Do Slum Upgrading Programs Impact School Attendance?

This paper analyzes how slum upgrading programs impact elementary school childrens attendance in Uruguay. We take advantage of the eligibility rule that deems slums eligible for a SUP program if they have 40 or more dwelling units. Using a fuzzy regression discontinuity estimator, we find that students exposed to SUPs are 17 percent less likely to be at the 90th percentile of the yearly count of school absences. That effect appears to be driven by how SUPs impact girls. These interventions have effects that last for more than five years after their implementation. We discuss some critical urban and education policy implications of our findings.

Congestion charge, air pollution and children’s school attendance: benefitting the poor

Air pollution negatively affects individuals’ health and human capital accumulation. For example, students school performance is hampered by air pollution as it decreases cognitive abilities and increases absences. Moreover, low-income students are the most exposed and vulnerable to the negative effects of air pollution lacking protective resources and suffering from pre-existing health conditions. Here, we inquire how more stringent traffic regulations implemented in Central London from late 2015 affected pollution levels and school absences. First, we observe a substantive decrease in pollution in the area affected by the regulations from 2016. Secondly, we use a Difference in Difference approach to estimate the causal effect of the policy on air pollution and school absences. For all schools combined, findings do not show any substantive improvement in attendance. However, when looking at the heterogeneous effects of the policy, we observe a larger decrease in absences for Low socioeconomic status (SES) students. Moreover, we find a positive spillover effect for schools just outside the area targeted by the policy. Consequently, the findings highlight the efficacy of environmental policy in diminishing pollution levels and to benefit the poor.

The Longitudinal Effects of Age of Alcohol Initiation on School Outcomes

Previous research has demonstrated initiation of alcohol during adolescence has a negative effect on both school attendance and missed instruction due to behavior. Utilizing longitudinal data comprised of primarily Black American youth between ages 11 and 18 ( N = 931), both the short-term and long-term effects of alcohol initiation were analyzed using a piecewise growth curve model. Results indicated that alcohol initiation negatively impacted school absences, but this effect was unique and independent of the age at which alcohol was initiated. The number of days of missed instruction due to school violations was also negatively impacted, but the effect was found to be more detrimental to those youth initiating alcohol at a younger age. By age 18, early alcohol initiators had a significantly greater number of days of missed instruction due to school violations, yet did not differ in the number of school absences. Implications of the results are discussed.

An analysis of school absences in England during the COVID-19 pandemic

Background The introduction of SARS-CoV-2, the virus that causes COVID-19 infection, in the UK in early 2020, resulted in the introduction of several control policies to reduce disease spread. As part of these restrictions, schools were closed to all pupils in March (except for vulnerable and key worker children), before re-opening to certain year groups in June. Finally, all school children returned to the classroom in September. Methods Here, we analyse data on school absences in late 2020 as a result of COVID-19 infection and how that varied through time as other measures in the community were introduced. We utilise data from the Department for Education Educational Settings database and examine how pupil and teacher absences change in both primary and secondary schools. Results Our results show that absences as a result of COVID-19 infection rose steadily following the re-opening of schools in September. Cases in teachers declined during the November lockdown, particularly in regions previously in tier 3, the highest level of control at the time. Cases in secondary school pupils increased for the first 2 weeks of the November lockdown, before decreasing. Since the introduction of the tier system, the number of absences with confirmed infection in primary schools was observed to be (markedly) lower than that in secondary schools. In December, we observed a large rise in the number of absences per school in secondary school settings in the South East and London, but such rises were not observed in other regions or in primary school settings. We conjecture that the increased transmissibility of the new variant in these regions may have contributed to this rise in secondary school cases. Finally, we observe a positive correlation between cases in the community and cases in schools in most regions, with weak evidence suggesting that cases in schools lag behind cases in the surrounding community. Conclusions We conclude that there is no significant evidence to suggest that schools are playing a substantial role in driving spread in the community and that careful monitoring may be required as schools re-open to determine the effect associated with open schools upon community incidence.

Using graphic modelling to identify modifiable mediators of the association between area-based deprivation at birth and offspring unemployment

Background Deprivation can perpetuate across generations; however, the causative pathways are not well understood. Directed acyclic graphs (DAG) with mediation analysis can help elucidate and quantify complex pathways in order to identify modifiable factors at which to target interventions. Methods and findings We linked ten Scotland-wide databases (six health and four education) to produce a cohort of 217,226 pupils who attended Scottish schools between 2009 and 2013. The DAG comprised 23 potential mediators of the association between area deprivation at birth and subsequent offspring ‘not in education, employment or training’ status, covering maternal, antenatal, perinatal and child health, school engagement, and educational factors. Analyses were performed using modified g-computation. Deprivation at birth was associated with a 7.3% increase in offspring ‘not in education, employment or training’. The principal mediators of this association were smoking during pregnancy (natural indirect effect of 0·016, 95% CI 0·013, 0·019) and school absences (natural indirect effect of 0·021, 95% CI 0·018, 0·024), explaining 22% and 30% of the total effect respectively. The proportion of the association potentially eliminated by addressing these factors was 19% (controlled direct effect when set to non-smoker 0·058; 95% CI 0·053, 0·063) for smoking during pregnancy and 38% (controlled direct effect when set to no absences 0·043; 95% CI 0·037, 0·049) for school absences. Conclusions Combining a DAG with mediation analysis helped disentangle a complex public health problem and quantified the modifiable factors of maternal smoking and school absence that could be targeted for intervention. This study also demonstrates the general utility of DAGs in understanding complex public health problems.

An analysis of school absences in England during the Covid-19 pandemic

The introduction of SARS-CoV-2, the virus that causes COVID-19 infection, in the UK in early 2020, resulted in the UK government introducing several control policies in order to reduce the spread of disease. As part of these restrictions, schools were closed to all pupils in March (except for vulnerable and key worker children), before re-opening to certain year groups in June. Finally all school children returned to the classroom in September. In this paper, we analyse the data on school absences from September 2020 to December 2020 as a result of COVID-19 infection and how that varied through time as other measures in the community were introduced. We utilise data from the Educational Settings database compiled by the Department for Education and examine how pupil and teacher absences change in both primary and secondary schools.Our results show that absences as a result of COVID-19 infection rose steadily following the re-opening of schools in September. Cases in teachers were seen to decline during the November lockdown, particularly in those regions that had previously been in tier 3, the highest level of control at the time. Cases in secondary school pupils increased for the first two weeks of the November lockdown, before decreasing. Since the introduction of the tier system, the number of absences owing to confirmed infection in primary schools was observed to be significantly lower than in secondary schools across all regions and tiers.In December, we observed a large rise in the number of absences per school in secondary school settings in the South East and Greater London, but such rises were not observed in other regions or in primary school settings. We conjecture that the increased transmissibility of the new variant in these regions may have contributed to this rise in cases in secondary schools. Finally, we observe a positive correlation between cases in the community and cases in schools in most regions, with weak evidence suggesting that cases in schools lag behind cases in the surrounding community. We conclude that there is not significant evidence to suggest that schools are playing a significant role in driving spread in the community and that careful monitoring may be required as schools re-open to determine the effect associated with open schools upon community incidence.

Predicting virologically-confirmed influenza using school absences in Allegheny County, Pennsylvania, USA during the 2007-2015 influenza seasons

Background Children are important in community-level influenza transmission. School-based monitoring may inform influenza surveillance. Methods We used reported weekly confirmed influenza in Allegheny County during the 2007, and 2010-2015 influenza seasons using Pennsylvania’s Allegheny County Health Department all-age influenza cases from health facilities, and all-cause and influenza-like illness (ILI)-specific absences from nine county school districts. Negative binomial regression predicted influenza cases using all-cause and illness-specific absence rates, calendar week, average weekly temperature and relative humidity, using four cross-validations. Results School districts reported 2,184,220 all-cause absences (2010-2015). Three one-season studies reported 19,577 all-cause and 3,012 ILI-related absences (2007, 2012, 2015). Over seven seasons, 11,946 confirmed influenza cases were reported. Absences improved seasonal model fits and predictions. Multivariate models using elementary school absences outperformed middle and high school models (relative mean absolute error (relMAE)=0.94, 0.98, 0.99). K-5 grade-specific absence models had lowest mean absolute errors (MAE) in cross-validations. ILI-specific absences performed marginally better than all-cause absences in two years, adjusting for other covariates, but markedly worse one year. Conclusions Our findings suggest seasonal models including K-5th grade absences predict all-age confirmed influenza and may serve as a useful surveillance tool.