Small-Area Analyses Using Public American Community Survey Data: A Tree-Based Spatial Microsimulation Technique

Quantitative sociologists and social policymakers are increasingly interested in local context. Some city-specific studies have developed new primary data collection efforts to analyze inequality at the neighborhood level, but methods from spatial microsimulation have yet to be broadly used in sociology to take better advantage of existing public data sets. The American Community Survey (ACS) is the largest household survey in the United States and indispensable for detailed analysis of specific places and populations. The authors propose a technique, tree-based spatial microsimulation, to produce “small-area” (census-tract) estimates of any person- or household-level phenomenon that can be derived from ACS microdata variables. The approach is straightforward and computationally efficient, based only on publicly available data, and it provides more reliable estimates than do prevailing methods of microsimulation. The authors demonstrate the technique’s capabilities by producing tract-level estimates, stratified by race/ethnicity, of (1) the proportion of people in the census-tract population who have children and work in an essential occupation and (2) the proportion of people in the census-tract population living below the federal poverty threshold and in a household that spends greater than 50 percent of monthly income on rent or owner costs. These examples are relevant to understanding the sociospatial inequalities dramatized by the coronavirus disease 2019 pandemic. The authors discuss potential extensions of the technique to derive small-area estimates of variables observed in surveys other than the ACS.

Introduction

This chapter serves as an introduction to the book Practical Microsimulation Modelling. It provides as context a description of microsimulation modelling, a simulation-based tool with a micro-unit of analysis that can be used for ex-ante analysis. The methodology is motivated as a mechanism of abstracting from reality to help us understand complexity better. It describes the main analytical objectives of users of microsimulation models in the field of income distribution analysis. The chapter then describes in turn the main methods of microsimulation considered in the book: hypothetical models, static models, behavioural models (labour supply and consumption), environmental models, decomposing inequality, dynamic microsimulation models, and spatial microsimulation models. The chapter concludes by providing an outline of the book.

Spatial Inequality

There has been a growing emphasis on the spatial targeting of policy options in the areas of poverty and social exclusion. In this chapter, the focus will be on using spatial microsimulation models to look at the local impact of policies related to inequality and poverty. Spatial data typically exist in national census datasets, but very frequently these data do not contain information on incomes. The challenge, therefore, is to generate datasets that are spatially consistent, in order to facilitate the linkage of spatially defined data, such as local-area census data, with nationally representative surveys that contain labour, demographic, and income information. Spatial microsimulation modelling helps with this. The purpose of this chapter is to provide an insight into the rationale, development, and application of the spatial microsimulation method for analysing the spatial distribution of inequality. The policy context for spatial-inequality analysis is discussed initially, before considering the statistical method for synthetically generating spatially consistent, household-income-distribution data. Approaches to validating these methods are then discussed, before applying quantitative methods to measuring spatial inequality in a national setting.

Estimating Health over Space and Time: A Review of Spatial Microsimulation Applied to Public Health

There is an ongoing demand for data on population health, for reasons of resource allocation, future planning and crucially to address inequalities in health between people and between populations. Although there are regular sources of data at coarse spatial scales, such as countries or large sub-national units such as states, there is often a lack of good quality health data at the local level. One method to develop reliable estimates of population health outcomes is spatial microsimulation, an approach that has its roots in economic studies. Here, we share a review of this method for estimating health in populations, explaining the different approaches available and examples where the method is applied successfully for creating both static and dynamic populations. Recent notable advances in the method that allow uncertainty to be represented are highlighted, along with the evolving approaches to validation that are an ongoing challenge in small-area estimation. The summary serves as a primer for academics new to the area of research as well as an overview for non-academic researchers who consider using these models for policy evaluations.

Estimating the Uncertainty of a Small Area Estimator Based on a Microsimulation Approach

Spatial microsimulation encompasses a range of alternative methodological approaches for the small area estimation (SAE) of target population parameters from sample survey data down to target small areas in contexts where such data are desired but not otherwise available. Although widely used, an enduring limitation of spatial microsimulation SAE approaches is their current inability to deliver reliable measures of uncertainty—and hence confidence intervals—around the small area estimates produced. In this article, we overcome this key limitation via the development of a measure of uncertainty that takes into account both variance and bias, that is, the mean squared error. This new approach is evaluated via a simulation study and demonstrated in a practical application using European Union Statistics on Income and Living Conditions data to explore income levels across Italian municipalities. Evaluations show that the approach proposed delivers accurate estimates of uncertainty and is robust to nonnormal distributions. The approach provides a significant development to widely used spatial microsimulation SAE techniques.