In a field experiment, we provide financial incentives to teachers framed either as gains, received at the end of the year, or as losses, in which teachers receive upfront bonuses that must be paid back if their students do not improve sufficiently. Pooling two waves of the experiment, loss-framed incentives improve math achievement by an estimated 0.124 standard deviations (σ) with large effects in the first wave and no effects in the second wave. Effects for gain framed incentives are smaller and not statistically significant, approximately 0.051σ. We find suggestive evidence that effects on teacher value added persist post-treatment.
We introduce a model of two-sided statistical discrimination in which worker and firm beliefs are complementary. Firms try to infer whether workers have made investments required for them to be productive, and simultaneously, workers try to deduce whether firms have made investments necessary for them to thrive. When multiple equilibria exist, group differences are sustained by both sides of the interaction – workers and firms. Strategic complementarity complicates both empirical analysis designed to detect discrimination and policy meant to alleviate it. Affirmative action is much less effective than in traditional statistical discrimination models. More generally, we demonstrate the futility of policies that are designed to correct gender and racial disparities but do not address both sides of the coordination problem. We propose a two-sided version of “investment insurance” – a policy in which the government (after observing a noisy version of the employer’s signal) offers to hire any worker who it believes to be qualified and whom the employers does not offer a job – and show that it (weakly) dominates any alternative. The paper concludes by proposing a way to identify statistical discrimination by employers when beliefs are complements.
This study examines the impact on student achievement of high-dosage reading tutoring for middle school students in New York City Public Schools, using a school-level randomized field experiment. Across three years, schools offered at least 130 hours of 4-on-1 tutoring based on a guided reading model, which consisted of 1-on-1 read alouds, independent reading, vocabulary review, and group discussion. We show that, at the mean, tutoring has a positive and significant effect on school attendance, a positive, but insignificant, effect on English Language Arts (ELA) state test scores and no effect on math state test scores. There is important heterogeneity by race. For black students, our treatment increased attendance by 2.0 percentage points (control mean 92.4 percent) and ELA scores by 0.09 standard deviations per year – two times larger than the effect of the Promise Academy Middle School in the Harlem Children’s Zone and KIPP Charter Middle Schools on reading achievement. For Hispanic students, the treatment effect is 0.8 percentage points on attendance (control mean 92.0 percent) and 0.01 standard deviations per year on ELA scores. We argue that the difference between the effectiveness of tutoring for black and Hispanic students is best explained by the average tutor characteristics at the schools they attend.
This study examines the impact on student achievement of implementing management training for principals in traditional public schools in Houston, Texas, using a school-level randomized field experiment. Across two years, principals were provided 300 hours of training on lesson planning, data-driven instruction, and teacher observation and coaching. The findings show that offering management training to principals significantly increases student achievement in all subjects in year one and has an insignificant effect in year two. We argue that the results in year two are driven by principal turnover, coupled with the cumulative nature of the training. Schools with principals who are predicted to remain in their positions for both years of the experiment demonstrate large treatment effects in both years – particularly those with principals who are also predicted to implement the training with high fidelity – while those with principals that are predicted to leave have statistically insignificant effects in each year of treatment.
We estimate the impact of charter schools on early-life labor market outcomes using administrative data from Texas. We find that, at the mean, charter schools have no impact on test scores and a negative impact on earnings. No Excuses charter schools increase test scores and four-year college enrollment, but have a small and statistically insignificant impact on earnings, while other types of charter schools decrease test scores, four-year college enrollment, and earnings. Moving to school-level estimates, we find that charter schools that decrease test scores also tend to decrease earnings, while charter schools that increase test scores have no discernible impact on earnings. In contrast, high school graduation effects are predictive of earnings effects throughout the distribution of school quality. The paper concludes with a speculative discussion of what might explain our set of facts.
This article describes a randomized field experiment in which parents were provided financial incentives to engage in behaviors designed to increase early childhood cognitive and executive function skills through a parent academy. Parents were rewarded for attendance at early childhood sessions, completing homework assignments with their children, and for their child’s demonstration of mastery on interim assessments. This intervention had large and statistically significant positive impacts on both cognitive and non-cognitive test scores of Hispanics and Whites, but no impact on Blacks. These differential outcomes across races are not attributable to differences in observable characteristics (e.g. family size, mother’s age, mother’s education) or to the intensity of engagement with the program. Children with above median (pre-treatment) non cognitive scores accrue the most benefits from treatment.
This paper describes randomized ﬁeld experiments in eighty-four urban public schools in two cities designed to understand the impact of aligned incentives on student achievement. In Washington DC, incentives were “horizontal” – provided to one agent (students) for various inputs in the education production function (i.e. attendance, behavior, interim assessments,homework, and uniforms). In Houston, TX, incentives were “vertical” – provided to multiple agents (parents, teachers, and students) for a single input (math objectives). On outcomes for which we provided direct incentives, there were large and statistically signiﬁcant effects from both treatments. Horizontal incentives led to increases in math and reading test scores. Vertical incentives increased math achievement, but resulted in decreased reading, science, and social studies test scores. We argue that the data is consistent with agents perceiving academic achievement in various subjects as substitutes, not complements, in education production.
This study examines the impact on student achievement of high-dosage reading tutoring for middle school students in New York City public schools, using a school-level randomized field experiment. Across three years, schools offered at least 130 hours of 4-on-1 tutoring based on a guided reading model. We demonstrate that, at the mean, tutoring has a positive and significant effect on school attendance, a positive, but insignificant, effect on English Language Arts (ELA) state test scores and no effect on math state test scores. There is important heterogeneity by race. For black students, our treatment increased attendance by 2.0 percentage points (control mean 92.4 percent) and ELA scores by 0.09 standard deviations per year – two times larger than the effect of KIPP Charter Middle Schools on reading achievement. We argue that the increased effectiveness of tutoring for black students is best explained by the average tutor characteristics at the schools they attend.
We introduce a model in which agents observe signals about the state of the world, some of which are open to interpretation. Our decision makers first interpret each signal and then form a posterior on the sequence of interpreted signals. This ‘double updating’ leads to confirmation bias and can lead agents who observe the same information to polarize. We explore the model’s predictions in an on-line experiment in which individuals interpret research summaries about climate change and the death penalty. Consistent with the model, there is a significant relationship between an individual’s prior and their interpretation of the summaries; and - even more striking - over half of the subjects exhibit polarizing behavior.
Starting in the 2013-2014 school year, I conducted a randomized field experiment in fortysix traditional public elementary schools in Houston, Texas designed to test the potential productivity benefits of teacher specialization in schools. Treatment schools altered their schedules to have teachers specialize in a subset of subjects in which they have demonstrated relative strength (based on value-add measures and principal observations). The average impact of encouraging schools to specialize their teachers on student achievement is -0.11 standard deviations per year on a combined index of math and reading test scores. Students enrolled in special education and those with less experienced teachers demonstrated marked negative results. I argue that the results are consistent with a model in which the benefits of specialization driven by sorting teachers into a subset of subjects based on comparative advantage is outweighed by inefficient pedagogy due to having fewer interactions with each student, though other mechanisms are possible.
This paper explores racial diﬀerences in police use of force. On non-lethal uses of force, blacks and Hispanics are more than ﬁfty percent more likely to experience some form of force in interactions with police. Adding controls that account for important context and civilian behavior reduces, but cannot fully explain, these disparities. On the most extreme use of force –oﬃcer-involved shootings – we ﬁnd no racial diﬀerences in either the raw data or when contextual factors are taken into account. We argue that the patterns in the data are consistent with a model in which police oﬃcers are utility maximizers, a fraction of which have a preference for discrimination, who incur relatively high expected costs of oﬃcer-involved shootings.
We propose a theory of social interactions based on self-selection and comparative advantage. In our model, students choose peer groups based on their comparative advantage within a social environment. The eﬀect of moving a student into a diﬀerent environment with higher-achieving peers depends on where in the ability distribution she falls and the shadow prices that clear the social market. We show that the model’s key prediction—an individual’s ordinal rank predicts her behavior and test scores—is borne out in one randomized controlled trial in Kenya as well as administrative data from the U.S. To test whether our selection mechanism can explain the eﬀect of rank on outcomes, we conduct an experiment with nearly 600 public school students in Houston. The experimental results suggest that social interactions are mediated by self-selection based on comparative advantage.
We present a two-armed bandit model of decision making under uncertainty where the expected return to investing in the "risky arm" increases when choosing that arm and decreases when choosing the "safe" arm. These dynamics are natural in applications such as human capital development, job search, and occupational choice. Using new insights from stochastic control, along with a monotonicity condition on the payo dynamics, we show that optimal strategies in our model are stopping rules that can be characterized by an index which formally coincides with Gittins' index. Our result implies the indexability of a new class of restless bandit models
Randomized ﬁeld experiments designed to better understand the production of human capital have increased exponentially over the past several decades. This chapter summarizes what we have learned about various partial derivatives of the human capital production function, what important partial derivatives are left to be estimated, and what – together – our collective efforts have taught us about how to produce human capital in developed countries. The chapter concludes with a back of the envelope simulation of how much of the racial wage gap in America might be accounted for if human capital policy focused on best practices gleaned from randomized ﬁeld experiments.
This paper describes a field experiment in Oklahoma City Public Schools in which students were provided with free cellular phones and daily information about the link between human capital and future outcomes via text message in one treatment and minutes to talk and text as an incentive in a second treatment. Students’ reported beliefs about the relationship between education and outcomes were influenced by the information treatment. However, there were no measurable changes in student effort, attendance, suspensions, or state test scores, though there is evidence that scores on college entrance exams four years later increased. The patterns in the data appear most consistent with a model in which students have present-bias or lack knowledge of the educational production function, though other explanations are possible.