The COVID-19 pandemic has led governments worldwide to impose extensive restrictions on citizens, some of which may have long-term impact after their removal. Education is arguably the policy domain where closure policies are anticipated to lead to greatest lasting loss, in this case learning loss. Currently, limited data exists from which researchers and practitioners can draw insightful conclusions about how to remedy the problem. In this paper, we outline the global pattern in pandemic school-closure periods and illustrate data needs through the examples drawn from Brazil and India, two large countries with decentralised education systems, and which experienced prolonged periods of school closures during the pandemic. We conclude with a series of recommendations for building an improved data environment at government, school and household levels, to serve the building back agenda in education.
Although the proportion of black, brown and indigenous electoral candidates in Brazil is close to the proportion of blacks, browns and indigenous in the general population, the proportion elected to the country's Federal Congress is significantly lower. Statistical techniques such as linear or logistic regression are typically used to estimate the effect of a particular variable such as color/race or gender on a candidate's electoral performance. However, in Brazilian elections, characterized by substantive, asymmetrical differences such as extreme variations in campaign finance distribution, the efficacy of these types of regression models is limited. Such being the case in Brazil's open list proportional representation system, we propose quantile regression as the most suitable means for estimating the relationship between voting and other variables such as race/color, because it enables us to estimate relationships between the variables of interest across several distribution quantiles. Quantile regression models show that black and brown candidates get as many as 40% fewer votes than white candidates in higher vote distribution quantiles. Furthermore, analysis of access to campaign financing finds that black and brown candidates on average garner only 75% of the funds available to white candidates at quantile 80 of campaign finance distribution. This drops to 65% at quantile 90.
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