Jens Ludwig's research while affiliated with University of Chicago and other places
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Publications (41)
Improving academic outcomes for economically disadvantaged students has proven challenging, particularly for children at older ages. We present two large-scale randomized controlled trials of a high-dosage tutoring program delivered to secondary school students in Chicago. One innovation is to use paraprofessional tutors to hold down cost, thereby...
Significance
Encouraging vaccination is a pressing policy problem. Our megastudy with 689,693 Walmart pharmacy customers demonstrates that text-based reminders can encourage pharmacy vaccination and establishes what kinds of messages work best. We tested 22 different text reminders using a variety of different behavioral science principles to nudge...
Policy-makers are increasingly turning to behavioural science for insights about how to improve citizens’ decisions and outcomes¹. Typically, different scientists test different intervention ideas in different samples using different outcomes over different time intervals². The lack of comparability of such individual investigations limits their po...
Algorithms (in some form) are already widely used in the criminal justice system. We draw lessons from this experience for what is to come for the rest of society as machine learning diffuses. We find economists and other social scientists have a key role to play in shaping the impact of algorithms, in part through improving the tools used to build...
In response to budget problems, many urban school systems reduced resources for getting students to come to school, such as truancy officers. Chicago, for instance, in 1991, went from 150 truancy officers down to a total of zero. Is that a good idea? In this study, we explore the effects of increased support by a pro‐social adult, or “social capita...
Preventing discrimination requires that we have means of detecting it, and this can be enormously difficult when human beings are making the underlying decisions. As applied today, algorithms can increase the risk of discrimination. But as we argue here, algorithms by their nature require a far greater level of specificity than is usually possible...
There are widespread concerns that the growing use of machine learning algorithms in important decisions may reproduce and reinforce existing discrimination against legally protected groups. Most of the attention to date on issues of “algorithmic bias” or “algorithmic fairness” has come from computer scientists and machine learning researchers. We...
This article reviews current federal housing assistance policies and briefly summarizes research evidence about the efficacy of the different programs. We identify three key challenges that these programs face in meeting their stated objectives and suggest strategies for addressing them. The first challenge is the large variation in market conditio...
Concerns about the dissemination of spurious results have led to calls for pre-analysis plans (PAPs) to avoid ex-post “p-hacking.” But often the conceptual hypotheses being tested do not imply the level of specificity required for a PAP. In this paper we suggest a framework for PAPs that capitalize on the availability of causal machine-learning (ML...
The law forbids discrimination. But the ambiguity of human decision-making often makes it extraordinarily hard for the legal system to know whether anyone has actually discriminated. To understand how algorithms affect discrimination, we must therefore also understand how they affect the problem of detecting discrimination. By one measure, algorith...
We respond to the new article by Hayo, Neumeier, and Westphal (HNW), which is a critique of our 2006 article. The principal contribution of that article was to use a greatly improved proxy for gun prevalence to estimate the effect of gun prevalence on homicide rates. While the best available, our proxy, the ratio of firearms suicides to total suici...
The law forbids discrimination. But the ambiguity of human decision-making often makes it hard for the legal system to know whether anyone has discriminated. To understand how algorithms affect discrimination, we must understand how they affect the detection of discrimination. With the appropriate requirements in place, algorithms create the potent...
Concerns that algorithms may discriminate against certain groups have led to numerous efforts to 'blind' the algorithm to race. We argue that this intuitive perspective is misleading and may do harm. Our primary result is exceedingly simple, yet often overlooked. A preference for fairness should not change the choice of estimator. Equity preference...
Can machine learning improve human decision making? Bail decisions provide a good test case. Millions of times each year, judges make jail-or-release decisions that hinge on a prediction of what a defendant would do if released. The concreteness of the prediction task combined with the volume of data available makes this a promising machine-learnin...
Evaluating whether machines improve on human performance is one of the central questions of machine learning. However, there are many domains where the data is selectively labeled, in the sense that the observed outcomes are themselves a consequence of the existing choices of the human decision-makers. For instance, in the context of judicial bail...
A core challenge in the analysis of experimental data is that the impact of some intervention is often not entirely captured by a single, well-defined outcome. Instead there may be a large number of outcome variables that are potentially affected and of interest. In this paper, we propose a data-driven approach rooted in machine learning to the pro...
We present the results of three large-scale randomized controlled trials (RCTs) carried out in Chicago, testing interventions to reduce crime and dropout by changing the decision-making of economically disadvantaged youth. We study a program called Becoming a Man (BAM), developed by the non-profit Youth Guidance, in two RCTs implemented in 2009–10...
Policymakers and researchers are increasingly interested in using experimental methods to inform the design of social policy. The most common approach, at least in developed countries, is to carry out large-scale randomized trials of the policies of interest, or what we call here policy evaluations. In this chapter, we argue that in some circumstan...
Economists have become increasingly interested in studying the nature of production functions in social policy applications, with the goal of improving productivity. Traditionally models have assumed workers are homogenous inputs. However, in practice, substantial variability in productivity means the marginal productivity of labor depends substant...
The standard economic view suggests that people will commit an action if its expected benefits outweigh its costs. But before people weigh the costs and benefits of an action, what affects whether they think of the action in the first place? We argue that actions are more likely to enter into consideration when they are cognitively accessible. We d...
Most empirical policy work focuses on causal inference. We argue an important class of policy problems does not require causal inference but instead requires predictive inference. Solving these “prediction policy problems” requires more than simple regression techniques, since these are tuned to generating unbiased estimates of coefficients rather...
The evidence base for the study of guns and violence begins with data on such fundamental issues as the number and distribution of guns, the number of people shot each year in criminal assaults, and the frequency of gun use in self-defense. It seems that these simple descriptive statistics should be readily available, and in fact the rhetoric of th...
In this Article, we seek to help guide law enforcement activities targeting gun acquisition by high-risk people by examining two potentially important sources of crime guns: licensed retail dealers and traffickers. Limited data availability is a key reason more is not currently known about how criminals obtain guns. This Article assembles a unique...
The United States government devotes about $40 billion each year to means-tested housing programs, plus another $6 billion or so in tax expenditures on the Low Income Housing Tax Credit (LIHTC). What exactly do we spend this money on, why, and what does it accomplish? We focus on these questions. We begin by reviewing the history of low-income hous...
Federal firearms law divides the population into two groups: those prohibited from legally possessing a firearm due to their criminal record or certain other disqualifying conditions and everyone else. The vast majority of the adult public is allowed to acquire and possess all the firearms they want, thus preserving the personal right to "keep and...
Until now researchers have assessed the burden imposed by gunshot injuries and deaths in terms of medical costs and lost productivity. Here, the chapters widen the lens, developing a framework to calculate the full costs borne by Americans in a society where both gun violence and its ever-present threat mandate responses that touch every aspect of...
This article begins by characterizing the nature and scope of the gun violence problem, including a discussion of the potential benefit from use of guns in self-defense. The next section is devoted to a discussion of guns and it gives some basic facts on the patterns of private gun ownership and gun misuse. The article then discusses policies desig...
Citations
... At the same time, students do not learn from these technologies if they do not use them. Recent research providing human tutoring to increase student motivation to engage in difficult deliberate practice opportunities suggests promise in reducing achievement gaps by reducing opportunity gaps (Guryan et al., 2021;Chine et al., 2022). ...
... One estimate suggests that reducing incarceration rates from 2009 to 1984 levels, and investing the resulting savings in an increased police presence, would lead to a net decline in violent crime nationally of about 130,000 incidents per annum. 279 Therefore, even if racial minorities benefit from the public safety produced by the criminal justice system, it is at a highly disproportionate and unnecessary direct cost. ...
... 19 To generate findings relevant to the health care sector, we considered associations of health outcomes with (1) health system-partnered, structural primary prevention to promote housing affordability and stability as contextual conditions associated with the distribution of population risk 20 and (2) targeted primary prevention to help at-risk households remain stably and affordably housed via short-term (<1 year) and long-term (Ն1 year) interventions. Key intervention examples, informed by the public health and urban planning literature, 11,12,21,22 are defined in Table 1. ...
... GANs improve upon these methods by varying only the selected feature and producing high-quality realistic images; they can also be scaled to a larger set of images at low cost. Another paper that uses GANs for a related purpose is Ludwig and Mullainathan (2022). ...
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... Is this research approach suitable for the pressing question of vaccine hesitancy? After the exercise behavior megastudy, Milkman conducted a study on vaccine hesitancy in 2020 [40]. Studies have shown that only 79% of people who intended to receive the influenza vaccine did so [41]. ...
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... A study found that descriptive norms could increase physical activity among gym-members, but only if the message describes that "an increasing majority" or "minority" of the population exercises frequently. On the other hand, referring to "increasing minority" or "majority" of the population did not have significant effect on the frequency of gym visits (Milkman, Gromet, et al., 2021). These results highlight the necessity to gain further understanding on the factors influencing the effect of norms. ...
... We note that the class of social welfare functions that appear in this paper and [22] are justified axiomatically by Roberts [51]. As a result, recent approaches to fairness in machine learning relying on notions of social welfare [47,48] are closely related to our work. Namely, this paper's approach can be interpreted as a planner aiming to maximize social welfare for a particular class of functions. ...
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... Several of these questions and issues have been addressed in recent rigorous evaluations of other mentoring programs (see, for example, Axford et al., 2021;Guryan et al., 2021;Heppe et al., 2021;King et al., 2021;Millenky et al., 2014), as well as of other BBBS program models/ partnerships (e.g., Herrera et al., 2007;Lindstrom Johnson et al., 2022). However, to date, they have not been fully addressed in evaluations of BBBSA CBM-a mentoring program model with one of the largest reaches in the U.S. ...
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... But controversial applications-as in the criminal justice system to influence parole hearings and sentencing judgments-are being called out and questioned. 4 Further, old-fashioned profit maximization can visibly pollute and corrupt the presumed objectivity of the algorithmic output, as has become the case with Google's PageRank algorithm, a relatively early and triumphant machine learning technique. 5 S uccessive technological revolutions have transformed both the content and management of work before: for example, the mills of the First Industrial Revolution, with workers clocking on and off shifts, and the assembly line of the Second, with the disciplined microfragmentation of tasks. ...
Reference: High-Tech Modernism: Limits & Extensions
... In Finland, the corresponding figure was 8.2% (EUROSTAT, 2021). School dropout may begin in the early years of schooling (Guryan et al., 2021;Reschly, 2020), and truancy-skipping classes or school without a valid excuse (see Gentle-Genitty et al., 2015)-may be a warning sign of school disengagement (Keppens & Spruyt, 2020). Truants have been shown to have a 34.7% higher likelihood of dropping out of secondary education than regular school attendees (Cabus & De Witte, 2015), and having absent peers increases the probability of a student dropping out (De Witte & Csillag, 2014). ...
