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"Ludwig, Jens"
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HUMAN DECISIONS AND MACHINE PREDICTIONS
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-learning application. Yet comparing the algorithm to judges proves complicated. First, the available data are generated by prior judge decisions. We only observe crime outcomes for released defendants, not for those judges detained. This makes it hard to evaluate counterfactual decision rules based on algorithmic predictions. Second, judges may have a broader set of preferences than the variable the algorithm predicts; for instance, judges may care specifically about violent crimes or about racial inequities. We deal with these problems using different econometric strategies, such as quasi-random assignment of cases to judges. Even accounting for these concerns, our results suggest potentially large welfare gains: one policy simulation shows crime reductions up to 24.7% with no change in jailing rates, or jailing rate reductions up to 41.9% with no increase in crime rates. Moreover, all categories of crime, including violent crimes, show reductions; these gains can be achieved while simultaneously reducing racial disparities. These results suggest that while machine learning can be valuable, realizing this value requires integrating these tools into an economic framework: being clear about the link between predictions and decisions; specifying the scope of payoff functions; and constructing unbiased decision counterfactuals.
Journal Article
Prediction Policy Problems
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 than minimizing prediction error. We argue that new developments in the field of “machine learning” are particularly useful for addressing these prediction problems. We use an example from health policy to illustrate the large potential social welfare gains from improved prediction.
Journal Article
Neighborhood Effects on the Long-Term Well-Being of Low-Income Adults
Nearly 9 million Americans live in extreme-poverty neighborhoods, places that also tend to be racially segregated and dangerous. Yet, the effects on the well-being of residents of moving out of such communities into less distressed areas remain uncertain. Using data from Moving to Opportunity, a unique randomized housing mobility experiment, we found that moving from a high-poverty to lower-poverty neighborhood leads to long-term (10- to 15-year) improvements in adult physical and mental health and subjective well-being, despite not affecting economic self-sufficiency. A 1—standard deviation decline in neighborhood poverty (13 percentage points) increases subjective well-being by an amount equal to the gap in subjective well-being between people whose annual incomes differ by $13,000—a large amount given that the average control group income is $20,000. Subjective well-being is more strongly affected by changes in neighborhood economic disadvantage than racial segregation, which is important because racial segregation has been declining since 1970, but income segregation has been increasing.
Journal Article
Long-Term Neighborhood Effects on Low-Income Families: Evidence from Moving to Opportunity
We examine long-term neighborhood effects on low-income families using data from the Moving to Opportunity (MTO) randomized housing-mobility experiment. This experiment offered to some public-housing families but not to others the chance to move to less-disadvantaged neighborhoods. We show that ten to 15 years after baseline, MTO: (i) improves adult physical and mental health; (ii) has no detectable effect on economic outcomes or youth schooling or physical health; and (iii) has mixed results by gender on other youth outcomes, with girls doing better on some measures and boys doing worse. Despite the somewhat mixed pattern of impacts on traditional behavioral outcomes, MTO moves substantially improve adult subjective well-being.
Journal Article
Mechanism Experiments and Policy Evaluations
Randomized controlled trials are increasingly used to evaluate policies. How can we make these experiments as useful as possible for policy purposes? We argue greater use should be made of experiments that identify the behavioral mechanisms that are central to clearly specified policy questions, what we call “mechanism experiments.” These types of experiments can be of great policy value even if the intervention that is tested (or its setting) does not correspond exactly to any realistic policy option.
Journal Article
Fragile Algorithms and Fallible Decision-Makers
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 them.
Journal Article
Human bias in algorithm design
Algorithms are designed to learn user preferences by observing user behaviour. This causes algorithms to fail to reflect user preferences when psychological biases affect user decision making. For algorithms to enhance social welfare, algorithm design needs to be psychologically informed.
Journal Article
Neighborhood effects on use of African-American Vernacular English
African-American Vernacular English (AAVE) is systematic, rooted in history, and important as an identity marker and expressive resource for its speakers. In these respects, it resembles other vernacular or nonstandard varieties, like Cockney or Appalachian English. But like them, AAVE can trigger discrimination in the workplace, housing market, and schools. Understanding what shapes the relative use of AAVE vs. Standard American English (SAE) is important for policy and scientific reasons. This work presents, to our knowledge, the first experimental estimates of the effects of moving into lower-poverty neighborhoods on AAVE use. We use data on non-Hispanic African-American youth (n= 629) from a large-scale, randomized residential mobility experiment called Moving to Opportunity (MTO), which enrolled a sample of mostly minority families originally living in distressed public housing. Audio recordings of the youth were transcribed and coded for the use of five grammatical and five phonological AAVE features to construct a measure of the proportion of possible instances, or tokens, in which speakers use AAVE rather than SAE speech features. Random assignment to receive a housing voucher to move into a lower-poverty area (the intention-to-treat effect) led youth to live in neighborhoods (census tracts) with an 11 percentage point lower poverty rate on average over the next 10–15 y and reduced the share of AAVE tokens by ∼3 percentage points compared with the MTO control group youth. The MTO effect on AAVE use equals approximately half of the difference in AAVE frequency observed between youth whose parents have a high school diploma and those whose parents do not.
Journal Article