Analyzing mathematics and reading achievement outcomes from a district-level random assignment study fielded in over 500 schools within 59 school districts and seven states, the authors estimate the 1-year impacts of a data-driven reform initiative implemented by the Johns Hopkins Center for Data-Driven Reform in Education (CDDRE). CDDRE consultants work with districts to implement quarterly student benchmark assessments and provide district and school leaders with extensive training on interpreting and using the data to guide reform. Relative to a control condition, in which districts operated as usual without CDDRE services, the data-driven reform initiative caused statistically significant districtwide improvements in student mathematics achievement. The CDDRE intervention also had a positive effect on reading achievement, but the estimates fell short of conventional levels of statistical significance.

Theory and conventional wisdom suggest that errors undermine the credibility of tornado warning systems and thus decrease the probability that individuals will comply (i.e., engage in protective action) when future warnings are issued. Unfortunately, empirical research on the influence of warning system accuracy on public responses to tornado warnings is incomplete and inconclusive. This study adds to existing research by analyzing two sets of relationships. First, we assess the relationship between perceptions of accuracy, credibility, and warning response. Using data collected via a large regional survey, we find that trust in the National Weather Service (NWS; the agency responsible for issuing tornado warnings) increases the likelihood that an individual will opt for protective action when responding to a hypothetical warning. More importantly, we find that subjective perceptions of warning system accuracy are, as theory suggests, systematically related to trust in the NWS and (by extension) stated responses to future warnings. The second half of the study matches survey data against NWS warning and event archives to investigate a critical follow-up question--Why do some people perceive that their warning system is accurate, whereas others perceive that their system is error prone? We find that subjective perceptions are--in part-a function of objective experience, knowledge, and demographic characteristics. When considered in tandem, these findings support the proposition that errors influence perceptions about the accuracy of warning systems, which in turn impact the credibility that people assign to information provided by systems and, ultimately, public decisions about how to respond when warnings are issued.

The recent increase in the number of students classified as English language learners (ELLs) has focused significant attention on reclassification policy, which governs the process by which ELLs move toward, and are deemed to reach, full English proficiency. In this paper, we draw on a data set containing annual individual‐level records for every Wisconsin student ever classified as an ELL between the 2006–07 and 2012–13 school years to estimate the effects of being reclassified at the end of 10th grade—a crucial period on the pathway to postsecondary education—on several measures related to students’ postsecondary attainments. We estimate these effects in a regression discontinuity framework, exploiting Wisconsin's policy rule that automatically reclassifies ELLs who score above a specified cutoff on the state's English language proficiency exam. Our analysis indicates that being reclassified as fully English proficient in 10th grade has a positive effect on students’ ACT scores. It also provides some evidence of a positive effect on high school graduation and the probability of enrolling in a postsecondary institution the fall after graduation. Together, our analyses provide evidence on the effects of a policy directly relevant to the country's fastest growing student population, and we close the paper with a discussion of the implications for research and policy.

Effective communication about severe weather requires that providers of weather information disseminate accurate and timely messages and that the intended recipients (i.e., the population at risk) receive and react to these messages. This article contributes to extant research on the second half of this equation by introducing a “real time” measure of public attention to severe weather risk communication based on the growing stream of data that individuals publish on social media platforms, in this case, Twitter. The authors develop a metric that tracks temporal fluctuations in tornado-related Twitter activity between 25 April 2012 and 11 November 2012 and assess the validity of the metric by systematically comparing fluctuations in Twitter activity to the issuance of tornado watches and warnings, which represent basic but important forms of communication designed to elicit, and therefore correlate with, public attention. The assessment finds that the measure demonstrates a high degree of convergent validity, suggesting that social media data can be used to advance our understanding of the relationship between risk communication, attention, and public reactions to severe weather.

In this paper we estimate the effect of housing voucher receipt on the composition of recipient households and the quality of the neighborhoods in which recipient households reside. Drawing on a dataset that contains extensive information on a large and diverse panel of low-income families for up to five years following voucher receipt, we isolate the effects of voucher receipt using propensity score matching techniques together with regression adjustment. Full-sample results show voucher receipt to have little effect on neighborhood quality in the short-term, but some positive long-term effects. We also find that voucher receipt is tied to a higher probability of change in household composition in the year of voucher receipt, but greater stability in subsequent years. Our large sample allows us to explore differential responses of geographic and socioeconomic subgroups. Our findings have several implications for both research and policy. JEL Classifications: I30, I38 Keywords: housing vouchers; neighborhood quality; household composition; matching Notes and acknowledgments: The research presented in this paper was generously supported by a grant from the John D. and Catherine T. MacArthur Foundation. We gratefully acknowledge that support. We would like to thank Larry Buron, Carolyn Heinrich, Jeffrey Kling, John Mullahy, Gary Painter, and Jim Riccio for helpful comments on this research. We would also like to thank Dan Ross for his work in securing, cleaning, and organizing the data, Deborah Johnson for editorial assistance and referees of this journal for very helpful suggestions.2