This paper studies the effect of school safety on absenteeism and performance in a large school district. Perception of school safety was measured as a part of the Conditions for Learning (CFL) survey that is administered each year. We define a “low safety” treatment as consisting of students whose reported safety scores are in the lowest decile. We combine the survey data with school administrative records and set up a quasi-experimental design to study the causal effects of low safety treatment on absenteeism and performance. Average treatment effect (ATE) of low safety is +2.20 days on absenteeism and -0.18 points on GPA. We extend this analysis by fitting causal decision trees proposed by Athey & Imbens (2015) to estimate heterogeneity in the causal effects on several covariates including demographics, other perceived conditions for learning, and neighborhood level characteristics. We find that the effects of low safety on absenteeism are the largest for African American males with a low perception of peers’ Social and Emotional Learning (SEL) competency. Conditional Average Treatment Effect (CATE) of low safety on absenteeism for this group is 11 days and represents a 58% increase at the mean. On the other hand, the negative effects of low safety on performance are largest in low-income neighborhoods. Elementary and Middle school students living in low-income neighborhoods who reported low SEL competency of peers saw a drop of -0.55 points on their GPA due to low safety. This effect corresponds to a decline of 22% at the mean for this subgroup.
With legal frameworks changing, administrative data can increasingly be utilised both for official statistics and to facilitate new research, enabling the development of evidence-based policy for the public benefit. Secure access conditions generally apply to using these rich, highly detailed data. However, using data from various sources is difficult when they are fragmented in “silos” between several Research Data Centres (RDCs) as can happen at a national level, and is very likely to be the case at an international level. This is a major obstacle for international comparative research. Based on user consultations, on discussions with international organisations such as OECD and Eurostat and based on lessons learned from projects as, “Data without Boundaries” and the “Nordic Microdata Access Network”, IDAN aims to create a concrete operational international framework enabling access to controlled data for research. IDAN, founded in 2018, involves six RDCs from France, Germany, the Netherlands and the United Kingdom. Initially, the partners’ access systems are being implemented in each partners' premise based on bilateral agreements. This process involves combining requirements of security and surveillance for Safe Rooms, thus paving the way for next steps toward an integrated RDCs network. This presentation will describe how IDAN is setting up a new concrete environment for researchers to work remotely with data from the other partners within their local RDC. The paper will present first project developments, lessons and impact for research that are also of interest for national contexts where administrative data are held in multiple data centres.