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This report summarizes major findings from an independent evaluation of 2020 census operations. The American Statistical Association 2020 Census Quality Indicators Task Force selected the authors to conduct the evaluation using nonpublic operations data provided by the Census Bureau. The evaluation focused on the quality of state-level population counts released by the Census Bureau for congressional apportionment. The authors first partitioned the census enumeration process into five operation phases. Within each phase, one or more activities considered to be critical to census data quality were identified. Operational data from each activity were then analyzed to assess the risk of error, particularly as they related to similar activities in the 2010 census. Overall, the evaluation found that census operations relied on higher risk activities at a higher rate in 2020 than in 2010, suggesting that the risk of error may be higher in 2020 than in 2010. However, the available data were insufficient to determine whether the apportionment counts are of lower quality in 2020 than in 2010.

Efforts to improve the public welfare and child welfare system sparked an unprecedented amount of federal legislation in the 1990s, including the Adoption and Safe Families Act of 1997 (ASFA), the Multiethnic Placement Act of 1994 and Interethnic Adoption Provisions of 1996 (MEPA-IEP), and welfare reform. Such reforms allow an unprecedented degree of flexibility, but little is known about their implementation. Researchers administered the Local Agency Survey to the first national probability sample of public child welfare agencies from 1999 to 2000. Findings indicate that ASFA has had the most effect on child welfare service delivery. Welfare reform has had less effect, and MEPA-IEP seems to have had little effect at all.

Open and crowdsourced data are becoming prominent in social sciences research. Crowdsourcing projects harness information from large crowds of citizens who voluntarily participate into one collaborative project, and allow new insights into people’s attitudes and perceptions. However, these are usually affected by a series of biases that limit their representativeness (i.e. self-selection bias, unequal participation, underrepresentation of certain areas and times). In this chapter we present a two-step method aimed to produce reliable small area estimates from crowdsourced data when no auxiliary information is available at the individual level. A non-parametric bootstrap, aimed to compute pseudosampling weights and bootstrap weighted estimates, is followed by an area-level model based small area estimation approach, which borrows strength from related areas based on a set of covariates, to improve the small area estimates. In order to assess the method, a simulation study and an application to safety perceptions in Greater London are conducted. The simulation study shows that the area-level model-based small area estimator under the non-parametric bootstrap improves (in terms of bias and variability) the small area estimates in the majority of areas. The application produces estimates of safety perceptions at a small geographical level in Greater London from Place Pulse 2.0 data. In the application, estimates are validated externally by comparing these to reliable survey estimates. Further simulation experiments and applications are needed to examine whether this method also improves the small area estimates when the sample biases are larger, smaller or show different distributions. A measure of reliability also needs to be developed to estimate the error of the small area estimates under the non-parametric bootstrap.