decision rules for priority manually chosen or inductively derived from a predictive model. Concluding, we show how the counterfactual perspective underscores both the understudied costs of categorical prioritization in social policy and the understudied potential of predictive algorithms to narrow inequalities. [Paper]

    Simone Zhang, Rebecca A. Johnson, John Novembre, Edward Freeland, and Dalton Conley. 2021. Public attitudes toward genetic risk scoring in medicine and beyond. Social Science and Medicine 274:113796.

    • Description: Advances in genomics research have led to the development of polygenic risk scores, which numerically summarize genetic predispositions for a wide array of human outcomes. Initially developed to characterize disease risk, polygenic risk scores can now be calculated for many non-disease traits and social outcomes, with the potential to be used not only in health care but also other institutional domains. In this study, we draw on a nationally-representative survey of U.S. adults to examine three sets of lay attitudes toward the deployment of genetic risk scores in a variety of medical and non-medical domains: 1. abstract belief about whether people should be judged on the basis of genetic predispositions; 2. concrete attitudes about whether various institutions should be permitted to use genetic information; and 3. personal willingness to provide genetic information to various institutions. Results demonstrate two striking differences across these three sets of attitudes. First, despite almost universal agreement that people should not be judged based on genetics, there is support, albeit varied, for institutions being permitted to use genetic information, with support highest for disease outcomes and in reproductive decision-making. We further find significant variation in personal willingness to provide such information, with a majority of respondents expressing willingness to provide information to health care providers and relative finder services, but less than a quarter expressing willingness to do so for an array of other institutions and services. Second, while there are no demographic differences in respondents’ abstract beliefs about judging based on genetics, demographic differences emerge in permissibility ratings and personal willingness. Our results should inform debates about the deployment of polygenic scores in domains within and beyond medicine. [Paper] [Online Supplement] [Replication Package]

    Conley, Dalton and Simone Zhang. 2018. “The promise of genes for understanding cause and effect.” Proceedings of the National Academy of Sciences 115(22): 56265628.

    • Description: This article discusses methodological challenges associated with causal inference using genetic instrumental variables. [Paper]

    Work in Progress

    Pretrial Risk Assessment Algorithms in the Courtroom

    • Description: This project investigates how algorithmic risk assessments used in criminal courts shape decision outcomes, courtroom debate, and the claims that judges, prosecutors, and defense attorneys advance about defendants. Focusing on a tool intended to help judges set pretrial release conditions, I build on a pre-existing randomized controlled trial in a U.S. county that randomized whether the risk assessment report for a given arrested individual was provided to the court or withheld. Using a mix of qualitative and quantitative methods, I analyze administrative data and a sample of court hearings transcripts from the county. Part I explores the effect of access to risk assessment reports on how often judges order cash bail, with a focus on how the information influences whether prosecutors and defense attorneys secure their requested bail conditions. The results indicate that risk assessments can have an asymmetric effect, with recommendations for cash bail exerting more influence than recommendations to not require cash bail. Part II identifies two additional ways that the risk assessment tool altered the behavior of judges: how often they required that defendants be subject to pretrial supervision and how they spoke to defendants. Part III tests theoretical predictions that risk assessments might affect how defendants are evaluated and shape courtroom actors’ understandings of the goals of their decision-making. It demonstrates that risk assessment tools may not lead courtroom actors to fundamentally reorient their approach to evaluating defendants, but may spur shifts in some specific domains of their practices. Some of these shifts may be regarded as counterproductive, while others may help align practices with legal ideals. Together, this project contributes to our understanding of the on-the-ground impact of algorithmic decision aids and highlights some of the trade-offs that the adoption of such tools may entail.

    Optimizing Schools? Public Perceptions of Algorithmic versus Status Quo Prioritization in K-12 Schooling (with Rebecca Johnson)

    • Description: A growing body of research investigates algorithmic justice in domains like lending, hiring, and criminal justice. Less examined is the use of predictive algorithms in K-12 schools, particularly those used to target scarce supportive resources. This project focuses on the case of school districts making decisions about which students to prioritize for intensive tutoring in the wake of the COVID-19 pandemic. The project asks: how do two sets of stakeholders—current parents of K-12 students; the general public—view the procedural justice of using predictive models to decide which students receive help? We present quantitative and qualitative results from a nationally-representative survey experiment (N ~ 5,400), which contrasts perceptions of predictive models as a method to allocate scarce resources with perceptions of other longstanding methods, such as parent requests, lotteries, counselor discretion, and bureaucratic rules.

    Why’s the Power Out? Organizational Responsiveness to Everyday Resident Requests, Questions, and Complaints

    • Description: Many everyday services are increasingly structured by resident-initiated requests and complaints. Concerns that this development might exacerbate urban inequality has prompted research on where service requests originate. Less well understood is whether local service providers vary in their responsiveness to different requests. In this paper, I address two key questions: to whom are local service providers responsive? And what styles of claims-making are more likely to elicit responses? I analyze the case of public client service interactions on Twitter between residents and local governments, electric utilities, broadband Internet service providers, and cell phone service providers. Using supervised machine learning and matching, I find that these public interactions between residents and service providers do not replicate patterns of bias based on demographic cues commonly observed in other settings. They may, however, disadvantage groups that are more collectively under-served as organizations are more responsive to claims framed in terms of individualized needs and preferences, rather than collective ones.

    How Local Discretion Shapes Racial and Gender Inequality: The Case of Small Business Relief Funding (with Elizabeth Bell, Heather Kappes, Crystal C. Hall, Rebecca Johnson, and Miles Williams)

    • Description: Organizations that distribute limited resources, such as government agencies and non-profit organizations, face conflicts over how to select and evaluate recipients. Questions of fair selection were salient as local economic development organizations allocated COVID-19 relief to U.S. small businesses. We leverage observational data, simulations, and quantitative and qualitative survey responses to better understand how local discretion affected inequality in access to relief. Qualitative coding of selection procedures (Study 1) showed that local organizations varied in who they defined as the beneficiary of relief and the criteria they used to prioritize recipients. To test how selection procedures affected access to help, we used real application data from three cities (Study 2). Simulations showed that when selection procedures used economic criteria to give higher priority to “meritorious” businesses, women- and minority-owned businesses were disadvantaged. However, selection procedures that give a demographic plus factor to these businesses correct for these inequalities. Finally, we used a vignette-based experiment to understand whether members of the public perceive these inequalities as fair, finding strong political polarization (Study 3). Together, these studies show how and why local discretion over scarce relief can contribute to racial, ethnic, and gender disparities in access to resources.