A comparison of value-added models for school accountability

School accountability systems increasingly hold schools to account for their performances using value-added models purporting to measure the effects of schools on student learning. The most common approach is to fit a linear regression of student current achievement on student prior achievement, where the school effects are the school means of the predicted residuals. In the literature, further adjustments are usually made for student sociodemographics and sometimes school composition and 'non-malleable' characteristics. However, accountability systems typically make fewer adjustments: for transparency to end users, because data is unavailable or of insufficient quality, or for ideological reasons. There is therefore considerable interest in understanding the extent to which simpler models give similar school effects to more theoretically justified but complex models. We explore these issues via a case study and empirical analysis of England's 'Progress 8' secondary school accountability system

Feeding Hungry Families:Food Banks in Schools in England

This article investigates the number and distribution of food banks in schools in England. Drawing on a novel source of nationally representative data, we show that there are over 4,000 school-based food banks across the country. This important finding shows that there are now more food banks inside schools than outside of them. Our analysis, using responses from representative Teacher Tapp data (n= 8,665) also shows that whilst food banks are found in primary and secondary schools across the whole of England, they are disproportionally found in schools with high numbers of socio-economically disadvantaged children. We identify regional differences in the proportion of schools that run a food bank. The large numbers of school-based food banks suggest they are rapidly becoming a commonplace response to how schools support families. Our findings highlight the need for debate about the extent of child food insecurity in England and the development of effective policymaking with regard to charitable food aid in schools

A review and evaluation of secondary school accountability in England: Statistical strengths, weaknesses, and challenges for 'Progress 8' raised by Covid-19

School performance measures are published annually in England to hold schools to account and to support parental school choice. This article reviews and evaluates the ‘Progress 8’ secondary school accountability system for state-funded schools. We assess the statistical strengths and weaknesses of Progress 8 relating to: choice of pupil outcome attainment measure; potential adjustments for pupil input attainment and background characteristics; decisions around which schools and pupils are excluded from the measure; presentation of Progress 8 to users, choice of statistical model, and calculation of statistical uncertainty; and issues related to the volatility of school performance over time, including scope for reporting multi-year averages. We then discuss challenges for Progress 8 raised by the COVID-19 pandemic. Six simple recommendations follow to improve Progress 8 and school accountability in England. Context and implication Rationale for this study This study fulfils a need for a comprehensive statistical evaluation of the Progress 8 school accountability system in England, which has been in place since 2016. Why the new findings matter A review of Progress 8 is an important contribution as decisions on its calculation and presentation have important consequences for schools and pupils. Implications for educational researchers and policy makers Our findings on the statistical strengths and weaknesses of Progress 8 and the resulting recommendations made for improvements to the measure and school accountability have clear implications for informing policy makers in the educational system. This includes wider lessons extending beyond Progress 8 to other performance metrics and to other school systems internationally where similar issues apply. Additionally, the review highlights key concerns that the public and school practitioners may like to consider when drawing upon Progress 8 to make decisions around school choice and in assessing how well a school is performing

Socioeconomic differences in Body Mass Index in Spain:an intersectional multilevel analysis of individual heterogeneity and discriminatory accuracy

Many studies have demonstrated the existence of simple, unidimensional socioeconomic gradients in body mass index (BMI). However, in the present paper we move beyond such traditional analyses by simultaneously considering multiple demographic and socioeconomic dimensions. Using the Spanish National Health Survey 2011–2012, we apply intersectionality theory and multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA) to analyze 14,190 adults nested within 108 intersectional strata defined by combining categories of gender, age, income, educational achievement and living situation. We develop two multilevel models to obtain information on stratum-specific BMI averages and the degree of clustering of BMI within strata expressed by the intra-class correlation coefficient (ICC). The first model is a simple variance components analysis that provides a detailed mapping of the BMI disparities in the population and measures the accuracy of stratum membership to predict individual BMI. The second model includes the variables used to define the intersectional strata as a way to identify stratum-specific interactions. The first model suggests moderate but meaningful clustering of individual BMI within the intersectional strata (ICC = 12.4%). Compared with the population average (BMI = 26.07 Kg/m2), the stratum of cohabiting 18-35-year-old females with medium income and high education presents the lowest BMI (-3.7 Kg/m2), while cohabiting 36-64-year-old females with low income and low education show the highest BMI (+2.6 Kg/m2). In the second model, the ICC falls to 1.9%, suggesting the existence of only very small stratum specific interaction effects. We confirm the existence of a socioeconomic gradient in BMI. Compared with traditional analyses, the intersectional MAIHDA approach provides a better mapping of socioeconomic and demographic inequalities in BMI. Because of the moderate clustering, public health policies aiming to reduce BMI in Spain should not solely focus on the intersectional strata with the highest BMI, but should also consider whole population polices

Systematic sensitivity analysis of the full economic impacts of sea level rise

The potential impacts of sea level rise (SLR) due to climate change have been widely studied in the literature. However, the uncertainty and robustness of these estimates has seldom been explored. Here we assess the model input uncertainty regarding the wide effects of SLR on marine navigation from a global economic perspective. We systematically assess the robustness of computable general equilibrium (CGE) estimates to model’s inputs uncertainty. Monte Carlo (MC) and Gaussian quadrature (GQ) methods are used for conducting a Systematic sensitivity analysis (SSA). This design allows to both explore the sensitivity of the CGE model and to compare the MC and GQ methods. Results show that, regardless whether triangular or piecewise linear Probability distributions are used, the welfare losses are higher in the MC SSA than in the original deterministic simulation. This indicates that the CGE economic literature has potentially underestimated the total economic effects of SLR, thus stressing the necessity of SSA when simulating the general equilibrium effects of SLR. The uncertainty decomposition shows that land losses have a smaller effect compared to capital and seaport productivity losses. Capital losses seem to affect the developed regions GDP more than the productivity losses do. Moreover, we show the uncertainty decomposition of the MC results and discuss the convergence of the MC results for a decomposed version of the CGE model. This paper aims to provide standardised guidelines for stochastic simulation in the context of CGE modelling that could be useful for researchers in similar settings